Can Artificial Intelligence Give a Hand to Open and Distributed Learning? A Probe into the State of Undergraduate Students’ Academic Emotions and Test Anxiety in Learning via ChatGPT
DOI:
https://doi.org/10.19173/irrodl.v25i3.7742Keywords:
AI-empowered applications,, undergraduate students, academic emotions, test anxietyAbstract
Artificial Intelligence (AI), as an innovation in technology, has greatly affected human life. AI applications such as ChatGPT have been used in different fields, particularly education. However, the use of AI applications to enhance undergraduate students’ academic emotions and test anxiety has not been appropriately investigated. This study addresses the effects of undergraduate students’ test anxiety and academic emotions. A total of 160 undergraduate students majoring in different fields of study were selected through convenience sampling and divided into control and experimental groups. Both groups received test anxiety and academic emotions scales at the onset of the treatment. The students assigned to the experimental group were trained to use ChatGPT and monitored for learning and doing their assignments outside the classroom during the semester. The two groups received the scales at the end of the semester, which lasted 16 weeks. Independent samples t-tests were used for analyzing the data. Results revealed that using AI-empowered applications significantly reduced the students’ test anxiety and negative academic emotions but enhanced their positive academic emotions. Students can use ChatGPT as an auxiliary instrument to overcome their negative emotions and enhance their educational attainment. Findings affect teachers, educational technologists, educational psychologists, and students.
References
Aldosari, S. A. M. (2020). The future of higher education in the light of artificial intelligence transformations. International Journal of Higher Education, 9(3), 145-151. https://files.eric.ed.gov/fulltext/EJ1248453.pdf
Ali, S. S., & Choi, B. J. (2020). State-of-the-art artificial intelligence techniques for distributed smart grids: A review. Electronics, 9(6), Article 1030. https://doi.org/10.3390/electronics9061030
Amornkitpinyo, T., Yoosomboon, S., Sopapradit, S., & Amornkitpinyo, P. (2021). The structural equation model of using cloud learning for higher education students in the 21st century. Journal of e-Learning and Knowledge Society, 17(1), 72–80. https://doi.org/10.20368/1971-8829/1135300
Anderson, T. (2016). Theories for learning with emerging technologies. In Handbook of emerging technologies for learning (pp. 89–103). Springer.
Artino, A. R., & Jones, K. D. (2012). Exploring the complex relations between achievement emotions and self-regulated learning behaviors in online learning. The Internet and Higher Education, 15(3), 170–175. https://doi.org/10.1016/j.iheduc.2012.01.006
Bahado-Singh, R. O., Vishweswaraiah, S., Aydas, B., Mishra, N. K., Guda, C., & Radhakrishna, U. (2019). Deep learning/artificial intelligence and blood-based DNA epigenomic prediction of cerebral palsy. International Journal of Molecular Sciences, 20(9), 2075. doi: 10.3390/ijms20092075
Basaknezhad, S., Saeidi, R., & Mehrabizadeh, M. (2013). The study of perceived threat of test, cognitive test anxiety, looming maladaptive style, attributions to performance and fear of negative evaluation predictors of test anxiety in female high school students. Journal of Educational Sciences, 20(1), 189–202. https://education.scu.ac.ir/article_10110.html?lang=en
Bates, A. W. (1997). The impact of technological change on open and distance learning. Distance Education, 18(1), 93–109. https://doi.org/10.1080/0158791970180108
Bates, A. W. (2015). Teaching in a digital age: Guidelines for designing teaching and learning. BCcampus. https://openlibrary-repo.ecampusontario.ca/jspui/handle/123456789/276
Bisen, I. E., Arslan, E. A., Yildirim, K., & Yildirim, Y. (2021). Artificial intelligence and machine learning in higher education. In Z. Gulzar & A. Leema (Eds.), Machine learning approaches for improvising modern learning systems (pp. 1–17). IGI Global. https://doi.org/10.4018/978-1-7998-5009-0.ch001
Blake, R. J. (2009). The use of technology for second language distance learning. The Modern Language Journal, 93, 822–835. https://doi.org/10.1111/j.1540-4781.2009.00975.x
Bozkurt, A., Akgun-Ozbek, E., Yilmazel, S., Erdogdu, E., Ucar, H., Guler, E., Sezgin, S., Karadeniz, A., Sen-Ersoy, N., Goksel-Canbek, N., Dincer, G.D., Ari, S., & Aydin, C. H. (2015). Trends in distance education research: A content analysis of journals 2009–2013. The International Review of Research in Open and Distributed Learning, 16(1), 330–363. https://doi.org/10.19173/irrodl.v16i1.1953
Calvo, R. A., & D’Mello, S. (2010). Affect detection: An interdisciplinary review of models, methods, and applications. IEEE Transactions on Affective Computing, 1(1), 18–37. https://doi.org/10.1109/T-AFFC.2010.1
Cerniglia, L., Cimino, S., & Ammaniti, M. (2021). What are the effects of screen time on emotion regulation and academic achievements? A three-wave longitudinal study on children from 4 to 8 years of age. Journal of Early Childhood Research, 19(2), 145-160. https://doi.org/10.1177/1476718X20969
Chaudhry, I. S., Sarwary, S. A. M., El Refae, G. A., & Chabchoub, H. (2023). Time to revisit existing student’s performance evaluation approach in higher education sector in a new era of ChatGPT—A case study. Cogent Education, 10(1), Article 2210461. https://doi.org/10.1080/2331186X.2023.2210461
Chedrawi, C., Howayeck, P., & Tarhini, A. (2019). CSR and legitimacy in higher education accreditation programs, an isomorphic approach of Lebanese business schools. Quality Assurance in Education, 27(1), 70–81. https://doi.org/10.1108/QAE‐04‐2018‐0053
Chen, J. J., & Li, S. F. (2012). The paths of junior school students’ achievement attribution and academic emotions forecasting their academic achievement. Chinese Journal of Clinical Psychology, 2012(3), 392–394. https://caod.oriprobe.com/articles/29870105/The_Paths_of_Junior_School_Students__Achievement_Attribution_and_Acade.htm
Clark, J. T. (2020). Distance education. In E. Iadanza (Ed.), Clinical engineering handbook (2nd ed., pp. 410–415). Academic Press. https://doi.org/10.1016/B978-0-12-813467-2.00063-8
Cocoradă, E. (2016). Achievement emotions and performance among university students. Bulletin of the Transilvania University of Braşov, Series VII: Social Sciences and Law, 9(2-Suppl), 119–128. https://www.ceeol.com/search/article-detail?id=510917
Dhir, S. K., Verma, D., Batta, M., & Mishra, D. (2017). E-learning in medical education in India. Indian Pediatrics, 54(10), 871–877. https://doi.org/10.1007/s13312-017-1152-9
Dieguez, T., Loureiro, P., & Ferreira, I. (2021, November 8–9). Entrepreneurship and leadership in higher education to develop the needed 21st century skills. In F. Bezzina (Ed.), ECMLG 2021: 17th European Conference on Management, Leadership and Governance (p. 143). Academic Conferences.
D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157. https://doi.org/10.1016/j.learninstruc.2011.10.001
Dong, Y., & Yu, G. L. (2010). Effects of adolescents’ academic emotion on their academic achievements. Journal of Psychological Science, 33(4), 934–937.
Dung, D. T. H. (2020). The advantages and disadvantages of virtual learning. IOSR Journal of Research & Method in Education, 10(3), 45–48. https://doi.org/10.9790/7388-1003054548
Essien, A., Chukwukelu, G., & Essien, V. (2020). Opportunities and challenges of adopting artificial intelligence for learning and teaching in higher education. In M. Ali & T. Wood-Harper (Eds.), Fostering communication and learning with underutilized technologies in higher education (pp. 67–78). IGI Global. https://doi.org/10.4018/978-1-7998-4846-2.ch005
Fleming, S., & Hiple, D. (2004). Distance education to distributed learning: Multiple formats and technologies in language instruction. CALICO Journal, 22(1), 63–82. http://www.jstor.org/stable/24149444
Garrison, D. R., Anderson, T., & Archer, W. (2003). A theory of critical inquiry in online distance education. In M. Moore and G. Anderson (Eds.), Handbook of distance education (pp. 113–127). Erlbaum. http://hdl.handle.net/2149/764
Ghafourian, P., Ghoshuni, M., & Vosoogh, I. (2020). Evaluation of exam anxiety in healthy subjects using brain signals analysis. The Neuroscience Journal of Shefaye Khatam, 8(3), 61–69. https://doi.org/10.29252/shefa.8.3.61
Hayat, A. A., Salehi, A., & Kojuri, J. (2018). Medical student’s academic performance: The role of academic emotions and motivation. Journal of Advances in Medical Education and Professionalism, 6(4), 168–175. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6191829/
Horton, W. (2006). E-learning by design. Wiley Publishing, Inc.
Johnson, C. C., Walton, J. B., Strickler, L., & Elliott, J. B. (2023). Online teaching in K-12 education in the United States: A systematic review. Review of Educational Research, 93(3), 353–411. https://doi.org/10.3102/00346543221105550
Khan, B. H. (2003). National virtual education plan: Enhancing education through e-learning in developing countries. Quarterly of the Commonwealth Educational Media Centre for Asia, 9(1), 2–5.
Khan, S., Zaman, S. I., & Rais, M. (2022). Measuring student satisfaction through overall quality at business schools: A structural equation modeling. South Asian Journal of Social Review, 1(2), 34–55. https://doi.org/10.57044/SAJSR.2022.1.2.2210
Kim, C., & Hodges, C. B. (2012). Effects of an emotion control treatment on academic emotions, motivation and achievement in an online mathematics course. Instructional Science, 40, 173–192. https://doi.org/10.1007/s11251-011-9165-6
King, N. J., Ollendick, T. H., & Gullone, E. (1991). Test anxiety in children and adolescents. Australian Psychologist, 26(1), 25–32. https://doi.org/10.1080/00050069108258829
Kistyanto, A., Rahman, M. F. W., Adhar Wisandiko, F., & Setyawati, E. E. P. (2022). Cultural intelligence increase student’s innovative behavior in higher education: The mediating role of interpersonal trust. International Journal of Educational Management, 36(4), 419–440. https://doi.org/10.1108/IJEM-11-2020-0510
Koçak, O., Koçak, Ö. E., & Younis, M. Z. (2021). The psychological consequences of COVID-19 fear and the moderator effects of individuals’ underlying illness and witnessing infected friends and family. International Journal of Environmental Research and Public Health, 18(4), 1836. https://doi.org/10.3390/ijerph18041836
Lei, H., & Cui, Y. (2016). Effects of academic emotions on achievement among mainland Chinese students: A meta-analysis. Social Behavior and Personality: An International Journal, 44(9), 1541–1554. https://doi.org/10.2224/sbp.2016.44.9.1541
Li, J., Li, J., Yang, Y., & Ren, Z. (2021). Design of higher education system based on artificial intelligence technology. Discrete Dynamics in Nature & Society, 2021, Article 3303160. https://doi.org/10.1155/2021/3303160
Lufi, D., & Awwad, A. (2013). Using the Minnesota Multiphasic Personality Inventory-2 to develop a scale to identify test anxiety among students with learning disabilities. Learning Disability Quarterly, 36(4), 242–249. https://doi.org/10.1177/0731948712471199
Minkevics, V., & Kampars, J. (2021). Artificial intelligence and big data driven IS security management solution with applications in higher education organizations. In P. Chemouil (Ed.), 17th International Conference on Network and Service Management, CNSM 2021 (pp. 340–344). Institute of Electrical and Electronics Engineers. https://doi.org/10.23919/CNSM52442.2021.9615575
Moreno, R., & Mayer, R. E. (2005). Role of guidance, reflection, and interactivity in an agent-based multimedia game. Journal of Educational Psychology, 97(1), 117–128. https://doi.org/10.1037/0022-0663.97.1.117
Neumann, M., & Baumann, L. (2021, October 13–16). Agile methods in higher education: Adapting and using eduScrum with real world projects. In 2021 IEEE Frontiers in Education Conference (FIE) (pp. 1–8). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/FIE49875.2021.9637344
Ngah, A. H., Kamalrulzaman, N. I., Mohamad, M. F. H., Rashid, R. A., Harun, N. O., Ariffin, N. A., & Osman, N. A. A. (2022). The sequential mediation model of students’ willingness to continue online learning during the COVID-19 pandemic. Research and Practice in Technology Enhanced Learning, 17(1), Article 13. https://doi.org/10.1186/s41039-022-00188-w
Odhiambo, G. (2016). Higher education in Kenya: An assessment of current responses to the imperative of widening access. Journal of Higher Education Policy & Management, 38(2), 196–211. https://doi.org/10.1080/1360080X.2016.1150551
O’Driscoll, D., & McAleese, M. (2023). The protective role of self-compassion on test anxiety among adolescents. Pastoral Care in Education, 41(2), 211-224. https://doi.org/10.1080/02643944.2022.2054021
Pekrun, R., Goetz, T., Frenzel, A. C., Barchfeld, P., Perry, R. P. (2011). Measuring emotions in students’ learning and performance: The Achievement Emotions Questionnaire (AEQ). Contemporary Educational Psychology, 36(1), 36–48. https://doi.org/10.1016/j.cedpsych.2010.10.002
Pekrun, R., Lichtenfeld, S., Marsh, H. W., Murayama, K., & Goetz, T. (2017). Achievement emotions and academic performance: Longitudinal models of reciprocal effects. Child Development, 88(5), 1653–1670. https://doi.org/10.1111/cdev.12704
Picard, R. W., & Healey, J. (1997). Affective wearables. Personal Technologies, 1, 231–240. https://doi.org/10.1007/BF01682026
Piroozmanesh, A., & Imanipour, M. (2018). The effect of formative evaluation on test anxiety of nursing students. Journal of Medical Education Development, 10(28), 18–26. https://doaj.org/article/7b6baa79dda7411c8f54af86e3cf3dc4
Pradana, M., Elisa, H. P., & Syarifuddin, S. (2023). Discussing ChatGPT in education: A literature review and bibliometric analysis. Cogent Education, 10(2), Article 2243134. https://doi.org/10.1080/2331186X.2023.2243134
Roa, C. C., Matutes, K. C. B., & Ombajen, J. D. (2022). Learning management: A review on emerging technologies for distance education. World Wide Journal of Multidisciplinary Research and Development, 8(03), 121–125. https://doi.org/10.17605/OSF.IO/WVB45
Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning & Teaching, 6(1), 342–363. https://doi.org/10.37074/jalt.2023.6.1.9
Rumble, G. (1989). “Open learning,” “distance learning,” and the misuse of language. Open Learning: The Journal of Open, Distance and e-Learning, 4(2), 28–36. https://doi.org/10.1080/0268051890040206
Salmalian, H., Maleki Pirbazari, M., & Salehi, S. (2020). The relationship early maladaptive schemas of students with their social anxiety and academic burnout (a canonical correlation). Journal of Educational Sciences, 27(1), 183–202. https://doi.org/10.22055/edus.2020.32778.3005
Schlesier, J., Roden, I., & Moschner, B. (2019). Emotion regulation in primary school children: A systematic review. Children and Youth Services Review, 100, 239-257. https://doi.org/10.1016/j.childyouth.2019.02.044
Shen, B., Wang, Y., Yang, Y., & Yu, X. (2023). Relationships between Chinese university EFL learners’ academic emotions and self-regulated learning strategies: A structural equation model. Language Teaching Research. Advance online publication. https://doi.org/10.1177/13621688221144832
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.
Talib, N., & Sansgiry, S. S. (2012). Determinants of academic performance of university students. Pakistan Journal of Psychological Research, 27(2), 365–378. https://psycnet.apa.org/record/2014-02064-007
Turner, J. E., & Schallert, D. L. (2001). Expectancy–value relationships of shame reactions and shame resiliency. Journal of Educational Psychology, 93(2), 320–329. https://doi.org/10.1037/0022-0663.93.2.320
Villavicencio, F. T. (2011). Critical thinking, negative academic emotions, and achievement: A meditational analysis. The Asia-Pacific Education Researcher, 20(1), 118–126. https://animorepository.dlsu.edu.ph/faculty_research/1277
Villavicencio, F. T., & Bernardo, A. B. (2013). Positive academic emotions moderate the relationship between self‐regulation and academic achievement. British Journal of Educational Psychology, 83(2), 329-340.
Wang, A. P., & Che, H. (2005). A research on the relationship between learning anxiety, learning attitude, motivation and test performance. Psychological Development and Education, 2005(1), 55–59, 86. https://caod.oriprobe.com/articles/8589649/A_Research_On_the_Relationship_Between_Learning_Anxiety_Learning_Attit.htm
Wang, R., Ryu, H., & Katuk, N. (2015). Assessment of students' cognitive-affective states in learning within a computer-based environment: Effects on performance. Journal of Information and Communication Technology, 14, 153-176. DOI:10.32890/jict2015.14.0.8161
Wenge, M. (2021). Artificial intelligence-based real-time communication and AI-multimedia services in higher education. Journal of Multiple-Valued Logic and Soft Computing, 36(1–3), 231–248. https://dblp.org/rec/journals/mvl/Ma21.html
Yavorsky, K. (2017). Academic emotion and self-efficacy impacting sense of math class belonging in college students (Publication no. 2415) [MA thesis, Rowan University]. Rowan University Theses and Dissertations. https://rdw.rowan.edu/etd/2415
Yazdani, Z., & Asadi, F. (2022). Investigating the relationship between virtual education and exam anxiety mediated by academic engagement. Technology of Instruction and Learning, 5(15), 57–76. https://doi.org/10.22054/jti.2023.72129.1361
Zembylas, M., & McGlynn, C. (2012). Discomforting pedagogies: Emotional tensions, ethical dilemmas and transformative possibilities. British Educational Research Journal, 38(1), 41-59. https://www.jstor.org/stable/23211443
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