Timothy WONG*, CHAN Kok Hoe, and ONG EeCheng
Department of Economics, Faculty of Arts and Social Sciences (FASS)
Wong, T. C. J., Chan, K. H., & Ong, E. C. (2024). Econ on the Go: Chatbot-guided scavenger hunt for large economic classes [Poster presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-twong-chankh-ongec/
SUB-THEME
Others – Opportunities from AI
KEYWORDS
Experiential learning, field trip, scavenger hunt, chatbot
CATEGORY
Poster Presentation
EXTENDED ABSTRACT
We designed a chatbot-guided scavenger hunt for students in our core introductory course, EC1101E: “Introduction to Economic Analysis”, which enrols 300–600 students each semester. This project is funded by a Teaching Enhancement Grant (TEG).
To design the scavenger hunt, we first identified economic concepts that can be matched to locational features and histories. For example, the concept of technological change as a decrease in inputs is manifested in the transition post World War II from rickshaws to trishaws; the inputs here are labor hours and energy. This story is explained on a plaque at Jinricksha Station.
Students visit various locations to collaboratively solve the puzzles by applying economic knowledge. As Mayer (2004) states, “meaningful learning occurs when the learner strives to make sense of the presented material by selecting relevant incoming information, organizing it into a coherent structure, and integrating it with other organized knowledge.” Here, the “presented material” is the puzzle, the “relevant incoming information” is their surroundings, and “other organized knowledge” is the economic theories they have learned in class. After students solve a puzzle, they are guided by the chatbot to the next location where a new puzzle is revealed.
The chatbot will record students’ responses to the puzzles and clues, enabling us to evaluate students’ strengths and weaknesses in their understanding of specific economic concepts and in their ability to apply such concepts to novel contexts. We may use this information to adjust how we teach a particular concept, to modify the puzzles, and/or to improve the chatbot in future iterations.
Chatbots offer scalability and accessibility, and their potential as a pedagogical tool is being explored (Wollny et. al., 2021) including in the area of experiential learning (Casillo et al., 2022). While experiential field trips provide unique opportunities for students to identify economic concepts at play in the real world and to recognize the limits of economic theories and models (Ong & Wong, 2023), these field trips are typically led by human guides who can lead only a small group of students at a time. Meanwhile, chatbot guides can accommodate hundreds of students and permit flexible scheduling.
We believe that the chatbot-guided scavenger hunt promotes several positive learning outcomes. First, we hope that this activity will help students to see the value of economics in understanding the world, thereby increasing their engagement with economics (Eccles & Wigfield, 2020). Studies show that intrinsic interest in a subject drives deep learning while a focus on extrinsic rewards leads to surface learning (Laird & Garver, 2010; Entwistle, 2009).
Second, this experiential learning activity presents a unique opportunity for students to practice applying economic concepts in the field. The ability to recognise which economic theories apply to which real-world scenarios will enable them to transfer their learning to their subsequent courses and careers (Green et al., 2013).
Third, we aim to inculcate in our students intellectual curiosity, where they continually observe, question, and discover. Students engage with one another in a social, collaborative setting to solve puzzles. This experience may shape their perception of how and when learning happens, leading them to recognise that learning is not circumscribed to the classroom.
We will conduct surveys at the beginning of the semester and after the field trip to evaluate the efficacy of the chatbot-guided field trip. We are interested in the following outcomes: (i) students’ knowledge of economic theory; (ii) students’ ability to apply economic theory to new contexts; (iii) students’ interest in economics; (iv) students’ perception of the relevance of economics in their lives; (v) students’ perception of and attitudes toward learning; (vi) students’ engagement with their classmates.
REFERENCES
Casillo, M., De Santo, M., Mosca, R., & Santaniello, D. (2022). An ontology-based chatbot to enhance experiential learning in a cultural heritage scenario. Frontiers in Artificial Intelligence 5. https://doi.org/10.3389/frai.2022.808281
Eccles, J. S., & Wigfield, A. (2020). From expectancy-value theory to situated expectancy-value theory: A developmental, social cognitive, and sociocultural perspective on motivation. Contemporary Educational Psychology, 61, Article 101859. https://doi.org/10.1016/j.cedpsych.2020.101859
Entwistle, N. (2009). Teaching for understanding at university: Deep approaches and distinctive ways of thinking. Universities into the 21st Century Series. Palgrave Macmillan.
Green, G., Bean, J., & Peterson, D. (2013). Deep learning in intermediate microeconomics: using scaffolding assignments to teach theory and promote transfer. Journal of Economic Education, 44(2). 142-157. https://doi.org/10.1080/00220485.2013.770338
Laird, T. N., & Garver, A. K. (2010). The effect of teaching general education courses on deep approaches to learning: How disciplinary context matters. Research in Higher Education, 51(3), 248–265, https://doi.org/10.1007/s11162-009-9154-7
Mayer, R. E. (2004). Should there be a three-strikes rules against pure discovery learning? The case for guided methods of instruction. American Psychologist, 59(1), 14. https://doi.org/10.1037/0003-066X.59.1.14
Ong, E. C., & Wong, T. (2023). Bringing the classroom to the real world: Field trips to marginalized neighborhoods. Journal of Economic Education, 54:(3), 267–280. 10.1080/00220485.2023.2200409
Wollny, S., Schneider, J., Di Mitri, D., Weidlich, J., Rittberger, M., & Drachsler, H. (2021). Are we there yet? A systematic literature review on chatbots in education. Frontiers in Artificial Intelligence 4. http://dx.doi.org/10.3389/frai.2021.654924