Learning and teaching in STEM fields using “authentic problems”

by Peter D. Huck

In STEP-WISE, my co-instructors and I sought to leverage our experience with computer programming to instruct fourth year biology students in what type of analysis was possible with the aid of open-source computational resources, like Python. We sought to teach students how to think about and approach problems relevant in our research. Little did we know that we were adhering to an approach that the education literature calls teaching with “authentic problems”.

A perennial concern in higher education is how to ensure that recent college graduates can solve real-world problems they encounter, despite having completed a program of rigorous course work. Price and coworkers (2021) address this concern. They hypothesize that the ability to solve problems is assessed by challenging exercises with well-defined answers reached by straightforward analysis, but do not require the use of judgement to make decisions based on limited or incomplete information. Therefore, to improve students’ ability to solve problems, instructors should offer problems that are more unstructured, lacking a clear solution path or that are not certain to have any solution at all, to come closer to real world situations. These are the problems that, according to Price et al. are authentic.

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Rethinking the Share in the Think-Pair-Share

By Joseph Groom

Cooper KM, Schinske JN, and Tanner KD. Reconsidering the Share of a Think-Pair-Share: Emerging Limitations, Alternatives, and Opportunities for Research. CBE Life Sciences Education (2021). Vol. 20: fe1. doi: 10.1187/cbe.20-08-0200

Random Call Anxiety

In their article in the March 2021 issue of CBE Life Sciences Education, K.M. Cooper et al. use recent studies to make a case for altering or abolishing the “share” portion of the widely used Think-Pair-Share method. The Think-Pair-Share is a popular active learning technique that allows students to come up with ideas on their own, bounce those ideas off of a classmate, hear a variety of student voices, and refine and articulate their own ideas by sharing them with the whole class. Cooper et al. succinctly explain how the “share” portion in particular can lead to inequities in learning, how certain assumptions about benefits of the “share” are not necessarily true, and how to effectively modify or remove the “share”.

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Curating content to teach concepts

By Mugdha Sathe

When I started my Masters in Biochemistry, I encountered huge biology textbooks. The large content was overwhelming, and I was questioning my decision to enter biochemistry when I had been a chemistry undergrad. But then, over the next two years, I realized I was learning all kinds of concepts along with these new-to-me biology facts.

As instructors, why do we focus on content instead of concepts? Petersen and colleagues address this question in their recent article about “the tyranny of content”. Instructors reported that the ‘Need to cover content’ was one of the barriers that kept them from implementing active learning in their classrooms. Basic courses are often prerequisites for advance course creating a perceived need to cover particular content. These concerns are legitimate. Learning facts does matter. But, still faculty-centered teaching persists despite the effectiveness of student-centred learning practices.

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A quantifiable measure of classroom engagement using skin biosensors

By Michael W. Cashman

In the second whole-group training session, my STEP-WISE colleagues and I learned about active learning techniques, their benefits, and why we should incorporate active pedagogy in the biology seminars we are designing and teaching. Based on personal experience as a student, participating in a flipped classroom learning environment that relies on just-in-time pedagogy works! So, I was surprised to learn that reformed-based teaching and learning practices are only gaining traction among academics responsible for teaching undergraduate STEM courses. The flipped classroom actually works for lots of people: a steadily increasing amount of data support active learning techniques and the positive impacts they can have on student learning.

Enter a group of researchers from Auburn University determined to generate data providing insight about what impact reformed-based teaching and learning practices have on student learning outcomes. Their article, “Biosensors show promise as a measure of student engagement in a large introductory biology course,” published in CBE—Life Sciences Education journal (December 2020) explains a masterfully crafted, meticulously designed, and utterly creative test.

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Training undergraduates through research networks

By Joseph Groom


Jensen-Ryan D, Murren CJ, Rutter MT, and Thompson JJ. Advancing Science while Training Undergraduates: Recommendations from a Collaborative Biology Research Network. CBE – Life Sciences Education (2020). 19: es13. doi: 10.1187/cbe.20-05-0090.

As a postdoc interested in a faculty position at a primarily undergraduate institution, I think a lot about how to effectively mentor students in my lab but also maintain a productive research program. I looked to this article for suggestions from faculty members with extensive experience leading an undergraduate research network.

What it is

Jensen-Ryan et al (2020) highlight a successful undergraduate biology research network (BRN) in CBE-Life Sciences Education. They wanted to synthesize what has been learned about running a BRN by identifying key successes, challenges, and recommendations. So, they interviewed a bunch of faculty members who have been a part of an 8-year BRN, and then coded the transcripts of those interviews.. They found that the BRN diversified access to scientific research, and improved student experiences, scientific outcomes, and faculty professional development. But they also found “goal conflict”: producing data and mentoring students are not necessarily aligned. Nonetheless, while data production was slower than anticipated, the positive student outcomes were very apparent. They recommend that mentors (1) use stringent laboratory protocols that can be modified through student work, (2) have dedicated personnel for management of the project, and (3) choose appropriate collaborators with agreed-upon expectations.

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The “Startup Mindset”: A Model for Pandemic Pedagogies

by Amal Katrib

Schooltime has gained new meaning in today’s world of social distancing, with the educational system pressured to embrace, and accordingly adapt to, the “new norm”. The pandemic’s abrupt onset had left many students trapped in a convoluted maze of uncertainties, having to fly relatively blind through a less familiar learning environment—the virtual classroom. In order to mitigate disruptions to student learning, educators started experimenting with a variety of online resources and technologies. While some focused on assembling a broad menu of solutions to effectively engage students from a distance, others conjured up new pedagogical modalities to best strategize for times ahead. And without the time to dive into research that guides both online and crisis teaching, academic institutions were opting to deploy flexible action plans so they can respond to such unprecedented challenges and pivot, if and when necessary.

This high degree of organizational adaptability is something I used to only associate with startups, failing to realize its prevalence, let alone its importance, in education.

Many early-stage startups emphasize the need to plan(a) ahead, while staying both lean(b) and agile(c) —what I refer to as the “startup mindset”—in order to survive an ever-changing volatile environment. They implement a “build-measure-learn” framework, cycling their ideas through a feedback loop of validated learning and quickly iterating through incremental development to optimize product value and market fit. They also are predominantly led by smaller, multifunctional teams that continue to collaborate across organizational boundaries without restraints. As a result, they are able to readily assess circumstantial changes as they come up, and strategically embrace them to continue driving innovation.

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Institutions must take responsibility for the mentorship of their trainees

by Meredith CoursePhoto of Dr. Meredith Course

My colleague (an incredible mentor herself!) Dr. Irini Topalidou and I were frustrated to see poor mentorship treated as a failing of individuals, when we felt it was clearly a larger, cultural issue in STEM. We were also galvanized to publish this article when we saw over and over evidence that good mentorship is disproportionately unavailable to underrepresented minorities in STEM, despite the fact that it is particularly beneficial to them. Without structural changes in place, those in mentorship positions would be allowed to mentor as they saw fit, rather than deliberately and with evidence-based practices, similar to those we learn about in STEP-WISE. Improved mentorship skills, we argue, benefits not just trainees, but mentors and institutions as well. Therefore, it behooves the institutions who hire and promote mentors and who admit and confer degrees on trainees to implement effective mentorship incentives, accountability, and training.

Institutions should take responsibility for trainee mentorship


Tracking Careers After a PhD

by Tanya Brown

One of the many questions I asked myself during my PhD training was “What kind of job will I get once I graduate?” This is quite a common question with many possible answers. With so many PhDs hitting the job market, where do all of us go?

It can be quite a challenge to figure out how to answer this question. Where do students go once they graduate? What do postdocs do once they are ready to move on? This sort of information would be useful for prospective graduate students and postdocs to know before they tackle academia. Career outcomes can reflect, at least in part, institutional training goals and values. Trainees deserve to know the environment that they are entering before devoting years to graduate school or postdoctoral training. Since this sort of tracking information is required for NIH training grants, it seems like this type of information is collected, at least by some programs, but may be challenging to sort through and organize at departmental and institutional levels. Communicating the results then presents a whole other set of challenges.   Continue reading