Risks of Active Learning Approach: Missing the Learning Goals

At the onset of the COVID‐19 pandemic, many traditional in‐person courses were converted to synchronous, asynchronous, or hybrid online formats. Instructors of a large undergraduate Basic Human Anatomy course at Indiana University ‐ Bloomington leveraged these changes to convert the lecture portion of the course to a flipped‐classroom model with two components: asynchronous online course content delivery, and live in‐person interactive learning sessions. The in‐person interactive learning sessions introduced students to active learning strategies and techniques (drawing, memory matrices, flow charts, diagrams, etc.), and the students actively participated in these sessions through the use of an online interactive learning platform ‐ TopHat. Here we sought to assess the efficacy and outcomes of the active learning sessions on student performance in the course.Basic Human Anatomy (ANAT‐A215) is a 5 credit hour undergraduate (baccalaureate) course that has an enrollment of 480 students with 1 large lecture section and 12 smaller lab sections. In Fall 2021, the instructors modified the lecture component of the course into a flipped classroom format. Lecture content was delivered via asynchronous online lectures, and the F2F class time was listed as optional (but strongly encouraged) for attendance. To facilitate greater student‐instructor interaction, one half of the class attended on Mondays and the other half attended on Fridays, and participated in similar interactive sessions. Wednesday F2F interactive lecture sessions were open to the entire class. To encourage attendance and participation in the optional F2F interactive lecture sessions, extra credit points could be earned through participation in TopHat anatomy questions. Points were earned for participation only (.5 points per question). TopHat questions were created by the instructors and consisted of both lower order and higher order questions that tested lecture and lab anatomical content. At the end of each lecture exam block of material, the instructors selected multiple TopHat questions at random for extra credit, with a range of 2‐4 extra credit points that could be earned per exam block.We found a statistically significant positive correlation between the number of points accumulated for participating in the F2F active learning lecture session questions and lecture exam scores. Using the number of points accumulated via the TopHat questions as a proxy for lecture attendance, we found that more TopHat points accumulated by students resulted in higher mean lecture exam scores – the greatest benefits occurring if students participated in multiple (>3) F2F lecture sessions per block. Students who regularly attended F2F interactive sessions saw the greatest gains in their lecture exam scores. These findings indicate that active learning strategies could be efficacious in supplementing an asynchronous lecture content delivery format. We shared our findings with the class to encourage them to attend the active learning sessions, but encouraging some student populations to attend these sessions remains a challenge. Future research should be conducted on methods to encourage student involvement and participation in the course.

Active learning is one form of supervised machine learning. In supervised learning, a set of labeled samples is passed to a learning algorithm for training a classifier. However, labeling large amounts of training samples can be costly and error-prone. Active learning deals with the development of algorithms that interactively select a subset of the available unlabeled samples for labeling, and aims at minimizing the labeling effort while maintaining classification performance. The key challenge for the development of so-called active learning strategies is the balance between exploitation and exploration: On the one hand, the estimated decision boundary needs to be refined in feature space regions where it has already been established, while, on the other hand, the feature space needs to be scanned carefully for unexpected class distributions. In this thesis, two approaches to active learning are presented that consider these two aspects in a novel way. In order to lay the foundations for the first one, it is proposed to express the uncertainty in class prediction of a classifier at a test point in terms of a second-order distribution. The mean of this distribution corresponds to the common estimate of the posterior class probabilities and thus is related to the distance of the test point to the decision boundary, whereas the spread of the distribution indicates the degree of exploration in the corresponding region of feature space. This allows for the evaluation of the utility of labeling a yet unlabeled point with respect to classifier improvement in a principled way and leads to a completely novel approach to active learning. The proposed strategy is then implemented and evaluated based on kernel density classification. The generic active learning strategy can be combined with any other classifier, but it performs best if the derived second-order distributions are sufficiently good approximations to the sampling distribution. Although second-order distributions for random forests are derived in this thesis, they do not approximate sufficiently well the sampling distribution and mainly allow only for the relative comparison of prediction uncertainty between test points. In order to combine the state of the art classification performance of random forests with the principal ideas of the first active learning approach, a related second approach for random forests is derived. It is, in addition, tailored to the demands in industrial optical inspection: bag-wise labeling with weak labels and strongly imbalanced classes. Moreover, an outlier detection scheme based on random forests is derived that is used by the proposed active learning algorithm. Finally, a new computational scheme for Gaussian process classification is presented. It is compared to two standard methods in geostatistics, both with respect to theoretical consistency and practical performance. The method evolved as a by-product when considering using Gaussian process models for active learning.

Carle Illinois College of Medicine.

The world is changing so fast, and therefore education needs to adapt to the challenges of times. In order to update the content of school education in the Republic of Kazakhstan modern trends are going to be used. These trends contain pedagogical methods that can be used to preserve and even increase internal motivation, as active learning. Active learning method is an treatment where students participate or interact with the learning process, as opposed to passively taking in the information.The goal of this study is to identify the impact of active learning method on 10th grade students’ attitude towards mathematics of the students the second semester of the school year 2019-2020. More specifically, it attempted to determine and compare the attitude toward mathematics of students’ exposure to active learning and traditional teaching strategy. The Likert scale used to evaluate the attitude of students toward mathematics. Mean, Cronbach value, T-test were the statistical tools used in anatomizing and interpreting the research data. The discovering showed that the students in the active learning group had auspicious attitude than students in the conventional teaching group. According to the findings after research, we saw the direct relation between attitude and active learning. It is concluded that the students’ attitude toward mathematics was better by using active learning strategy. It is recommended that mathematics teacher should use active learning strategy in order to improve the attitude toward mathematics of the students.Keywords: attitude, mathematics, active learning

Heart rate can be used as a measure of cognitive engagement. We measured average student heart rates during medical school lecture classes using wristwatch-style monitors. Analysis of 42 classes showed a steady decline in heart rate from the beginning to end of a lecture class. Active learning sessions (peer-discussion based problem solving) resulted in a significant uptick in heart rate, but this returned to the average level immediately following the active learning period. This is the first statistically robust assessment of changes in heart rate during the course of college lecture classes and indicates that personal heart rate monitors may be useful tools for assessment of different teaching modalities. The key findings suggest that the value of active learning within the classroom resides in the activity itself and not in an increase in engagement or reset in attention during the didactic period following an active learning session.

Simple SummaryPreclinical drug screening experiments for anti-cancer drug discovery typically involve testing candidate drugs against cancer cell lines. This process can be expensive and time consuming since the possible experimental space can be quite huge, involving all of the combinations of candidate cell lines and drugs. Guiding drug screening experiments with active learning strategies could potentially identify promising candidates for successful experimentation. This study investigates various active learning strategies for selecting experiments to generate response data for identifying effective treatments and improving the performance of drug response prediction models. We have demonstrated that most active learning strategies are more efficient than random selection for identifying effective treatments.It is well-known that cancers of the same histology type can respond differently to a treatment. Thus, computational drug response prediction is of paramount importance for both preclinical drug screening studies and clinical treatment design. To build drug response prediction models, treatment response data need to be generated through screening experiments and used as input to train the prediction models. In this study, we investigate various active learning strategies of selecting experiments to generate response data for the purposes of (1) improving the performance of drug response prediction models built on the data and (2) identifying effective treatments. Here, we focus on constructing drug-specific response prediction models for cancer cell lines. Various approaches have been designed and applied to select cell lines for screening, including a random, greedy, uncertainty, diversity, combination of greedy and uncertainty, sampling-based hybrid, and iteration-based hybrid approach. All of these approaches are evaluated and compared using two criteria: (1) the number of identified hits that are selected experiments validated to be responsive, and (2) the performance of the response prediction model trained on the data of selected experiments. The analysis was conducted for 57 drugs and the results show a significant improvement on identifying hits using active learning approaches compared with the random and greedy sampling method. Active learning approaches also show an improvement on response prediction performance for some of the drugs and analysis runs compared with the greedy sampling method.

The purpose of this chapter is to explore the need for change from passive instructional approaches to active learning strategies and to provide theoretical frameworks, information, and tools to guide a next-generation leader in the process of changing the organizational culture through active learning. An overview of active learning, cooperative learning, and collaborative learning will be presented. Several specific forms of active learning will be explored in detail: Case-Based Learning (CBL), Problem-Based Learning (PBL), and Team-Based Learning (TBL). The overlapping characteristics among the approaches will be presented, along with the distinctive features of each approach. Assets and limitations will be discussed, along with specific examples and techniques. Outcomes from research on active learning strategies will be summarized. Considerations for determining active learning strategies that are the best fit for particular learners, instructors, and content areas will be examined.

Active learning approaches require a lot of time investment in student activities and engagement during the class period, which often leads to incomplete coverage of the course syllabus. Furthermore, it requires significant amount of time for the instructor to design and implement active learning strategies. All these shortcomings are often cited as the common hindrance in adopting student active learning. To address these widely recognized inhibiting factors, we recently implemented a new student active learning approach. This approach is based on the students’ presentation and hence termed as student presentation-based active learning (SPAL). Under this approach, students are given a reading assignment to prepare a PowerPoint presentation on well-defined conceptual topics, questions, or chapter modules. Reading assignments on a topic are administered 1–2 weeks before covering them in the class. This allows reasonable time for the self-comprehension of the suggested material for presentation preparation. Students were expected to rehearse the presentation and be prepared to complete it in the suggested time duration. During each lecture, one group of student would present the assigned topic to the class, and their presentations were graded according to the rubric focusing on the coverage of suggested topics, quality of presentation, and after presentation discussions. Peers and instructor provided feedback about the students’ presentation and unclear concepts. To understand the efficacy of this approach, this approach was implemented in Mechanical Engineering Senior Capstone Project I course in Fall 2016. Here the responses and insights garnered from this practice were presented, and discussion on the advantages and challenges associated with the adaptation of this approach in teaching engineering courses as compared to lecture based classroom education system. This paper can provide useful insights for instructors considering this approach or similar student active teaching approach in their courses.

Therapists’ active learning increases treatment fidelity, but research is needed on supervisory strategies to engage therapists in active learning. This study used sequential analysis to examine consultant behaviors associated with increased and decreased probability of eliciting therapists’ active learning. The study included 162 consultation sessions from 27 community therapists implementing Attachment and Biobehavioral Catch-up. Consultants’ client discussion, information provision, and modeling were associated with reduced likelihood of active learning. Consultants’ questions, engagement in active learning strategies, use of video, and silence were associated with greater likelihood of therapist active learning. These findings inform supervisors’ attempts to encourage active learning.

Over the past several years, we have taught a graduate level gross anatomy course using prosected cadavers. The laboratory component of the course constituted instructor‐led demonstrations of the anatomical structures. To create a more engaging and active learning environment, we developed a new teaching strategy that allowed students to construct their own anatomical knowledge through cooperative discovery‐ learning exercises. In the new method, instructors tagged structures on the cadavers prior to the laboratory sessions and then students worked in small groups to identify the tagged structures. We then compared the two methods by assessing both students’ learning and attitude. Four traditional demonstration sessions were alternated with 4 active learning sessions. Each lab session was followed by a short practical identification quiz. A survey was administered to evaluate students’ attitudes. Quiz scores were significantly higher following active learning sessions (mean score 54 vs 38) and continued to improve as students became more effective learners. The survey clearly showed a unanimous preference of the improved design. Structured exercises that emphasized kinesthetic manipulation of the anatomical specimens in conjunction with a cooperative learning environment advanced the learning process significantly and provided the motivation for advanced self‐learning.Grant Funding Source: none

What is active learning? While active learning has been demonstrated to have positive impacts on student learning and performance, defining the concept has been elusive. Previous research examining active learning definitions in STEM fields found that the vast majority of published articles did not define active learning, and those that did defined active learning as interacting, engaging, or not lecturing. The current research extends this STEM-focused work by examining both social science and STEM science publications. A restricted systematic review of literature was conducted using the SCOPUS database, resulting in 547 relevant articles focused on active learning from 2017 to 2022. An examination of the articles indicated that 71% of the reviewed articles did not define active learning and that the instructional strategies most often cited as fostering active learning emphasized social interactive learning strategies (e.g., small groups, team-based learning, discussion, and cooperative learning), as well as critical thinking strategies (e.g., problem-based learning, case-based learning, and inquiry-based learning). In addition, an in-depth qualitative analysis of the 161 definitions provided within the articles yielded three main emergent themes: (a) active learning is defined as grounded in student-centered constructivist theory, (b) active learning is defined as promoting higher-order thinking and deep learning, and (c) active learning is defined as an instructional strategy involving activity, participation, and engagement. Given these main findings, a representative definition was created: Active learning is a student-centered approach to the construction of knowledge focused on activities and strategies that foster higher-order thinking.

Seminars and workshops on active learning techniques are being held at many universities. This essay compares traditional classroom interaction with the new active learning approach and offers a personal account of one instructor's attempts to introduce the active learning method within a criminology curriculum. Small group interaction, weekly written assignments, and student's editing of papers are discussed. Overall the instructor found that students responded positively to the new approaches and that their examination and paper grades improved.

While many active learning strategies are evidence-based, it is unclear as to which methods are preferred by most students. In the present study students in an undergraduate health professions microbiology course were assigned four projects (“types” 1 – 4) that included elements of active learning and that required students to apply microbiological concepts to health care or public health. A type 1 project involved the student consulting professional journals, textbooks and/or websites and preparing an informative brochure on an infectious disease suitable for reading by the lay public. A type 2 project involved the student shadowing a health care professional. A type 3 project involved the student performing a community service. A type 4 project involved the student designing and conducting an original lab research study. A survey was used to assess student attitudes about doing these projects. I report that while some students preferred particular project types, no project type was preferred by a majority of students. In addition, students ranked all four project types highly and considered them all to have equal educational value and relevance to course content. These results suggest that instructors should avoid using a “one size fits all” active learning approach. Students value the use of diverse pedagogical approches that engage their learning in different ways and that connect course concepts to real-world applications.

Faculty motivations to use active learning among pharmacy educators

Objective: Implementation of active learning methods into classroom enhances the learning experience of students. Instructional strategy followed by proper active learning activity increases engagement, attention, thinking skill of students. This study was carried out to incorporate an instructional strategy i.e visualization followed by active learning activity- crossword puzzle to first year B.Tech students and to find out its effectiveness in the learning process Methods: The animated videos available on internet were used for visualization. Then by using free online resources, crossword puzzles were created and provided to the students. Evaluation was done by crossword puzzle as well as by feedback in the form of questionnaire using a 5-point Likert scale. The data was collected and analyzed.Results: More than 90% students indicated that visualization and crossword puzzle enhanced their learning, oriented to the important topics and served as good tool in effective learning of engineering physics.Conclusions: This activity contributed to a helpful learning experience which allows the students to engage better in the learning, to review the lecture material as well as examination and also provides a more relaxed and friendly classroom atmosphere by proper break in the lecture without breaking learning. Keywords: Active learning, Visualization, Crossword puzzles, Questionnaire.