Education Research

Education Research

The Department of Physiology is committed to enhancing educational outcomes through innovative approaches and pedagogical research. Educational research in the Department is actively looking into effectively leveraging AI to enhance learning, motivation, patient education, and assessment effectiveness We emphasize mental health education and leverage mobile apps for remote learning and motivation in exercise physiology. Our leadership in the scholarship of teaching and learning is demonstrated through numerous publications in medical education research journals, teaching enhancement grants and pioneering efforts in blended learning and e-books.

Project Culture of Change

Mobile Application-assisted Graded Exercise Practical: A Remote Teaching Strategy to Promote Motivation and Experiential Learning in Exercise Physiology

Principal Investigators

Ivan Low

Description

Practical classes are critical instructional activities in facilitating learning and motivation in health sciences education. With increasing pedagogical activities being conducted in virtual or remote settings, this study assessed how a remote practical assisted by physiological monitoring smartphone applications impacted student motivation and the achievement of intended learning outcomes in exercise physiology teaching. A total of 24 students (out of 30; 80%) were surveyed via mixed-methods questionnaire containing 27 closed-ended and three open-ended questions and their responses were compared after attending the remote or the traditional in-class practical in randomized order. Unpaired student’s t-tests were performed for comparisons between interventions with significance level set at p <0.05. Students reported that both remote and in-class practical strongly facilitated the achievement of learning outcomes. Self-reported scores for student satisfaction and perceived achievement of learning outcomes were similar between the two practical methodologies. Student motivation score assessed using the Lab Motivation Scale revealed that students were more motivated during the remote practical, particularly in the effort domain (p <0.05). This was in line with the identified themes from the qualitative responses which indicated that the remote practical was more engaging than in-person practical, with greater opportunities for experiential learning and class involvement being the main factors underlying these findings. Taken together, remote practicals can be critical aspects of a blended learning curriculum that encourages student engagement and experiential learning. With further advancements in physiological monitoring wearables and smartphone technologies, remote practicals can be potential alternatives to traditional in-person practical in exercise physiology teaching.

Publications

Human vs AI: Evaluating Large Language Models (LLMs) in the Grading of Scientific Inquiry Assessments

Principal Investigators

Ivan Low, Swapna Haresh Teckwani, Amanda Huee-Ping Wong

Description

The advent of AI, particularly Large Language Models (LLMs) like ChatGPT and Gemini, has significantly impacted the educational landscape, offering unique opportunities for learning and assessment. In the realm of written assessment grading, traditionally viewed as a laborious and subjective process, this study sought to evaluate the accuracy and reliability of these LLMs against human graders in an interdisciplinary course on scientific inquiry. Human graders and three LLMs, GPT-3.5, GPT-4o, and Gemini, were tasked with scoring submitted student assignments according to a set of rubrics aligned with various cognitive domains, namely ‘Understand’, ‘Analyse’, ‘Evaluate’ from the revised Bloom’s taxonomy, and ‘Scientific inquiry competency’. Our findings revealed that whilst LLMs demonstrated some level of competency, they do not yet meet the assessment standards of human graders. Specifically, inter-rater reliability (percentage agreement and correlation analysis) between human graders were superior as compared to between two grading rounds for each LLM respectively. Furthermore, concordance and correlation between human and LLM graders were mostly moderate to poor in terms of overall scores and across the pre-specified cognitive domains. The results suggest a future where AI could complement human expertise in educational assessment, but underscores the importance of adaptive learning by educators and continuous improvement in current AI technologies to fully realize this potential.

A jigsaw-based pedagogy to promote interdisciplinary learning in Health Sciences Education

Principal Investigators

Ivan Low, Amanda Wong, Swapna Haresh Teckwani

Description

The changing global healthcare landscape necessitates a workforce skilled in interdisciplinary approaches to address evolving health challenges. To meet this need, NUS School of Medicine introduced a new Minor Programme in Integrative Health to promote interdisciplinary learning among students from diverse academic backgrounds. The programme employs a webbed curriculum framework where multidisciplinary lectures converge around central health themes, complemented by small-group tutorials employing both traditional and jigsaw pedagogical methods. The jigsaw method groups students by their disciplines to identify health issues, then reassigns them into mixed-discipline groups to develop interdisciplinary solutions.

Employing a mixed-methods design, this study compares the effectiveness of jigsaw and conventional methods in enhancing interdisciplinary learning outcomes through (1) Interdisciplinary Understanding Questionnaire (IUQ), (2) scoring of tutorial assignments with rubrics framed by the SOLO taxonomy, and (3) semi-structured interviews. Of the 20 students enrolled in the programme's inaugural course, 18 participated in the study. Quantitative analysis revealed that the jigsaw pedagogy, when compared with conventional methods, enhanced knowledge of interdisciplinary and collaborative skills. This was supported by improved scores in multi- and interdisciplinary (but not transdisciplinary) assignment domains, as well as the commons themes identified from the semi-structured interviews.

Our approach contrasts traditional learning models by engaging students in a jigsaw-based tutorial system, which facilitates initial discipline-specific mastery followed by interdisciplinary collaboration. This study contributes to the sparse empirical evidence on effective interdisciplinary education strategies, offering insights that could inform future curriculum developments aimed at preparing students for the complexities of modern healthcare challenges.

Comparative study of ChatGPT performance in tutorial and case-based learning questions in physiology and biochemistry

Principal Investigators

Nathasha Luke, Reshma Taneja, Celestial Yap, Chen Zhi Xiong, Amanda Wong

Course Description

This study explored generative artificial intelligence (ChatGPT) in answering Physiology and biochemistry modified essay questions, intending to understand the capabilities and limitations of generative AI in this domain.

Recipient of the NUS Teaching Enhancement Grant - Learning Community FY 22/23

Publications

Luke WANV, Seow Chong L, Ban KH, Wong AH, Zhi Xiong C, Shuh Shing L, Taneja R, Samarasekera DD, Yap CT. Is ChatGPT 'ready' to be a learning tool for medical undergraduates and will it perform equally in different subjects? Comparative study of ChatGPT performance in tutorial and case-based learning questions in physiology and biochemistry. Med Teach. 2024 Jan 31:1-7. doi: 10.1080/0142159X.2024.2308779.

Generative AI as a tool in developing patient education skills for medical students

Investigators

Nathasha Luke, Reshma Taneja, Celestial Yap

Description

Medical students should develop skills in effective patient education needing expertise in the particular domain and pacing to be tailored to the patient’s level of comprehension. We evaluated the performance of non-domain-specific generative AI as an assistive tool for medical students in this aspect.

Recipient of the NUS Teaching Enhancement Grant - Learning Community FY 22/23.

Exploring the Effects of an Artificial Intelligence (AI) Chatbot on Learning and Motivation Among Medical and Pharmacy Students

Investigators

Wong Lik Wei, Amanda Wong, Hooi Shing Chuan

Description

The rapid advancements in Artificial Intelligence (AI) technologies have prompted us to re-evaluate the future of our education. Although AI has great potential to enhance teaching and learning, its role in pedagogy and instruction has not been fully studied. This study aims to explore the impact of an AI chatbot on learning and motivation among medical students. Specifically, we seek to examine whether the AI chatbot stimulates the development of questioning skills, and whether it promotes engagement and motivation. The findings of this study will significantly contribute to a better understanding of the role of this technology in education.

Recipient of Swee-Liew Wadsworth Education Grant 2023 and NUS Teaching Enhancement Grant 2023.

Implementing Mental Health Education in Undergraduate Curriculum

Investigators

Ira Agrawal, Wong Lik Wei, Ajay Mathuru, Celestial Yap, Swapna Teckwani, Nathasha Luke

Description

Mental health issues such as stress, anxiety, depression, and burnout are common among undergraduate students, and pose a serious challenge to student well-being and their success in university and life. Introducing mental health education as part of the undergraduate curriculum could be a promising early intervention. We aim to study the effectiveness of such initiatives in improving student well-being and self-awareness via the new, interdisciplinary elective, Building Mental Wellbeing and Resilience at the NUS College of Humanities & Sciences. Students will engage in experiential learning and impact measurements to develop their own good practices and strategies to manage distress and mental wellbeing in the course.

Recipient of Swee-Liew Wadsworth Education Grant 2023.

Education Research