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Active Learning in BIS2A - Biology

Active Learning in BIS2A - Biology


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Active learning in BIS2A

In every lecture, we will ask you to answer questions, either in a small group or individually. These questions serve several purposes:

Functions of in-class questions

  • Questions stimulate students to examine a topic from a different perspective, one that the instructor considers relevant to their learning.

  • Questions act as mini "self-tests" for students. If you are uncertain about what question is being asked or how to answer it, this is a good time to (a) ask the instructor for clarification and/or (b) take note to review this immediately after class with a TA, the instructor, classmates, or the internet. If the instructor took the time to ask you the question in class, this is a big clue that he/she thinks that both the question and the answer are important.

  • Some in-class questions will ask students to formulate questions themselves. This is typically an exercise that is designed to force the student to reflect on and try to articulate the point of the lesson. These are critical exercises that force you to think more deeply about a topic and to place it in the broader context of the course.

  • Some questions may ask the student to interpret data or to create a model (e.g., perhaps a picture) and to communicate what they see to the class. This exercise asks the student to practice explaining something out loud. This can be a great self-test and learning experience, both for the person answering and fellow students who should also be using the time to examine how they would have answered the question and how that compares with the feedback of the instructor.

  • Questions in the discussion that follows and the thought process involved in solving a problem or answering the questions are opportunities for the instructor to model expert behavior in an interactive way—sometimes it is equally important to understand HOW we arrive at an answer as it is to understand the answer.

Some questions are designed to stimulate thought and discussion rather than to elicit a discrete answer. If called on, you should not feel compelled to have one "right" answer!! Understanding this is very important. Once you realize that it is perfectly acceptable (and sometimes desirable) to not know all of the answers (if you did, what would be the point of coming to class), it can take away a lot of the anxiety of getting called on. While it is okay to not know "the answer", it is nevertheless important for you to attempt to make a contribution to the discussion. Examples of other meaningful contributions might include: asking for clarification; associating the question with another class topic (trying to make connections); and expressing what you are comfortable with and what confuses you about the question. Don't be afraid to say "I don't know". That's perfectly okay and even expected sometimes. Be prepared for the instructor to follow up with a different question, however, that will try to either highlight something that you likely do know or to ask for your help with identifying a point of confusion.

Getting ready for lecture

To help you get ready for each lecture, we provide study guides that include instructions on how to prepare for class. You should do your best to complete the assigned reading and suggested "self-assessments" before coming to class. This will ensure that you are ready for discussions and that you can make the most of your time during class. We do not expect you to be an expert before lecture, but we do expect you to do the pre-reading and by doing so make yourself familiar with the required vocabulary and spend some time thinking about the concepts that will be discussed. We will build on that basic knowledge in lecture. If you do not have at least some of the basic building blocks before hand, you will make less efficient use of your time in class.

We cannot emphasize too strongly that YOU have the primary responsibility for learning the material in this (or any other) course. Although we are invested in your success, your instructors and TAs cannot magically implant knowledge. Like any other discipline that requires mastery (e.g., sports, music, dance, etc.), we can help guide you and critique your performance, but we can not replace the hours of practice necessary to become good at something. You would never expect to become a proficient pianist by going to lessons once or twice a week and never practicing. To most of us, it seems self-evident that you need practice to become good at something like music, art, or sports. It should not be surprising that the same rule applies with learning biology or any other academic subject.

We see ourselves as your coaches for this class; we want all of you to succeed. However, for this to happen, you have to take your practice seriously. This means coming to class prepared, participating in class, studying the material covered in class as soon as possible, identifying where you are uncertain and getting help to clarify those topics as soon as possible, and trying to make thoughtful contributions to the online discussions (not just the bare minimum required to "get the points").

Bottom line: you need to be active participants in your learning.


Active Learning in BIS2A - Biology

iclicker2, ready for action. (Photo by M. Hoefnagels)

I just learned of an article that should interest anyone contemplating the power of active learning. The title of the article is Active Learning Not Associated with Student Learning in a Random Sample of College Biology Courses, and it appeared in the Winter 2011 edition of CBE Life Sciences Education. I’ll leave you to read the full article if you’d like, but the point is that active learning, in and of itself, doesn’t automatically produce learning gains. The authors write: “Our … most important result was that we did not find an association between the weekly frequency of active-learning exercises used in introductory biology courses and how much students learned about natural selection.”

Why the difference between their result and the large number of previous studies reporting success using active learning methods? The authors attribute the discrepancy to the population of teachers being studied. That is, most studies on active learning use classes taught by instructors with extensive experience in science education, whereas this one was based on a nationwide sampling of biology teachers as a whole. Therefore, the instructor’s skill at using the techniques may be more important than the techniques themselves. The authors conclude: “These results imply active learning is not a quick or easy fix for the current deficiencies in undergraduate science education. Simply adding clicker questions or a class discussion to a lecture is unlikely to lead to large learning gains.”

Upon reflection, this result is not surprising. Think about the pencil, a tool that can be used to write amazing literature or pointless drivel. Or think about the dreadful PowerPoint presentations you have endured — you know, the type that inspired the phrase “Death by PowerPoint.” Used poorly, PowerPoint presentations can be maddeningly dull. But they don’t have to be in the hands of a charismatic speaker who uses it well, PowerPoint can add excitement and visual interest to any presentation.

Active learning techniques are no different. They can be useful tools, but only when used in conjunction with the other requirements for effective teaching. In my opinion, these are a deep knowledge of (and passion for) the subject respect for students and their current state of knowledge and a way to connect with students so that they’ll want to move from “I don’t get it” to “Ah ha! That’s it!”

Like many teachers, I struggle to find questions and activities that help me make that elusive connection with my students. That’s why I take every opportunity to observe and talk to experienced teachers in biology, allied health, and other disciplines. It’s hard to step outside your comfort zone to try something new, but it’s only through practice that you can build the skills necessary to engage your students in real learning. Keep careful notes about what works and what doesn’t work I suggest typing notes directly into your PowerPoint slides immediately after class, so you’ll see them the next time you teach. And don’t be afraid to make adjustments mid-semester if things don’t work out as planned.


Helping students develop their own ideas

“I was teaching human genetics to biology majors at UMD. The class was working in small groups, discussing an article. I overheard my students debating alternative interpretations of the data. I stopped for a moment and realized: this is what college education should be—using content to develop your own ideas,” said Quimby.

To foster this, Quimby—with the help of five postdoctoral fellows— recently developed a flipped cell biology curriculum course using the well-known textbook, Molecular Biology of the Cell written by Bruce Alberts and colleagues. In the classroom, instead of listening to a lecture or memorizing facts, students work together in small groups analyzing real data—including microscopy images and western blots. These exercises teach students to draw their own conclusions—as well as acquainting them with the scientific process.

UMD is one of many colleges and universities that have begun to embrace active learning. Research shows active learning increases student performance in science, technology, engineering and math (STEM) fields (see papers here and here). Now educators are putting these results into practice. For example, William Jeffries, associate dean at the University of Vermont College of Medicine, cited this study in the Burlington Free Press as a primary reason the medical school decided to replace all of its traditional lecture courses with active learning.


Biology Professor Highlights Active Learning in Science Education

“As an instructor, I try to teach how the topic has relevance from different approaches in biology,” said Erik Herzog, Professor of Biology at Washington University in St. Louis. Herzog teaches undergraduate biology courses at the university. His lab uses a variety of techniques to study the cellular and molecular basis of circadian rhythms, biological clocks that drive near 24-hour rhythms in living beings including animals and plants.

Herzog has a Ph.D. in biomedical engineering and neuroscience from Syracuse University. He spent six years as a postdoc at the University of Virginia before joining the Biology Department at Washington University. He has taught undergraduate classes at Wash U for 18 years. In 2014, Herzog became the director of ENDURE, a pipeline program that prepares undergraduates from diverse backgrounds for neuroscience Ph.D. programs. In 2018, Herzog received the Award for Education in Neuroscience from the Society for Neuroscience (SfN).

In an interview with The Teaching Center, Herzog discussed the challenges of teaching biology and how to address them with a broad curriculum. He also emphasized the importance of acting learning in science education by citing examples from his own teaching.

What was your path to becoming a teacher?

I started thinking that I might want to be a teacher when I was in high school and I was a swim coach. I coached kids of all ages. I also taught adults and pride myself on having taught swimming to a 70-year-old who was afraid of the water. I really enjoyed that. It was an opportunity to say, if you’re out there you get people’s attention and they listen to what you have to say. The idea that I could get in front of people and share my ideas was foreign to me at the time.

How did you become interested in circadian rhythms?

When I was a grad student working with Robert Barlow, we discovered that horseshoe crabs see equally well during the day and night. I was fascinated by the idea that somehow, these crabs put on night vision goggles to anticipate the night and that they did this everyday in order to be able to navigate and do the things they do.

Then I discovered that there’s this thing called a circadian clock that allowed them to anticipate these millionfold changes. I went to the lab of Dr. Gene Block at the University of Virginia to learn more about circadian clocks. It was a coincidence: I saw it in my data that these crabs were seeing equally well day and night, and then I went to study the biological clock.

What are some of the challenges of teaching biology?

One challenge that we all face in biology is that we don’t agree on the fundamental laws. We agree that to be a biologist, you need to think about evolution, genetics, cells, and systems, but we all approach it with a different set of fundamental facts, so building from there and integrating the lessons of biology is a challenge.

At Washington University, we’ve done a good job in the Biology Department of defining a curriculum that trains students broadly in the biological sciences. We’ve refused to subdivide the majors. As an instructor, I try to teach how the topic has relevance from different approaches in biology.

How do you use active learning in your teaching?

I teach two classes to undergrads at Wash U: One is the lab of neurophysiology and the other is about biological clocks. The first class is hands-on. The students take the data they collect, analyze it, interpret it, and put it into a manuscript. They end up writing three full-length scientific manuscripts with drafts and reviews by their peers. That’s one way in which active learning works well in the lab setting.

In the biological clocks class, students work on a Wikipedia project. During the first half of the class, they’re introduced to famous scientists in the field. Then they look at Wikipedia to see who is missing from the public database. They then nominate and vote who they want to create in Wikipedia. We divide students into teams of three and they work on around 40 Wikipedia sites. They generate a living document for the public. Students enjoy getting to interact with scientists in the field to check the validity of the story and they enjoy working together as a team to ensure the best quality product.

Why is supporting diversity in STEM important?

Among the many reasons, I feel that science is for everybody. Subsets of our community have not always felt welcome or invited to become scientists, so opening doors is a first step. Another reason is, the demographics of our country are changing such that who is a minority will be different by 2050. If we don’t start training those who are in the minority now and will soon be in the majority, it will be a problem for everybody.

How do you see science education changing in the future?

I think most of us have bought into the idea that independent research experiences are an efficient and important way to train future scientists. The future of science education at Washington University will hopefully involve training for research mentors that benefits the mentors without being burdensome, training that teaches best practices and is based upon real data. I’m excited for Washington University to invest in training for research mentors and resources for mentors.

Who were the teachers who most impacted you and why?

There are many people who have contributed to who I am as a scientist. My postdoc mentor, Gene Block, taught me how to do science and how to enjoy the collaborative process. I was also inspired by two professors in undergrad, Dr. Dan Rittschof and Dr. Richard Forward Jr. They gave me my first taste of independent research.

Here at Washington University, I arrived and started working closely with Nobuo Suga (Professor Emeritus of Biology) and Paul Stein (Professor of Biology and Physical Therapy), who were instrumental in my teaching. From Nobuo, I learned to always have a piece of scratch paper to draw pictures and I learned how to write upside down. From Paul I learned how to use the board and always have a plan for where you’re going and what you’ll test.


MATERIALS AND METHODS

The course in question enrolls ∼345 students and is usually taken by sophomores. A typical demographic makeup is 58% female, 46% white, 30% ESL, and 7% URM students, of whom ∼17% are in the Educational Opportunity Program (EOP). EOP students have been identified by the UW as economically or educationally disadvantaged. The course is offered every quarter of the year and is taken by a total of ∼1200 students per year or almost 25% of an average incoming class at our university. The course prerequisites are two quarters of inorganic chemistry.

On a grading scale from 0.0 to 4.0, a grade of 1.5 or higher in the initial course is required to register for the next course in the year-long (3-quarter) sequence. Thus, students who do not receive a grade of 1.5 or higher fail to advance in the life sciences. Students who do not receive 2.5 or higher must average 2.0 or higher over the three courses in the series in order to declare biology as their major. Thus, students who receive less than 2.5 are at high risk of failing to advance in biology or other life sciences majors.

The study was organized in three steps: 1) analyzing the characteristics of students who had taken the course previously to better understand the reasons for failure and to predict student performance a priori 2) implementing four contrasting course designs during the spring quarter 2005 and 3) repeating one of these four course designs and contrasting it with a fifth course design in the fall quarter 2005.

Risk Analysis

To better understand the reasons for the high failure rate, we analyzed data on 3338 students who had started the introductory biology series at the UW between the autumn quarters of 2001 and 2004. Specifically, we attempted to correlate the following demographic variables and measures of prior academic performance with failure in one or more of the courses in the series: gender, ethnicity as Caucasian, Asian, or URM (African-American, Native American, Hispanic, or Pacific Islander), chronological age, average grade in UW chemistry classes at the time of entering the course, overall UW grade point average (GPA) at the time of entering the course, UW class standing (freshman, sophomore, etc.) at the time of entering the course, high school GPA, Scholastic Aptitude Test (SAT) verbal score, SAT math score, American College Test (ACT) score, score on the UW math placement test given to matriculating students, Test of English as a Foreign Language (TOEFL) score, and EOP status.

After analyzing these variables for covariation and evaluating them for missing data, we performed a factor analysis to determine which variables could be omitted from the analysis or aggregated into a single index. These steps led us to drop UW chemistry GPA, high school GPA, ACT score, TOEFL score, and UW math placement score from the model.

To determine which of the remaining variables were most capable of predicting failure in the course, we performed bivariate logistic regression with backward elimination. After determining which variables were most important, we used them to design a regression model that predicted student grades in the first course in the sequence. In this way, we could identify students who were at low risk or high risk of failing the course. We defined high-risk students as those predicted to get below a grade of 2.5 in the course, and low-risk students as those predicted to get a grade of 2.5 or higher.

Spring 2005 Course Design

The spring 2005 course was listed as two equal-size sections during registration, so that students signed up for sections unaware of contrasting course designs. The two sections were taught back-to-back by the same instructor (S.F.) in the same room, using identical notes. Students from the two sections were mixed randomly in lab sections and required field trips.

During each class the instructor posed four multiple-choice questions that required a response from all students. The period started with a question based on the previous session's material and a question on the reading for that day. Twice during a lecture delivered in a modified Socratic style, the instructor posed questions based on the material being discussed and that had to be answered by all students. The questions were designed to be difficult. Specifically, they attempted to either test students' ability to analyze an aspect of the topic or apply a concept in a new situation. If less than ∼60% of the answers to these questions were correct, the instructor told the students to discuss the question among themselves and then reanswer (this is the peer-instruction technique see Mazur, 1997 Crouch and Mazur, 2001). Students answered from four to eight formal questions each day, with an average of 5.6.

In the “clickers section,” students were given an electronic response device that they registered with their name and student number. After each class session, the instructor would choose three of the four to eight responses to grade. Correct answers on these three questions were worth 1 point each. A total of 100 clicker points were possible for the quarter, representing ∼14% of the total points.

In the “cards section,” students were given four cards with A, B, C, or D printed on them. Students held up these cards to answer the same in-class questions posed to the clicker section. Because other students could look at the cards if they desired, card responses were public. To enforce or prescribe participation, the instructor “stared down” students who occasionally did not hold up a card, so that virtually all students responded to all questions. Card responses were public but ungraded clicker responses were private but graded.

The full class met 4 d per week during the fifth class period each week students were given five written, exam-style questions to complete in 35 min. Answers were then randomly assigned to another student for a 15-min grading period based on a key and rubric provided by the instructor. Correct answers were to be given 2 points, partial credit answers 1 point, and blank or unintelligible answers 0 points. Both the answerer and grader were anonymous to each other only course staff knew their identities. Students did nine of these practice exams for a total of 90 possible points—roughly 15% of the total grade.

Students in the cards and clickers sections were randomly assigned to one of two methods of completing identical practice exams. Within each section, half the students did the practice exams and grading by themselves online, and half did the practice exams and grading as part of a study group. Students who did the assignment online could do so anywhere, but had to log in and submit answers and grades on the 35-min + 15-min schedule during the class period. Students assigned to study groups met in a classroom on campus and were proctored by a staff member. The staff member did not answer content questions or assist the groups in any way. The staff member distributed the hard-copy questions, accepted answers after 35 min, randomly assigned answer sheets for the 15-min grading period, and collected the graded sheets.

Students were assigned to study groups by the instructor, on the basis of their course grade predicted by the regression model from the risk analysis. Each study group had one student who was predicted to receive below 1.5 in the course, two students who were predicted to receive between 1.5 and 3.0, and one student who was predicted to receive 3.0 or higher. Students were unaware of this structure, however. Each week, study group members signed up to serve as their group's “manager,” who coordinated the exercise “strategist,” who considered ways to approach each question “recorder,” who wrote the answers or “encourager,” who gave positive feedback to participants. These roles were explained by the staff proctor but were not enforced by peer evaluation or other techniques.

To summarize, the spring quarter tested the following four designs: clickers + online practice, clickers + study group practice, cards + online practice, and cards + study group practice. All students took a common final and a common second midterm. On the first midterm, several questions on the exams given to the two sections were identical or formally equivalent. Thus, there were a total of 336 out of 400 total exam points available to use in evaluating student performance on identical exam questions. Students who dropped the course or who were caught cheating on exams were not included in any of the analyses.

We also collected data on attendance. This was done automatically from clicker responses and from the cards section by counting the number of students present during each class period. Although we could only evaluate overall class attendance in the cards section, in the clickers section we could also analyze the number of classes attended by each student.

Fall 2005 Course Design

As in spring 2005, students registered blindly for two equal-sized sections in the fall 2005 course. The sections were again taught back-to-back by the same instructor (S.F.) in the same room using identical notes, with students from the two sections mixed randomly for labs and field trips. In both sections, students completed and graded weekly practice exams by themselves online.

All students in the course were required to purchase a clicker and register it to their name and student number. The instructor posed identical, daily, in-class, multiple-choice questions to each section. In one section, the questions were graded for right/wrong answers using the same format as spring 2005. In the other section, students were given points for participation, with two points per day possible if students answered all questions posed—irrespective of whether their answers were correct or incorrect.

To summarize, the fall quarter repeated the clickers (graded) + online course design and added a clickers (participation) + online course design. Students again took a common final exam although the sections were given different midterm exams, enough midterm questions were identical or formally equivalent that there were a total of 335 of the 400 total exam points to use in evaluating student performance on common exam questions. Unless otherwise noted, all statistical tests reported here are two-tailed tests.


Abstract

To test the hypothesis that lecturing maximizes learning and course performance, we metaanalyzed 225 studies that reported data on examination scores or failure rates when comparing student performance in undergraduate science, technology, engineering, and mathematics (STEM) courses under traditional lecturing versus active learning. The effect sizes indicate that on average, student performance on examinations and concept inventories increased by 0.47 SDs under active learning (n = 158 studies), and that the odds ratio for failing was 1.95 under traditional lecturing (n = 67 studies). These results indicate that average examination scores improved by about 6% in active learning sections, and that students in classes with traditional lecturing were 1.5 times more likely to fail than were students in classes with active learning. Heterogeneity analyses indicated that both results hold across the STEM disciplines, that active learning increases scores on concept inventories more than on course examinations, and that active learning appears effective across all class sizes—although the greatest effects are in small (n ≤ 50) classes. Trim and fill analyses and fail-safe n calculations suggest that the results are not due to publication bias. The results also appear robust to variation in the methodological rigor of the included studies, based on the quality of controls over student quality and instructor identity. This is the largest and most comprehensive metaanalysis of undergraduate STEM education published to date. The results raise questions about the continued use of traditional lecturing as a control in research studies, and support active learning as the preferred, empirically validated teaching practice in regular classrooms.

Lecturing has been the predominant mode of instruction since universities were founded in Western Europe over 900 y ago (1). Although theories of learning that emphasize the need for students to construct their own understanding have challenged the theoretical underpinnings of the traditional, instructor-focused, “teaching by telling” approach (2, 3), to date there has been no quantitative analysis of how constructivist versus exposition-centered methods impact student performance in undergraduate courses across the science, technology, engineering, and mathematics (STEM) disciplines. In the STEM classroom, should we ask or should we tell?

Addressing this question is essential if scientists are committed to teaching based on evidence rather than tradition (4). The answer could also be part of a solution to the “pipeline problem” that some countries are experiencing in STEM education: For example, the observation that less than 40% of US students who enter university with an interest in STEM, and just 20% of STEM-interested underrepresented minority students, finish with a STEM degree (5).

To test the efficacy of constructivist versus exposition-centered course designs, we focused on the design of class sessions—as opposed to laboratories, homework assignments, or other exercises. More specifically, we compared the results of experiments that documented student performance in courses with at least some active learning versus traditional lecturing, by metaanalyzing 225 studies in the published and unpublished literature. The active learning interventions varied widely in intensity and implementation, and included approaches as diverse as occasional group problem-solving, worksheets or tutorials completed during class, use of personal response systems with or without peer instruction, and studio or workshop course designs. We followed guidelines for best practice in quantitative reviews (SI Materials and Methods), and evaluated student performance using two outcome variables: (i) scores on identical or formally equivalent examinations, concept inventories, or other assessments or (ii) failure rates, usually measured as the percentage of students receiving a D or F grade or withdrawing from the course in question (DFW rate).

The analysis, then, focused on two related questions. Does active learning boost examination scores? Does it lower failure rates?


INTRODUCTION

The promotion of undergraduate biology knowledge in the United States has immediate and long-term implications for increasing national science literacy, providing high-quality education to the science, technology, engineering, and mathematics (STEM) workforce, and contributing to critical scientific advances. To meet these objectives, calls to action formalized priorities and made specific recommendations aimed at improving undergraduate biology education nationwide. For example, after extensive discussions among biology faculty, students, and administrators, the American Association for the Advancement of Science (2009) published a formative document, Vision and Change: A Call to Action, which advocated for “student-centered classrooms” and outlined six core competencies intended to guide undergraduate biology education: 1) apply the process of science 2) use quantitative reasoning 3) use modeling and simulation 4) tap into the interdisciplinary nature of science 5) communicate and collaborate with other disciplines and 6) understand the relationship between science and society. Another call to action came from the President’s Council of Advisors on Science and Technology (2012), who proposed five recommendations to change undergraduate STEM education, including the adoption of “evidence-based teaching practices.”

Although these pushes for “student-centered” and “evidence-based” practices are relatively recent, they stem from ideologies that are more than a century old. Specifically, Dewey (1916) wrote, “Learning means something which the individual does when he studies. It is an active, personally conducted affair” (p. 390). Based upon this work, Pesavento et al. (2015) identified Dewey as one of the earliest and most influential advocates of what we now know as active learning. Subsequently, others expanded on and institutionalized terms such as “student-centered” and “evidence-based” practices (Piaget, 1932 Montessori, 1946 Vygotsky, 1987 Papert, 1980 Brown et al., 1989 Turkle and Papert, 1990 Ackermann, 2001, Cook et al., 2012). While this body of work is critical to our understanding of active learning, the ways in which practitioners and researchers currently use the term are often vague.

Despite this ambiguity, research concerning the effectiveness of active learning in the classroom has continued. For example, a landmark meta-analysis compared student achievement and failure rates between undergraduate science, engineering, and mathematics classes that used active-learning approaches and those that used lecture (Freeman et al., 2014). Findings demonstrated that active learning decreased failure rates by 55% and increased student examination performance by approximately half a standard deviation. To define active learning for the purposes of clarity and transparency in their research, Freeman et al. (2014) developed a definition based on responses from 338 biology departmental seminar audience members: “Active learning engages students in the process of learning through activities and/or discussion in class, as opposed to passively listening ໿to an expert. It emphasizes higher-order thinking and often involves group work” (pp. 8413–8414). This definition guided their inclusion criteria for the study, and it is one of the few examples of clearly defined parameters.

Although many articles do not define the exact parameters of active learning, the research has demonstrated the positive effects of active learning on student achievement and affect across multiple contexts. For example, researchers demonstrated that active learning yields disproportionate learning gains among the most at-risk student groups, such as first-generation college attendees and those who identify with races/ethnicities historically underrepresented in STEM fields (Beichner et al., 2007 Haak et al., 2011 Eddy and Hogan, 2014 Ballen et al., 2017 Wilton et al., 2019 Bauer et al., 2020). Additionally, a meta-analysis conducted by Theobald et al. (2020) demonstrated that active learning narrows achievement gaps for underrepresented students in undergraduate STEM disciplines. However, it is important to note that the definitions of active learning used in these articles vary from the antithesis of lecture (Theobald et al., 2020) to listing the specific strategies that characterize the term (e.g., in-class activities, prelecture preparation, and frequent low-risk assessment Ballen et al, 2017).

Despite the varying parameters of the term, postsecondary institutions have increasingly embraced the use of the term “active learning” (Pfund et al., 2009 Aragón et al., 2018). Examples include institution-wide initiatives (e.g., the Science Education Initiatives at University of Colorado and University of British Colombia, and the Active Learning Initiative at Cornell University), the Summer Institutes on Scientific Teaching (www.summerinstitutes.org), and the Obama Administration’s Active Learning Day (https://obamawhitehouse.archives.gov/blog/2016/10/25/active-learning-day-america). Additionally, more than three-fourths of colleges and universities in the United States provide some type of active-learning classrooms, defined as those that offer flexibility in design to facilitate different types of teaching (Alexander et al., 2019).

Despite these institutional supports and documented positive impacts, the term “active learning” itself is difficult to ascertain from a review of literature. For example, ໿Eddy et al. (2015) explained that active learning is a complex process that encompasses both teaching methods and student learning. Drew and Mackie (2011) noted the meaning of active learning may be dichotomous, as it has been considered a theory of learning as well as a set of pedagogical strategies. Although attempts have been made to define active learning as a theory (Freeman et al., 2014 Connell et al., 2016 Moss-Racusin et al., 2016 Auerbach and Schussler, 2017 Jeno et al., 2017) as well as a set of strategies in biology education research (BER Tanner, 2013 Miller and Tanner, 2015), these attempts are not always 1) streamlined or easy to follow, 2) regularly used in the literature, 3) supported by literature or data, and/or 4) comprehensive. This outcome is problematic when trying to understand what exactly active learning encompasses.

Notably, the variation in the conceptualization of active learning reflects a state of scientific revolution. According to Kuhn (1970), the development of a science has alternating phases (i.e., normal and revolutionary). Normal science, equated to puzzle-solving, comes with a reasonable chance of solution via familiar methods and can be solved by one person. On the other hand, a revolutionary phase involves a collectively negotiated revision to an existing belief or practice. While discipline-based education researchers address questions about the efficacy of recently developed teaching strategies, those strategies are commonly being binned under active learning, which is an ill-defined term. To improve our field, it is important to negotiate how the community interprets and understands this term.

Furthermore, demystifying active learning in undergraduate biology has direct applications for teaching and research. The broad interpretation of active learning may discourage instructors from trying new instructional practices and may ultimately serve as a barrier to implementation (Kreber and Cranton, 2000 O’Donnell, 2008 Stains and Vickrey, 2017). It may additionally serve as a barrier to experimental replication in discipline-based education research (DBER) communities, because there are no agreed-upon standards or criteria for inclusion or exclusion. Given this, we investigated the following four questions in the context of undergraduate biology courses: 1) How does the BER literature use and define the term “active learning”? 2) How does the BER community define the term “active learning”? 3) How are active-learning strategies described in the BER literature? and 4) How are active-learning strategies described by the BER community? We addressed these research questions through a review of BER literature and a survey of the BER community. We expect that, by developing ways to efficiently communicate active learning in the context of biology education, we will encourage teaching innovations and the adoption of common research-based practices.


Discussion

A yearlong upper division biochemistry series is challenging to run in an active learning format because the topic is extremely diverse (physical chemistry, genetics, physiology, medical implications, enzymology, etc.), highly prone to information overload (memorization) which makes it challenging to convey core concepts. Further, the students in this study were not previously exposed to an active learning format at the university level.

Broadly acknowledged problems with contemporary university level science education have been identified by multiple groups 2, 12 and have various unacceptable consequences, including attrition from classes and from STEM majors, poor performance in science courses, poor understanding of an ever increasing amount of course content 2, 12 , and the lack of student understanding of how science is actually done 13 . A number of well-researched approaches to these problems have been described, which provide strong evidence as to their effectiveness. While the number of large university courses which embrace such practices appears to be increasing 4 , no course with an active learning approach is being taught by ladder science faculty at UCSB, and no equivalent active learning Biochemistry course is being taught at any of the University of California campuses. We sought to determine if a single instructor with limited resources could effectively implement a conversion from a CL format to an active learning format to address the problems mentioned above.

Our results with a three quarter upper division biochemistry course are highly encouraging, since the overall 10% increase in student test performance in each of the two quarters which were analyzed, is clearly above the average increase (6%) observed in a comparison of >225 studies making use of similar approaches 4 . Furthermore, attendance improved from the 35–65% range for the CL course to over 90% in the AL series. Fewer students received failing grades in the AL series when compared to students in the CL course, as a direct result of their improved test scores.


Conclusion

The teaching technique described here aimed to incorporate active learning into the undergraduate biology classroom by emphasizing the importance of discussion-based learning and understanding of the scientific literature. While journal clubs typically exist outside of the traditional classroom, the experience described here incorporated a journal club into the classroom by selecting articles that reinforced the delivery of the required content and allowed students to engage in experiential learning by reading and discussing how the concepts they learned in the lecture were applied in the scientific literature. The challenges with this study were twofold. The first challenge was the ability to facilitate student-engaged discussion. Teaching students how to write discussion-generating questions, choosing a paper that students could connect with, and choosing a paper that was not overly technical all aided in overcoming this challenge. The second challenge was in developing a quantitative method of assessment. In attempts to evaluate the journal-club experience, I found that lowering the stakes of the assignment led to more thoughtful participation by the students (currently worth 10% of the overall class grade and based on participation, as described above). In past semesters, when stakes were higher (25–30% of overall class grade), students were more hesitant to admit a lack of understanding because they assumed that this would reflect poorly on their grade. With a high-stakes assignment, students often aimed to impress rather than to learn. On the other hand, when less emphasis was placed on the grade, students were more likely to genuinely engage in discussion and admit to their confusion or discontent with aspects of the paper. Despite these challenges, simple exposure to the journal-club experience has proved effective for the students. Previous students who participated in the journal club and are currently enrolled in graduate programs have verbally reported feeling more advanced than their classmates, in terms of familiarity with the scientific literature and their ability to critically evaluate it and in being more at ease when presented with literature-based assignments.


Watch the video: Bio Parodies (May 2022).