Paper Presentations – Page 4

Bridging the Gap: Comparing Student Beliefs and Existing Empirical Data on ChatGPT’s Job Market Impact

cdtlsd Paper Presentations 18 November, 202418 November, 2024ChatGPT, job characteristics, labour market trends, Opportunities from Generative AI, student beliefs

Jingcheng FU
Residential College 4 (RC4), NUS

jingcheng.fu@nus.edu.sg

Fu, J. (2024). Bridging the gap: Comparing student beliefs and existing empirical data on ChatGPT’s job market impact [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-jingchengfu/

SUB-THEME

Opportunities from Generative AI

KEYWORDS

ChatGPT, student beliefs, job characteristics, labour market trends

CATEGORY

Paper Presentation

INTRODUCTION

The integration of AI technologies, particularly language models like ChatGPT, is poised to transform various job sectors and higher education (Dempere et al., 2023). This study explores student beliefs about ChatGPT’s impact on the labour market. Specifically, students are asked to predict about the association between different skill requirements and other job characteristics and the exposure to ChatGPT. The beliefs are elicited in a class survey after which the students are informed of the latest empirical findings. Understanding these beliefs is crucial for educators and career advisors to guide students effectively in a rapidly evolving job market.

LITERATURE REVIEW

Eloundou et al. (2023) developed an “exposure index” to measure the extent to which different jobs are affected by ChatGPT. Specifically, based on a detailed description of the work activities and tasks for each job, the researchers use some rubrics to determine the proportion of tasks that ChatGPT is expected to make at least 50% faster. This index is computed for 1,016 occupations in the US labour market. The occupation dataset also provides quantitative measures of job characteristics, including the importance of different skills. Many of those characteristics are significantly correlated with the exposure index.

RESEARCH QUESTIONS

What are students’ perceptions of the relationship between a job’s requirements of different types of skills and the exposure to ChatGPT?
How do these student beliefs compare with the existing empirical findings?

METHODOLOGY

To investigate these questions, a survey was administered to 34 first-year NUS students who took the course UTC1702G “Thinking in Systems – Markets and Inequality” in April 2023. The class was 55% female, and the faculty composition was 32% NUS Business School, 16% Faculty of Arts and Social Sciences (FASS), 14% Faculty of Science (FOS), 27% School of Computing (SOC), 9% College of Design and Engineering (CDE), and 2% from the Multi-disciplinary Programme. Students were given a brief explanation of the study design of Eloundou et al. (2023), together with the definitions of the skills, before they answered the multiple-choice questions (MCQs) to make guesses on the findings. I first asked students which skills are positively associated with substitutability, followed by which skills are negatively associated; the answers were coded as “Negative”, “Positive”, and “Neutral” for each skill (2% of the answers for a particular skill were inconsistent and not included in the analysis).

KEY FINDINGS

Figure 1 summarises the findings of Eloundou et al. (2023) and the distribution of student beliefs. Of the 11 skills tested, all are significantly associated with exposure except for speaking. For the three skills that are most strongly positively associated with exposure—reading, writing, and programming—the vast majority of student beliefs were accurate. For the other skills, however, their guesses departed from the paper’s findings. For the two skills that are most strongly negatively associated with exposure, only around 50% of students correctly predicted the association, and around 30% of the students believed the opposite. For the other two process-related skills, learning strategies and monitoring, which have a small negative association with exposure, the guesses were split, with more than 40% being incorrect positive guesses. Only 20-30% of students correctly saw the positive association for active listening, and the negative association for mathematics and science.

HECS2024-a86-Fig1 Figure 1. Beliefs about associations between ChatGPT exposure and different skill requirements compared to empirical findings.

SIGNIFICANCE OF THE STUDY

The findings indicate a significant disparity between student beliefs and the empirical data from the exposure index study. Many students hold incorrect assumptions about which skills and job characteristics are most vulnerable to AI substitution. This gap underscores the need for educational interventions to align student perceptions with actual labour market trends. By identifying these misconceptions, educators can develop targeted strategies to enhance career guidance and support, ensuring students have accurate information about AI’s effects on job characteristics and skills. This is essential for preparing them to navigate future career paths effectively. Addressing these misconceptions is crucial for students’ future success, providing a foundation for further research and practical applications in higher education and career planning..

REFERENCES

Dempere, J., Modugu, K., Hesham, A., & Ramasamy, L. K. (2023). The impact of ChatGPT on higher education. In Frontiers in Educatio, 8, 1206936. https://doi.org/10.3389/feduc.2023.1206936

Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. https://doi.org/10.48550/arXiv.2303.10130

Cultivating Student Wellbeing and Skills: The Role of Arts and Cultural Participation in Student Development

cdtlsd Paper Presentations 18 November, 202418 November, 2024arts and cultural participation, Co-curricular activities, Opportunities from Wellbeing, skills, Wellbeing

Rimi Parvin Khan

Department of Communications and New Media,
Faculty of Arts and Social Sciences (FASS), NUS

rimikhan@nus.edu.sg

Khan, R. P. (2024). Cultivating student wellbeing and skills: The role of arts and cultural participation in student development [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-rimikhan/

SUB-THEME

Opportunities from Wellbeing

KEYWORDS

Co-curricular activities, arts and cultural participation, skills, wellbeing

CATEGORY

Paper Presentation

EXTENDED ABSTRACT

Institutional conversations about student wellbeing are leading to a greater emphasis on co-curricular activities. There is growing recognition of the multi-dimensionality of student wellbeing, including the need for students’ ‘self-actualisation’ as part of their university experience (Baik & Larcombe, 2023). This paper explores the particular role that cultural participation plays within these practices of self-actualisation.

‘Culture’ is a broad and ambiguous term that is used to describe both ‘art’ (Arnold, 1869) and ‘ordinary’ practices of identity- and meaning-making (Williams, 1958; Ang, 1993). In both the Singapore and international policy contexts, ‘cultural participation’ encompasses activities ranging from music, literature, audio-visual and new media, as well as sports and other leisure activities. For several decades, cultural studies scholars and policymakers have been debating the impact of cultural participation, and whether these can, or should, be tied to a range of creative, cultural, or economic objectives (Galloway, 2006; Miles & Gibson, 2016; Yue & Khan, 2014). Over this period, economic justifications for arts and cultural participation have come to the fore, emphasising the importance of creative industries in contributing to national economies.

It is in this context that my research, which is part of a larger collaborative project funded by the Australian Research Council (ARC LP200301027), explores the role of arts and cultural participation in contributing to transferable skill development for the future economy.

The paper discusses NUS co-curricular activities (CCAs) as both facilitating student wellbeing and contributing to transferable skill development, highlighting the potential tensions that arise between these goals. It draws on qualitative research with students involved in NUS Centre for the Arts’ CCA programmes. University-based student clubs supporting a range of arts and creative activities have long been part of the student experience in Singapore. These clubs, ranging from music ensembles to theatre and performing arts groups, provide students with opportunities to engage in creative expression and collaboration outside the formal academic curriculum. This paper examines the role of these CCAs in the cultivation of skills such as critical thinking, social and cultural awareness, problem-solving, and collaboration, aligning with the World Economic Forum’s 21st Century Skills Framework and the Singaporean government’s ‘SkillsFuture’ policy, both of which directly inform curriculum and labour market planning. The research asks: 1) How do young people acquire 21st-century skills through arts participation?; 2) Why do young people participate in CCA arts activities?; and 3) What are the longer-term vocational outcomes and career pathways for young people who participate in these activities?

The data suggests that creative participation does lead to such skills and contributes not only to students’ ‘lifelong’ but also to ‘lifewide’ learning (Barnett, 2011), emphasising individuals’ holistic development. However, students do not necessarily see the value of their CCA participation in these terms and are more ambivalent about the contribution of these activities to their longer-term employment trajectories. The paper asks whether activities that promote wellbeing should be tied to economic and educational outcomes, or whether they should offer a space for personal development that exists outside these objectives. Is such a space possible given the competitive structures of educational and career attainment that students participate in? Given these questions and tensions, how might we best advocate for students’ cultural participation and the continuing value of CCAs?

REFERENCES

Arnold, M. (1994). Culture and anarchy. 1869. Ed. Samuel Lipman. Yale UP, 1, 164.

Baik, C., & Larcombe, W. (2023). Student wellbeing and students’ experiences in higher education. In Research Handbook on the Student Experience in Higher Education. Edward Elgar Publishing.

Barnett, R. (2011). Lifewide education: A new and transformative concept for higher education. Learning for a Complex World: A Lifewide Concept of Learning, Education and Personal Development, 22-38.

Williams, R. (2011). Culture is ordinary (1958). Cultural theory: An anthology, 5359.

Ang, I. (1993). To be or not to be Chinese: Diaspora, culture and postmodern ethnicity. Asian Journal of Social Science, 21(1), 1-17. https://doi.org/10.1163/030382493X00017

Galloway, S. (2006). Cultural participation and individual quality of life: A review of research findings. Applied Research in Quality of Life, 1, 323-342. https://doi.org/10.1007/s11482-007-9024-4

Miles, A., & Gibson, L. (2016). Everyday participation and cultural value. Cultural Trends, 25(3), 151-157. https://doi.org/10.1080/09548963.2016.1204043

Yue, A., & Khan, R. (2014). Accounting for multiculturalism: the utility of cultural indicators and the politics of diversity and participation. Conjunctions, 1(1), 1-28. https://doi.org/10.7146/tjcp.v1i1.18600

Building Nature into the Curriculum: Wellbeing Through Nature Education

cdtlsd Paper Presentations 18 November, 20242 December, 2024experiential learning, general education, nature education, Opportunities from Wellbeing, Outdoor learning, Wellbeing

Patricia LORENZ

Ridge View Residential College (RVRC), NUS

plorenz@nus.edu.sg

Lorenz, P. (2024). Building nature into the curriculum: Wellbeing through nature education [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-plorenz/

SUB-THEME

Opportunities from Wellbeing

KEYWORDS

Wellbeing, outdoor learning, nature education, experiential learning, general education

CATEGORY

Paper Presentation

EXTENDED ABSTRACT

This paper presentation in the sub-theme of “Opportunities From Wellbeing” examines how nature education, built into the General Education (GE) courses at NUS, can benefit student wellbeing. Albeit not being the main focus of the courses, extensive opportunities to spend time in nature promotes a sense of wellbeing in students by countering widespread Nature Deficit Disorder (Lee, 2023). The term “Nature Deficit Disorder” was first described by Louv (2008) as a condition in which children and young people are deprived of spending time in nature and the opportunity to play outdoors. Recent research has demonstrated direct links between Nature Deficit Disorder and mental health in adolescents (Dong & Geng, 2023). Thus, universities have the potential to benefit students’ health and wellbeing by exposing enrolled students to nature through the formal or informal curriculum.

Ridge View Residential College (RVRC) focuses on teaching sustainability and labels itself the “College in Nature”. As such, it offers a range of extracurricular nature-based activities, such as the RVRC Leopard Cat Quest, RVRC Intertidal Walk and Clean, and the RVRC Citizen Science Programme. The college also offers two courses under the GE “Community and Engagement” pillar, namely RVN2001 “The Great Extinction”, focusing on the current biodiversity loss and mass extinction, and RVN2002 “Wild Asia”, discussing conservation issues and strategies in Southeast Asia. While both courses were designed with a focus on biodiversity loss and conservation, increasingly reconnecting students to the natural world has become an additional focal point.

Observational evidence and survey questionnaires have demonstrated that nearly all students enrolling in the courses suffer from Nature Deficit Disorder. Hence, a larger focus was placed on creating time throughout the course schedule to reconnect to nature. RVN2001 engages students in four local fieldtrips, and one outdoor learning session on campus, while RVN2002 engages students on a highly immersive 10-day overseas fieldtrip to Pahang, Malaysia, which is nearly entirely dominated by outdoor learning. While this provides ample outdoor learning time, specific techniques were employed to facilitate greater awareness of the natural world and the benefits immersion in nature provides to the individual. Great emphasis is placed on being still or quiet in nature, to silently observe wildlife or habitats, as well as to engage in a structured Forest Bathing session.

As a result, students have demonstrated a greater appreciation for nature in post-course surveys. Moreover, through experiential learning student were able to identify how these nature engagement sessions benefit their own health and wellbeing. Feedback from RVN2001 demonstrates the understanding “That nature is important to our wellbeing” and “how environmentalism can be directly linked with health”. Students were also able to connect the personal experiences to society: “Going on the field trips to nature parks really helped me take my mind off school work (which, if extended to larger society, could have really beneficial effects too if they would realise)”, and the bigger picture: “This kind of environmental action underscores the interconnectedness of all life and the critical role that conservation plays in our own survival and well-being”. Likewise, a noticeable number of students who took RVN2002 declared that “Forest bathing really opened my eyes and made me truly realise how much I love nature and how much I enjoy it”, and understood the health benefits of the activity: “Being silent in nature was very therapeutic”.

Thus, with little outdoor engagement built into the Singapore school curriculum, it can be argued that it is highly beneficial for educators to make an effort to incorporate extensive nature-based outdoor education into the curriculum in order to benefit students’ mental and physical wellbeing. This however ought not to be done in addition to existing classroom teaching, thus further adding to students’ already heavy workload, but instead outdoor learning ought to replace conventional classroom teaching.

REFERENCES

Dong, X. & Geng, L. (2023). Nature deficit and mental health among adolescents: A perspectives of conservation of resources theory. Journal of Environmental Psychology, 87(101995). https://www.sciencedirect.com/science/article/abs/pii/S0272494423000439

Lee, K. (2023). Addressing the Nature-Deficit Disorder in Singapore. Nature Watch, 31(1), 14-15. https://www.nss.org.sg/articles/492463b1-bAllPagesNW23Q1FINAL-5MB.pdf

Louv, R. (2008). Last child in the woods: saving our children from nature-deficit disorder. Algonquin Books of Chapel Hill. https://richardlouv.com/books/last-child/

Accuracy and Reliability of Large Language Models in Assessing Learning Outcomes Achievement Across Cognitive Domains

cdtlsd Paper Presentations 18 November, 202420 November, 2024deep learning, high impact educational practice, Opportunities from Engaging Communities, Overseas study trips

Swapna Haresh Teckwani*, Amanda Huee-Ping WONG, and Ivan Cherh Chiet LOW*

Department of Physiology,
Yong Loo Lin School of Medicine, NUS

*swapnaht@nus.edu.sg; phsilcc@nus.edu.sg

Teckwani, S. H., Wong, A. H.-P., & Low, I. C. C. (2024). Accuracy and reliability of large language models in assessing learning outcomes achievement across cognitive domains [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-teckwani-et-al/

SUB-THEME

Opportunities from Generative AI

KEYWORDS

ChatGPT, large language model, grading, assessment, Bloom’s taxonomy

CATEGORY

Paper Presentation

INTRODUCTION

Rapid advancements in artificial intelligence (AI) have significantly impacted various sectors, notably education. AI, particularly through Large Language Models (LLMs) such as ChatGPT and Gemini, has introduced new opportunities in higher education, offering personalised feedback, developing problem-solving skills, and enhancing learning experiences (Kasneci et al., 2023; Moorhouse et al., 2023; Yan et al., 2024). However, the integration of AI in educational assessment, especially in grading written assignments, remains controversial. This study evaluates the accuracy and reliability of LLMs compared to human graders in assessing learning outcomes in a scientific inquiry course on sports physiology. Efficacy of LLM in feedback provision for the graded assignments was evaluated as well.

METHODS

This study involved 40 undergraduate students enrolled in the HSI2002 course “Inquiry into Current Sporting Beliefs and Practices”. Students attended three tutorial sessions, each focusing on different topics related to sports physiology. After each tutorial, students submitted a one-page written assignment evaluated on the ability to “Understand”, “Analyse”, “Evaluate” from the revised Bloom’s taxonomy, and ‘Scientific inquiry competency.’

A total of 117 assignments were independently scored by two human graders and three LLMs: GPT-3.5, GPT-4o, and Gemini. The assessment rubrics, aligned with the revised Bloom’s taxonomy, were engineered into language prompts for the LLMs. Each LLM graded the assignments twice to assess scoring reliability. Paired t-tests and Pearson correlation coefficients, were conducted to compare mean scores and inter-rater reliability (IRR).

RESULTS

Mean overall scores and mean scores for each learning taxa were comparable between the first and second raters for human and LLM graders. However, GPT-3.5 consistently scored lower and GPT-4o scored higher than human graders, while Gemini’s scores were similar to human graders.

IRR analysis of overall assignment scores revealed excellent (80%) agreement and correlation (r = 0.936) between human raters. Contrastingly, Gemini showed good agreement (71%) and correlation (r = 0.672), whereas GPT-3.5 and GPT-4o showed only moderate agreement (40% and 49%, respectively) and no correlation between raters. When comparing human with LLM raters, all LLMs were only in moderate agreement with human raters and a weak correlation (Pearson r = 0.271) observed only between GPT-4o and human graders.

Human graders exhibited excellent inter-rater agreement (≥ 80%) in the “Understand,” “Evaluate,” and “Scientific Inquiry Competency” categories, with slightly lower agreement (72%) in the “Analyse” category. LLMs demonstrated poorer inter-rater agreement compared to human graders. Among the LLMs, Gemini showed the highest inter-rater agreement, with good agreement (50-79%) in three categories and excellent agreement (80%) in the “Analyse” category. GPT-3.5 exhibited the lowest inter-rater agreement, with moderate agreement (30-49%) across all categories. GPT-4o showed slightly better inter-rater agreement than GPT-3.5, with good agreement (56%) in “Scientific Inquiry Competency” and moderate agreement (45-47%) in the other categories. All LLMs showed only moderate agreement (30-49%) with human graders across all learning categories.

Correlation analysis revealed that Gemini had strong correlations for the “Understand” and “Analyse” categories but only moderate correlations for “Evaluate” and “Scientific Inquiry Competency.” GPT-3.5 and GPT-4o had no significant correlation in scores within their grading rounds. In contrast, human graders displayed strong correlations across all categories. Comparing LLM scores with human scores revealed no significant correlation, highlighting the current limitations of LLMs in achieving human-level grading reliability.

CONCLUSION

While LLMs demonstrated potential in grading written assignments, they do not yet match the assessment standards of human graders. The study highlighted superior consistency among human graders and moderate concordance between human and LLM graders. These findings underscore the need for continuous improvement in LLM technologies and adaptive learning by educators to fully harness AI’s potential in educational assessment. LLMs nonetheless exhibited promising capabilities in providing personalised and constructive feedbacks.

REFERENCES

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., Stadler, M., Weller, J., Kuhn, J., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/https://doi.org/10.1016/j.lindif.2023.102274

Moorhouse, B. L., Yeo, M. A., & Wan, Y. (2023). Generative AI tools and assessment: Guidelines of the world’s top-ranking universities. Computers and Education Open, 5. https://doi.org/10.1016/j.caeo.2023.100151

Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112. https://doi.org/https://doi.org/10.1111/bjet.13370

Planting the Seeds for Meaningful and Effective Community Engagement Experiences through University Overseas Study Trips

cdtlsd Paper Presentations 16 November, 20246 December, 2024community engagement, course design, deep learning, high impact educational practice, Opportunities from Engaging Communities, Overseas study trips

Corinne ONG*, WONG Soon Fen, Eunice NG, and LIM Cheng Puay
Ridge View Residential College (RVRC)

*corinne@nus.edu.sg

Ong, C. P. P., Wong, S. F., Ng, E. S. Q., & Lim, C. P. (2024). Planting the seeds for meaningful and effective community engagement experiences through university overseas study trips [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-ong-et-al/

SUB-THEME

Opportunities from Engaging Communities

KEYWORDS

Overseas study trips, high-impact educational practice, deep learning, community engagement, course design

CATEGORY

Paper Presentation

INTRODUCTION

This paper documents the reflective experiences of the authors in designing a new undergraduate course involving a 10-day overseas learning component in a public university in Singapore. We illustrate how community engagement can be integrated into a course which focuses on culture and sustainability in Southeast Asia. The benefits of community-based learning experiences are increasingly well-established in the higher education landscape, constituting a form of high-impact educational practice, especially when facilitated by deep learning teaching strategies (Laird, 2008). Its accruable benefits range from developing greater civic interest and engagement, increased social capital, competency development, personal growth, and improved academic achievement among students (O’Brien, 2014). Deep learning, which furthers the impact of community engagement experiences, are enabled through integrative learning experiences (e.g. perspective-taking, interdisciplinary problem-solving), higher-order learning experiences (e.g. theoretical applications, idea analyses, and synthesis), and reflective learning experiences (Warburton, 2003).

PURPOSE/SIGNIFICANCE OF STUDY

Planning a study trip that integrates community engagement opportunities is a manifold process that this paper seeks to demystify. For instance, such engagements can exist in (a) multiple forms (between educators and the partners, between organisations, between students and community partners), and are (b) managed and enacted at various temporal junctures (course design to implementation and post-trip). Designing community engagement encounters also involves the deliberate introduction of (c) student learning objectives as guided by certain principles and values (e.g. social equity), and (d) intentional learning activities/assessments (e.g. reflections, stakeholder interviews, awareness-building projects) capable of maximising benefits for all stakeholders.

By documenting, conceptualising, and evaluating community engagement in the above ways, this paper is expected to provide educators, keen to introduce community engagement opportunities in undergraduate overseas study trips, with considerations on how community engagement activities can be integrated in impactful ways in overseas study trips. The following research questions (RQs) are examined:

1. How can community engagements for overseas study trips be designed to maximise its positive benefits for all stakeholders, including students?

Through this research question, we discuss the importance of context in shaping the design of these engagements, such as choice of issues of coverage and partners in order to meet course learning objectives. For instance, Southeast Asia, with its cultural diversity, natural resource endowments, and economic potential, offers significant scope for learning about sustainability (tensions) and the Sustainable Development Goals (SDGs). Partners who were actively contributing to promoting cultural and/or environmental sustainability in local communities in East Malaysia (e.g. WWF-Sarawak, Shell Sabah, Borneo Marine Institute, Sabah, Sarawak Biodiversity Centre) for instance, were identified and engaged as our partners who created learning content and insight-sharing opportunities with students.

The interdisciplinary nature of sustainability further lends itself to learning and inquiry from multiple disciplines. We share examples of how students from different disciplines were engaged in cross-disciplinary learning in the process of community engagement, and how course activities (e.g. pre-seminar activities ranging from videos, case analyses), in-trip post-engagement reflections, and post-trip activities (video documentaries), were designed with the intent of helping students make critical culture and sustainability connections, while leveraging on their engagement experiences. These aspects of course design are expected to be instructive to educators of diverse disciplines.

2. What are the benefits of learning activities facilitated around community engagement encounters for students?

This includes a discussion of how community engagement skills (e.g. cultural sensitivity, interview skills), acquired through experiences from these study trips, could be applied to contexts beyond Malaysia and to different disciplines or topics of study.

METHODS

The findings of this paper are informed and derived from the triangulation of multiple data points: from the authors’ reflections of engagement efforts and encounters from course design to implementation, observations of student learning, and students’ works and course feedback.

PRELIMINARY FINDINGS

In response to RQ1, we outline key phases of the engagement planning process and accompanying considerations in three phases, namely pre-trip, in-trip, and post-trip:

Table 1
Conceptualisation of phases, actions/activities, and considerations involved in community engagement planning (click on the table to view a full-sized version)

In response to RQ2, final course evaluations from students showed that nearly all students (at least 90%) who responded (N=12) indicated their agreement with the perceived achievement of learning outcomes (Figure 1), and satisfaction with the course’s design and structure (Figure 2).

HECS2024-a89-Fig1

Figure 1: Students’ self-reported evaluation of the extent to which course learning outcomes were achieved.

HECS2024-a89-Fig2

Figure 2: Students’ evaluation of the effectiveness of the course structure and design.

Finally, students’ qualitative course feedback (some examples of anonymous student feedback shared below) reinforced the value of learning activities, especially pertaining to planned community engagements and instructor-facilitated class debriefs:

“The most effective learning strategy was definitely interacting with the locals and the people working in the NGO’s since they do not necessarily have the same views as the organisations they are working for/the views that are prevalent in academic literature. It was really eye opening how many of the social issues faced by the people and the challenges faced by organisations were not readily available or easy to find solely through research…”

Another student shared how the community interactions and reflections proved transformative, offering them new insights on privilege and the value of context in perspective-making:

“I think what was most effective was interacting with different stakeholders, ranging from students to villagers, and experiencing the homestays, especially Kampung Menuang…It also reminded me of how small we are compared to the world. Through daily reflections from the trip, I really feel and learned a lot from our peers, professors and our partners as we all have different perspectives due to different backgrounds.”

These findings validate the effectiveness of community engagement encounters in promoting meaningful, deep, and transformational learning for students.

REFERENCES

Grauerholz, L. (2001). Teaching holistically to achieve deep learning. College Teaching, 49(2), 44–50. http://www.jstor.org/stable/27559032

Laird, N. et al. (2008). The effects of discipline on deep approaches to student learning and college outcomes. Research in Higher Education, 49, 469–494. https://doi.org/10.1007/s11162-008-9088-5

Roberts, J. W. (2012;2011;). Beyond learning by doing: theoretical currents in experiential education (1st ed.). Routledge.

Mezirow, J. (2003). Transformative learning as discourse. Journal of Transformative Education, 1(1), 58-63. https://doi.org/10.1177/1541344603252172

O’Brien, W., & Sarkis, J. (2014). The potential of community-based sustainability projects for deep learning initiatives. Journal of Cleaner Production, 62, 48-61. https://doi.org/10.1016/j.jclepro.2013.07.001

Warburton, K. (2003). Deep learning and education for sustainability. International Journal of Sustainability in Higher Education, 4(1), 44-56. https://doi.org/10.1108/14676370310455332

Whisper AI: Enhancing Feedback on Oral Assessments and Facilitating Research and Analysis

cdtlsd Paper Presentations 21 October, 202421 October, 2024feedback, Opportunities from Generative AI, speaking assessments, speech corpus, transcribing, Whisper AI

Muzzammil Yassin

Centre for Language Studies (CLS), Faculty of Arts and Social Sciences (FASS)

clsmmy@nus.edu.sg

Muzzammil Yassin (2024). Whisper AI: Enhancing feedback on oral assessments and facilitating research and analysis [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-m-yassin/

SUB-THEME

Opportunities from Generative AI

KEYWORDS

Whisper AI, feedback, speaking assessments, speech corpus, transcribing

CATEGORY

Paper Presentation

INTRODUCTION

Feedback in its various forms plays an integral role in learning a foreign language. In the Arabic Studies Programme at the Centre for Language Studies (CLS), oral assessments have traditionally involved either face-to-face interviews or presentations delivered by learners. The latter may be done “live” or submitted as a recording. Feedback provided on these assessments in the Arabic Studies Programme has usually been scarce, and when provided upon request by the student, is usually general and without much detail or correction. Furthermore, unlike a written assignment, the data from oral assignments might usually not be collected or stored. Thus, when students are provided with feedback, they do not usually get the opportunity to hear what they have said, or how they said it, or visualise it.

This presentation, based on action research, explores the potential of using Whisper from Open AI to enhance the feedback mechanism for oral assignments in language classrooms. The usage of AI discussed here falls within the detect-diagnose-act framework (mentioned in Molenaar, 2022). Over the period of AY 2023/24, Whisper AI was used to transcribe a relatively large amount of spoken data in a bid to provide enhanced feedback (Figures 1 and 2). This facilitated the analysis of students’ linguistic output in order to identify areas for improvement in pronunciation, grammatical structures, idiomaticity, and vocabulary choice. Research has demonstrated the usefulness of transcripts for students in the feedback process (Lynch, 2001, 2007). In addition to facilitating enhanced feedback, the transcription of the spoken data allows for it to be stored and compiled into a corpus. This will subsequently allow for reflection, error analysis, and data-driven design of activities. Thus, utilising Whisper on data collected from oral assignments can help facilitate research in the long run.

HECS2024-a105-Fig1 — *Figure 1*. Sample of the code run on Google Colab

HECS2024-a105-Fig2 — *Figure 2*. Sample of the code run on Google Colab on multiple audio files

OUTCOMES

The outcome of this intervention has an impact on two aspects: feedback provided to the learner, and data compiled for further reflection and research.

Regarding the first, students are provided with a feedback table containing the transcript of their presentation or oral interview. This is usually done in the second half of the semester, after the oral assessment test has been conducted. Written comments are provided alongside the transcript. Students are presented with this feedback table during a consultation session, held face-to-face or over Zoom. The feedback contained within the table is discussed and suggestions are provided for students on how to improve their speaking proficiency. If required, excerpts from the recording can be played. This allows students to hear what they said and visualise it as well. Students take note of the errors made and seek further clarification if necessary. The written comments and the transcript are theirs to keep for future reference.

Student feedback from AY 2023/24 shows positive comments regarding the feedback provided throughout the semester, a large part of which included feedback on oral assessments which were enhanced with the help of Whisper AI (Figures 3 and 4). Such feedback also plays a role in further developing students’ speaking skills (Al Jahromi, 2020).

HECS2024-a105-Fig3 — *Figure 3*. Sample feedback on an oral assessment question with areas for improvement colour coded

HECS2024-a105-Fig4 — *Figure 4*. Sample feedback table with areas for improvement colour coded

The other area which benefits from this intervention using Whisper AI relates to collecting and compiling spoken data. This would be extremely useful for reflection and research purposes, especially data that is collected on a longitudinal basis; from the beginner course—LAR1201 “Arabic 1” to the highest advanced course—LAR4202 “Arabic 6”. Such a corpus would allow teachers to document part of the development of learners’ speaking abilities. Such rich and varied transcribed data, along with the audio recordings, has the potential to contribute to a better understanding of areas related to language acquisition and a data-driven design of teaching material.

The following table provides a brief ‘before’ and ‘after’ overview of my teaching practice with regardS to providing feedback on oral assessments.

Table 1
Overview of my teaching practice and the quality of feedback given to oral assessments ‘before’ and ‘after’ the application of Whisper AI

HECS2024-a105-Table1

In summary, WhisperAI has helped to fill a gap relating to providing more detailed feedback to students of the CLS Arabic Studies Programme for oral assessments. In addition to the benefit of helping students ‘notice’ and ‘visualise’ (Lynch, 2001) their speech, the data from such interventions can be used to create activities that aim towards correcting common errors in learners’ speech/written production. Technology is a force multiplier which enhances the learning experience when used appropriately. The augmentative approach to using AI demonstrated above seeks to benefit both the learner and the teacher. This is part of what the detect-diagnose-act framework advocates (Molenaar, 2022).

REFERENCES

Al Jahromi, D. (2020). Can teacher and peer formative feedback enhance L2 university students’ oral presentation skills? In Hidri, S. (eds) Changing Language Assessment. Palgrave Macmillan. https://doi.org/10.1007/978-3-030-42269-1_5

Lynch, T. (2001). Seeing what they meant: transcribing as a route to noticing. ELT Journal, 55(2), 124–132. https://doi.org/10.1093/elt/55.2.124

Lynch, T. (2007). Learning from the transcripts of an oral communication task. ELT Journal, 61(4), 311–320. https://doi.org/10.1093/elt/ccm050

Molenaar, I. (2022). Towards hybrid human-AI learning technologies. European Journal of Education, 57, 632–645. https://doi.org/10.1111/ejed.12527

Stillwell, C., Curabba, B., Alexander, K., Kidd, A., Kim, E., Stone, P., & Wyle, C. (2010). Students transcribing tasks: noticing fluency, accuracy, and complexity, ELT Journal, 64(4), 445–455. https://doi.org/10.1093/elt/ccp081

A Tool for Learning via Productive Struggle Using Generative AI

cdtlsd Paper Presentations 20 October, 20242 December, 2024Generative AI, large language models, Opportunities from Generative AI, student learning, symbolic regression

Mehul MOTANI^1,2*, Kei Sen FONG¹, and John Chong Min TAN¹

¹Department of Electrical and Computer Engineering,
College of Design and Engineering (CDE),
²Institute of Data Science, Institute for Digital Medicine (WisDM), N.1 Institute for Health,
National University of Singapore (NUS)

*motani@nus.edu.sg

Motani, M., Fong, K. S., & Tan, J. C. M. (2024). A tool for learning via productive struggle using generative AI [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-motani-et-al/

SUB-THEME

Opportunities from Generative AI

KEYWORDS

Student learning, generative AI, symbolic regression, large language models

CATEGORY

Paper Presentation

EXTENDED ABSTRACT

Learning is often facilitated through struggle. This principle is embodied by an approach called productive struggle (PS), in which students are persuaded that struggling is part of learning and should be embraced. Productive struggle is related to an idea called productive failure (PF), in which students are given a task without prior instruction on how to solve it and allowed (even encouraged) to fail (Kapur, 2016). The key idea is that the initial struggle and the experience of failure can enhance learning and understanding when the correct solutions and underlying principles are subsequently taught. In this work, we adopt PS and combine it with generative AI tools, e.g., large language models and symbolic regression, to help students make progress on a learning task by providing fast, personalised feedback (Peng, 2019). We demonstrate our ideas on a specific learning task, namely building students’ intuition about the structure of mathematical equations and show how this could work via a prototype system. Our work contributes to the broader movement to explore the positive impacts of AI in education (Chen, 2020).

METHODS

In this work, we present an interactive tool, with graphing and large language model (LLM) capabilities, to build students’ intuition about the structure of mathematical equations. We name this tool Guess the Equation (GTE). GTE starts with a set of equations designed by the instructor. For each of these equations, we provide a visual graphical plot of sampled points from the equation and the students’ task is to guess the functional form of the equation without any prior knowledge (see Figure 1).

HECS2024-a100-Fig1 — *Figure 1.* GTE prototype of student user interface

The student will first have to make an initial guess using natural language text. Then, GTE explains, qualitatively, the differences between the guess and the true equation, along with displaying a graphical plot of the guess and sampled points (see Figure 2). The student will then have to modify the guess, which GTE evaluates and provides qualitative hints to guide the student towards the answer. This iteration repeats until the student achieves the answer. Throughout this process, the student experiences multiple failures, each supplemented with a qualitative hint towards the right answer.

HECS2024-a100-Fig2 — *Figure 2*. GTE provides fast personalised feedback to the student

In developing GTE, we utilise two main technical components:

TaskGen (Tan, 2024), a generative AI tool, that reformulates a complex task down into subtasks. As an improvement to free-form text output common in LLMs, TaskGen uses a special output, i.e., StrictJSON, for each part of the process. StrictJSON is an LLM output parser for the JSON format with type checking, ensuring extractable outputs that are compatible with downstream tasks. i.e., graph plotting, code execution.
Symbolic Regression (SR) (Fong, 2023), which is an approach that learns closed-form functional expressions from data. SR is used to suggest incorrect yet reasonably well-fitting equations to the student, allowing GTE to provide meaningful hints without divulging the true answer. SR is the key component that generates “near-successes”, which function as productive negative examples.

DISCUSSION

GTE provides reasoning in the form of Thoughts (Figure 3, green text) and a Summary of Conversation (Figure 3, purple text) that helps instructors in troubleshooting and verifying the hints given to students. This also reduces the chance of LLM hallucinations (Ji, 2023).

GTE is robust and provides appropriate responses to diverse student input (see Figures 4 and 5). Note that in Figure 2, the input is not even in equation form. This is an improvement from traditional learning tools, which require manual planning and design of edge cases.

HECS2024-a100-Fig3 — *Figure 3*. GTE provides extra information, such as *Thoughts* and *Summary of Conversation*.

HECS2024-a100-Fig4 — *Figure 4*. GTE responds appropriately even when no equations are provided.

HECS2024-a100-Fig5 — *Figure 5*. GTE handles exceptions with explanations, in contrast to traditional tools which simply dismiss such responses without constructive feedback

CONCLUSION

This study presents a novel PS-based approach to using generative AI and SR to enhance student learning. By utilising state-of-the-art AI tools, GTE allows students to obtain fast, iterative, automated and personalised feedback, allowing them to obtain more experience on failures. Future work involves doing a quantitative comparison to conventional teaching methods. We note that our approach can be generalised to learning tasks in other areas, such as machine learning, engineering, and physics, demonstrating the potential of AI in education (Chen, 2020).

REFERENCES

Kapur, M. (2016). Examining productive failure, productive success, unproductive failure, and unproductive success in learning. Educational Psychologist, 51(2), 289-299. https://doi.org/10.1080/00461520.2016.1155457

Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(1), 1-14. https://doi.org/10.1186/s40561-019-0089-y

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510

Tan, J. C. M., Saroj, P., Runwal, B., Maheshwari, H., Sheng, B. L. Y., Cottrill, R., … & Motani, M. (2024). TaskGen: A task-based, memory-infused agentic framework using StrictJSON. arXiv preprint arXiv:2407.15734. https://doi.org/10.48550/arXiv.2407.15734

Fong, K. S., Wongso, S., & Motani, M. (2023). Rethinking symbolic regression: Morphology and adaptability in the context of evolutionary algorithms. In The Eleventh International Conference on Learning Representations.

Ji, Z., Yu, T., Xu, Y., Lee, N., Ishii, E., & Fung, P. (2023). Towards mitigating LLM hallucination via self reflection. In Findings of the Association for Computational Linguistics: Empirical Methods in Natural Language Processing (pp. 1827-1843). Retrieved from https://ar5iv.labs.arxiv.org/html/2310.06271.

Leveraging AI Tools for Engagement and Higher Learning Outcomes: An Exploratory Study

cdtlsd Paper Presentations 17 October, 20242 December, 2024AI, Higher-order learning, Interdisciplinarity, Opportunities from Generative AI

Sadaf Ansari

Ridge View Residential College (RVRC)

sadaf@nus.edu.sg

Ansari, S. (2024). Leveraging AI tools for engagement and higher learning outcomes: An exploratory study [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-s-ansari/

SUB-THEME

Opportunities from Generative AI

KEYWORDS

Higher-order learning, AI, interdisciplinarity

CATEGORY

Paper Presentation

EXTENDED ABSTRACT

The release of open-access Generative artificial intelligence (GenAI) has made its use in higher education inevitable (Mollick, 2024). Educators’ initial concerns, which centred on unreliability and plagiarism, have since been replaced by universities embracing the technology, with a focus on the positive potential of AI in learning (NUS AI COP, 2023; CTI Cornell, 2023). Emerging literature on the use of open-access GenAI indicates the possibilities for enhancing student learning and transfer of knowledge when paired with specific inquiry-related skills such as critical thinking and ideation (Gregersen, 2023; Mollick, 2024). This is rapidly shifting the pedagogic focus in using GenAI from ‘what works’ to the possibilities offered by ‘what if?” (Ross, 2023). Set within such a context, this exploratory case study evaluated the impact of integrating GenAI on student engagement and higher-order learning outcomes for an interdisciplinary course.

BACKGROUND

Recent studies indicate that the use of GenAI can positively impact transfer; once students have gained foundational knowledge, GenAI can provide a time-efficient mode to test multiple ideas for complex problems, beyond traditional modes (Eapen, 2023; Mollick, 2022). This can enhance student engagement by simplifying the process of knowledge application, and build their proficiency in cognitively-demanding higher learning outcomes (Kirschner et al., 2022)

PROJECT AIMS AND SCOPE

The study was conducted with 32 students reading an interdisciplinary NUS General Education course—RVSS1000 “Exploring Sense of Place”—taught in small classes of 14-18 students using a seminar-style format. The class composition was multidisciplinary; it included students in their first and second year of undergraduate studies from six different NUS faculties. The course instructor’s observations from teaching the course across several semesters highlighted that students often lacked engagement and struggled with the cognitive demands of ideation—a higher learning outcome—while working on the final project. This was compounded by the inherent challenge of integrating creative ideas within complex spatial parameters of a real site and communicating these ideas effectively to stakeholders. End-of-semester time constraints further exacerbated these challenges for students. The course instructor evaluated the impact on these three interconnected problems encountered during ideation, by enabling students to automate the process of visualisation using GenAI – Microsoft CoPilot.

METHOD

The impact of integrating GenAI was evaluated using three types of data – a) the course instructor’s observations of student engagement with ideating project solutions using GenAI, b) analysis of students’ visualisations for the project using GenAI, and c) a student perception survey about using GenAI. An exploratory case study method was adopted for data collection and evaluation, due to the fast-evolving context-specific nature of GenAI capabilities.

KEY FINDINGS

Overall, findings from the study demonstrated the amelioration of the three interconnected problems encountered by students. Firstly, GenAI helped integrate students’ ideas within the complex spatial parameters of any real-world site by drawing on its vast database of site-specific photographs. Secondly, GenAI produced realistic three-dimensional visualisations that enhanced effective communication of students’ creative ideas to external partners and stakeholders. Lastly, automating the visualisations freed students’ time to focus instead on the higher cognitive demand of generating creative ideas for the project and iterative refinements.

The course instructor’s observational data indicated an increase in positive student engagement during project discussions and consultations; compared to previous cohorts, students experimented more with complex creative ideas, provided meaningful peer feedback, and undertook effective improvements of their projects to incorporate peer and instructor feedback. During the formal presentation, the use of GenAI also enabled students to fully explain their creative ideas and engage stakeholders in discussions about implementation, a limitation faced by previous cohorts.

Students’ use of GenAI for the project demonstrated their ability to undertake the cognitively-demanding task of synthesising creative ideas anchored by robust interdisciplinary analysis. This was evidenced in the use of GenAI to scale the spaces correctly and connect site-specific details about materials, landscape, biodiversity, stakeholder priorities, and function. In some exemplary projects, students used GenAI to further elaborate spatial features and cited relevant literature justifying their ideas for implementation.

The student perception survey conducted at the end of the course included questions about the impact of using GenAI on the three challenges identified by previous cohorts, using a Likert scale. Students strongly agreed about the interconnected benefits of using GenAI for enabling the cognitively demanding task of ideation by automating the process of visualisation and iterative refinement, with scores for the six questions ranging from 73%-79%.

While it is difficult to establish causation, data from NUS student feedback also showed minor improvements in students’ expected grades and course difficulty levels with the use of GenAI. Similar improvements were noted for ‘teacher’s ability to enhance interest in the subject’ and ‘overall effectiveness’. The addition of a new AI-related question in the NUS Student Feedback Survey in AY2024/25 offers an opportunity to clarify this further.

Despite the learning affordances of using GenAI identified by students, survey data about their recommendation for its use by future cohorts showed mixed results, with only 56% supporting it. 24% decided against using GenAI due to a perceived steep learning curve. 21% were unsure due to their own struggles with using GenAI, as they discovered that prompt engineering was not an intuitive skill. Hence, the study concluded that decisions about the continued integrative use of GenAI in this course (and perhaps others) must also address such valid concerns and avoid creating new digital disparities in student learning (NUS CTLT, 2024).

REFERENCES

CU Committee Report: Generative Artificial Intelligence for Education and Pedagogy | Center for Teaching Innovation. (n.d.). Retrieved December 14, 2023, from https://teaching.cornell.edu/generative-artificial-intelligence/cu-committee-report-generative-artificial-intelligence-education

Eapen, T. T., Finkenstadt, D. J., Folk, J., & Venkataswamy, L. (2023, July 1). How generative AI can augment human creativity. Harvard Business Review. https://hbr.org/2023/07/how-generative-ai-can-augment-human-creativity.

Gregersen, H., & Bianzino, N. M. (2023, May 26). AI can help you ask better questions-and solve bigger problems. Harvard Business Review. https://hbr.org/2023/05/ai-can-help-you-ask-better-questions-and-solve-bigger-problems

Jensen, T., Dede, C., Tsiwah, F., & Thompson, K. (2023, July 27). Who Does the Thinking: The Role of Generative AI in Higher Education [Video]. YouTube. International Association of Universities. Retrieved December 12, 2023, from https://www.youtube.com/watch?v=gE_GKsdTPAs.

Kirschner, P. A., Hendrick, C., & Heal, J. (2022). How Teaching Happens: Seminal Works in Teaching and Teacher Effectiveness and What They Mean in Practice. Routledge.

Mollick, E. (2024). Co-intelligence: Living and working with AI. Portfolio/Penguin.

Mollick, E.R., and Mollick, L., (December 13, 2022). New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments. Available at SSRN: https://ssrn.com/abstract=4300783 or http://dx.doi.org/10.2139/ssrn.4300783

NUS CTLT (2024, August 7). Policy for Use of AI in Teaching and Learning. Retrieved August 25, 2024, from https://ctlt.nus.edu.sg/wp-content/uploads/2024/08/Policy-for-Use-of-AI-in-Teaching-and-Learning

Ross, J. (2023). Digital Futures for Learning: Speculative Methods and Pedagogies. Routledge.

A Sense of Belonging – What It Means To Science Students

cdtlsd Paper Presentations 17 October, 202417 October, 2024Belonging, Community, mentorship, Opportunities from Wellbeing

LIU Mei Hui¹*, YONG Lai Cheng², and CHNG Shu Sin³

¹Department of Food Science and Technology, Faculty of Science (FOS)
²Department of Mathematics, FOS
³Science Dean’s Office, FOS

*fstlmh@nus.edu.sg

Liu, M. H., Yong, L. C., & Chng, S. S. (2024). A sense of belonging – What it means to Science students [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-liu-et-al/

SUB-THEME

Opportunities from Wellbeing

KEYWORDS

Belonging, community, mentorship

CATEGORY

Paper Presentation

INTRODUCTION

Learning environments within the university can facilitate social interactions and collaborations, and that formal and informal interactions in such spaces are related to an increased sense of belonging (Peacock & Cowan, 2019). A greater sense of belonging also correlates to better wellbeing and mental health outcomes (Gopalan et al., 2022). While there are several definitions to what a sense of belonging is, Van Ryzin’s (2011) concept of belonging as the perception of support students receive from their peers and teachers highlights the importance of meaningful interactions in a student’s education experience.

Despite the value, understanding what affects a student’s sense of belonging, especially in the Asian context (Tambyah & Mukhopadhyay, 2018), is limited. Students spend the majority of their time at the university to learn in spaces where they also interact with teachers and faculty staff. However, we are unclear how different types of interactions in these spaces may influence a student’s sense of belonging. Therefore, our research questions are as follows:

What are the types of student-teacher/staff interactions within the faculty that may contribute to students’ sense of belonging?
What are other factors that contribute to students’ sense of belonging to the faculty?

METHODOLOGY

In 2022, graduating students from the Faculty of Science (FOS) were invited to participate in an exit survey. The graduating cohort were students who underwent in-person learning in Years 1 and 2 (pre-COVID) and online/hybrid learning in Years 3 and 4 (COVID). The exit survey consists of a series of questions related to their experience in the university, and the questions analysed here are a subset of questions in the whole survey. Both quantitative and qualitative data relevant to our research question were analysed. A total of 125 unique respondents were collected and this represents 12.9% of the graduating cohort. There were respondents representing every department and programme in FOS.

RESULTS

Students were asked to rate, on a scale of 1 to 5, their sense of belonging to their Department. Among the respondents, 82 (64.6%) of the respondents rated their sense of belonging to be 3 and above. Next, all respondents were categorised into five groups according to their response to this question and further differentiated according to their response to three additional questions related to student-teacher interaction (Table 1). Their responses were counted and reported as a percentage within each group. In all groups, there was a high percentage of students (77-100%) who reported to have at least one person at NUS who made them excited about learning. However, less than 50% of students who rated 1 or 2 for their sense of belonging reported that they had someone who provided “value-add” beyond the classroom or have a mentor to guide them.

Table 1
Percentage of student responses to three statements within each subgroup of students who rated, from a scale of 1 to 5, for a sense of belonging. Red fonts represent percentages below 50% for ‘Yes”

HECS2024-a26-Table1

Students were further asked to explain their rating for the question on sense of belonging. When the responses of low raters and high raters were compared, several themes with contrasting comments between the two groups of raters were identified (Table 2).

Table 2
Thematic analysis of student qualitative response

HECS2024-a26-Table2

CONCLUSION AND SIGNIFICANCE

Our quantitative results suggest that having good teachers in the classroom may not be sufficient for students to have a sense of belonging with their learning spaces. Meaningful forms of interactions beyond the classroom are required to further cultivate this belonging. Our qualitative data further show that students look for a supportive environment of peers and teachers where they can feel connected and a belonging to a part of a community. Students also recognised the impact of the COVID pandemic on their sense of belonging. Students’ sense of belonging to their learning environment can be influenced by a number of factors, including student-teacher interactions or circumstantial changes like COVID-19 (Dost & Smith, 2023). Understanding the factors which affects the sense of belonging for our students can inform future strategies to enhance this quality, beyond only relying on resources that directly improve wellbeing.

REFERENCES

Gopalan, M., Linden-Carmichael, A., & Lanza, S. (2022). College students’ sense of belonging and mental health amidst the COVID-19 pandemic. Journal of Adolescent Health 70(2): 228–233. https://doi.org/10.1016/j.jadohealth.2021.10.010

Dost, G., & Smith, L M. (2023). Understanding higher education students’ sense of belonging: A qualitative meta-ethnographic analysis. Journal of Further and Higher Education 47(6), 822-849. https://doi.org/10.1080/0309877X.2023.2191176

Tambyah, S. K., & Mukhopadhyay, K. (2018). Belonging, engagement and growth: Evaluating learning outcomes of a residential college in the Asian context. Asian Journal of the Scholarship of Teaching and Learning, 8(2), 201-222. https://ctlt.nus.edu.sg/wp-content/uploads/2024/04/pdf_pastissuenov2018_article3_tambyah-kankana-1.pdf

Peacock, S., & Cowan, J. (2019). Promoting sense of belonging in online learning communities of inquiry in accredited courses. Online Learning, 23(2), 67–81. https://doi.org/10.24059/olj.v23i2.1488

Van Ryzin, M. J. (2011). Protective factors at school: Reciprocal effects among adolescents’ perceptions of the school environment, engagement in learning, and hope. Journal of Youth and Adolescence, 40(12), 1568–1580. https://doi.org/10.1007/s10964-011-9637-7

‘Code for Community’ Project: Promoting Community Engagement among NUS Computing Students

cdtlsd Paper Presentations 16 October, 202418 October, 2024community engagement, computing education, Opportunities from Engaging Communities, volunteerism

Bimlesh WADHWA

Department of Computer Science, School of Computing (SoC)

bimlesh@nus.edu.sg

Wadhwa, B. (2024). 'Code for Community' Project: Promoting community engagement among NUS Computing students [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-bwadhwa/

SUB-THEME

Opportunities from Engaging Communities

KEYWORDS

Community engagement, computing education, volunteerism

CATEGORY

Paper Presentation

INTRODUCTION

In today’s technology-driven world, coding and robotics are not just valuable skills but essential ones. The “Code for Community” project, initiated by NUS Computing (also known as the School of Computing [SoC]) in 2015, seeks to bridge the digital divide by teaching coding and robotics to underserved children and youth. Through the involvement of SoC students and alumni as volunteers, the project fosters mutual growth and benefits. Community engagement plays a crucial role in computing education. The rapid pace of technological advancements, combined with the often solitary nature of coding, can lead to isolation and barriers to connection. When SoC students participate in community projects, they are more likely to collaborate, share knowledge, and support one another, encouraging active learning, critical thinking, and problem-solving skills essential in computing.

The project introduces underserved children to coding and robotics through an engaging curriculum that utilises tools such as Dash, Dot, and McQueen robots. Weekly lessons build the children’s skills and confidence progressively, and the participants are also trained for hackathons and taken to tech fairs for valuable exposure and learning opportunities.

OBJECTIVES OF THE ‘CODE FOR COMMUNITY’ PROJECT

The project’s three primary objectives are:

Empowerment Through Education: Equip underserved children with essential coding and robotics skills.
Community Engagement: Foster a sense of community involvement and responsibility among SoC students.
Skills Development: Enhance the technical and soft skills of SoC student volunteers through teaching and mentorship roles.

WORKSHOPS AND ACTIVITIES

Workshops typically span 8-10 weeks with 60-90 minute weekly sessions held at selected partner centres such as the Autism Resource Centre, the Ulu Pandan Study Centre, the Sembawang Family Service Centre, and the Brighton Connects student care centres. These workshops offer a balanced approach, combining computing theory with hands-on practice. Participants learn to code and build simple robotic projects (Figures 1 and 2). Hackathon preparation enhances problem-solving and teamwork skills, while visits to tech fairs expose participants to real-world technology applications. The project is inclusive, extending participation to neurodivergent children and youth.

HECS2024-a112-Fig1 — *Figure 1*. SoC volunteers introducing coding to course participants

HECS2024-a112-Fig2 — *Figure 2*. Coding and robotics-building activities.

PERCEIVED IMPACT AND RATIONALE

Though formal evidence has not been collected, we believe the project is impactful based on several observations. Participants demonstrate increased confidence and curiosity as they develop coding and robotics skills, particularly after successfully completing projects or engaging in hackathons. Exposure to tech fairs broadens their understanding of technology’s potential and encourages further exploration. These activities are designed to spark interest in technology and create a positive learning environment, which we believe is key to fostering long-term engagement in the field.

For SoC volunteers, the experience of teaching and mentoring underserved children offers unique personal and professional growth. Volunteers report deeper technical proficiency, improved leadership and communication skills, and a heightened sense of empathy. Engaging with these communities encourages students to think critically about societal needs and develop technology-driven solutions.

HECS2024-a112-Fig3 — *Figure 3*. SoC volunteers with centre staff and the course participants.

CONCLUSION

The ‘Code for Community’ project is a unique blend of computing education and community engagement, creating a transformative impact on both participants and volunteers. While formal data collection is yet to be conducted, the observed outcomes suggest a positive influence on all involved. This initiative showcases the powerful role technology can play in driving social change and preparing the next generation of compassionate, skilled computing professionals.