EXPLORING PRE-SERVICE TEACHERS’ BELIEFS ABOUT THE ROLE OF ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION IN GHANA

Keywords: Artificial Intelligence, Learning Analytics, Pre-service Teachers, Educational Data Mining, Ghana

Abstract

The role of Artificial Intelligence (AI) in education has been well-documented in developed societies. However, the phenomenon has received little attention in developing countries such as Ghana. This study examined pre-service teachers from one of the teacher education universities in Ghana beliefs about the role of artificial intelligence in higher education. This study employed the quantitative descriptive design to obtain data from a convenience sample of 231 pre-service teachers. The study revealed that the majority of the pre-service teachers are very much aware of AI systems and that using AI-related systems will have a positive effect on pre-service teachers’ performance and that AI has the potential to replace teacher’s absence. Further, the majority of the respondents indicated that AI is relevant as it provides new ways of attaining distinction in teaching and learning. On the contrary, it was discovered that the majority of the pre-service teachers indicated that they feel anxious when it comes to using AI-related systems in learning. It is therefore recommended that more training and support systems be put in place to help and support pre-service teachers when using AI systems during teaching and learning during the post-pandemic era.

References

Agudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2014). Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning. Computers in Human Behavior, 34, 301-314. https://doi.org/10.1016/j.chb.2013.10.035.

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://doi.org/10.5430/ijhe.v9n3p145.

Alexander, P. A., Schallert, D. L., & Reynolds, R. E. (2009). What is learning anyway? A topographical perspective considered. Educational Psychologist, 44(3), 176-192. https://doi.org/10.1080/00461520903029006.

Alias, N. A., & Zainuddin, A. M. (2005). Innovation for better teaching and learning: Adopting the learning management system. Malaysian online journal of instructional technology,2(2),27-40. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.119.9362&rep=rep1&type=pdf.

Allen, I. E., & Seaman, J. (2017). Digital Compass Learning: Distance Education Enrollment Report 2017. Babson survey research group. https://eric.ed.gov/?id=ed580868.

Arnold, K. E., & Pistilli, M. D. (2012, April). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267-270). https://doi.org/10.1145/2330601.2330666.

Ausubel, D. P. (1969). A cognitive theory of school learning. Psychology in the Schools, 6(4), 331-335.https://doi.org/10.1002/1520-6807(196910)6:4<331.

Awang, T. S., & Zakaria, E. (2013). Enhancing students’ understanding in integral calculus through the integration of Maple in learning. Procedia-Social and Behavioral Sciences, 102, 204-211. https://www.sciencedirect.com/science/article/pii/S1877042813042705.

Baars, M., Leopold, C., & Paas, F. (2018). Self-explaining steps in problem-solving tasks to improve self-regulation in secondary education. Journal of Educational Psychology, 110(4), 578. https://psycnet.apa.org/record/2017-42391-001.

Baker, R. S. J. d. (2011). Data Mining for Education (3rd ed). In International Encyclopaedia of Education.https://www.upenn.edu/learninganalytics/ryanbaker/Encyclopedia%20Chapter%0Draft%20v10%20-fw.pdf.

Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of educational data mining, 1(1), 3-17. https://doi.org/10.5281/zenodo.3554657.

Banihashem S. K, Aliabadi K, Ardakani S. P., Delaver A, Ahmadabadi M., N. (2018). Learning analytics: A critical literature review. Interdisciplinary Journal of Virtual Learning in Medical Sciences. 9(2). https://dx.doi.org/10.5812/ijvlms.63024.

Bates, T., Cobo, C., Mariño, O., & Wheeler, S. (2020). Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 17(1), 1-12. https://doi.org/10.1186/s41239-020-00218-x.

Buckingham Shum, S. (2012). Learning analytics policy brief. UNESCO. http://iite.unesco.org/pics/publications/en/files/3214711.pdf.

Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE review, 42(4), 40. https://er.educause.edu/articles/2007/7/academic-analytics-a-new-tool-for-a-new-era.

Cazan, A. M., Cocoradă, E., & Maican, C. I. (2016). Computer anxiety and attitudes towards the computer and the internet with Romanian high-school and university students. Computers in Human Behavior, 55, 258-267. https://doi.org/10.1016/j.chb.2015.09.001.

Chatti, M. A., & Muslim, A. (2019). The PERLA framework: Blending personalization and learning analytics. International review of research in open and distributed learning, 20(1). https://doi.org/10.19173/irrodl.v20i1.3936.

Chaurasia, S. S., & Rosin, A. F. (2017). From Big Data to Big Impact: analytics for teaching and learning in higher education. Industrial and Commercial Training. https://doi.org/10.1108/ICT-10-2016-0069.

Chaussignol, M, Khoroshavin, A., Klimova, A., & Bilyadinova (2018). Artificial intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16-24. https://doi.org/10.1016/j.procs.2018.08.233.

Chou, C. (2003). Incidences and correlates of internet anxiety among high school teachers in Taiwan. Computers in Human Behavior, 19(6), 731-749. https://www.sciencedirect.com/science/article/pii/S0747563203000104.

Chuo, Y. H., Tsai, C. H., Lan, Y. L., & Tsai, C. S. (2011). The effect of organizational support, self-efficacy, and computer anxiety on the usage intention of e-learning system in hospital. African Journal of Business Management, 5(14), 5518-5523. https://doi.org/10.5897/AJBM11.725.

Cicchinelli, A., Veas, E., Pardo, A., Pammer-Schindler, V., Fessl, A., Barreiros, C., & Lindstädt, S. (2018). Finding traces of self-regulated learning in activity streams. In Proceedings of the 8th international conference on learning analytics and knowledge,191-200. https://doi.org/10.1145/3170358.3170381.

Clark-Gordon, C. V., Bowman, N. D., Goodboy, A. K., & Wright, A. (2019). Anonymity and online self-disclosure: A meta-analysis. Communication Reports, 32(2), 98-111. https://www.tandfonline.com/doi/abs/10.1080/08934215.2019.1607516.

Domjan, M. (2010). Principles of learning and behavior (6th ed.). Belmont, CA: Educause Review Online, 46(5),31-40.

Elliott, S.N., Kratochwill, T.R., Littlefield Cook, J. & Travers, J. (2000). Educational psychology: Effective teaching, effective learning.

Esterhuyse, M. P., Scholtz, B. M., & Venter, D. (2016). Intention to use and satisfaction of e-learning for training in the corporate context. Interdisciplinary Journal of Information, Knowledge, and Management, 11, 347. http://www.informingscience.org/Publications/3610.

Fahimirad, M., & Kotamjani, S. S. (2018). A review on application of artificial intelligence in teaching and learning in educational contexts. International Journal of Learning and Development, 8(4), 106-118. https://doi.org/10.5296/ijld.v8i4.14057.

Falcão, T.P., Ferreira, R., Rodrigues, R.L., Diniz J., & Gasevic, D., (2019). "Students' Perceptions about Learning Analytics in a Brazilian Higher Education Institution," 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), 204-206, 2161 https://doi.org/10.1109/ICALT.2019.00049.

Fenwick, T., Edwards, R., & Sawchuk, P. (2015). Emerging approaches to educational research: Tracing the socio-material. Routledge. https://doi.org/10.4324/9780203817582.

Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6), 304-317. https://www.inderscienceonline.com/doi/abs/10.1504/IJTEL.2012.051816.

Forrest, E., & Hoanca, B. (2015). Artificial intelligence: Marketing's game changer. Trends and innovations in marketing information systems, 45-64. https://doi.org/10.4018/978-1-4666-8459-1.ch003.

Fryer, L. K., Nakao, K., & Thompson, A. (2019). Chatbot learning partners: Connecting learning experiences, interests and competence. Computers in human behaviors, (93), 279- 289. https://doi.org/10.1016/j.chb.2018.12.023.

Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71. https://link.springer.com/article/10.1007/s11528-014-0822-x.

Greller, W., & Drachsler, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Journal of Educational Technology & Society, 15(3), 42-57. https://www.jstor.org/stable/10.2307/jeductechsoci.15.3.42.

Huang, T.W., & Smith, C. (2006). The History of Artificial Intelligence. improve self-regulation in secondary education. Journal of Educational Psychology, 110(4), 578-595. https://doi.org/10.1037/edu0000223 improving students’ engagement and learning outcomes in an MOOCs enabled collaborative programming course. Interactive Learning Environments, 25(2), 220-234. https://doi.org/10.1080/10494820.2016.1278391

Khare, K., Stewart, B., & Khare, A. (2018). Artificial intelligence and the student experience: An institutional perspective. The International Academic Forum (IAFOR). https://doi.org/0.22492/ije.6.3.04.

Kim, D., Yoon, M., Jo, I. H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women's university in South Korea. Computers & Education, 127, 233-251. https://doi.org/10.1016/j.compedu.2018.08.023.

Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2020). My teacher is a machine: Understanding students’ perceptions of AI teaching assistants in online education. International Journal of Human–Computer Interaction, 36(20), 1902-1911. https://doi.org/10.1080/10447318.2020.1801227.

Köhl, K., Gremmels, J. (2015). A software tool for the input and management of phenotypic data using personal digital assistants and other mobile devices. Plant Methods, 11, 25. https://doi.org/10.1186/s13007-015-0069-3.

Korobili, S., Togia, A., & Malliari, A. (2010). Computer anxiety and attitudes among undergraduate students in Greece. Computers in Human Behavior, 26(3), 399-405. https://doi.org/10.1016/j.chb.2009.11.011.

Kuzilek, J., Hlosta, M., Herrmannova, D., Zdrahal, Z., Vaclavek, J., & Wolff, A. (2015). OU Analyse: analysing at-risk students at The Open University. Learning Analytics Review, 1-16. http://www.laceproject.eu/learning-analyticsreview/analysing-at-risk-students-at-open-university/.

Lachman, S. J. (1997). Learning is a process: Toward an improved definition of learning. The Journal of psychology, 131(5), 477-480. https://doi.org/10.1080/00223989709603535.

Li, W., Jin, G., Cui, X., & See, S. (2015). "An Evaluation of Unified Memory Technology on NVIDIA GPUs," 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 1092-1098, https://doi.org/10.1109/CCGrid.2015.105.

Lim, L. A., Dawson, S., Gašević, D., Joksimović, S., Pardo, A., Fudge, A., & Gentili, S. (2021). Students’ perceptions of, and emotional responses to, personalised learning analytics-based feedback: an exploratory study of four courses. Assessment & Evaluation in Higher Education, 46(3), 339-359. https://doi.org/10.1080/02602938.2020.1782831.

Long, P., & G. Siemens. (2011), “Penetrating the fog: analytics in learning and education”, Educause Review Online, 46(5),31-40. https://eric.ed.gov/?id=EJ950794.

Lu, O. H., Huang, J. C., Huang, A. Y., & Yang, S. J. (2017). Applying learning analytics for improving students’ engagement and learning outcomes in an MOOCs enabled collaborative programming course. Interactive Learning Environments, 25(2), 220-234. https://doi.org/10.1080/10494820.2016.1278391.

Ma, Y., & Siau, K. L., "Artificial Intelligence Impacts on Higher Education" (2018). MWAIS 2018 Proceedings 42.https://aisel.aisnet.org/mwais2018/42.

MacCallum, K., Jeffrey, L., & Kinshuk. (2014). Factors impacting teachers’ adoption of mobile learning. Journal of Information Technology Education: Research, 13. https://stel.pubpub.org/pub/01-02-balliammanda-2021#n7h843o9f41.

Mansfield, M. (2019). The Best Learning Management Systems in Higher Education. Pagely https://pagely.com/blog/learning-management-systems-in-higher-education/.

Manyika, J., Chui,. M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011, June). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute. https://catalog.lib.kyushu-u.ac.jp/ja/recordID/3144682/.

Marcoulides, G. A. (1989). Measuring computer anxiety: The computer anxiety scale. Educational and Psychological Measurement, 49(3), 733-739.

Mwalumbwe, I., & Mtebe, J. S. (2017). Using learning analytics to predict students’ performance in Moodle learning management system: A case of Mbeya University of Science and Technology. The Electronic Journal of Information Systems in Developing Countries, 79(1), 1-13. https://doi.org/10.1002/j.1681-4835.2017.tb00577.x.

Newell, A. (1982). The knowledge level. Artificial intelligence, 18(1), 87-127. http://lidecc.cs.uns.edu.ar/~grs/InteligenciaArtificial/Allen%20Newell%20-%20The%20knowledge%20level.pdf.

Nicol, D. J., & Macfarlane‐Dick, D. (2006). Formative assessment and self‐regulated learning: A model and seven principles of good feedback practice. Studies in higher education, 31(2), 199-218. https://doi.org/10.1080/03075070600572090.

Nomura, T., Suzuki, T., Kanda, T., & Kato, K. (2006, September). Measurement of anxiety toward robots. In ROMAN 2006-The 15th IEEE International Symposium on Robot and Human Interactive Communication (pp. 372-377). IEEE. https://ieeexplore.ieee.org/abstract/document/4107836/.

Oracle Corporation. (2019). What is Big Data? https://www.oracle.com/a/ocom/docs/what-is-big-data-ebook-4421383.pdf.

Ormrod, J. E. (1999). Human learning (3rd ed.). Englewood Cliffs, NJ: Prentice Hall. https://www.scirp.org/(S(351jmbntvnsjt1aadkposzje))/reference/ReferencesPapers.aspx?ReferenceID=589826.

Park, J., Ahn, J., Thavisay, T., & Ren, T. (2019). Examining the role of anxiety and social influence in multi-benefits of mobile payment service. Journal of Retailing and Consumer Services, 47, 140-149. https://doi.org/10.1016/j.jretconser.2018.11.015.

Pfeifer, R., & Scheier, C. (1999). Understanding intelligence’the mit press. Cambridge, MA. Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of asynchronous learning networks, 16(3), 9-20. https://eric.ed.gov/?id=EJ982669.

Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 1-13. https://doi.org/10.1186/s41039-017-0062-8.

Puustinen, M., & Pulkkinen, L. (2001). Models of self-regulated learning: A review. Scandinavian journal of educational research, 45(3), 269-286. https://doi.org/10.1080/00313830120074206.

Raja, R., & Nagasubramani, P. C. (2018). Impact of modern technology in education. Journal of Applied and Advanced Research, 3(1), 33-35. https://www.academia.edu/download/63887921/Impact_of_modern_technology_in_education20200710-27957-jsmaeg.pdf.

Rubin, A., & Babbie, E. (1997). Research methods for social work (3rd ed.). Pacific Grove, CA: Brooks/Cole Siau, K. (2017, August). Impact of artificial intelligence, robotics, and automation on higher education. In Twenty-third Americas Conference on Information Systems (pp. 10-12). https://core.ac.uk/download/pdf/301372667.pdf.

Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177%2F0002764213498851.

Suhirman, Zain, J. M., & Herawan, T. (2014). Data Mining for Education Decision Support: A Review. International Journal of Emerging Technologies in Learning, 9(6), 4–19. https://doi.org/10.3991/ijet.v9i6.3950.

Sun, Z., Lu, L., & Xie, K. (2016). The effects of self-regulated learning on students’ performance trajectory in the flipped math classroom. International Society of the Learning Sciences. (1)66-73 https://dx.doi.org/10.22318/icls2016.11.

Thiede, K. W., Griffin, T. D., Wiley, J., & Redford, J. S. (2009). Metacognitive monitoring during and after reading. Handbook of metacognition in education, 85, 106.

Tuomi, I. (2018). The impact of artificial intelligence on learning, teaching, and education. Luxembourg: Publications Office of the European Union. http://mx.nthu.edu.tw/~cshwang/data-economics/course-infoecon/INFE12-Jobs/Tuomi=AI%20on%20Education-EU-2018.pdf.

U.S. Department of Education, Office of Educational Technology (2012). Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief. Washington, D.C. http://www2.ed.gov/about/offices/list/os/technology/index.html.

Van Harmelen, M., & Workman, D. (2012). Analytics for learning and teaching. CETIS Analytics Series, 1(3), 1-40.

Viberg, O., Khalil, M., & Baars, M. (2020, March). Self-regulated learning and learning Wadsworth/Cengage.

Wang, Y. S. (2007). Development and validation of a mobile computer anxiety scale. British Journal of Educational Technology, 38(6), 990-1009. https://doi.org/10.1111/j.1467-8535.2006.00687.x.

West, D., D. Heath, and H. Huijser. (2016). “Let’s Talk Learning Analytics: A Framework for Implementation in Relation to Student Retention.” Online Learning 20 (2): np. https://doi.org/10.24059/olj.v20i2.792.

Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223-235. https://doi.org/10.1080/17439884.2020.1798995.

Winne, P. H. (2017). Learning analytics for self-regulated learning. Handbook of learning analytics, 241-249. https://www.zybus.org/wp-content/uploads/2017/05/chapter21.pdf.

Winstone, N. E., Nash, R. A., Parker, M., & Rowntree, J. (2017). Supporting Learners' Agentic Engagement with Feedback: A Systematic Review and a Taxonomy of Recipience Processes. Educational Psychologist, 52(1), 17-37. https://doi.org/10.1080/00461520.2016.1207538.

Wong, J., Khalil, M., Baars, M., de Koning, B. B., & Paas, F. (2019). Exploring sequences of learner activities in relation to self-regulated learning in a massive open online course. Computers & Education, 140, 103595. https://doi.org/10.1016/j.compedu.2019.103595.

Wu, Y. H., Wrobel, J., Cornuet, M., Kerhervé, H., Damnée, S., & Rigaud, A. S. (2014). Acceptance of an assistive robot in older adults: a mixed-method study of human–robot interaction over a 1-month period in the Living Lab setting. Clinical interventions in aging, 9, 801. https://www.ncbi.nlm.nih.gov/pmc/articles/pmc4020879/.

Yang, C., Huan, S., & Yang, Y. (2020). A practical teaching mode for colleges supported by artificial intelligence. International Journal of Emerging Technologies in Learning (IJET), 15(17), 195-206. https://www.learntechlib.org/p/218012/.

Yang, H., Alphones, A., Xiong, Z., Niyato, D., Zhao, J., & Wu, K. (2020). Artificial-intelligence-enabled intelligent 6G networks. IEEE Network, 34(6), 272-280. https://ieeexplore.ieee.org/abstract/document/9237460/.

Yu, H., Miao, C., Leung, C., & White, T. J. (2017). Towards AI-powered personalization in MOOC learning. NPJ Science of Learning, 2(1), 1-5. https://doi.org/10.1038/s41539-017-0016-3.

Zeide, E. (2019). Artificial intelligence in higher education: applications, promise and perils, and ethical questions. Educause Review, 54(3), 31-39.

Zhang, K., & Aslan, A. B. (2021). AI technologies for education: Recent research & future directions. Computers and Education: Artificial Intelligence, 2, 100025. https://doi.org/10.1016/j.caeai.2021.100025.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into practice, 41(2),64-70. https://www.tandfonline.com/action/showCitFormats?doi=10.1207/s15430421tip4102_2.

Views:

301

Downloads:

148

Published
2023-09-30
Citations
How to Cite
Butakor, P. K. (2023). EXPLORING PRE-SERVICE TEACHERS’ BELIEFS ABOUT THE ROLE OF ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION IN GHANA. International Journal of Innovative Technologies in Social Science, (3(39). https://doi.org/10.31435/rsglobal_ijitss/30092023/8057