Leveraging active learning through student-generated questions and electronic posters in a physiology course.

Question generation is an important metacognitive learning activity for students that encourages the development of higher-order thinking skills. Question generation can also serve as an alternative assessment method. Yet, it is rarely incorporated into learning settings. I propose a study to investigate the effects of using Multiple Choice Question (MCQ) generation in data science MOOC assessments in two different settings where - i) MCQ generation is a required task, and ii) students have a choice between creating an MCQ and answering MCQs. The findings from this study are intended to provide evidence of the learning benefits of MCQ generation in MOOC assessments, the impact of giving students a choice on the quality of the MCQs they create, and the factors that influence students' choice.

The COVID‐19 pandemic required a shift to online learning that posed particular challenges for the education of dental students. Basic science courses were presented in an online, asynchronous format to facilitate small group rotations through preclinical work. The success of active learning was previously examined in a Dental Physiology course, and it was desired to reassess those methods in an online environment. It was unclear whether the loss of the collaborative opportunities during the active learning sessions would negatively impact student perceptions or performance. In the asynchronous, online environment, students were unable to easily share their ideas with their classmates or ask questions of the lecturer in real time. The 120 students enrolled in the first‐year Dental Physiology course were provided with pre‐recorded sections of content containing learning activities, and the effectiveness was compared with sections of traditional lecture recordings. On surveys, dental students had more positive perceptions of online recordings that incorporated active learning strategies, with higher perceived effectiveness of the lecturer, ability to pay attention, motivation to study, and confidence with the material. This was similar to the previous study, in which face‐2‐face (F2F) active learning was more positively reviewed than traditional lectures. Compared with these past sections, the online cohort expressed similarly positive perceptions of the active learning sections, but the online traditional lectures were perceived to be less effective than F2F lectures. Despite the differences in student perceptions, unit exam scores were similar for traditional didactic lectures in both online and F2F settings (P=1.000, one‐way ANOVA). In the online setting, active learning exam scores were 8.13% higher than traditional lecture recordings (P<0.001, one‐way ANOVA and Bonferroni post‐hoc test). However, the previous F2F active learning produced significantly higher scores than online active learning on unit exams (P=0.04). While active learning strategies continue to be effective, further research is needed to optimize these methods and engage students in online coursework. The results suggest the importance of active and collaborative learning opportunities in the education of students in physiology coursework.

Multiple Choice Questions (MCQs) are an established medium of formal educational contexts. The collaborative generation of MCQs by students follows the perspectives of constructionist and situated learning and is an activity that fosters learning processes. The MCQs generated are—besides the learning processes—further outcomes of collaborative generation processes. Quality MCQs are a valuable resource, so that collaboratively generated quality MCQs might also be exploited in further educational scenarios. However, the quality MCQs first need to be identified from the corpus of all generated MCQs. This article investigates whether Likes distributed by students when answering MCQs are viable as a metric for identifying quality MCQs. Additionally, this study explores whether the process of collaboratively generating MCQs and using the quality MCQs generated in commercial quiz apps is achievable without additional extrinsic motivators. Accordingly, this article describes the results of a two-stage field study. The first stage investigates whether quality MCQs may be identified through collaborative inputs. For this purpose, the Reading Game (RG), a gamified, web-based software aiming at collaborative MCQ generation, is employed as a semester-accompanying learning activity in a bachelor course in Urban Water Management. The reliability of a proxy metric for quality calculated from the ratio of Likes received and appearances in quizzes is compared to the quality estimations of domain experts for selected MCQs. The selection comprised the ten best and the ten worst rated MCQs. Each of the MCQs is rated regarding five dimensions. The results support the assumption that the RG-given quality metric allows identification of well-designed MCQs. In the second stage, MCQs created by RG are provided in a commercial quiz app (QuizUp) in a voluntary educational scenario. Despite the prevailing pressure to learn, neither the motivational effects of RG nor of the app are found in this study to be sufficient for encouraging students to voluntarily use them on a regular basis. Besides confirming that quality MCQs may be generated by collaborative software, it is to be stated that in the collaborative generation of MCQs, Likes may serve as a proxy metric for the quality of the MCQs generated.

The COVID-19 pandemic required a shift to online learning that posed particular challenges for the education of dental students. Basic science courses were presented in an online, asynchronous format to facilitate small group rotations through preclinical work. The success of active learning was previously examined in a Dental Physiology course, and it was desired to reassess those methods in an online environment. Students were provided with prerecorded lectures containing learning activities, and the effectiveness was compared with traditional lecture recordings. On surveys, dental students had more positive perceptions of online recordings that incorporated active learning strategies, with higher perceived effectiveness of the lecturer, ability to pay attention, motivation to study, and confidence with the material. This was similar to the previous study, in which face-2-face (F2F) active learning was more positively reviewed than traditional lectures. Compared with these past sections, the online cohort expressed similarly positive perceptions of the active learning sections, but the online traditional lectures were perceived to be less effective than F2F lectures. Despite the differences in student perceptions, unit exam scores were similar for didactic lectures in both online and F2F settings. Unit exam scores were significantly higher when active learning strategies were employed, with the highest performance levels in the F2F cohort. While active learning strategies continue to be effective, further research is needed to optimize these methods and engage students in online coursework. The results suggest the importance of active and collaborative learning opportunities in the education of students in physiology coursework.

Multiple-choice questions (MCQs) are widely used in health science education because they are an efficient way to evaluate knowledge from simple recall to complex clinical reasoning. The creation of high-quality MCQs, however, can be time-consuming and requires expertise in question composition. Advancements in artificial intelligence (AI), especially large language models (LLMs), offer the potential to allow for the rapid generation of high-quality, consistent, and course-specific MCQs. Here we discuss the potential benefits and drawbacks of the use of this technology in the generation of MCQs, including ensuring the accuracy and fairness of questions, along with technical, ethical, and privacy considerations. We offer practical guiding principles for the implementation of AI-generated MCQs and outline future research areas related to their impact on student learning and educational quality.

Physiology encompasses examining the body's everyday activities, which is crucial for understanding health before disease onset. The physiology course at Hasanuddin University is included in the biomedical science curriculum. Traditionally delivered via a teacher-centered methodology, this strategy frequently results in rote memorization. To resolve this, active learning methodologies utilizing many media enabled students to choose their preferred instructional tools and augment their knowledge and drive. This study seeks to evaluate the influence of different learning media on undergraduate students' knowledge, presenting skills, and motivation in physiology courses. We executed a mixed-method study, including quantitative and qualitative approaches, utilizing a sequential explanatory design, wherein quantitative data was initially gathered. We proceeded with qualitative data collecting to elucidate and reinforce the previous findings. We engaged 100 students to assess their knowledge and presenting skills. The evaluative instruments employed were multiple-choice questions and assessment rubrics. To ascertain the reasons and motivations for student engagement in learning physiology, we engaged nine student respondents, categorizing them into three groups: active participants, neutral participants, and non-participants. We assessed the motivations for student learning engagement with a qualitative questionnaire, which was then complemented by Focus Group Discussion (FGD) interviews. Findings indicated a greater involvement of female students (67%) and individuals from non-SBMPTN paths (62%). Most pupils favored PowerPoint (52%) and video (20%) as educational material. Substantial enhancements in posttest scores were observed following the utilization of these media. Researchers observed significant enhancements in posttest scores following presentations that utilized PowerPoint and video media (Wilcoxon, p<0.01). Students who presented the genitourinary system via video medium achieved superior scores compared to those who utilized PowerPoint (Mann-Whitney p=0.001, p<0.01). Qualitative studies indicated that active learning strategies enhanced student involvement and motivation, making learning more engaging and pleasurable. Active learning using specific media such as PowerPoint and video markedly improves knowledge, engagement, and motivation among students in physiology courses.

Automatic multiple choice question (MCQ) generation from a text is a popular research area. MCQs are widely accepted for large-scale assessment in various domains and applications. However, manual generation of MCQs is expensive and time-consuming. Therefore, researchers have been attracted toward automatic MCQ generation since the late 90's. Since then, many systems have been developed for MCQ generation. We perform a systematic review of those systems. This paper presents our findings on the review. We outline a generic workflow for an automatic MCQ generation system. The workflow consists of six phases. For each of these phases, we find and discuss the list of techniques adopted in the literature. We also study the evaluation techniques for assessing the quality of the system generated MCQs. Finally, we identify the areas where the current research focus should be directed toward enriching the literature.

this letter is intended to contribute to the discussion of active versus passive learning in physiology. Here are some thoughts for consideration: are we, as teachers, making a difference in the learning process? How do we ensure that we emphasize the acquisition of knowledge rather than short-term

In this paper, we present an unsupervised dependency-based approach to extract semantic relations to be applied in the context of automatic generation of multiple choice questions (MCQs). MCQs also known as multiple choice tests provide a popular solution for large-scale assessments as they make it much easier for test-takers to take tests and for examiners to interpret their results. Manual generation of MCQs is a very expensive and time-consuming task and yet they often need to be produced on a large scale and within short iterative cycles. We approach the problem of automated MCQ generation with the help of unsupervised relation extraction, a technique used in a number of related natural language processing problems. The goal of Unsupervised relation extraction is to identify the most important named entities and terminology in a document and then recognise semantic relations between them, without any prior knowledge as to the semantic types of the relations or their specific linguistic realisation. We use these techniques to process instructional texts and identify those facts (terminology, entities, and semantic relations between them) that are likely to be important for assessing test-takers' familiarity with the instructional material. We investigate an approach to learn semantic relations between named entities by employing a dependency tree model. Our findings show that an optimised configuration of our MCQ generation system is capable of attaining high precision rates, which are much more important than recall in the automatic generation of MCQs. We also carried out a user-centric evaluation of the system, where subject domain experts evaluated automatically generated MCQ items in terms of readability, usefulness of semantic relations, relevance, acceptability of questions and distractors and overall MCQ usability. The results of this evaluation make it possible for us to draw conclusions about the utility of the approach in practical e-Learning applications.

Active learning is increasingly promoted in nursing education as a student-centred and andragogical approach that enhances engagement, competence, and clinical reasoning. However, limited qualitative evidence explores how students and educators experience active learning and its influence on learning outcomes within real educational contexts. To provide a comprehensive description of how active learning strategies influence learning outcomes and educational experiences among undergraduate nursing students and educators, and to identify multilevel factors shaping their implementation. A qualitative descriptive design was employed. Four focus groups were conducted with purposively selected undergraduate nursing students and educators from the Faculty of Nursing at the Arab American University-Palestine (n = 17). Interviews were conducted in Arabic, audio-recorded, transcribed verbatim, and translated into English. Data were analysed using Braun and Clarke's reflexive thematic analysis with NVivo to identify patterns across participants' perspectives. Trustworthiness was ensured through reflexivity, reflective journaling, and member checking. Five themes emerged. Participants described exposure to diverse active learning strategies, including team-based learning, simulation, case-based learning, flipped classroom activities, peer learning, and gamification. Active learning was perceived to enhance confidence, engagement, knowledge retention, and clinical reasoning. However, its effectiveness was shaped by multilevel conditions, including learner readiness, psychological safety, instructional facilitation, classroom dynamics, and institutional support. Educators further conceptualized active learning as an andragogical process involving intentional instructional design, experiential learning, and reflective feedback practices. Active learning was experienced as a meaningful approach that supports competence, engagement, and professional preparedness. The findings highlight the importance of psychologically safe learning environments, structured facilitation, and institutional readiness in shaping the implementation and sustainability of active learning in undergraduate nursing education.

Aim: Writing high-quality items (questions) is a resource-intensive task. Particularly, the development of one context-rich multiple-choice question (MCQ) for assessing higher-order cognitive skills may cost hours of medical teachers. The aim of this study was to find out whether it is possible the use of Automatic Item Generation (AIG) in Turkish to generate case-based MCQs that assess clinical reasoning skills. Methods: By following the template-based AIG method developed by Gierl et al., MCQs on hypertension were generated with the help of software after the development of a cognitive model and an item model. The cognitive model and the item model was developed by a medical doctor and a cardiologist by considering Turkish Hypertension Consensus Report. The software was built as a Python-based code intended for single use without a user interface. The items were recorded in a MySQL database. Of these questions, 10 questions were randomly chosen to be reviewed by three subject matter experts (cardiologists). The evaluation was based on the quality of the questions and whether the questions assess higher-order skills such as clinical reasoning rather than factual recall. Results: In 1.73 seconds, 1600 MCQs on hypertension were generated. Although there were some minor revision suggestions in a few questions, each question was stated by all cardiologists as an acceptable item. The cardiologists also stated that the questions assess clinical reasoning skills rather than factual recall. Conclusions: This study demonstrated for the first time that AIG for assessing clinical reasoning skills in the context of medical education in Turkish is possible. This method of augmented intelligence to generate items can be used in Turkish as it has been used in other five languages. The use of this method could bring about more questions to assess clinical reasoning skills. It may also lead medical teachers to spend less amount of time and effort compared to traditional item writing.

Students require continuous feedback for effective learning. Multiple choice questions (MCQs) are extensively used among various assessment methods to provide such feedback. However, manual MCQ generation is a tedious task that requires significant effort, time, and domain knowledge. Therefore, a system must be present that can automatically generate MCQs from the given text. The automatic generation of MCQs can be carried out by following three sequential steps: extracting informative sentences from the textual data, identifying the key, and determining distractors. The dataset comprising of various topics from the 9th and 11th-grade computer science course books are used in this work. Moreover, TF-IDF, Jaccard similarity, quality phrase mining, K-means, and bidirectional encoder representation from transformers techniques are utilized for automatic MCQs generation. Domain experts validated the generated MCQs with 83%, 77%, and 80% accuracy, key generation, and distractor generation, respectively. The overall MCQ generation achieved 80% accuracy through this system by the experts. Finally, a desktop app was developed that takes the contents in textual form as input, processes it at the backend, and visualizes the generated MCQs on the interface. The presented solution may help teachers, students, and other stakeholders with automatic MCQ generation.

In order to meet the need for both a STEM‐competent workforce, and a STEM‐literate electorate, efforts must be made to increase retention and performance of students from groups historically underrepresented in STEM, including women, low‐income students, and minority groups. Active learning strategies have been shown to narrow the achievement gap for many of these students, but the effects of specific strategies are relatively understudied. In addition, recent studies have shown improvements in student success in courses using alternative formats (e.g., flipped classes), although these gains may be due to the inherent inclusion of active learning, rather than a result of the flipped format itself. To address these questions, we are developing a novel active learning strategy, Graduated Engagement Tools (GET), that has multiple points for student engagement and utilizes (1) online and in‐class activities of increasing levels of Bloom's taxonomy and (2) directed metacognitive exercises to promote student self‐examination of strategies and motivations for learning. We are in the process of testing the general effectiveness of the GET strategy in an introductory physiology course with a very large (~1300), diverse student enrollment. Our initial focus is on improving content mastery and reducing failure rates. We will assess the effectiveness of these strategies across groups of students to measure differential effects on specific demographics of students. We also plan to investigate the effect of the GET strategy on self‐efficacy, or student's perceived ability to succeed; a metric shown to correlate with academic success and retention that is relatively low in students from groups historically underrepresented in STEM. These efforts are ongoing and we present examples of the GET activities, preliminary data showing high levels of student engagement and critical thinking, and a plan for creating and assessing a scalable and transferrable model for deploying these activities in both lecture and hybrid‐ / flipped‐classroom formats.

To advance efforts for assessing professional development in undergraduate physiology coursework, our study investigates how students described aspects of course structure in an introductory physiology course in relation to transferable skills for other professional pursuits. The study context was a large-enrollment undergraduate physiology course that was redesigned to use weekly classroom assessment techniques and active learning. Based on the theoretical framework of transfer of learning and the principles of Universal Design for Learning (UDL) informed by critical disability theory, we conducted a qualitative inquiry of written responses through deductive and inductive content analysis. We found that the vast majority of students described how aspects of course structure supported their transferable skill development, often highlighting the weekly rhythm: Muddy Mondays, Working Wednesdays, and Freaky Fridays. Furthermore, students provided evidence of applying strategies from a single course to other courses, in alignment with UDL principles as well as the Physiology Majors Interest Group (P-MIG) career competencies. Our study offers a comprehensive overview of how multiple components of course structure can support transferable skill development, with implications for education researchers who assess and instructors who apply active learning strategies in instructional design for undergraduate physiology courses.NEW & NOTEWORTHY In alignment with Universal Design for Learning principles as well as the Physiology Majors Interest Group (P-MIG) career competencies, this study offers a comprehensive overview of how multiple components of course structure can support transferable skill development, with implications for education researchers who assess and instructors who apply active learning strategies in instructional design for undergraduate physiology courses.

Albert Einstein College of Medicine.