Author: cdtlsd

The Usage of Generative Artificial Intelligence (AI) Avatars in Psychiatric Medical Education

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Charlene GOH Xing Le1, Eugene WANG1, Judy C. G. SNG2,*

1Yong Loo Lin School of Medicine (YLLSOM), NUS
2Department of Pharmacology, YLLSOM, NUS 

*phcsngj@nus.edu.sg

Sng, J. C. G., Goh, C. X. L., & Wang, E. (2024). The usage of generative artificial intelligence (AI) avatars for psychiatric medical education [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-jcgsng-et-al/

SUB-THEME

Opportunities from Generative AI 

KEYWORDS

Opportunities from Generative AI, video avatars, patient simulation, medical education

CATEGORY

Lightning Talk

BACKGROUND

The integration of generative artificial intelligence (AI) into our daily lives has significantly increased in recent years, with numerous healthcare institutions leveraging AI and medical technology to enhance diagnostic and treatment accuracy. However, its utilisation in medical education remains limited. This project aims to bridge this gap by promoting the incorporation of AI into medical education. This is done by supplementing existing resources with AI-generated videos of simulated patients. Currently, the range of available avatars online is limited and not entirely relevant to the context of Singapore. By providing virtual scenarios featuring patients with psychiatric medical conditions, we aim to develop essential skills in medical students, such as empathy and sensitivity, within a controlled environment. This approach seeks to enhance students’ clinical skills and decision-making, ensuring they are well-prepared for real-world patient interactions as practitioners. 

METHODS

AI technology was employed at multiple stages of building the video avatars. Initially, a doctor- patient conversation is generated using ChatGPT-4, after inputting a prompt that corresponds to a specific psychiatric patient profile. The patient profile includes the positive and negative signs presented by the patient, their socio-economic background, past medical history, and the suggested treatment plan. Medical experts in the psychiatric field meticulously crafted the simulated patient’s information to ensure an accurate representation of the condition. Two main symptoms were chosen: low mood (including adjustment disorder, dysthymia, and major depressive disorder) and chest tightness (not amounting to chest pain, including adjustment disorder, panic disorder, and somatic symptom disorder). After fine-tuning the script to ensure the scenario was localised and culturally accurate to Singapore’s context, the D-iD software was utilised to generate an AI video of a patient, emulating sentiments and synchronising speech with facial expressions. 

 

The video is then shown to students, whose objective is to accurately diagnose the patient. The patient avatar video aims to increase interactivity with students in a classroom setting, enhancing their diagnostic skills and understanding of psychiatric conditions. 

RESULTS

Preliminary feedback from medical professionals suggests its great potential to become a staple resource for healthcare students. In the coming months, we plan to pilot this with a small group of healthcare students to gather feedback and suggestions to refine the ensemble of avatars we currently have. The data collection will include metrics such as diagnostic accuracy and effectiveness in improving the students’ knowledge. Further studies will be conducted to determine the usage and effectiveness in nurturing the holistic development of medical students, encompassing both academic and psychosocial skills. 

CONCLUSION

As AI integrates more seamlessly into the medical curriculum, it is crucial to cultivate an engaging, interactive, and safe space for students, bridging the gap between theoretical knowledge and clinical practice. We believe that incorporating AI as early as possible in medical education is vital for developing holistic practitioners for a sustainable future, ultimately improving patient care in healthcare institutions. 

 

Infusing Contextual Elements With Generative AI Tools To Reinforce Learning For Students

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TAN Chun Liang

Department of Architecture,
College of Design and Engineering (CDE), NUS

tcl@nus.edu.sg 

Tan, C. L. (2024). Infusing contextual elements with generative AI tools to reinforce learning for students [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-cltan/

SUB-THEME

Opportunities from Generative AI 

KEYWORDS

Urban Greening, Site context, Video assignment, Peer review 

CATEGORY

Lightning Talk

EXTENDED ABSTRACT

The recent rise of AI content generators has had significant impact on both learning and teaching in educational institutions. Although the university encourages the responsible use of AI content-generation tools, a point of concern is on the authenticity of student report submissions. How do we ensure that usage of such platforms can help us augment the learning experience and not become a tool for students to conveniently churn out AI-generated reports at the eleventh hour to submit as their own?  

 

This is an important question to address for the course LA5303 “Urban Greening: Technologies and Techniques”, where students are taught ways to utilise urban greenery to improve the environment. Without a real understanding of the course content, students may not learn about the proper ways of urban greening and fall back to more superficial and cosmetic treatments of the landscape, leading to greenwashing. 

 

Drawing on the Significant Learning pedagogical framework (Fink, 2013), I try to tap on the Human Dimension, in learning about oneself in others to reinforce learning. I explored peer learning and the theory of Distributed Cognition (Hutchins, 2020) to acquire knowledge through an individual’s social and physical environment. In this manner, cognitive resources can be shared socially, and students can achieve more as a group than with just individual effort.  

 

Strategy 1: Experiential learning, peer learning, and industry talks 

Singapore is adorned with many examples of urban greenery projects, ranging from ground-level parks to sky-rise greenery projects. Since this course is about urban greenery, I began to introduce more activities to encourage more experiential and peer learning: where students go for site visits, assess real projects in Singapore and learn from each other instead of having me give them second- or third-hand information via lectures.

 

Figure 1. Learning journey to SproutHub @ Henderson  

 

Learning journeys to prominent urban greening spaces such as SproutHub@Henderson (Figure 1) and talks by industry partners (Figure 2) were organised to let students gain first-hand experience of the components of urban greenery covered in this course.  

Figure 2. Talks by Mr Christopher Leow, prominent urban farmer (Top row) and Representatives from Elmich Pte Ltd, sharing green wall and green roof products (Bottom row) 

 

In addition, students were tasked to visit urban greenery projects of their choosing and to create bite-sized (1-minute) videos documenting their learning (Figure 3). The videos were subsequently uploaded onto an online bulletin board (Miro) for others to review and comment on (Figure 4). Students were given the opportunity to provide comments on the videos produced by their peers. This directly increased their learning of urban greenery projects by 50 (total number of students in the course), as knowledge is now crowdsourced and contextual.  

 

Figure 3. Screenshots of a video done by a student, documenting urban greenery learning  

 

Figure 4. Comments from peers on the Miro board 

 

Strategy 2: Pre-emptive strike to nullify the impact of A.I. generated content  

Instead of warning students not to use AI-generated content, I insisted that it was the first thing they did for their assignment. Students had to append their prompts and AI-generated results in their report and show how they built on the AI content to come up with their final report. The AI-generated content thus became another layer of educational scaffolding for the students. More importantly, students were instructed to include specific examples of how to improve on urban greening using examples of videos done by their peers from Strategy 1. In this case, it became less likely for students to cheat with AI content generators such as ChatGPT because the examples are unique. 

REFERENCES​

Fink, L. D. (2013). Creating significant learning experiences: An integrated approach to designing college courses. John Wiley & Sons.

Hutchins, E. (2020). The distributed cognition perspective on human interaction. In Roots of human sociality (pp. 375-398). Routledge. 

Harnessing Generative AI As A Personalised Tutor: Enhancing Interdisciplinary Learning Outcomes For Biotechnology Graduate Students

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Xin Xiang LIM 

Department of Biological Sciences,
Faculty of Science, NUS 

xinxiang@nus.edu.sg 

Lim, X. X. (2024). Harnessing generative AI as a personalised tutor: Enhancing interdisciplinary learning outcomes for biotechnology graduate students [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-xxlim/

SUB-THEME

Opportunities from Generative AI 

KEYWORDS

ChatGPT, Student’s Prompt Analysis, Interdisciplinary learning, Generative AI, Personalised Tutor

CATEGORY

Lightning Talk

EXTENDED ABSTRACT

The escalating complexity of societal issues necessitates that graduates from higher educational institutions engage in problem-solving endeavours that transcend singular disciplines (Mansilla & Duraising, 2007; Repko., 2007). When students grasp and interconnect a diverse array of knowledge and skills, their educational experiences become more fulfilling, and their employment prospects broaden (Ivanitskaya & Montgomery., 2002). This phenomenon is particularly pertinent in biotechnology, where innovation is paramount in transforming laboratory discoveries into marketable products that address societal challenges. The innovation process extends beyond biology, requiring an understanding of market needs, funding sources, business models, risk management, and competitor analysis—critical real-world considerations. Thus, fostering innovation demands collaborative, interdisciplinary approaches to facilitate the cross-pollination of ideas and the integration of multiple perspectives. Equipping students with the skills to identify problems, develop prototypes, and conduct market research can provide a robust framework for innovation (Boms et al., 2022). 

 

Pharmaceutical sciences, chemistry, and biotechnology, typically have limited exposure to essential business considerations necessary for evaluating product market viability. This gap underscores the need for substantial scaffolding in business and innovation concepts to enable students to integrate their biotechnological expertise with business innovation, ultimately facilitating product creation. 

 

Generative AI offers significant potential to enrich learning experiences, fostering creativity, critical thinking, and motivation among students. ChatGPT, for instance, has demonstrated efficacy in enhancing interactive learning and personalised tutoring (Baidoo-Anu & Ansah., 2023). Leveraging ChatGPT promotes inquiry-based learning and student-centric approaches, both of which are effective in enhancing learning outcomes. Previous studies indicate that generative AI tools increase intrinsic motivation, conversational engagement, and continuous idea expression among students (Ryan & Deci., 2020) This context presents two educational opportunities: 1) inquiry-based active learning through prompt generation (prompt engineering), and 2) learning from generative AI responses. Developing writing prompts and prompting strategies has become a critical skill in handling generative AI. Prompt datasets collected from generative AI, including log data, can capture students’ learning processes in non-invasive ways. Prompt analysis thus provides an opportunity for educators to gain insights into students’ perceptions, motivations, and behaviors concerning interdisciplinary learning. 

 

Evaluation of students’ interdisciplinary learning outcomes will be conducted through three primary methods: 1) pre- and post-course surveys to capture students’ self-perceptions of their interdisciplinary skills and knowledge, providing insights into their metacognition and epistemology (Lattuca et al., 2012); 2) Assessment of student assignments and presentations using published, peer-reviewed rubrics for interdisciplinarity; and 3) Analysis of student prompts generated with ChatGPT. The student prompts will be analysed using natural language processing techniques, as outlined in recent studies, to gain insights into students’ cognitive processes and modes of thinking (Lee et al., 2023). Through this triangulated approach, the overall aim of the study is to comprehensively and rigorously evaluate the cognitive processes underpinning interdisciplinary learning. 

 

This lightning talk will specifically describe the insights gleaned from the analysis of students’ prompts so as to identify and evaluate possible learning barriers faced by students in the process of interdisciplinary learning. 

 

Figure 1. Interdisciplinary approach to biotechnology innovation. 

REFERENCES

Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. psychology, 61, 101860. https://doi.org/10.1038/s41587-022-01253-x

Boms, O., Shi, Z., Mallipeddi, N., Chung, J. J., Marks, W. H., Whitehead, D. C., & Succi, M. D. (2022). Integrating innovation as a core objective in medical training. Nature Biotechnology, 40(3), 434-437. 

Boix Mansilla, V., & Dawes Duraising, E. (2007). Targeted assessment of students’ interdisciplinary work: An empirically grounded framework proposed. Journal of Higher Education, 78(2), 215-237. https://doi.org/10.1080/00221546.2007.11780874

Ivanitskaya, L., Clark, D., Montgomery, G. et al. (2002). Interdisciplinary Learning: Process and Outcomes. Innovative Higher Education 27, 95–111. https://doi.org/10.1023/A:1021105309984 

Lattuca, L. R., & Knight, D. B., & Bergom, I. M. (2012, June), Developing a Measure of Interdisciplinary Competence for Engineers. Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. https://dx.doi.org/10.18260/1-2—21173 

Lee, U., Han, A., Lee, J., Lee, E., Kim, J., Kim, H., & Lim, C. (2023). Prompt Aloud!: Incorporating image-generative AI into STEAM class with learning analytics using prompt data. Education and Information Technologies, 1-31. https://doi.org/10.1007/s10639-023-12150-4

Repko, A. F. (2007). Integrating interdisciplinarity: How the theories of common ground and cognitive interdisciplinarity are informing the debate on interdisciplinary integration. Issues in Integrative Studies, 25, 1-31. http://hdl.handle.net/10323/4501

Reimagining Data Storytelling with Generative AI

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Evelyn ANG 

Data Literacy Programme
Office of the President, NUS

eve.ang@nus.edu.sg

Ang, E. (2024). Reimagining data storytelling with generative AI [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-eang/

SUB-THEME

Opportunities from Generative AI 

KEYWORDS

Data storytelling, Generative AIs, adult learners, ChatGPT-4o, custom GPTs 

CATEGORY

Lightning Talk

EXTENDED ABSTRACT

Data storytelling is a new superpower for making complex data accessible and engaging (Loewen, 2024a). Schwabish (2014) as well as Green and Brock (2000) highlight how visual and narrative elements enhance comprehension and persuasion, essential for effective data communication. Dykes (2020) demonstrates through real-world examples how compelling data stories can lead to more informed business decisions. Loewen (2024b) describes data storytelling as the art behind the science—the art of making sense out of a deluge of data, shaping it into something that sticks. The integration of generative AIs in storytelling creates more engaging narratives, akin to how bards once used music to enliven stories. Despite myths about data storytelling being just simplistic visualisation, it can be said to be a misconception. Dykes explained that effective data storytelling uses coherent narratives supported by meaningful visualisations to engage audiences deeply. Moreover, Generative AIs democratise the ability to analyse vast datasets, allowing humans to focus on creativity and emotional intelligence (Dykes, 2024). By combining AI capabilities with human adaptability, data storytellers can make data insights more compelling and actionable. Li (2024) has done a detailed scan into data storytelling tools available, and most are prototypes for research purposes. McKinsey & Company (2024) published an article reporting a surge in AI adoption in at least one business function in early 2024. Generative AI adoption is moving beyond professional setting and is much more likely to be used in both work and personal settings. 

 

Generative AI is here to stay and beckons the question how we can purpose generative AIs in data storytelling. 

 

In this lightning talk, I will highlight broadly what is good data storytelling as suggested by Knaflic (2015) in her book Storytelling with Data in areas (1) understanding the context, (2) choose appropriate visual display, (3) eliminate clutter, (4) focus attention where you want it, (5) think like a designer, and lastly (6) tell a story. Now to address the elephant in the room—how will Gen AI fit into this picture? Recent work by Li (2024) proposed four distinct levels of AI involvement in working with data from the data workers’ perspectives, based on the levels of human agency versus AI automation. However, today’s advancement of AI has yet to be able to only perform a singular role with simple prompt inputs effectively. Kesari (2024) proposed a matrix of how different tools with GenAI are suited for different kinds of decisions to be made. 

 

How do we put all these together towards better data storytelling? I will broadly show how we can position fit-for-purpose use of GenAIs into the data storytelling preparatory work based on customGPTs. I will also weave in how GenAIs can be purposefully deployed so leaving us humans to do what we do best—creativity and connecting with our audience (Dyke, 2024). We will also visit how the most popular generative AI—ChatGPT—will be able to become your new companion in data storytelling through my CustomGPT—NarratEve. I will also touch on using Custom GPTs (Loewen, 2024) that can make your data storytelling and preparation even more effective. 

REFERENCES

Dykes, B. (2020). Effective data storytelling: How to drive change with data, narrative, and visuals. John Wiley and Sons, Inc. 

Dykes, B. (2024). The Future of Data Storytelling is Augmented, not Automated. Forbes. https://www.forbes.com/sites/brentdykes/2024/02/27/the-future-of-data-storytelling-is-augmented-not-automated

Green, M. C., & Brock, T. C. (2000). The role of transportation in the persuasiveness of public narratives. Journal of Personality and Social Psychology, 79(5), 701-721. https://doi.org/10.1037//0022-3514.79.5.701

Kesari, G. (2024, 17 January). The Enduring Power of Data Storytelling in the Generative AI Era. MIT.edu. https://sloanreview.mit.edu/article/the-enduring-power-of-data-storytelling-in-the-generative-ai-era/

Knaflic, C. N. (2015).  Storytelling with Data: a data visualization guide for business professionals. Wiley. 

Li, H. (2024). Why is AI not a Panacea for Data Workers? An Interview Study on Human- AI Collaboration in Data Storytelling. arXiv 

Li, H. (2024). Where are we so far? Understanding Data Storytelling Tools from the perspective of Human-AI collaboration. arXiv 

Loewen, J. (2024). Custom GPT Creation For Data Visualization: A Step-by-Step Guide. Towardsai.net. https://towardsai.net/p/data-analysis/custom-gpt-creation-for-data-visualization-a-step-by-step-guide

Loewen, J. (2024a). Why Data Storytelling is Your New Superpower. Medium https://medium.com/data-storytelling-corner/why-data-storytelling-is-your-new-superpower-9f76e62762ce 

Loewen, J. (2024b). What the Heck is Data Storytelling Anyways? Here Are The Basics. Medium. https://medium.com/data-storytelling-corner/what-the-heck-is-data-storytelling-anyways-here-are-the-basics-c47c72cba44b

McKinsey & Company (2024). The state of AI in early 2024: Gen AI adoption spikes and starts to generate value. McKinsey https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai 

Schwabish, J. A. (2014). An economist’s guide to visualizing data. Journal of Economic Perspectives, 28(1), 209-234. https://dx.DOI.org/10.1257/jep.28.1.209

Reverse Engineering Pedagogy To Bridge Diverse Learning Background In Classrooms

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Da Yang TAN* and Yoke Leng LOO 

NUS College

*dytan@nus.edu.sg

Tan, D. Y., & Loo, Y. L. (2024). Reverse engineering pedagogy to bridge diverse learning backgrounds in classrooms [Poster presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-dytan-ylloo/

SUB-THEME

Others 

KEYWORDS

Reverse engineering, interdisciplinary education, honours education, programming 

CATEGORY

Poster Presentation

BACKGROUND

With the push for interdisciplinary education within the higher education settings, many of such programmes are designed to help students gain a wider perspective and develop a diverse skill set essential for addressing the complex challenges of the modern world ​(World Economic Forum, 2023)​. This means that students from wide-ranging backgrounds now learn together within the same classroom settings. While the exchange of perspectives from students’ different backgrounds add tremendous value to the classroom learning experience, the varying learning abilities and starting points of students bring new challenges to the instructors, especially in subjects that seek to impart technical knowledge, such as quantitative reasoning. The same learning experience now must seek to accommodate a spectrum of students: students with prior knowledge and who are likely to find the learning objective trivial; while students who have little background will struggle to play catchup when faced with new technical knowledge that they need to pick up within the short duration of the course.  

 

To mitigate the challenge of diverse learning backgrounds, reverse engineering pedagogy (REP) ​(Tan, Cheah, & Lee, 2021)​ as a teaching method has been piloted in an earlier run of the authors’ sections of GEA1000N: Quantitative Reasoning with Data ​(Tan & Loo, 2024)​. Within the course, one of the learning objectives is for students to be familiar with R programming to handle large datasets (see Figure 1 for a sample of such activity). Students with limited prior programming knowledge may struggle to reconcile the theory with the programming tasks, while those with programming experience may find the tasks underwhelming. Below, we highlight some of the considerations in designing such activities: 

Figure 1. Sample of reverse engineering learning activity implemented in the class.

DESIGN AND USER CONSIDERATIONS 

Resources

The design of the activities needs to consider the resources allocated to the class, such as contact time within the classroom. For the REP activities, they were all designed to be completed within 30 minutes of the class, so that there will be sufficient time to complete other activities within the class.

Curriculum Structure

The activities need to situate itself well within the curriculum of the course, so that both instructors and students will not find the activity out of place during the learning process. This requires a careful consideration of the suitable timing to contact the learning activities. For the REP activities, they were introduced at the midpoint of the course in Week 5 and 7, since a theoretical minimum such as basic syntax, data structure, logical operators and loops would still be needed to be introduced earlier in the course to help students successfully navigate the RE activities; on the other hand, introducing the activities at midpoint will allow students to expose themselves R programming and subsequently use them in the later part of the course, especially in other in-class activities and projects. The RE activities also situates itself within the original activities that the teaching team have designed.  

Instructors

The role of the instructor should not be overlooked as a key user of the activities, especially in the team-teaching context of the course. Given that the designer of the learning activities and the instructors delivering the materials may not be the same person, the learning activities should be intuitive and clear for the instructors for the buy-in.  

Students

Students are the primary stakeholders of the learning activities and their appreciation towards the learning activities would therefore be important. To reconcile the challenges of varying learning background, the RE activities are deliberately conducted in small groups during the class, so that the less experienced students could learn from experienced students, while the experienced students could gain new perspectives from the questions or gaps in understanding from the less experienced students. 

CONCLUDING REMARKS 

In this work, the use of reverse engineering in the teaching of R programming is introduced. Earlier preliminary results have hinted at the potential of REP learning activities as potential strategy for classes with varying learning background. By highlighting the possible design considerations in the development of materials, it is hoped the approach would be useful for generalisation of such reverse engineering approaches to other courses of similar students’ background in technical subjects. 

REFERENCES

​​Tan, D. Y., & Loo, Y. L. (2024). Reverse Engineering Pedagogy To Promote Confidence and Motivation in Programming Among Honors College Students. 2024 IEEE Global Engineering Education Conference (EDUCON) (pp. 1 – 3). Kos Island, Greece: IEEE. 

​Tan, D. Y., Cheah, C. W., & Lee, C. H. (2021). Reverse Engineering Pedagogy as an Educational Tool to Promote Symbiosis between Design and Physics. IEEE International Conference on Engineering, Technology and Education (IEEE TALE). Wuhan, China. doi:10.1109/TALE52509.2021.9678692 

​World Economic Forum. (2023). The Future of Jobs Report 2023. Geneva: World Economic Forum.

The Efficacy of Instructional Practice and Support on Student Engagement and Wellbeing in Blended Learning

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LEE Ming Cherk*, Netty Haiffaq Binte Zaini MATTAR, CAO Feng, and Norhayati Ismail

Centre for English Language and Communication (CELC), NUS

*elclmc@nus.edu.sg 

Cao, F., Lee, M. C., Netty Haiffaq Zaini Mattar, & Norhayati Ismail (2024). The efficacy of instructional practice and support on student engagement and wellbeing in blended learning [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-caof-et-al/

SUB-THEME

Opportunities from Wellbeing 

KEYWORDS

Instructional practice, support, student engagement, well-being 

CATEGORY

Paper Presentation

 

EXTENDED ABSTRACT

The blended learning approach, which combines both online and face-to-face teaching and resources (Albiladi & Alshareef, 2019), has become a “new normal” in higher education (Luo et al., 2017; Porter et al., 2016). It necessitates that students switch between synchronous and asynchronous modes of learning, and between multiple e-learning and communication platforms. Such changes have felt sudden and imposed (Finlay et al., 2022), and is further complicated by differences in online accessibility (Bayyat et al., 2021). Navigating through these various modes and platforms, even as they continue to shift and fluctuate over the years, has been demanding for students. This has caused increased distress and anxiety in students (Hagedorn et al., 2022) such that scholars have now turned their attention to the wellbeing of students in blended learning environments (Huang, 2023; Mendoza & Venables, 2023).  

 

Wellbeing is defined as a positive state of mental health. Wellbeing also indicates feelings of competence, agency, self-motivation, positive relationships, and personal growth (Baik, et al, 2017, p. 3). Scholars have suggested that student engagement in learning has an impact on student well-being which in turn has a strong bearing on academic achievement (Baik, et al, 2017; Houghton & Anderson, 2017; Alvarado et al., 2019; Ward-Griffin et al., 2018). In higher education, engagement is linked to students’ involvement with academically meaningful activities (Kuh, 2001).  

 

Working on the notion that student engagement is indicative of well-being, this study examines the engagement levels of undergraduates at the National University of Singapore in a range of blended-learning courses offered by the Centre for English Language Communication (CELC), NUS. The purpose was to measure students’ sense of engagement vis-a-vis the instructional practices and support (e.g., issuing reminders, clarifying instructions, and answering questions) given to promote engagement and student well-being and to identify aspects of learning where students felt least engaged.   

 

The three main research questions were: 

  1. To what extent were the students well engaged in learning?
  2. How did the students’ engagement correlate with the instructional practice and support given? 
  3. What actions can be taken to improve student engagement and, ultimately, their well-being? 

 

Based on the Community of Inquiry framework (Garrison et al., 2000), a questionnaire survey was devised with the assumption that student engagement is an indicator of student well-being. The effects of the instructional structure and practices delivered through blended learning were tested on behavioural, cognitive, and emotional engagement (Fredericks et. al., 2016). Statistical analyses were subsequently performed to compare the data and identify correlations among the variables. The findings were then corroborated by interviews with students.  

 

The results demonstrate highly positive perceptions towards teacher support, student participation, collaboration, and students’ sense of belonging.  Moreover, the analysis of students’ responses shows either moderate or strong correlations between students’ engagement levels and instructional practices and support. 

 

Understanding how various instructional practices and support provided for blended learning environments in CELC courses can help to inform the improvement of blended learning courses, such that student mental well-being is enhanced. 

 

REFERENCES

Albiladi, W. S., & Alshareef, K. K. (2019). Blended Learning in English Teaching and Learning:  A Review of the Current Literature. Journal of Language Teaching and Research, 10(2), 232. https://doi.org/10.17507/jltr.1002.03 

Baik, C.; Larcombe, W., Brooker, A., Wyn, J., Allen, L., Brett, M., Field, R., & James, R (2017).  Enhancing student mental wellbeing. A Handbook for Academic Educators., 26(8), 879-896. 

Bayyat, M., Muaılı, Z. H. A., & Aldabbas, L. (2021). Online component challenges of a blended learning experience: A comprehensive approach. Turkish Online Journal of Distance Education, 22(4), 277-294. http://dx.doi.org/10.17718/tojde.1002881

Finlay, M. J., Tinnion, D. J., & Simpson, T. (2022). A virtual versus blended learning approach to  higher education during the COVID-19 pandemic: The experiences of a sport and exercise science student cohort. Journal of hospitality, leisure, sport & tourism education, 30, 100363. https://doi.org/10.1016/j.jhlste.2021.100363

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School Engagement: Potential of the  Concept, State of the Evidence. Review of Educational Research, 74(1), 59-109. https://doi.org/10.3102/00346543074001059 

Fredricks, J. A., Filsecker, M., & Lawson, M. A. (2016). Student engagement, context, and  adjustment: Addressing definitional, measurement, and methodological issues. Learning and Instruction, 43, 1–4. https://doi.org/10.1016/j.learninstruc.2016.02.002 

Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment:  Computer conferencing in higher education model. The Internet and Higher Education, 2(2-3), 87-105. https://doi.org/10.1016/S1096-7516(00)00016-6

Hagedorn, R. L., Wattick, R. A., & Olfert, M. D. (2022). “My entire world stopped”: College students’ psychosocial and academic frustrations during the COVID-19 pandemic. Applied Research in Quality of Life, 17(2), 1069-1090. http://dx.doi.org/10.1007/s11482-021-09948-0

Houghton, A-M. & Anderson, J. (2017) Embedding mental wellbeing in the curriculum:  maximising success in higher education. Higher Education Academy. 

Huang, J. C. (2023). Implementation effect of integrating cooperative inquiry into blended learning: analysis of students’ goal setting, task value, and well-being. Interactive Learning Environments, 1-16. https://doi.org/10.1080/10494820.2023.2205896

Kuh, G. D. (2001). Assessing what really matters to student learning: Inside the National Survey of Student Engagement. Change, 33(3), 10-17. https://doi.org/10.1080/00091380109601795

Liem, G. A. D., & Chong, W. H. (2017). Fostering student engagement in schools: International best practices. School Psychology International, 38(2), 121-130. https://journals.sagepub.com/doi/pdf/10.1177/0143034317702947 

Luo, L., Cheng, X., Wang, S., Zhang, J., Zhu, W., Yang, J., & Liu, P. (2017). Blended learning with Moodle in medical statistics: An assessment of knowledge, attitudes and practices relating to e-learning. BMC Medical Education, 17(1), 170. https://doi.org/10.1186/s12909-017-1009-x 

Mendoza, A., & Venables, A. (2023). Attributes of blended learning environments designed to foster a sense of belonging for higher education students. Journal of Information Technology Education. Research, 22, 129. http://dx.doi.org/10.28945/5082 

National Survey of Student Engagement (2023). Engagement indicators and high-impact practices. https://nsse.indiana.edu/nsse/survey-instruments/engagement-indicators.html 

Porter, W. W., Graham, C. R., Bodily, R. G., & Sandberg, D. S. (2016). A qualitative analysis of institutional drivers and barriers to blended learning adoption in higher education. The Internet and Higher Education, 28, 17–27. https://doi.org/10.1016/j.iheduc.2015.08.003 

“Is This How You Feel?” Role Playing For Social Work Students Using Gen-AI: Bridging Technology And Pedagogy

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Gerard CHUNG Siew Keong1,*, Jonathan Y. H. SIM2,*, Bryan ONG1, and NEO Jie Xiang3 

1Department of Social Work, Faculty of Arts and Social Sciences (FASS), NUS 
2AI Centre for Educational Technologies (AICET) and Department of Philosophy, FASS, NUS
3School of Computing, NUS 

*gerard@nus.edu.sg; 2jyhsim@nus.edu.sg

Chung, G. S. K., & Sim, J. Y. H. (2024). "Is this how you feel?”: Role playing for social work students using GenAI: Bridging technology and pedagogy [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-gcskeong-jyhsim-et-al/

SUB-THEME

Opportunities from Generative AI

KEYWORDS

LLM, role-playing, social work, counselling, experiential learning

CATEGORY

Paper Presentation

 

EXTENDED ABSTRACT

Role-playing is a vital active learning method in social work education, crucial for developing students’ communication and engagement skills (Fulton et al., 2019). It involves participants simulating real-life scenarios to practice managing situations they might encounter in their professional lives. While evidence supports its effectiveness in skill development (Skoura-Kirk et al., 2021), role-playing in educational settings faces several challenges: Time constraints in classrooms often limit opportunities for extensive practice. First-time participants may feel anxious about peer assessment, impacting their engagement (Gómez-Poyato et al., 2020). Moreover, the pedagogical effectiveness can be compromised if the role-playing lacks authenticity or proper debriefing (Lazar, 2014). 

 

To address these issues, our project is developing a Role-Playing Tool (RPT) using Generative AI (GenAI) to simulate service user interactions with social workers (role played by students). This tool— the first of its kind in social work training in Singapore—offers students the opportunity to engage in various realistic scenarios relevant to the local social work context. The RPT allows for practice at the student’s convenience, focuses on specific skills for improvement, and provides instant feedback. The RPT’s level of control and accessibility enhances learning by enabling deliberate practice, a key factor in skill acquisition and confidence building. Students can use the feedback from the RPT to enrich classroom discussions with instructors and peers, fostering a collaborative learning environment. By leveraging GenAI technology, the tool aims to overcome the limitations of traditional role-playing methods while maintaining the benefits of experiential learning in social work education. 

 

However, we also face the challenge of developing the pedagogical content and approach of the tool. For instance, how can we make the GenAI-powered role-playing realistic to typical scenarios faced by social workers? What characteristics of the service users’ profile should the GenAI display when it “role-plays” as a service user? How can the tool be appropriately used in existing courses that teach practice skills to social work undergraduate students? 

 

To address these crucial questions, our study employed two primary methods of investigation. First, we conducted surveys with social work students and instructors to understand how they envision using the tool in their training and courses. This approach provided us valuable user-centric and user-generated insights into making the GenAI role-play profiles more realistic and relevant to actual social work practice scenarios. Second, we conducted a comprehensive review of existing studies from social work literature on the use of role-playing in university education. This review was crucial in informing the design of our pedagogy, allowing us to build upon proven conventional methods and adapt them for the AI-powered context. 

 

Our key findings from these investigations were twofold. The interviews with students and instructors gave us practical insights for enhancing the realism of the GenAI tool’s profiles and scenarios. For instance, students requested that service users role-played by GenAI could show more variations in emotional moods and linguistic styles. Our user interviews also highlighted the importance of integrating existing clinical frameworks familiar to current social work practice. Additionally, our literature review uncovered time-proven guidelines from current social work education practices that can be effectively adapted for our GenAI-based Role-Playing Tool. 

 

The significance of this study lies in its comprehensive approach to technological integration in social work education. While developing the GenAI-powered Role-Playing Tool is an important first step, we recognise that crafting an appropriate pedagogy is equally crucial and challenging. Our research emphasises the need for a thoughtful, evidence-based approach to integrating this technology into existing curricula, ensuring it complements and enhances traditional teaching methods rather than replacing them.

 

REFERENCES

Fulton, A. E., Dimitropoulos, G., Ayala, J., McLaughlin, A. M., Baynton, M., Blaug, C., Collins, T., Elliott, G., Judge-Stasiak, A., Letkemann, L., & Ragan, E. (2019). Role-Playing: A Strategy for Practicum Preparation for Foundation Year MSW Students. Journal of Teaching in Social Work, 39(2), 163–180. https://doi.org/10.1080/08841233.2019.1576573 

Gómez-Poyato, M. J., Aguilar-Latorre, A., Martínez-Pecharromán, M. M., Magallón-Botaya, R., & Oliván-Blázquez, B. (2020). Flipped classroom and role-playing as active learning methods in the social work degree: Randomized experimental study. Social Work Education, 39(7), 879–892. https://doi.org/10.1080/02615479.2019.1693532 

Lazar, A. (2014). Setting the Stage: Role-Playing in the Group Work Classroom. Social Work with Groups, 37(3), 230–242. https://doi.org/10.1080/01609513.2013.862894 

Skoura-Kirk, E., Brown, S., & Mikelyte, R. (2021). Playing its part: An evaluation of professional skill development through service user-led role-plays for social work students. Social Work Education, 40(8), 977–993. https://doi.org/10.1080/02615479.2020.1764521

Enhancing Educational Outcomes in Quality and Productivity Management Through ChatGPT Integration

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Alexander LIN*, Anqi SHI, and Stephen En Rong TAY 

Department of the Built Environment, College of Design and Engineering (CDE), NUS

*bdgal@nus.edu.sg 

Lin, A., Shi, A., & Tay, S. E. R. (2024). Enhancing educational outcomes in quality and productivity management through ChatGPT integration [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-alin-et-al/

SUB-THEME

Opportunities from Generative AI

KEYWORDS

Artificial Intelligence, Educational Technology, Interactive Learning, Management Education, Student Engagement 

CATEGORY

Paper Presentation

 

INTRODUCTION 

This study examines the impact of integrating Generative AI tools, specifically ChatGPT, into the course PF2203 “Quality and Productivity Management (QPM)”. It focuses on fostering Collaborative Constructivism and Authentic Application of QPM concepts. Collaborative Constructivism enhances understanding and retention (Brooks & Brooks, 1999; Piaget, 2013), while Authentic Application translates theoretical knowledge into practical scenarios (Rahmawati et al., 2021; Weeks et al., 2019). These methodologies significantly boost critical thinking, as evidenced by studies like Sandu et al. (2024) and Rahman and Watanobe (2023), which suggest that student interactions with ChatGPT promote such outcomes. By integrating ChatGPT, the study anticipates improved student engagement and critical thinking in practical QPM applications. The research questions are:   

  1. Does ChatGPT integration enhance learning, as shown by student work? 
  2. What are student perceptions of ChatGPT integration? 

 

METHODOLOGY

This study employs methods similar to those used by Sandu et al. (2024) and Rahman and Watanobe (2023), enhancing student engagement and critical thinking through interactions with ChatGPT in PF2203. Groups of 2 to 3 students utilised ChatGPT to prepare their group projects, which included gathering information, synthesising content, and discussing a QPM-related topic through oral presentations. The presentations also had a segment on students’ sharing of their ChatGPT usage, followed by a class discussion. The effectiveness of ChatGPT was assessed through both qualitative and quantitative student feedback, as well as students’ presentation scores, which were evaluated based on the marking criteria in Table 1. The study received ethical approval from the Learning and Analytics Committee in April 2024. 

Table 1 
Marking scheme for project presentation 

HECS2024-a42-Fig1

 

DISCUSSION AND RESULTS

Through tutorial discussions, the lecturer promoted creative use of ChatGPT, including integrating theoretical knowledge with practical cases. As illustrated in Figures 1 and 2, which depict examples from students’ Q&A with ChatGPT and presentation slides, respectively, students learned this approach and applied lean principles at different phases of a construction project in their presentation. Concurrently, the students were aware of the potential errors in the output generated by ChatGPT. For instance, Table 2 reveals their capability to discern when the output does not align closely with the required information or context. Consequently, they could exclude irrelevant data, showcasing their critical thinking skills in assessing and utilising the information from ChatGPT effectively. Overall, student presentation scores, which were evaluated based on criteria emphasising concept integration and practical understanding (see Table 1), show significant improvement in the Academic Year 2023/24 Semester 2 with the use of ChatGPT, compared to the previous year without ChatGPT (see Table 3). Averaged scores rose from 13.25 to 14.75 for the 1st Presentation and from 14.25 to 15 for the 2nd Presentation, showing improved capability of integration of QPM knowledge, aligning with the course’s learning objectives. However, as there were only four groups, statistical analysis was not feasible due to the small sample size. 

 

As indicated in Table 4, feedback collected using a 5-point Likert scale demonstrates a consensus that ChatGPT enhances knowledge integration (Question 1) and motivates learning (Question 2). Responses to Questions 3 and 4 generally confirm improvements in scenario-based learning and critical thinking through ChatGPT. The qualitative feedback from students, presented in Table 5, shows that integrating ChatGPT bolsters students’ critical thinking in applying managerial concepts in real-world contexts—effectively bridging the identified research gap. 

Figure 1. Student’s prompt for ChatGPT 

 

Figure 2. Student’s work for second presentation

 

Table 2
Students’ remark on ChatGPT’s output. The first column records the prompts used and the responses from ChatGPT, and the second column contains their remarks on how they utilised ChatGPT’s output, as stipulated by a template provided by the lecturer, along with their evaluations of the output’s relevance and accuracy. 

 

Table 3 
Averaged presentation scores (For each semester, number of student groups = 4)

 

Table 4 
End-of-course survey results indicating the average response based on a five-point Likert scale with 1 (Strongly Disagree) and 5 (Strongly Agree) (n= 10). 

HECS2024-a42-Fig4

 

Table 5 
Qualitative student feedback  

HECS2024-a42-Fig5

 

CONCLUSION

Integrating ChatGPT into the QPM course has led to measurable improvements in both student engagement and academic performance. This suggests a scalable model that is sufficiently applicable for broader adoption across various educational disciplines. Future research will focus on optimizing AI tool integration within these contexts.

 

REFERENCES

Brooks, J. G., & Brooks, M. G. (1999). In search of understanding: The case for constructivist classrooms. Ascd. 

Piaget, J. (2013). The construction of reality in the child. Routledge. 

Rahman, M. M., & Watanobe, Y. (2023). ChatGPT for education and research: Opportunities, threats, and strategies. Applied Sciences, 13(9), 5783. https://doi.org/10.3390/app13095783

Rahmawati, Y., Taylor, E., Taylor, P. C., & Koul, R. (2021). Student empowerment in a constructivist values learning environment for a healthy and sustainable world. Learning Environments Research, 24, 451-468. https://doi.org/10.1007/s10984-020-09336-9

Sandu, R., Gide, E., & Elkhodr, M. (2024). The role and impact of ChatGPT in educational practices: insights from an Australian higher education case study. Discover Education, 3(1), 71. https://doi.org/10.1007/s44217-024-00126-6

Weeks, K. W., Coben, D., O’neill, D., Jones, A., Weeks, A., Brown, M., & Pontin, D. (2019). Developing and integrating nursing competence through authentic technology-enhanced clinical simulation education: Pedagogies for reconceptualising the theory-practice gap. Nurse Education in Practice, 37, 29-38. https://doi.org/10.1016/j.nepr.2019.04.010  

Interdisciplinary Education In NUS: A Scan Of Current Courses And Development Of An Evaluation Framework

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Olivier LEFEBVRE1,*, Alex MITCHELL2, Marissa Kwan Lin E3, Stephen En Rong TAY4, Li Neng LEE5 

1Department of Civil and Environmental Engineering, College of Design and Engineering (CDE), NUS
2Department of Communications and New Media, College of Humanities and Sciences, NUS
3Centre for English Language Communication, NUS
4Department of the Built Environment, CDE, NUS
5Department of Psychology, College of Humanities and Sciences, NUS 

*ceelop@nus.edu.sg

Lefebvre, O. P., Mitchell, A., E, M. K. L., Tay, S. E. R., & Lee, L. N. (2024). Interdisciplinary education in NUS: A scan of current courses and development of an evaluation framework [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/olefebvre-et-al/

SUB-THEME

Others – Interdisciplinary Education 

KEYWORDS

Interdisciplinary learning, course design, course evaluation, community engagement  

CATEGORY

Paper Presentation

 

CONTEXT

The establishment of the College of Humanities and Sciences (CHS) in 2020 and the College of Design and Engineering (CDE) in 2022 exemplifies the efforts towards interdisciplinary education in the National University of Singapore (NUS) in response to current problems that require various disciplines to work together (National University of Singapore, 2021). These efforts have produced courses that look beyond the classroom to engage with the wider community.  

 

For example, in the CHS, the course HS2911 “Social Media and Mental Health” provides students with interdisciplinary training to analyse the impact of social media in real-world scenarios. Similarly, in the CDE, the course CDE2501 “Liveable Cities” provides students the opportunity to approach community development through the lens of urban policymakers, planners, architects, engineers, real estate consultants and managers. 

 

While these efforts provide students with an interdisciplinary learning experience, the diverse nature of the courses, along with the lack of a common yardstick in evaluating interdisciplinary education makes it challenging to determine if these efforts have been fruitful, especially in terms of real-world applicability that impacts our communities. Hence, the NUS Teaching Academy (NUSTA) has developed the following research questions: 

  1. What is the current state of interdisciplinary studies at NUS?  
  2. What evaluation metrics could be used to support interdisciplinary courses? 
  3. How can interdisciplinary education in NUS be refined? 

 

METHODOLOGY

An interdisciplinary team within the NUSTA was formed to include views from CHS and CDE. In this study, the operational definition of interdisciplinary learning from the NUS Board of Undergraduate Studies (BUS) was adopted:  

Interdisciplinary courses integrate perspectives, theoretic frameworks, concepts, tools, and techniques and approaches from two or more conventional disciplines to understand the chosen theme, its challenges, and potential solutions. 

A scan of interdisciplinary courses in both colleges was conducted. Subsequently, the NUS Futures Office was engaged to better develop the study, from which the findings were presented to the NUSTA for feedback and refinement. In developing an evaluation framework, the Accreditation Manual from the Institution of Engineers Singapore was referenced (Institution of Engineers Singapore, n.d.). 

 

RESULTS AND DISCUSSION

The scan reveals that interdisciplinary courses can be predominantly categorised as either knowledge-/application-focused and adjacent/orthogonal (refer to Figure 1).

Figure 1. Dimensions describing interdisciplinary courses in NUS. 

 

Subsequently, an evaluation framework inspired by the engineering accreditation board (EAB) was developed. The proposed framework consists of 1) interdisciplinary educational objectives (IEOs), and 2) interdisciplinary learning outcomes (ILOs). The former describes the objectives at the programme level, while the latter describes attributes that students should achieve. A total of six ILOs were developed, which are presented in Table 1. Note that courses need not fulfil all the ILOs as courses within the same programme can complement each other to cover the ILOs. 

 

Next, the Course Learning Outcomes (CLOs) in the proposed framework would describe how the specific course maps to the ILOs. An example for CDE2501 “Liveable Cities” is presented in Table 2 as an example. 

 Table 1 
List of interdisciplinary learning outcomes developed in the study 

 

Table 2 
List of interdisciplinary learning outcomes developed in the study 

 

CONCLUSION AND SIGNIFICANCE

With these findings, a suggestion towards more application-focused courses is made to provide students to create and test solutions for the community at large. In addition, the proposed framework could be used by faculty members to develop interdisciplinary courses and education programmes, and provide a tool for reflection on current interdisciplinary courses. Finally, the framework could be utilised at the programme level, coupled with alumni and employer surveys and engagements, to assess if the ILOs have been achieved. These suggestions are provided to enhance the interdisciplinary education within NUS on community impact. 

 

REFERENCES

Institution of Engineers Singapore. (n.d) Engineering Accreditation Board Accreditation Manual. https://www.ies.org.sg/Accreditation/EAB10249  

National University of Singapore (2021, 27 August). Two new colleges at NUS to deliver flexible, interdisciplinary education more accessibly, and at greater scale https://news.nus.edu.sg/two-new-colleges-at-nus-to-deliver-flexible-interdisciplinary-education-more-accessibly-and-at-greater-scale/ 

Pedagogical Practices for Study Trips: A Factor Analysis of Key Variables

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K. Mukhopadhyay*, S. K. Tambyah, K. J. FONG, J. S. YIP

College of Alice and Peter Tan (CAPT), NUS

*kankana.m@nus.edu.sg

Mukhopadhyay, K., Tambyah, S. K., Fong, K. J., Yip, J. S. J. (2024). Pedagogical practices for study trips: A factor analysis of key variables [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-kmukhopadhyay-et-al-2/

SUB-THEME

Opportunities from Engaging Communities

KEYWORDS

Factor analysis, vignette survey, intentional pedagogy, experiential learning, study trips

CATEGORY

Paper Presentation

 

EXTENDED ABSTRACT

This paper will share insightful findings on the effective pedagogies for engaging with communities during short-term overseas experiential study trips. The findings are based on a robust factor analysis of variables that define pre-trip and actual-trip pedagogies. The data was obtained from a vignette survey conducted as part of a larger research study funded by Ministry of Education (MOE). This larger study is a comprehensive assessment of the pedagogies used and the learning outcomes achieved in a residential college through such trips. For more than a decade, the trips, guided by experiential learning theories (Kolb 1984; Moon, 2004; Roberts, 2012; Lovett, 2020) have been conducted in different geographical locations (India, Myanmar, Balkans, Botswana, and Nepal), and by different faculty-student teams.

 

The intentional pedagogical practices—pre-trip and during the actual trip—provide the foundation to achieve the deep learning outcomes for students who embark on these trips (VandeBerg, et al, 2012; Matsushita, 2018; Mukhopadhyay, et al., 2022). The question that this paper addresses is: What aspects of intentional pedagogies are a) most productive, and b) challenging for achieving deep learning outcomes in short-term overseas experiential study trips?

 

VIGNETTE SURVEY METHODOLOGY

The study uses a mixed-methods case study design, combining existing data from past study trips and fresh data from surveys and interviews with former student participants and overseas partners (organisation/institution/social business) from 2012 to 2020. The vignette survey was one of the primary data collection methods. The vignettes were constructed through systematic analysis of the existing data and approximated situations from the lived experiences of the students during the study trips. The vignettes aided students’ recall of what they experienced during the trips, given that there was a lag between the actual trips and this study (Hyman & Steiner, 1996; Hopkins &
King, 2010).

 

Factor analysis results

Exploratory factor analysis was conducted on the variables related to pre-trip and actual trip pedagogies, based on a sample size of 145 respondents. The results revealed interesting latent constructs and evidence of the robustness of the experiential learning pedagogies (Table 1). Using principal axis factoring with direct oblimin rotation, three latent factors from nine pre-trip pedagogical variables were identified, corresponding to classroom sessions, guest speakers, and peer presentations. These factors mapped on perfectly to the respective vignettes, demonstrating the strong construct validity of the survey. Cronbach’s Alpha tests confirmed the high reliability of the pre-trip latent factors. For actual trip engagements, the factor analysis revealed two latent factors: reflective activities and experiences from engagement. However, the reliability scores of these factors were moderate, suggesting that some components, such as reflection and the balance of activities during the actual trip, might be better treated as individual factors rather than a combined factor.

 

SIGNIFICANCE OF THE FINDINGS

These experiential study trips offer students an interactive platform to connect the concepts of engaging with communities to grounded practices through classroom discussions and field visits. This connection requires evidence-based pedagogical guidance which this paper provides through a robust factor analysis of pre-trip and actual-trip pedagogies. These results can enable a more informed understanding on how to improve the pedagogical design, implementation and learning outcomes for short-term overseas experiential study trips.

Table 1
Factor analysis results and descriptives of pre-trip and actual trip pedagogy-related variables

 

REFERENCES

Hopkins, D. J. & King, G. (2010). Improving anchoring vignettes: Designing surveys to correct interpersonal incomparability. Public Opinion Quarterly, 1-22. https://doi.org/10.7910/DVN/UU5EUI

Hyman, M. R. & Steiner, S. D. (1996). The vignette method in business ethics research: Current uses and recommendations. SMA Conference Paper.

Kolb, D. A. (1984). Experiential Learning. Prentice Hall Books.

Lovett, K. (2020). Introduction: Listening and Learning from Experiential Learning Educators. In K. Lovett (Ed.) Diverse Pedagogical Approaches to Experiential Learning:Multidisciplinary Case Studies, Reflections, and Strategies (pp. 1-11). Springer Nature. https://doiorg.libproxy1.nus.edu.sg/10.1007/978-3-030-42691-0

Moon, J. A. (2004). A Handbook of Reflective and Experiential Learning: Theory and Practice. Routledge.

Matsushita, K. (2018). An invitation to deep learning. In Matsushita, K. (Ed.) Deep Active Learning. Ch 2. Springer Nature. http://dx.DOI.org/10.1007/978-981-10-5660-4_2.

Mukhopadhyay, K., Balachandran, L., Wong S. F., Lai, J. C. Y., Tan, A. X. Y., McGahan, K., Toh T. C., Wong, R., & Tan L. Y. (2022). Steering towards the Internationalisation of Higher Education: Lessons from Pedagogical Interventions in Overseas Experiential Learning Programmes. Asian Journal of the Scholarship of Teaching and Learning, 12(1). 20-38. https://ctlt.nus.edu.sg/wp-content/uploads/2024/04/v12n1_mukhopadhyay-et-al-for-layout-2.pdf

Roberts, J. W. (2012). Beyond learning by doing: Theoretical currents in experiential education. Routledge.

Vande Berg, M., Paige, R. M., & Lou, K. H. (2012). Student learning abroad. In Vande Berg, M., Paige, R. M., & Lou, K. H. (Eds.), Student learning abroad: What our students are learning, what they’re not, and what we can do about it (pp. 3-28). Stylus.

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