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Share Your Research: How to Give a Good Talk

Share Your Research is a course that will guide learners through the steps of creating and delivering a good research talk. We designed this course for anyone who will be giving research-based scientific talks in the future. This list includes (but is not limited) to advanced undergraduate and graduate students, postdocs, as well as early-career or well-established researchers who are interested in learning more about giving a good scientific talk. While most of our instructors have a background in the life sciences, the lessons included in the course are broadly applicable to other disciplines.

By the end of this course, learners will have:

• A detailed outline plan for their research talk.
• Techniques and strategies for delivering an engaging and effective talk.
• Approaches for finding and refining their preferred speaking style.
• Strategies for practicing and receiving feedback on their talk.

This page is for educators to access the whole or parts of the course to use in their own teaching.

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Designing and Delivering Effective Research Talks: A Coaching Workshop

Increase your confidence in designing, preparing, and delivering purpose-driven oral presentations..

Please complete this survey to be contacted if we offer this workshop again

This pilot workshop is designed to support participants who will be delivering a 10-15 minute research talk within the next 3 months. Applicants will be asked to describe the audience, purpose, and topic of their upcoming talk in the application.

Accepted participants will:

• Submit an initial draft of a presentation slide deck for a 10-15 minute research talk.
• Complete 1-2 hours of pre-work, including viewing an introductory video and exploring resources from Harvard Catalyst's Writing and Communication Center .
• Revise and resubmit their presentation slide deck.
• Participate in a 2-hour workshop (via Zoom) in which they will: deliver their 10-15 minute research talk, receive feedback from their peers and a communication expert, and provide feedback for their peers' talks.

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Jaye Goldstein is the Founder + CEO of Founder to Leader, LLC , a coaching firm that equips biotech leaders for scale.  Previously, Jaye was on the founding team at Petri, where she built a new venture capital firm designed to accelerate formation-stage startups at the intersection of biology and engineering. In addition, Jaye founded and scaled the MIT Communication Lab, which helps engineers learn to communicate effectively, and oversaw education innovation across Harvard University.

Session Dates and Times

We will ask for your availability across these three options. If accepted,  you will attend a live coaching session via Zoom on one of these three days. 

• Monday, May 1, 2023 2:00pm - 4:00pm ET
• Thursday, May 4, 2023 1:00pm - 3:00pm ET
• Friday, May 5, 2023 9:00am - 11:00am ET

Sessions will be held via Zoom. Live captioning will be available. Persons with disabilities who wish to request accommodations or who have questions about access, please contact [email protected] in advance of the session.

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This workshop is open only to Harvard-affiliated schools and institutions . Availability is limited. Eligible applicants are accepted on a first come, first served basis through April 12, 2023. 

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• CAREER COLUMN
• 15 May 2019

Ways to give an effective seminar about your research project

• Ananya Sen 0

Ananya Sen is a PhD student in microbiology at the University of Illinois at Urbana-Champaign.

You can also search for this author in PubMed   Google Scholar

In my first year of graduate school, I was terrified of giving presentations. I would put too much information on my slides, talk too fast and constantly forget or trip over certain words. Unsuprisingly, the reception was lukewarm at best.

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doi: https://doi.org/10.1038/d41586-019-01574-z

This is an article from the Nature Careers Community, a place for Nature readers to share their professional experiences and advice. Guest posts are encouraged. You can get in touch with the editor at [email protected].

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MS Research: How to Give a Great Research Talk

Introduction.

Prof. Xiaokui recommended us a great speech about how to give a great research talk by Simon Peyton Jones from Microsoft weeks ago. I didn’t find it until clearing my E-mail box yesterday. Recursively speaking, this speech itself is a great talk with lots of practical suggestions as well. Thanks for your sharing, Prof. Simon and Prof. Xiaokui.

By exploring on the web page of this speech, I find that it comes from the Microsoft Research Cambridge Lab PhD Summer School in 2016 and 2017, and there are many other valuable courses, including one course on how to write a great research paper .

This post is to note down suggestions and good practices for a great research talk. There will be another post focusing on the one for writing a great research paper.

Here are related resources for your convenience:

How to Give a Great Research Talk

• Think: how often have you said “I’m really glad I went to that talk”.
• Some simple, actionable ideas that can make your talks much better.
• You will have more fun.
• A research talk gives you access to the world’s most priceless commodity: the time and attention of other people . Don’t waste it!
• To impress your audience with your brainpower.
• To tell them everything you know about your topic.
• To present all the technical details.
• To give your audience an intuitive feel for your idea.
• To make them foam at the mouth with eagerness to read your paper.
• To engage, excite, provoke them.
• To make them glad they came.
• Have read all your earlier papers
• Thoroughly understand all the relevant theory of cartesian closed endomorphic bifunctors
• Are all agog to hear about the latest developments in your work
• Are fresh, alert, and ready for action
• Have never heard of you.
• Have heard of bifunctors, but wish they hadn’t.
• Have just had lunch and are ready for a doze.
• Audience - Your mission is to WAKE THEM UP and make them glad they did.
• What to put in: Motivition (20%) + Your key idea (80%) + nothing else
• Why should I tune into this talk?
• What is the problem?
• Why is it an interesting problem?
• Does this talk describe a worthwhile advance?
• You must identify a key idea. “What I did this summer” is no good.
• Be specific. Don’t leave your audience to figure it out for themselves.
• Be absolutely specific. Say “If you remember nothing else, remember this”.
• Organise your talk around this specific goal. Ruthlessly prune material that is irrelevant to this goal.
• Avoid shallow overviews at all costs.
• Cut to the chase: the technical “meat”
• It’s ok to cover only part of your paper.
• To motivate the work.
• To convey the basic intuition.
• To illustrate the idea in action.
• To show extreme cases.
• To highlight shortcomings.
• “Outline of my talk”: conveys near zero information at the start of your talk.
• Worse, since your audience only gives you 2 minutes before dozing, you’ve just lost them.
• But maybe put up an outline for orientation after your motivation and signposts at pause points during the talk.
• But you absolutely must know the related work; respond readily to questions.
• But you can acknowledge co-authors (title slide), and pre-cursors (as you go along).
• But you can praise the opposition: X’s very interesting work does Y; I have extended it to do Z.
• Even though every line is drenched in your blood and sweat, dense clouds of notation will send your audience to sleep.
• Present specific aspects only; refer to the paper for the details.
• By all means have backup slides to use in response to questions.
• If you do not seem excited by your idea, why should the audience be?
• Enthusiasm makes people dramatically more receptive.
• It gets you loosened up, breathing, moving around.
• Your talk absolutely must be fresh in your mind.
• Ideas will occur to you during the conference, as you obsess on your talk during other people’s presentations.
• I didn’t have time to prepare this talk properly.
• My computer broke down, so I don’t have the results I expected.
• I don’t have time to tell you about this.
• I don’t feel qualified to address this audience.
• Deep breathing during previous talk.
• Script your first few sentences precisely (=> no brain required).
• Move around a lot, use large gestures, wave your arms, stand on chairs.
• Go to the loo first.
• Face the audience, not the screen.
• Know your material.
• Put your laptop in front of you, screen towards you.
• Don’t point much, but when you do, point at the screen, not at your laptop.
• Speak to someone at the back of the room, even if you have a microphone on.
• Make eye contact; identify a nodder, and speak to him or her (better still, more than one).
• Watch audience for questions…
• Questions are not a problem. Questions are a golden golden golden opportunity to connect with your audience.
• Specifically encourage questions during your talk: pause briefly now and then, ask for questions.
• Be prepared to truncate your talk if you run out of time. Better to connect, and not to present all your material.
• Eye contact with speaker.
• Nod frequently.
• Ask questions.
• Use a wireless presenter gizmo.
• Test that your laptop works with the projector, in advance.
• Laptops break: leave a backup copy on the web; bring a backup copy on a disk or USB key.
• Don’t reveal your points one by one or using animation effects.
• Audiences get restive and essentially stop listening when your time is up. Continuing is very counter productive.
• Simply truncate and conclude.
• Do not say “would you like me to go on?” (it’s hard to say “no thanks”.)
• What your talk is for: Your paper is the beef; your talk is the beef advertisement. Don’t cnofuse the two.
• Write a paper, and give a talk, about any idea, no matter how weedy and insignificant it may seem to you. Good papers and talks are a fundamental part of research excellence.
• Crystalise your ideas.
• Communicate them to others.
• Get feedback.
• Build relationships.
• (And garner research brownie points)

My Thoughts

This talk actually gives a long list of useful suggestions. The best way to apply what you agree is not to learn by rote, but to read this list when you are going to prepare for a research talk, especially when you are preparing the slides.

The most important things I learn from this talk are:

• 20% motivation + 80% key idea.
• Being seen and heard.
• Enthusiasm.

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Open Access

Ten simple rules for giving an effective academic job talk

* E-mail: [email protected] (SAS); [email protected] (JOLS)

¶ ‡ SAS, LLS, and MRA contributed equally to this work. CEGA, ACB, ACRG, MJ, GSK, JSM, JM, and ROM also contributed equally to this work.

Affiliation Department of Ecology and Evolutionary Biology, University of California Los Angeles, Los Angeles, California, United States of America

Affiliation Department of Human Genetics, University of California Los Angeles, Los Angeles, California, United States of America

• Shayna A. Sura, 
• Lauren L. Smith, 
• Monique R. Ambrose, 
• C. Eduardo Guerra Amorim, 
• Annabel C. Beichman, 
• Ana C. R. Gomez, 
• Mark Juhn, 
• Gaurav S. Kandlikar, 
• Julie S. Miller, 

Published: July 25, 2019

• https://doi.org/10.1371/journal.pcbi.1007163
• Reader Comments

Citation: Sura SA, Smith LL, Ambrose MR, Amorim CEG, Beichman AC, Gomez ACR, et al. (2019) Ten simple rules for giving an effective academic job talk. PLoS Comput Biol 15(7): e1007163. https://doi.org/10.1371/journal.pcbi.1007163

Editor: Fran Lewitter, Whitehead Institute for Biomedical Research, UNITED STATES

Copyright: © 2019 Sura et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The authors acknowledge support from the National Science Foundation Graduate Research Fellowship Program (to SAS, ACB, and JSM under grant #DGE-1144087, and to GSK and JSM under grant #DGE-1650604), the NSF Postdoctoral Research Fellowship in Biology (to JSM under grant #DBI-1812292), and NSF research grants OCE-1335657 and DEB-1557022 (to JOL-S and ACRG). ACRG was supported by the CAPES Science Without Borders Doctoral Fellowship. ROM and JOL-S were supported by the US Department of Defense Strategic Environmental Research and Development Program (RC-2635). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

You’ve finally completed your dissertation research and have your PhD in hand—yay! Maybe you’re also in the middle of a postdoctoral position. If you’re reading this article, chances are you are actively searching for and applying for faculty positions. (Check out reference [ 1 ] if you’re early in the application process and [ 2 ] for additional advice!) Unfortunately, many graduate students and postdocs are not taught the skills necessary for acquiring a faculty position after passing the “looks good on paper” part of the application and securing an on-campus interview. One of the last crucial steps in earning a faculty position is your academic job talk. No matter how great of a scientist you are, if you cannot give a compelling job talk, chances are low that you will be hired. Yet many candidates receive little guidance on how to ace this unique and vital test.

To help address this gap, we have put together these ten simple rules that will help you give an effective job talk. To be clear, these are rules developed for the academic job talk in a research-heavy department, which is typically in a seminar format. These rules are not targeted toward other formats such as chalk talks or teaching demonstrations, although some pointers may still apply. We are a group primarily composed of University of California, Los Angeles (UCLA) faculty, postdocs, and graduate students who participated in two recent job searches in the Ecology and Evolutionary Biology Department. We evaluated ten job talks over the span of 2 months and discussed their strengths and weaknesses in a weekly seminar course. These ten rules are based on our discussions of what worked (and what didn’t) across the variety of job talks we observed, as well as our various experiences on the job market and search committees over the years.

Rule 1: Know your audience

As with any seminar or presentation, when preparing your job talk, you want to target your specific audience. Therefore, you need to consider the background knowledge and interests of the audience members. Learn as much as you can about the position and what institutional needs the position is meant to address within the department and broader university. If you’re applying for a position within a specific department, what is the scope of the research in that department? Does it have a mission statement? Are any strategic aims or future plans publicly available? Does the department work closely with other academic units on campus, and does the position you’ve applied for have any formal ties to other units? To answer some of these questions, you should read the job ad closely, read about the current faculty’s research, and look through the department’s web page (see also Rule 7 [Understand your potential new workplace] and 8 [Understand your new colleagues] from reference [ 3 ]). If you’re lucky enough to have network connections to the department, use them now to get insights before you visit. We also recommend that after you receive an invitation to interview, you consider setting up a phone call with the chair of the search committee to inquire about the job and ask any specific questions you have regarding the job or department. In particular, it is a good idea to ask what the search committee is looking for—it may have been a long time since the job ad was released, and the search committee’s focus may have shifted from what was initially stated. We recommend a phone conversation as opposed to an emailed list of questions because it saves time; also, people are often more candid and may provide more useful insights over the phone. Depending on when your job talk occurs during your interview schedule, you might even make small changes to customize your talk based on interviews and meetings with department members prior to your talk.

Rule 2: Sell yourself

The faculty and search committee are trying to choose the candidate they’ll be most excited to have as a new colleague, so you need to showcase the reasons you’re their best choice! It is smart to include an explicit introduction about yourself—i.e., the kind of science you do, your grand aims, and your approach to research. You want to communicate your identity as a researcher and, if appropriate given your career stage and research plans, how this differentiates you from your mentors (reference [ 4 ] is an excellent resource).

You also want to convey other traits as a scientist and potential colleague. Reflect on the qualities that make you an exceptional researcher (creative, persistent, thoughtful, rigorous, multidisciplinary, etc.), as well as the specific traits that your audience will be looking for, and try to demonstrate them subtly to the audience over the course of the talk via examples in your work. Consider ways to demonstrate your fundamental strengths as a scientist, such as the ability to question your methods and results to pursue deeper and more robust conclusions. If you have any particular successes on your record, such as big grants or markers of professional stature, don’t be shy about mentioning them (but don’t brag!). Having your publication citations and/or grants listed in smaller text at the bottom of corresponding slides is one way to show your accomplishments without explicitly mentioning them. Finally, you can casually highlight additional non-research skills (e.g., mentoring, outreach, collaborations) throughout your talk. For example, give credit to an excellent mentee who contributed to the data collection or to a gifted collaborator who added a component to your study. Your application materials likely included many of these things, but if you can find ways to incorporate them in your talk, a broader audience can see the full package of who you are.

Keep in mind Rule 1 (Know your audience) when deciding how best to showcase yourself, as different disciplines and subfields may vary in their perceptions of what makes a good scientist. For example, disciplines may vary in their appreciation for deep thought into specific mechanisms and experimental designs versus mathematical elegance and rigor. Others may prize applied over fundamental research or vice versa. This may be especially challenging if your research is interdisciplinary, so make sure to investigate what factors are valued most highly by the decision makers in the audience for your talk so you can design your talk to emphasize those aspects of your work.

Rule 3: Impress the in-crowd…

Likely there will be people in the audience who work in the same field as you. Make sure to impress these experts with your knowledge and convince them you are worthy of being their colleague. You want to show them you have the sophistication and skills necessary to tackle advanced problems. Therefore, it’s a good idea to do at least one “deep dive” during your talk in which you include one or two “muscle-flexing” slides. By this we mean slides with technical content that the general audience member may not be able to fully understand but for which you can flex your intellectual muscles and showcase your skills. Importantly, do not bluff or bluster in this section—a technical error in your deep dive would be fatal.

These deep dives shouldn’t be long, or you risk losing most of your audience. However, a glimpse into the more advanced aspects of your work will convey that you’re able to play in the big leagues in your field. Just make sure to reengage your audience after this show of prowess, ideally providing a big-picture summary of what you’ve just shown.

Rule 4: … but also appeal to the out-crowd

In addition to impressing the specialists in the audience, you want to make sure the people who work outside your discipline are able to follow and enjoy your presentation. When preparing your talk, consider how you can present and frame the material so that even audience members from far-flung disciplines are engaged and can appreciate the broader relevance of your presentation. Be attuned to the breadth of the department you’re visiting, as this can present various communication challenges. The diverse interests of faculty in a broad department (e.g., biology) can make it difficult to make your research program appealing to everyone. However, it can also be difficult communicating to a more focused department (e.g., molecular genetics) if your research is not exactly in line with what everyone else does. It helps to summarize the important findings of your research as you present them, in addition to their implications and why they are exciting, in case not everyone followed the technical aspects of your results. You can also make it easier for audience members from other fields to follow your talk by avoiding excess jargon and keeping your messages clear.

Emphasize the themes in your work that relate to the job and department you’re interviewing for. If applicable and appropriate, it can help to subtly highlight connections between your research to research of other members of the department who have different specialties. But be careful not to overdo this, as it can become distracting.

Rule 5: Play the hand you’ve got to optimal effect

Strategic choice of topics to include in your talk from among your entire research portfolio is critical for giving an effective and memorable job talk. Depending upon what career stage you are in (just finished PhD, postdoc, assistant professor, etc.), you may have a smaller or larger research portfolio. For an hour-long job talk, it is unlikely you will be able to effectively discuss everything you have ever done. And that’s okay, because that is what a CV is for!

For your job talk, you need to assess your portfolio of published work, unpublished but completed work, and ongoing projects to determine which projects showcase your work most effectively and best match what the department is looking for in a future colleague. The most effective talk structures we observed were ones that focused on 2–3 research studies and that combined higher-level information with a few “deep dives” into the nitty gritty of a particular study ( Fig 1 ). This talk structure will help you satisfy Rules 3 and 4 above, which discuss how you want your whole audience to understand and appreciate your talk, while also presenting the “meat” of your research and impressing those most familiar with your field. If you feel that this design doesn’t convey the breadth or quantity of your productivity, consider adding a slide or two on the conceptual structure of your full research program in which you can show (with all your best citations) how all the pieces fit together.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

You want to start broad during the introduction to get everyone on board and then go into more depth on a few specific studies, including some “deep dives” to show off expert knowledge. Finally, you want to conclude your talk on a broad scale similar to your introduction. The dashed lines indicate flexibility in how many specific studies you incorporate into your talk, based upon your own research portfolio.

https://doi.org/10.1371/journal.pcbi.1007163.g001

In addition to presenting on your past and ongoing research, you need to clearly articulate your plan for your future research program. Tell the audience (and your potential future colleagues!) about your vision for your research lab both in the immediate future (next couple of years) and in the long term (5–10 years from now). This should also help differentiate you and your research from your previous mentors and their research programs. A critical part of establishing and maintaining a research program is your ability to generate funding. If you have already secured funding for your future research plans or you have a track record of successfully acquiring funding, then this is a great opportunity to bring this to your audience’s attention. If you don’t have independent funding yet, you can still demonstrate your awareness of the funding landscape and which funding opportunities are likely to support your research program. For example, in your future directions section, you might briefly touch on how one (or more) of your research questions aligns well with promising funding opportunities in your field, such as open research grants.

In organizing the structure of your talk and your transitions between topics, strive for a cohesive narrative that will make your talk more enjoyable to follow and easier to recall afterwards. What’s the progression of your research? How did one study lead to the next, and what shaped your decisions about how to proceed? What ideas do you have for future research at this new job? Telling a story is always a great way to keep your audience engaged and makes your science more memorable.

Rule 6: Give a good talk

A classic early paper in this series [ 5 ] provides ten useful rules for giving a good presentation. Read it! Showing you are a competent oral communicator is a vital component of giving an academic job talk. In addition to the universal suggestions from [ 5 ] (such as practicing for fluidity without over-rehearsing, making eye contact with the audience, and being enthusiastic and excited about your work), there are a few other pointers to bear in mind for a job talk. First, be aware that your job talk will be judged as an indicator of your ability to teach. Teaching is a crucial element of most academic jobs, but interview schedules often don’t allow time to address it explicitly, so this doubles your incentive to give a clear and engaging presentation. Bonus points if you are able to expand people’s understanding of technical aspects of your work—for instance, with a lucid explanation of your deep dive. Second, the job talk is a direct measure of your ability to sell your work and to act as an ambassador for the department in your future speaking engagements. Third, Rule 4 from [ 5 ] is “Make the take-home message persistent,” and this is a particular priority in the swirl of an academic search in which four or five candidates may visit over the span of a few weeks. We found that a strong thematic structure, including outline and summary slides, was an effective way to emphasize and reiterate your key points and make them memorable for the audience.

Our next three pointers are more pragmatic, but they are still useful to consider. First, be sure to ask for guidance on talk length if you’re unsure. For an hour-long seminar, the actual presentation length is typically 45–50 minutes, allowing for the fact that your host may burn precious minutes introducing you, and being certain to leave time for questions. Second, you should also make sure you understand the audiovisual equipment setup in the room where you are giving your presentation. If there isn’t seminar preparation time on your schedule, ask for it! This way, you can ensure your presentation is loaded properly, your presentation slides appear how you expect, and you are able to navigate through them without glitches. It is a good idea to save your presentation in multiple formats in case you encounter compatibility issues with the primary format (e.g., if your presentation is in PowerPoint, also save a PDF backup version). Third, don’t give your presentation while hungry. You want to exude energy and confidence, which may be difficult if you give a seminar later in the afternoon after many meetings and haven’t eaten since lunch—so take note of your schedule and, if necessary, bring a snack to revive your energy levels before your talk.

The pragmatic pointers we mentioned are great for planning ahead, but overall, you should be adaptable. Problems can arise unexpectedly, and it’s possible you’ll be delayed by interruptions or a lengthy introduction. Do your best to not get flustered, to handle yourself with grace, and to end your talk on time. Make a note of places in your talk where you can go into greater depth if you’re running ahead of schedule or places (particularly toward the end) where you can skim over the details more quickly if you’re behind schedule.

Rule 7: Be kind to your audience’s eyes

Your slides should enhance your presentation, not distract from what you are saying. Make sure your slide aesthetics are appealing to the audience. Your slides should be clear and concise, without too much text. When you have text-heavy slides, you lose some proportion of your audience’s attention while they read the text instead of listening to your words. So only display text that emphasizes the key points you will say out loud. Also, since the figures and images you present are especially important, you will want to construct figures specifically for your slides, keeping in mind that formatting for a presentation is typically different from formatting for a published paper. Refer to Box 1 for additional advice on qualities of good slides and common mistakes to avoid. You should also check out [ 5 , 6 ] for additional advice, noting that the rules in [ 6 ] are not specific to figures for presentations.

Box 1. Qualities of good slides versus slide qualities to be avoided

Slide qualities to aim for:

• ○. Minimal text.
• ○. Figures that are readable and easily understood.
• ○. Figures created specifically for talks (rather than pulled directly from a paper). Talk figures are generally simpler than figure panels from a paper, with fewer items per plot, a focus on the key points, larger labels and axes, etc. Avoid having to tell your audience to ignore parts of the figure by remaking the figure without extraneous information.
• ○. If you have a complicated figure, you can animate your slides to build up the complexity as you explain it to the audience. For example, you can start by showing only a very simple plot and then layer on additional pieces of information as you explain them.
• ○. Clean background.
• ○. Consistent design throughout the talk.
• ○. Color-blind-friendly color palettes or alternative ways to distinguish differences on figures besides just color (e.g., using dotted versus solid lines to represent different measures in a plot).
• ○. Simple visual markers (silhouettes or clip art) that link ideas across slides and jog your audience’s memory (e.g., a human silhouette next to parameters estimated from human data and a mouse silhouette next to data estimated from mice).

Slide qualities to avoid:

• Too much text.
• Text that’s too small to read or overlaid on an image so that it’s not legible.
• Busy background (e.g., photograph) that distracts from the text and/or figures you’re showing on the slide.
• Figures with no or unreadable axis labels.
• Poor color combinations, including combinations that are difficult for color-blind viewers to make out (e.g., red/green, blue/green).
• Visual markers that don’t convey any meaningful information, such as changing fonts and background colors. Even minor inconsistencies are distracting and convey a lack of attention to detail.

Rule 8: Embody the future

Remember that you are the exciting next generation of scientists! Make sure to share your enthusiasm and your fresh ideas for research. Emphasize how your work is new and innovative, whether by showing new solutions to old problems or by describing ways to approach problems that have only recently been recognized. If appropriate, highlight how you will harness the latest technologies and methodological developments to advance your research. This will get the audience thinking about applications to their own research programs and how you’d be a valuable colleague to have around.

You can also emphasize other forward-looking traits you would bring to the job. Maybe you have developed a new online resource or are using a new mentoring or teaching style that helps make research more broadly accessible for students. Find ways to showcase how you are moving science forward and how you’ll be a dynamic force for years to come.

Rule 9: Don’t blow it in the question-and-answer session

You’re almost done with your job talk, so don’t blow it during the question-and-answer (Q&A) session! You want to leave your audience with the best final impression and show that you can think and speak clearly in unscripted moments.

Here are some tips for a strong finish. When someone asks you a question, it can be helpful to paraphrase the question before beginning your answer. This gives you some extra time to compose your own thoughts and make sure you understood the question and ensures the rest of the audience hears the question. Regarding your actual responses, one cardinal rule is to never bluff. If you don’t know the answer, you can say so, but then show how you would think through the question, or relate it to something you have done or know about. If somebody voices a fair criticism, then acknowledge it and discuss approaches to addressing it. If you can, convey enthusiasm in this situation—if it’s truly an idea you’ve never considered, then treat this as an exciting and valuable scientific exchange, not an oral exam you are failing.

Remember that your audience likely includes people from outside your area of expertise, so it is possible you will get questions that seem to have missed key ideas from your talk. As with all questions, make sure you understand what the questioner is asking, and then take advantage of the opportunity to address any misunderstandings in a respectful, productive way. This is a great chance to demonstrate your ability to explain concepts clearly and concisely.

If there are predictable follow-up questions to your presentation, it can be helpful to have a few extra slides prepared. For example, if you presented a mathematical model using a schematic diagram, you may want to have a backup slide that shows the actual equations in case someone asks for more detail. If there is an extra data set or analysis that you’d love to include but just don’t have the time, then a spare slide or two might enable you to deliver a home-run response if you get asked the right question.

Finally, remember to handle yourself with grace during the Q&A session. Be poised, calm, and respectful, and demonstrate your intellectual maturity—all of these are qualities people admire and are seeking in a future colleague. Another past article in this series gives rules for building your scientific reputation [ 7 ]; Rules 1, 2, and 3 are useful during both the Q&A session and the whole interview process! Which brings us to Rule 10.

Rule 10: Be professional

Throughout this whole process, remember you are asking the host department to hire you as a (hopefully) long-term colleague in a small, tight-knit unit. Therefore, it is important to present a good image of yourself. You should dress appropriately for your job talk (i.e., not too casually). Even if you end up being a bit overdressed, it is better to leave that impression rather than showing up underdressed and being remembered as not having taken the job talk seriously. Be conscious of your body language and use of slang throughout your job talk and in any interactions you have during your visit. Humor can be a wonderful way to humanize and enliven your talk, but don’t overdo it, and steer well clear of anything potentially offensive. While you are answering questions, or if you happen to be interrupted during your talk, remember to show yourself in the best light by being polite and calm, even if an audience member is being confrontational or rude.

You are an amazing and productive scientist (you wouldn’t have been invited to give a job talk if you weren’t!), but it’s important to be clear about your specific contributions to the various research projects you present, particularly when the research is part of a big collaboration. It’s essential to acknowledge your collaborators, especially junior mentees. This shows your audience that you are ready to mentor undergraduates, graduates, postdocs, etc., and most importantly, that you do not take collaborators’ contributions for granted or claim them as your own. It’s also good practice to acknowledge relevant previous work that your research and ideas are building upon, as you never know who is in your audience, and you don’t want anyone to feel you are uninformed about or taking credit for this prior research. Again, you’re asking to be hired into an academic family, and you want your new family members to be comfortable and excited about pursuing new research opportunities with you.

Finally, it is a nice touch to write thank-you notes after your visit (but see Rule 10 from [ 3 ] for an alternative opinion). These notes can be sent by email within a few days after the end of your job interview. How many you send is up to you, but we suggest sending follow-up notes to at least the search chair and the other key players in your interview visit. And don’t forget about all the people who helped coordinate the logistical details for your visit!

In summary, the academic job talk is unlike most other seminars in its goals, context, and aspects of its execution. We have outlined some rules to help you put your best face forward in the job market (and to help all of us get the most out of the job search experience). There are additional resources online (e.g., [ 8 ] and [ 9 ] as two examples), and people should glean whatever insights they can from these sources. So do your preparation, nail the talk, and go get that job!

Acknowledgments

This paper arose from discussions in a graduate seminar course jointly led by KEL and JOL-S. We thank other participants in the course including Katie Gostic, Natalie Lozano, and Bernard Kim for their thoughts on these topics.

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• 8. Reis RM. Giving a job talk in the sciences. 30 March 2011 [cited 2019 May 15]. In: The Chronicle of Higher Education [Internet]. Available from: https://www.chronicle.com/article/Giving-a-Job-Talk-in-the/45375 .
• 9. Aguilar SJ. Tips for a successful job talk. 10 January 2018 [cited 2019 May 15]. In: Inside Higher Ed [Internet]. Available from https://www.insidehighered.com/advice/2018/01/10/advice-giving-effective-job-presentation-opinion .

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Academic job talks

An introduction to the genre of job talks, and how to make a great one.

Eleanor C Sayre

Academic job talks are their own special genre of talk, and it’s worth learning how to do them well.

Short guidelines for any talk

• Develop and use stylesheets
• Telegraph what you’re doing
• Stick to your allotted space
• Use hidden notes
• Give a sense of something bigger
• Be authentic, personable, and wise.
• Tailor your talk to your audience

The point of a talk is not to give a powerpoint of a paper (even if that’s what many people do anyways). The point of a talk is to give an engaging perspective on your material. Slides help if you use them well; they hinder if there’s extraneous visual information or too much text. Every time you want to put text on a slide, ask yourself, “Could I do this better with a picture?” Every time you want to put a graph on a slide, ask yourself, “How could I build this graph to more effectively draw attention to the salient features?”.

What is a job talk?

A job talk is more than just a research talk. A job talk is also an indicator of how well you lecture. It’s used to tell the department what you’ve already done, and where you’re going next: a sense that you can situate your research within your field(s), use your dissertation to build a successful research program, and perhaps become a leader (or a notable person) eventually. At a teaching school, they want you to treat student questions with respect and show evidence that you can conduct your research with their limited facilities using undergraduates as researchers. At a research school, they want you to engage intellectually with profs and show evidence that your research program will generate lots of grants.

You want to give them the sense that they would be lucky to have you, and you would feel honored to be there.

Job talks in my field are meant to be (and often are used as, at least at smaller schools) departmental colloquia. I aim my talks at the average student member of the audience (undergrad at liberal arts schools, grad at universities). They are usually meant to be 50 minutes with 10 for questions at the end; no one cares if you go a little under, but everyone hates it if you go over. (We still call them hourlong talks, even if they’re really 50 minutes.)

A job talk is not a business talk. You should be able to give 30-second, 2-minute, and 10-minute overviews of your research. However, none of them are the hourlong job talk.

How do I make it beautiful?

In my field (Physics), we have slides. We don’t read papers. If your field reads papers, this might be a little outside what you do. Honestly, I don’t know how I would give a talk without slides. How would I talk about my graphs?

When it’s time to make a new talk, I think about audience. Which research project are they most interested in? Are they cognitive scientists, physicists, or educators? Will there be sound (I have small video clips related to some projects)? Generally, I give background information close to the slides it pertains to, rather than a big chunk at the beginning. You’ll have to spend more time on motivation than on actual background material – you can slip in background information (“This is exactly like we expect because of prior research”) as you go.

I have three ways to think about beauty. There’s the beauty of each slide and the beauty of all them put together, and then the way they interface with your patter and timing. (I’ll leave aside the inherent beauty of your research. Your research is beautiful, right?)

Per-slide beauty

Do not make ugly slides.

• Use pleasing, harmonious colors. Projectors are different from your computer screen, and different from each other. I find that black, white, and blue or purple are best on most projectors. Yellows are often greenish grey; greens are either sickly or neon; reds are either brown or pink. Grays are nice, but hard to calibrate well because projectors are often darker than your screen.
• Black text. White background. Do not be tempted to reverse this, because it will inevitably look bad on the projector when the room isn’t dark enough or the black isn’t black enough. Do not be tempted to use other, lesser contrast schemes.
• Use a readable font. I use 32 for most text, 24 for small text, and 16 for references and slide numbers. I like Gill Sans or Helvetica light (except for numbers because 1 and l look the same, so I use Courier). Times New Roman looks fussy. Never use Comic Sans.
• In some (many?) rooms, you can’t see the bottom quarter of the slides. You may put references, the bottom parts of diagrams, and slide numbers down there. Do not put important conclusions at the bottom of the slide, no matter how tempted you are to finish a slide with a conclusion. Consider perhaps having it appear in the middle part of the slide as a build.
• If there is too much information on a slide, you must assist your audience with interpreting it. Build large diagrams from smaller pieces. Highlight the relevant parts of the graph in order as you talk about them. Get rid of your long bullet points. Make some whitespace.
• Do not do spiffy animations just because they are spiffy. Ask yourself, does this build add information? Does it draw focus inappropriately from my message? Generally, the appear and disappear builds are sufficient. Perhaps also fade in and fade out.

Many slide beauty

Your slides are all in one presentation. Make them look like they belong together, even if you stole them shamelessly from prior talks.

• Master slides are your friend. Know them. Use them. Love them. Put them together into a stylesheet and use it consistently.
• Builds are your friend. Don’t just duplicate slides with more info.
• Powerpoint themes are not your friend. They are ugly and distracting. The key to true beauty in a talk is functionality.
• Whatever your color scheme, keep the palette small and consistent. I usually have five or six colors: background (white), text (black), highlighting (baby blue), data color 1, and data color 2. If you can’t keep it small, at least keep it consistent.
• Think about making a follow-along ball or progress meter. In a talk with five sections, I often make five icons. I put them in the lower left of the talk, and highlight the one I’m on. It’s small and unobtrusive, and I replicate it (larger and centered) on the structural slides that introduce each section. This is more important in longer talks than shorter talks because it orients your audience to their location in the talk.
• If you’re part of a big collaboration, you’re probably going to get slides from your collaborators. You can use them, but try to make them fit visually with all your other slides.

Beautiful patter

It’s your presentation. Don’t look like you don’t know what’s coming.

• Rehearse your talk. Rehearse it from the beginning. Rehearse it from some place in the middle because they will interrupt you with questions.
• Set up your presenter display so that it shows the current slide, the next slide, the time you’ve spent (or the time remaining), and your presenter notes.
• Keep your presenter notes short. Two lines are good; one line is better. Or just skip them.
• If you’re pointing out features on the graph (with highlighting and builds), look at the graph that everyone else is looking at. It makes them feel like they are part of an experience with you instead of just being talked at.
• Deflect questions that come at inopportune times. My favorites are, “Great question! It leads me directly to the next part!” and “You are skipping ahead! Hold your question for a few minutes, and if you still have it at the end, ask it then” They let the audience feel clever for guessing where I’m going next (academics like to feel clever), and let me answer the question in the beautiful way I have planned.
• Encourage people to ask questions at the start, and maintain a respectful tone when answering (or deflecting) them.
• Make sure that that patter needs the slides and the slides need the patter. It’s one integrated talk, not a collection of pictures to accompany your speech!

Talks are not papers

In a talk, you want to give as many cues to listeners as possible about how to react especially if you’re going to deprive them of visual cues. Think about the cadence of speaking as separate from the cadence of writing.

It’s ok – desirable, even – to use phrases like:

• This is surprising, because
• That makes sense, because
• This is exciting, because
• Recall that (thing you said earlier)
• If that were true, we would expect X.

And to tell them where they are in the talk:

• So, we talked about X,Y,Z. Let’s see how that can play out in context A.
• The central problem is X. Before I talk about X, a brief digression into Y.
• Altogether then, the primary research questions are X,Y,Z. I’m just going to focus on X and Y in this talk.

And to ask questions deliberately:

• Why should we care about X?
• What would be the instructional implication for Y?

If your questions are important enough, put them on a slide by themselves.

Think about your style as a speaker. Find a way (within your style) to put funny parts into your presentation. I like to deliver mine deadpan. I like to have one laugh per ten minutes of talk; more is a fun audience, less is a flat audience.

Enough about style! Tell me what to say!

All that said, here’s a basic outline from one of my talks with two research projects (numbers of slides in parens). I gave this talk for the job interview at a faculty job that I accepted, so I know it was effective. It’s a little shorter than normal in terms of slide number, but a lot more of the slides have more builds. If each build were its own slide, there would be 105 slides (which is about right for my style – one build per 20-30 seconds on average).

• What is my field (4)
• What this talk is about (2)
• my postdoc (6)
• Transition slide (1)
• my dissertation (12)
• Curriculum design (7)
• Conclusions (3)
• What I can do for you (1)

Total: 38 slides

Where else can I find information about giving talks?

Matt Might is a computer scientist with lots of advice for academics entering the job market. Here’s his piece on academic talks .

xBlog is full of visual design tips. I like their piece on making movies in Keynote , as one way to think of a good talk is as a dynamic, live-action movie.

Colin Fredericks wrote Be pithy, damnit , a delightfully descriptive talk about how you should title your talks.

Additional topics to consider

Interviewing in a physics department.

The flow of a typical flyout visit and issues to look for.

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This article was first written on January 2, 2015, and last modified on February 8, 2024.

Image: Stephanie Okeyo, a Kenyan microbiologist, giving a flash talk at Falling Walls Engage 2019.

Script Practical Guide

How to give a science flash talk.

• A flash talk is a short presentation of your research to a non-expert audience
• A flash talk must be clear, concise and engaging

Don’t be a statue but avoid dancing

By:  Bernard Appiah

What is a science flash talk giving a science flash talk: two case studies know your audience top ten tips for an engaging and interesting flash talk.

Communication is an essential component of research. Before conducting your research, perhaps you had to convince funders or your institution to support it. And even when you have completed your research, you have to communicate it to your peers and others not in your field. Your potential to attract additional support depends on many factors including your ability to persuade institutions or people to be interested in your research.

When persuading people to be interested in your research or to even fund or prioritise your research, you often have to do so in a very short time. One way to fulfil this goal is to participate in a science flash talk.

What is a science flash talk?

A science flash talk is a presentation of your research or idea in a short time to engage with audiences who may not necessarily be experts in your field. These audiences may include judges who will be evaluating your presentation or others who may just be listening to you. Your task is to help them become aware of, or interested in, your research idea or concept.

Flash talks may be organised as a contest or as the format of a conference session. Examples of flash talk contests are  Three Minute Thesis  and  Falling Walls Engage . Each Falling Walls Engage finalist has five minutes to present their science engagement initiative to a distinguished audience including judges.

In such contests or presentations, you become the driver to lead your audience to a destination where they will at least be interested in your research or idea. By convention, you usually will not be expected to use PowerPoint. However, in some talks or contests such as the Falling Walls Engage, you are expected to use very few slides.  

In those five minutes your aim is to inspire the audience and help them to learn about the area of research that you are presenting.

Giving a science flash talk: two case studies

In 2018 the Geneva Health Forum organised a Science Flash Talk contest in Switzerland. Bonaventure Ikediashi, a doctoral student at the University of Lucerne in Switzerland, won. In three minutes, Ikediashi convinced his audience about the need to study knowledge deficit among patients of sickle disease.

Prior to his doctoral studies Ikediashi, a Nigerian, worked at a global logistics company in Mauritius and Madagascar. He capitalised on his global health experience to give an engaging science flash talk .

Stephanie Okeyo is a microbiologist and founder of Under The Microscope , a Kenyan-based non-profit that advances science education. Okeyo was one of the finalists at the 2019 Falling Walls Engage . Like all other finalists, she had only five minutes to speak about her initiatives.

“Because of the short time, it taught me how to filter unnecessary information,” she says. “I learned to be okay with moving out of ‘my script’ as I stuck to the core points.”

But beyond filtering unnecessary information, there are several strategies to help you give an effective science flash talk.

Know your audience

Pascal Heymann, pitch and presentation trainer at BerlinSpeaking.com coached the 2019 Falling Walls Engage finalists. He says that knowing one’s audience is key to delivering an effective science flash talk. He explains: “I advise any expert to ask themselves two questions before creating a pitch: First, how much does my audience already know about this topic? Second, how does my field of research relate to the audience’s everyday experience?”

“As an expert, you are closer to your topic than anyone else. You know the intricacies, the connections, the challenges and the breakthroughs,” he adds. “However, your audience does not. Your audience for all intents and purposes knows little to nothing about your field of research. And that’s fine. That’s why you’re pitching it to them. But it’s vital to keep this in mind when creating your pitch.”

Indeed, knowing the backgrounds of the audience can also help one to relate to them. For example at a health conference it is common to have audiences with health backgrounds. However, in some contests such as Falling Walls Engage, the audience may have diverse backgrounds: from anthropology to zoology. The overriding point is to ensure that your audience will not be lost.

You have to be sensitive to the culture of your audience too. For example, in parts of Africa, it is common to quote the wise saying “too many rats cannot dig a hole”. This implies that it can be messy to have various people doing the same thing in an uncoordinated manner. But this wise saying may offend audiences in Arab cultures where the use of rats in reference to people is derogatory.

Top ten tips for an enaging and interesting flash talk

Start and end well.

How you begin your science flash talk can determine the extent to which your audience move along with you or decide not to pay attention. “I imagine we all have an idea of pain. So if you had a muscle pull, or maybe childbirth, menstrual cramps…” began Ikediashi. “Now imagine having pain with an intensity that is probably 50 times worse. Terrible, isn’t it?” With this introduction, Ikediashi captivated the attention of the audience. Beginning with statements such as “The title of my talk is …” is unlikely to be as interesting. Equally important as the start of your talk, is how you end it, which can leave a lasting impression on the audience. Thus, you should practise your opening and ending lines well. Okeyo ended  her talk  with the statement, “Try in your various ways to see how we are going to burst this bubble of ‘climate change is not real’.”  Such a call for action can be effective. To start and end well, you will need to practise.

Make your talk relevant and relatable to the audience

You should try to make your story of human interest even if your talk is about abstract concepts. Okeyo asked, “What puzzles me as a scientist is how is it that the most intelligent specie is destroying its own home?” The expectation is that an intelligent people should not be destroying their environment, and this helps create intrigue amongst the audience.

Ikediashi used pain, something everyone can relate to, as an example. The two presenters made the audience relate to their topics.

Make your audiences appear as though they were touching, feeling, hearing or smelling what you are talking about. This helps them appreciate your talk and become interested in it.

Let your character(s) in your story come alive

Giving a science flash talk is like telling a story. Have you ever read a story that appeared as though the characters jumped out of the book to interact with you?  For example, the spider (called  Ananse  in Akan language of Ghana) is one of the most important characters of West African folklore. How this tiny creature is assumed to be the master of all knowledge is indeed a mystery. And as I listened to  Ananse  stories as a child in rural Ghana, it looked as if  Ananse  was directly interacting with me. You can employ this element of character in your science flash talk.

The main character in Ikediashi’s science flash talk is someone with sickle cell disease. Can you guess the main character of Okeyo’s talk? The answer is humans causing climate change. One way of making your characters stand out is to present your research as a crisis or dilemma that needs a solution.

While on the stage, all attention is on you. Use the stage well. To engage your audience effectively, you will have to move around the stage. But ensure that you don’t dance on the stage. Your time is short but resist the temptation to feel hurried or pressured.

If you are naturally shy, don’t display your shyness on the stage by remaining at one corner as if you are a statue. And if you’re naturally outgoing beware of unnecessary body movements as you talk.

Nonverbal communication including how we express our feelings as we talk is an important part of giving a science flash talk. If your talk has elements of surprise, let your audience see it from your face. “Have some eye contact with your audience,” Okeyo adds.

Avoid things that compete with your message

You are the centre of attraction but your message or story is even more important. Ensure that your dress and mannerisms don’t compete with your message. For example, in our everyday talk, repetitive words such as “uhm”, “like”, “you know” or “so” may be common. Not only are such words distracting but they also consume some of the limited time available for the talk. Consider recording your practice sessions or having friends or relatives act as an audience during your practice sessions to identify these habits. Other things to avoid include fiddling with an object in your hand or rocking nervously on your feet.

Avoid “big words” and “speed bumps”

In Africa, we sometimes believe that those who use big words in their talks are more knowledgeable in their field. And so people will say “purchase” instead of “buy”, “approximately” instead of “about” or “elucidate” instead “make clear”. Sometimes, researchers also forget that they are not speaking with their peers. As a result, they may introduce technical terms and abbreviations or acronyms that the audience may not be familiar with. These may create “speed bumps” for the audience, impeding their understanding.

“The best way … is to speak to lay people and have them drill you with questions until they understand what you do,” explains Heymann.

Use your voice effectively

Your voice is yours so don’t hide behind the microphone while on stage. Use it effectively. Speak with an energetic voice but also ensure that you are not shouting.

As you practise your flash talk, learn how to modify aspects of your speech including pitch (low or high), pace (slow or fast) and volume (soft or loud). Use these effectively to ensure that your delivery is not monotonous.

Moreover, you should present your talk in a more active voice (for example, the dog bit the woman) rather than passive voice (for example, the woman was bitten by the dog). Not only is the active voice often easier to understand but it is shorter, helping conserve precious time. Of course, there are instances when the passive voice will be better such as if the woman is a prominent person (for example, the queen mother was bitten by her dog).

If you have to use a prop, do so effectively

A prop can be any physical object relevant to your talk that you show to your audience. Okeyo used a balloon as prop to represent a climate change bubble. When using props, ensure that they can be seen by the audience and that they are meaningful to your talk. And to help create suspense, don’t reveal the prop early to your audience. Okeyo had someone bring the prop to the stage just when she was ready to burst it, thus helping create a suspense. If she had held the balloon earlier in the presentation, it would have been a distraction.

When things don’t go as planned, don’t panic

Sometimes, things don’t go as planned. But remember that the audience doesn’t know how you planned to present, so may well not notice. If something does go contrary to your plans, move on and don’t panic.

Tying it all together

Knowing these strategies will help you provide effective science flash talks. However, without practising these strategies, you are unlikely to achieve perfection.  And remember: “Be confident while presenting, “ says Okeyo. “Nobody knows what you are presenting as well as you.”

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Doing Research: A New Researcher’s Guide pp 1–15 Cite as

What Is Research, and Why Do People Do It?

• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  
• Open Access
• First Online: 03 December 2022

14k Accesses

Part of the book series: Research in Mathematics Education ((RME))

Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

Download chapter PDF

Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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Clinical Research: FDA Issues Draft Guidance on Informed Consent

The U.S. Food and Drug Administration (FDA), in collaboration other agencies, recently published draft guidance (Guidance) on effectively presenting key information regarding informed consent in FDA-regulated clinical investigations of medical products and Department of Health and Human Services (HHS) supported human subjects research. The Guidance aims to assist sponsors, investigators, and institutional review boards (stakeholders) involved in or tasked with overseeing human subject research. In particular, the Guidance provides an overview on how to effectively present key information, offering recommendations for the content, structure, and delivery of informed consent.

FDA regulations in 21 C.F.R. parts 50 and 56 are designed to protect human subjects participating in FDA-regulated clinical investigations, ensuring their rights, safety, and welfare. Following revisions announced by HHS on January 19, 2017 ( Revised Common Rule ), efforts have been made to enhance protection for human subjects and streamline research processes. Section 3023 of the Cures Act mandates the alignment of differences between HHS and FDA regulations, prompting a proposed rule to amend the aforementioned 21 C.F.R. sections for “harmonization.” FDA further plans to incorporate parts of the Revised Common Rule into 21 C.F.R. § 50.20(e) in support of its ongoing efforts to ensure consistency in regulations governing human subject protection.

Informed Consent Criteria

The Revised Common Rule dictates that consent information is to:

Begin with a concise and focused presentation of the key information that is most likely to assist a prospective study subject or legally authorized representative in understanding the reasons why one might or might not want to participate in the research. See 45 C.F.R. § 46.116(a)(5)(i)) .

The FDA, therefore, recommends that stakeholders carefully identify key information when obtaining informed consent to ensure a participant’s understanding of the research risks, benefits, and procedures, thereby promoting their ability to make informed decisions about participation.

Identification of Key Information

The FDA recommends starting the key information section of the consent form with an introductory statement to guide prospective study subjects. It is unnecessary for this section to include every element of informed consent; instead, stakeholders should prioritize including the most essential elements for a particular study, considering what would be important information to study participants. To aid stakeholders, FDA proposes the following content to include as part of the presentation of key information.

Voluntary Participation and Right to Discontinue Participation

The FDA recommends stating in the key information section that participation in the research is voluntary, with no penalties or loss of benefits for declining or withdrawing from participation in the study. Additionally, stakeholders should reassure prospective study subjects that their decision regarding study participation will not affect their relationship with health care providers or their medical care.

Purpose of the Research, Expected Duration, and Procedures to be Followed

The key information section of the consent form should effectively communicate essential details to prospective study subjects, facilitating their understanding of the study’s purpose and protocol, such as its design and inclusion criteria. Key details may include the expected duration of participation, procedures involved, the status of investigational products, and any experimental procedures. Additionally, stakeholders should consider including information on placebo use, randomization, post-study options, and how participation compares to standard care.

Reasonably Foreseeable Risks and Discomforts

The FDA recommends providing information upfront about the most common and serious risks associated with participation in the study to assist prospective study subjects when making informed decisions about participation. Key risks should be prioritized and clearly distinguished from other research interventions and may encompass details on risk monitoring and mitigation strategies to ensure prospective study subjects are thoroughly informed.

Reasonably Expected Benefits

The key information section should emphasize any potential benefits of research participation, as these may influence prospective study subjects’ decisions. However, it is important to ensure that prospective study subjects understand the distinction between research and clinical care, and any potential benefits should be presented in a clear and realistic manner without conveying overly optimistic expectations.

Appropriate Alternative Procedures

Incorporating a clear and concise description of alternative procedures or treatments, if applicable, is essential in the key information section to inform prospective study subjects’ decision-making regarding participation in the study. It is recommended to provide information about the care prospective study participants would receive outside the study first, followed by details on how the care provided in the context of the study differs, emphasizing awareness of alternatives tailored to individual values and preferences.

Compensation and Medical Treatments for Research-Related Injuries

For research involving more than minimal risk, it is recommended to include details about available medical treatments and compensation for prospective study subjects in case of injury as key information, particularly if there are no plans for compensating for treatment costs related to research-related injuries.

Costs Related to Subject Participation

Interested parties should consider including information about potential costs incurred by prospective study subjects, including whether health insurance may be charged and if reimbursement for study-related expenses will be provided. Additionally, incentives and payments for time, inconvenience, and discomfort should also be addressed as key information, as these issues can impact prospective study subjects’ decisions to participate.

Supplemental Information

Supplemental information beyond basic consent elements can be added to the key information section if it’s important to the prospective study subject’s decision about research participation. HHS Secretary’s Advisory Committee on Human Research Protections suggests considering various aspects, such as the novelty of the research and impacts on subjects outside of the study, to identify relevant information to include.

Presentation of Key Information

The Revised Common Rule also requires that:

Informed consent as a whole must present information in sufficient detail relating to the research and be organized and presented in a way that does not merely provide lists of isolated facts, but rather facilitates the prospective study subject’s or legally authorized representative’s understanding of the reasons why one might or might not want to participate. 45 C.F.R. § 46.116(a)(5)(ii)

Stakeholders are advised to prioritize presenting key information concisely at the outset of the informed consent process. The FDA emphasizes the need for clear and well-organized consent forms across various presentation formats, including written, oral, or electronic mediums. To enhance comprehension, stakeholders are encouraged to explore innovative methods like utilizing illustrations or tablet devices. Additionally, consent documents should adhere to plain language principles, prioritize essential information, and adopting a tiered structure, presenting key details first and additional information as needed. Customizing information to match the audience’s language proficiency, education, and cognitive abilities is critical for facilitating understanding and informed decision-making regarding participation in a study.

The FDA’s Guidance offers recommendations for effectively presenting key information in FDA-regulated clinical investigations and HHS-supported human subject research. The Guidance aligns with efforts to enhance protection for human subjects and streamline research processes. By prioritizing concise presentation and innovative methods, stakeholders can promote informed decision-making among prospective study participants, ensuring consistency and clarity in regulations governing human subject protection. Foley & Lardner will continue to monitor the development of this proposed rule to provide timely updates and insights to our readers.

Foley is here to help you address the short- and long-term impacts in the wake of regulatory changes. We have the resources to help you navigate these and other important legal considerations related to business operations and industry-specific issues. Please reach out to the authors, your Foley relationship partner, our  Health Care & Life Sector , or to our  Health Care Practice Group  with any questions.

Jordan H. Smiley

Kyle Y. Faget

Related insights, clinical trials: fda issues finalized charging guidance for investigational drug use , cancer drugs: antibody drug conjugates (adcs) keep growing, “let’s talk compliance”: medicare advantage: compliance issues and enforcement.

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'Love is Blind' says it's an experiment. Scientists say attraction is more arbitrary

Mansee Khurana

In the popular reality TV show Love is Blind, contestants date each other while in "pods" where they cannot see each other. Netflix hide caption

In the popular reality TV show Love is Blind, contestants date each other while in "pods" where they cannot see each other.

Netflix wraps up its sixth season of its popular reality TV show Love is Blind tomorrow with a highly anticipated reunion show. The show, which bills itself as a "social experiment," gathers contestants together who all seemingly believe in the premise that love is not based on physical appearance – and is instead based on a deep, intimate connection with another person.

In the show, men and women all "date" for about a week without ever seeing each other. To keep the contestants hidden from their possible love matches, they each conduct their dates inside a separate pod,with a wall separating them. After their week of "dating" is complete, the couples can choose to get engaged, after which they finally get to see each other.

Despite its far-fetched premise, the show is a hit – with the series being the most-watched reality show on Netflix in 2023 .

The success rate of its dating formula for contestants is also higher than other dating-focused reality TV shows like The Bachelor or Love Island . In six seasons, Love is Blind has made eight successful couples, while The Bachelor franchise has only had six.

The secret to lasting love might just be knowing how to fight

So does that mean love really is blind? Well, according to anthropologists and psychologists who study attraction, romantic love is based more on unique and distinctive characteristics than just physical attraction.

"Often, when we're talking about the question 'is love blind,' we're actually talking about our tendency to overlook certain qualities in a person when we're in love,". Helen Fisher, a biological anthropologist and the author of the book "Anatomy of Love."

But the possible idealizing of your partner in the early stages of dating doesn't mean you can completely ignore physical attraction. It plays an important role in the steps leading up to long-term, romantic love.

Abhishek Chatterjee and Deepti Vempati in season 2 of "Love Is Blind." Patrick Wymore/Netflix hide caption

Abhishek Chatterjee and Deepti Vempati in season 2 of "Love Is Blind."

In season five of the show, newly engaged couple Abhishek "Shake" Chatterjee and Deepti Vempati begin having trouble after exiting the "pods," where two couldn't see each other. Chatterjee, repeatedly tells other contestants on the show that he's not physically attracted to Vempati.

"It feels like I'm with my aunt or something," Chatterjee tells Jarette Jones, another contestant on the show.

Vempati ultimately calls the engagement off after multiple people, including Chatterjee's mother, tell her that she deserves better. Her decision to end the engagement was also influenced by her realizing that Chatterjee had been making comments about her behind her back. In the last episode of the season, Vempati revealed that she also had reservations about their physical chemistry, since Chatterjee wasn't someone who was normally her "physical type."

Romantic advice (regardless of your relationship status)

"I don't fault Shake for not finding me physically attractive or having that chemistry," she said in the season two finale of the show. "Like, you can't really fault someone for that."

That doesn't mean that couples have to see each other to develop romantic feelings for each other, Paul Eastwick, a professor of psychology at the University of California, Davis told NPR's Leila Fadel. After all, there are many instances of people having successful relationships without having seen the person first. But, people do desire romantic partners they personally find physically attractive.

"It can change the way you see someone," Eastwick told NPR's Morning Edition.

Cameron Hamilton and Lauren Speed attend a Love Is Blind screening in Atlanta. Paras Griffin/Getty Images hide caption

Cameron Hamilton and Lauren Speed attend a Love Is Blind screening in Atlanta.

In Eastwick's own research, he's proposed that there are fourteen core principles needed to study close relationships. None of these have to do with physical features, but rather things like cultural norms, responses to stress and the opportunities that partners have to integrate each other into their lives.

Relationships are also built on the unique patterns of partners that are created when two people decide to pursue a relationship.

"Love is idiosyncratic," Eastwick said. "It's based on your experience with another person."

There are signs that physical attraction changes as a relationship progresses. This shift in physical attraction includes couples reporting that they have less sex as they get older, though that doesn't mean that the two don't find each other physically attractive at all.

"Usually, couples still think their partner is the most attractive person in the room," Eastwick said. "Because they know that's their person."

Couples who have gotten married after meeting on Love is Blind have talked about how their relationship changed after they saw each other for the first time.

Cameron Hamilton, who was on the very first season of Love is Blind , called it a dream to be able to finally meet his fiancee, Lauren Speed Hamilton, for the first time.

"I'm not just in love with how gorgeous she is, but the person she is," Hamilton said on the show.

In an interview with Tamron Hall, his now wife, Speed Hamilton, said that the experience helped them keep their emotional connection at the forefront of their relationship.

"The way that we met each other, since we couldn't see each other, so it was all about conversation, we just kind of kept that going throughout our marriage," she told Hall.

The two are still together, and celebrated their fifth wedding anniversary in November 2023.

Speed-Hamilton admitted that before she saw her future husband, she was nervous that she wouldn't find him attractive. However, she was willing to give it a try because of the relationship they had developed before they had even seen each other.

"Sometimes, attraction grows," she said. "There is nothing more attractive than someone who treats you right and loves you properly."

The audio version of this story was produced by Ben Abrams. It was edited by Ashley Westerman.

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Nvidia’s Jensen Huang: The incredible future of AI

Jensen Huang, the CEO of tech titan Nvidia, has a message for the world about artificial intelligence: You ain’t seen nothing yet.

Speaking to a standing room-only audience at the 2024 SIEPR Economic Summit, Huang predicted that in as little as five years AI will be able to pass every test a human takes — not just the legal bar exams that it can complete today, but also highly specialized medical licensing exams.

In about 10 years, he said, the computational capabilities of AI systems will be a million times bigger than they are today. Systems synthetically generating data will have greater capacity to continuously learn, infer, and imagine. Instead of only instantly answering questions, forthcoming AI systems will also have the ability to think critically through problems over longer periods of time.

“In the future, the way you interact with AI will be very different” from what can be done with ChatGPT and other AI models today, said Huang in a keynote question-and-answer session led by John Shoven , a SIEPR senior fellow, emeritus; and the Charles R. Schwab Professor of Economics, emeritus, in Stanford’s School of Humanities and Sciences.

But does this mean AI technology will be able to mimic the human mind? Huang said he wasn’t sure. There needs to be a consensus about what it means to say AI has achieved human intelligence.

In order to have true artificial general intelligence, he said, “you need to know what the definition of success is.”

The gift of pain and suffering

Having co-founded Nvidia more than 30 years ago, Huang now finds himself at the center of the tech universe. His company, whose market value hit $2 trillion last month (after reaching $1 trillion the previous June), has rocketed thanks to its sophisticated and hugely expensive semiconductor chips and its estimated market share of more than 80 percent in AI chips.

“We sell the world’s first quarter-million-dollar chip,” Huang noted, referring to Nvidia’s powerful graphical processing unit system that weighs 70 pounds, consists of 35,000 parts and has the computing capacity of a data center.

During his Summit appearance, Huang regaled attendees with his insights and now-familiar deadpan humor. Asked about his signature outfit of black leather jacket, black shirt, and black pants, Huang said they are among the few pieces of clothing that don’t make him itch.

When asked his advice for Stanford students aspiring to be successful entrepreneurs, Huang talked about the importance of low expectations and high resilience. Greatness, he said, comes from smart people who have suffered from setbacks. This is why, at Nvidia, he talks openly about pain and suffering “with great glee.”

“For all of you Stanford students,” he said, “I wish upon you ample doses of pain and suffering.”

Watch the full discussion.

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More Screen Time Means Less Parent-Child Talk, Study Finds

By Emily Baumgaertner

According to new research, “technoference” is real.

Toddlers who are exposed to more screen time have fewer conversations with their parents or caregivers by an array of measures. They say less, hear less and have fewer back-and-forth exchanges with adults compared with children who spend less time in front of screens.

Those findings, published on Monday in the journal JAMA Pediatrics , make up one of the first sets of longitudinal evidence to confirm an intuitive reality: Screens are not just linked to higher rates of obesity, depression and hyperactivity among children; they also curb face-to-face interactions at home — with long-term implications that could be worrisome.

Some Background: What interrupts household chatter?

Researchers have long known that growing up in a language-rich environment is vital for early language development. More language exposure early in life is associated with social development , higher I.Q.s and even better brain function.

Given the value of such exposure, researchers in Australia were eager to investigate potential factors within the home environment that could be interrupting opportunities for parents to interact verbally with their children. Previous studies on the impact of technology mostly examined a parent’s use of a mobile device, rather than a child’s use of screens, and relied on self-reported measures of screen time rather than automated monitoring.

What Researchers Found: Every minute counts.

The new study, led by Mary E. Brushe , a researcher at the Telethon Kids Institute at the University of Western Australia, gathered data from 220 families across South Australia, Western Australia and Queensland with children who were born in 2017. Once every six months until they turned 3, the children wore T-shirts or vests that held small digital language processors that automatically tracked their exposure to certain types of electronic noise as well as language spoken by the child, the parent or another adult.

The researchers were particularly interested in three measures of language: words spoken by an adult, child vocalizations and turns in the conversation. They modeled each measure separately and adjusted the results for age, sex and other factors, such as the mother’s education level and the number of children at home.

Researchers found that at almost all ages, increased screen time squelched conversation. When the children were 18 months old, each additional minute of screen time was associated with 1.3 fewer child vocalizations, for example, and when they were 2 years old, an additional minute was associated with 0.4 fewer turns in conversation.

The strongest negative associations emerged when the children were 3 years old — and were exposed to an average of 2 hours 52 minutes of screen time daily. At this age, just one additional minute of screen time was associated with 6.6 fewer adult words, 4.9 fewer child vocalizations and 1.1 fewer turns in conversation.

What Happens Next: A look at “co-viewing.”

Lynn Perry, as associate professor of psychology at the University of Miami who was not involved in the study, said she was impressed by the way the study employed an objective measuring tool to demonstrate associations that “had previously only been assumed.”

Dr. Perry, who studies language and social interaction among preschool children, said experts in the field should next investigate how media designed to be viewed by parents and children together “might allow for more conversational turn-taking and bypass some of the negatives of screen time.”

Sarah Kucker, an expert in language development and digital media at Southern Methodist University in Dallas who was also not involved in the study, called the analysis “impressive” but emphasized that understanding the nuances of how and when media is used in a larger and more diverse population is “a critical next step.”

“Media is not going away,” Dr. Kucker said, “but paying attention to how and when media is used may be a good future avenue.”

Emily Baumgaertner is a national health reporter for The Times, focusing on public health issues that primarily affect vulnerable communities. More about Emily Baumgaertner

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Bernie Sanders wants the US to adopt a 32-hour workweek. Could workers and companies benefit?

FILE - Sen. Bernie Sanders, I-Vt., talks to the media as he walks to the House chamber before President Joe Biden’s State of the Union address at the U.S. Capitol, March 7, 2024, in Washington. Sanders, the far-left independent from Vermont, introduced a bill Thursday, March 14, that would shorten to 32 hours the amount of time many Americans can work each week before they’re owed overtime. (AP Photo/Jose Luis Magana, File)

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The 40-hour workweek has been standard in the U.S. for more than eight decades. Now some members of Congress want to give hourly workers an extra day off.

Sen. Bernie Sanders, the far-left independent from Vermont, this week introduced a bill that would shorten to 32 hours the amount of time many Americans can work each week before they’re owed overtime.

Given advances in automation, robotics and artificial intelligence, Sanders says U.S. companies can afford to give employees more time off without cutting their pay and benefits.

Critics say a mandated shorter week would force many companies to hire additional workers or lose productivity.

Here’s what to know about the issue:

What would Sanders’ proposal do?

The bill Sanders introduced Wednesday in the Senate would reduce the standard workweek from 40 hours to 32 hours. Employers would be prohibited from reducing their workers’ pay and benefits to match their lost hours.

That means people who currently work Monday through Friday, eight hours per day, would get to add an extra day to their weekend. Workers eligible for overtime would get paid extra for exceeding 32 hours in a week.

Sanders says the worktime reductions would be phased in over four years. He held a hearing on the proposal Thursday in the Senate Health, Education, Labor and Pensions Committee, of which Sanders is the chairman.

How would a shorter workweek affect employees and productivity?

One recent study of British companies that agreed to adopt a 32-hour workweek concluded that employees came to work less stressed and more focused while revenues remained steady or increased.

In 2022, a team of university researchers and the nonprofit 4 Day Week Global enlisted 61 companies to reduce working hours for six months without cutting wages. Afterward, 71% of the 2,900 workers said they were less burned out and nearly half reported being more satisfied with their jobs.

Meanwhile, 24 of the participating companies reported revenue growth of more than 34% over the prior six months. Nearly two dozen others saw a smaller increase.

“The majority of employees register an increase in their productivity over the trial. They are more energized, focused and capable,” Juliet Shor, a Boston College sociology professor and a lead researcher on the UK study, told Sanders’ Senate committee.

Critics say a 32-hour workweek might work for companies where employees spend most of their time at computers or in meetings, but could be disastrous for production at manufacturing plants that need hands-on workers to keep assembly lines running.

“These are concepts that have consequences,” Roger King, of the HR Policy Association, which represents corporate human resource officers, told the Senate committee. “It just doesn’t work in many industries.”

What’s the response in Washington?

With considerable opposition from Republicans, and potentially some Democrats, don’t expect Sanders’ proposal to get very far in the Senate. A companion bill by Democratic Rep. Mark Takano of California is likely doomed in the GOP-controlled House.

GOP Sen. Bill Cassidy of Louisiana said paying workers the same wages for fewer hours would force employers to pass the cost of hiring more workers along to consumers.

“It would threaten millions of small businesses operating on a razor-thin margin because they’re unable to find enough workers,” said Cassidy, the ranking Republican on the committee. “Now they’ve got the same workers, but only for three-quarters of the time. And they have to hire more.”

Sanders has used his platform as the committee’s chairman to showcase legislation aimed at holding big corporations more accountable to workers. He blamed greedy executives for pocketing extra profits as technology has boosted worker productivity.

“Do we continue the trend that technology only benefits the people on top, or do we demand that these transformational changes benefit working people?” Sanders said. “And one of the benefits must be a lower workweek, a 32-hour workweek.”

How did we decide a 40-hour workweek was the standard?

The Fair Labor Standards Act, signed into law by President Franklin D. Roosevelt in 1938, restricted child labor and imposed other workplace protections that included limiting the workweek to 44 hours. The law was amended two years later to make it a 40-hour week.

The landmark law followed a century of labor-union efforts seeking protections for the many overworked people in the U.S., said Tejasvi Nagaraja, a labor historian at Cornell University’s School of Industry and Labor Relations.

“The issue of time was always as important, or more important, than money for labor unions and labor advocates,” Nagaraja said.

In the 1830s, coal miners and textile workers began pushing back against workdays of up to 14 hours. After the Civil War, the abolition of slavery caused those in the U.S. to take a fresh look at workers’ rights. Unions rallied around the slogan: “Eight hours for work, eight hours for rest, eight hours for what you will.”

The federal government took tentative steps toward limiting working time. In 1869, President Ulysses S. Grant ordered an eight-hour workday for government employees. In 1916, Congress mandated the same for railroad workers.

Other reforms came from private industry. In 1926, Henry Ford adopted a 40-hour week for his automobile assembly workers more than a decade before Congress mandated it.

Ford wrote: “It is high time to rid ourselves of the notion that leisure for workmen is either lost time or a class privilege.”

Associated Press reporter Mary Clare Jalonick in Washington contributed.

First Lady Jill Biden is heading to North Carolina to talk women's health

DURHAM, N.C. (WTVD) -- First Lady Jill Biden is heading to North Carolina as part of the White House Initiative on Women's Health Research.

On Wednesday at 1:15 p.m., she is delivering remarks on women's health research in Durham.

President Joe Biden and Vice Present Kamala Harris are also making a trip to the Tar Heel state next week on March 26. One of their stops will be in Raleigh for a private fundraiser event.

The White House said there are more details to come.

Harris already made a trip to the state earlier this month ahead of Super Tuesday. She spoke in Durham about strengthening entrepreneurship and supporting small businesses, specifically focusing on the city's historic Black Wall Street to help black entrepreneurs launch and scale their businesses.

Harris also announced awards of $92 million in minority and women-led venture capital firms in the state through funding by the American Rescue Plan -- $32 million is from the federal government and another $60 million is from venture capitalists.

WATCH | VP Kamala Harris in Durham to discuss White House plan to invest millions in economy

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