정보교육학회논문지 (Journal of The Korean Association of Information Education)

  • 제26권4호
  • /
  • Pages.249-263
  • /
  • 2022
  • /
  • 1229-3245(pISSN)
  • /
  • 2733-8428(eISSN)
한국정보교육학회 (Korea Association of Information Education)
권호 표지
DOI QR코드

DOI QR Code

인공지능 기반으로 맞춤 및 적응형 학습 시스템의 고등 교육에서의 적용효과

Effects of AI-Based Personalized Adaptive Learning System in Higher Education

  • 투고 : 2022.07.06
  • 심사 : 2022.07.27
  • 발행 : 2022.08.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

인공지능 기반 맞춤 및 적응형 학습을 대학원 이상의 수업에 적용에 따른 실증 연구는 매우 부족한 상황이다. 본 연구는, 인공지능 기반 맞춤 및 적응형 학습을 대학원 수업에 적용한 경우, 만족도 및 충성도를 연구 했으며, 테크놀로지관련 인식, 컨텐츠 및 시스템 특성에 대한 인식, 및 인공지능 기반 맞춤형 학습과 강의를 병행한 교육에 대한 전반적인 인식이 만족도, 효과성, 유용성, 동기부여, 및 다른 수업에 적용에 따른 의사에 어떻게 영향을 주는 지 조사하였다. 인공지능 기반 맞춤 및 적응형 시스템인 알렉스를 적용한 강의 직후 온라인 설문조사를 통한 데이터를 사용하였으며, 요인분석, 회귀분석, 분산분석 등을 활용하여 가설검증을 하였다. 본 연구의 결과로, 어떤 요인들이 유의하게 영향을 주는 지와 효과의 크기를 비교 검증하였고, 더불어 만족도가 충성도에 영향을 미치는 이론이 교육효과에도 적용됨을 입증하였다. 또한, 인공지능 기반 맞춤 및 적응형 시스템의 고등교육 특히 대학원 수업에도 효과가 있고, 고객관계관리에 도움이 된다는 시사점을 제시한다.

The purpose of this study is to investigate the effects of assessment by adopting adaptive learning in higher education that are rarely examined in previous studies. In particular, this study applied research questions: 1) How does technical perception, perceived contents and features, and perceived integration of the AI-based adaptive system with lecture affect overall satisfaction, overall effectiveness, overall usefulness, overall motivation for the study, and intention to use it with other classes? 2) How do overall satisfaction, overall effectiveness, overall usefulness, motivation for the class, and intention to use affect loyalty on the AI-based adaptive system? This study conducted online surveys after the completion of the classes adopted AI-based adaptive learning system, ALEKS. This study applied ANOVA, regression, and factor analyses. The results of this study found that perceived integration of the AI-based adaptive learning system with the lectures on overall satisfaction, effectiveness, motivation, and intention to use for other classes showed significant with higher effect size. The results of this study provides implication that the AI-based learning system help improve learning outcomes in graduate level studies. The results provide policy and managerial implications that the AI-based adaptive learning system should improve better customer relationships in higher education.

키워드

참고문헌

  1. Acampora, G., Gaeta, M., & Loia, V. (2011). Combining Multi-agent Paradigm and Memetic Computing for Personalized and Adaptive Learning Experiences. Computational Intelligence Journal, 27(2), 141-165. https://doi.org/10.1111/j.1467-8640.2010.00367.x
  2. Albert, D., & Lukas, J. (1999). Knowledge Spaces: Theories, Empirical Research, and Applications. London, UK: Psychology Press, Routledge Taylor & Francis Group. Bulletin of the Graduate School of Education, Hiroshima University, 1(50), 25-34. Hiroshima, Japan.
  3. Albert, D., Schrepp, M., & Held, T. (1994). Construction of knowledge spaces for problemsolving in chess. In G. H. Fischer, & D. Laming (Eds.), Contributions to mathematical psychology, psychometrics, and methodology (pp. 123-135). Recent Research in Psychology. New York, USA: Springer New York.
  4. Albert, D., & Mori, T. (2001). Contributions of Cognitive Psychology to the Future of E-Learning. Bulletin of the Graduate School of Education, Hiroshima University, 1(50), Learning and Curriculum Development, 25-34.
  5. Cho, Y. (2021). Investigating Factors of Higher Education on Job Satisfaction, Globalization, and Hosting Country. Journal of Industrial Distribution & Business, 12(2), 7-17.
  6. Doignon, J. P., & Falmagne, J.C. (1985). Spaces for the assessment of knowledge. International Journal of Man-Machine Studies, 23, 175-196. https://doi.org/10.1016/S0020-7373(85)80031-6
  7. Doignon, J. P. & Falmagne, J. C. (1999). Knowledge Spaces. Berlin, Heidelberg, New York: Springer-Verlag.
  8. Doignon, J. P., & Falmagne, J. C. (2016). Knowledge spaces and learning spaces. In W. H. Batchelder, H. Colonius, & E. N. Dzhafarov (Eds.), New Handbook of Mathematical Psychology (pp.274-321), Cambridge, UK: Cambridge University Press.
  9. Elliott, A. J., & Dweck, C. S. (2005) Handbook of Competence and Motivation. New York, NY: The Guilford Press.
  10. El-Sabagh & Hamed, E. (2020). The Relationship between Learning-Styles and Learning Motivation of Students at Umm Al-Qura University. Journal of Egyptian Association for Educational Computer, 8(1), 1-28.
  11. El-Sabagh, H. A. (2021). Adaptive E-Learning Environment based on Learning Styles and its Impact on Development Students' Engagement. International Journal of Educational Technology in Higher Education, 18(53), 1-24. https://doi.org/10.1186/s41239-020-00238-7
  12. Espasa, A., & Meneses, J. (2010). Analyzing Feedback Processes in an Online Teaching and Learning Environment: An Exploratory Study. Higher Education, 59, 277-292. https://doi.org/10.1007/s10734-009-9247-4
  13. Eze, N. (2012). The Effect of Aleks on Students' Mathematics Achievement in an Online Learning Environment and the Cognitive Complexity of the Initial and Final Assessment. Georgia, US: Doctoral dissertation, Georgia State University, Georgia, U.S.
  14. Fang, Y., Ren, Z., Hu, X., & Graesser, A. C. (2019) A meta-analysis of the effectiveness of ALEKS on learning, Educational Psychology, 39(10), 1278-1292. https://doi.org/10.1080/01443410.2018.1495829
  15. Harati, H., Sujo-Montes, L., Tu, C., Armfield, S. J. W., & Yen, C. (2021). Assessment and Learning in Knowledge Spaces (ALEKS) Adaptive SystemImpact on Students' Perception and Self-Regulated Learning Skills. Education Sciences, 11, 1-21.
  16. Heller, J., Steiner, C., Hockemeyer, C., & Albert, D. (2006). Competence-based Knowledge Structures for Personalized Learning. International Journal on E-Learning, 5(1), 75-88.
  17. Hopkins, D. (2004). Assessment for Personalized Learning: The Quite Revolution. In General Teaching Council Conference, Perspectives on Pupil Assessment (pp.10-15). November 29, London, UK, GTC.
  18. Gifford, B. R. (2001). Transformational Instructional Material, Settings, and Economics. In The Case for the Distributed Learning Workshop. Minneapolis, MN: The Distributed Learning Workshop. 1-71.
  19. Jackson, M. J., Helms, M. M., Jackson, W. T., &Gum, J. R. (2011). Student Expectations of Technology-enhanced Pedagogy: A Ten-year Comparison. Journal of Education for Business, 86, 294-301. https://doi.org/10.1080/08832323.2010.518648
  20. Kerr, P. (2016). Adaptive Learning. ELT Journal, 70(1), 88-93. https://doi.org/10.1093/elt/ccv055
  21. Law, M. Y. (2021). Student's Attitude and Satisfaction towards Transformative Learning: AResearch Study on Emergency Remote Learning in Tertiary Education. Creative Education, 12, 494-528. https://doi.org/10.4236/ce.2021.123035
  22. Mainemelis, C., Boyatzis, R. E., & Kolb, D. A. (2002). Learning Styles and Adaptive Flexibility: Testing Experiential Learning Theory. Management Learning, 33(1), 5-33. https://doi.org/10.1177/1350507602331001
  23. National Research Council (2002). Technology and Assessment: Thinking ahead - Proceedings fromthe Workshop. Washington DC: National Academy Press.
  24. Normadhi et al., N. B., Shuib, L., Nasir, H. N. M., Bimba, A., Idris, N., & Balakrishnan, V. (2019). Identification of Personal Traits in Adaptive Learning Environment: Systematic Literature Review. Computers & Education, 130, 168-190. https://doi.org/10.1016/j.compedu.2018.11.005
  25. Nwaogu, E. (2012). The Effeect of Aleks on Students' Mathematics Achievement in an Online Learning Environment and the Cognitive Complexity of the Initial and Final Assessments. Georgia, U.S.: Doctoral Dissertation, Georgia State University, Georgia, U.S.
  26. Okoye, K. (2018). A Systematic Review of Process Modelling Methods and its Application for Personalized Adaptive Learning Systems. Journal of International Technology and Information Management, 27(3), 23-46. https://doi.org/10.58729/1941-6679.1379
  27. Oxman, S. & Wong W. (2014). White Paper: Adaptive Learning Systems. DeVry Education Group and Integrated Education Solutions.
  28. Popescu, E, Trigano, P., & Badica, C. (2007). Towards a Unified Learning Style Model in Adaptive Educational Systems. Seventh IEEE International Conference on Advanced Learning Technologies. July 18-20, Niigata, Japan. 804-808.
  29. Popescu, E. (2008). Dynamic Adaptive Hypermedia Systems for E-learning. France: Doctoral Dissertation, Universite de Technologie de Compiegne, Compiegne, France.
  30. Reddy, A. A., & Harper, M. (2013). ALEKS-based Placement at the University of Illinois. In J. Falmagne, D. Albert, C. Doble, D. Eppstein, & X. Hu (Eds.), Knowledge Spaces: Applications in Education (pp.51-68), Berlin, Heidelberg, Germany: Springer.
  31. Reimann, P., Bull, S., Kickmeier-Rust, M., Vatrapu, R., & Wasson, B. (2016). Measuring and Visualizing Learning in the Information-rich Classroom. New York, US: Routledge.
  32. Scalise, K., Bernbaum, D. J., & Timms, M. (2007). Adaptive Technology for E-Learning: Principles and Case Studies of an Emerging Field. Journal of the American Society for Information Science and Technology, 58(14), 2295-2309. https://doi.org/10.1002/asi.20701
  33. Steiner, C. M., Nussbaumer, A., & Albert, D. (2009). Supporting Self-Regulated Personalized Learning through Competence-based Knowledge Space Theory. P olicy Futures in Education, 7(6), 645-661. https://doi.org/10.2304/pfie.2009.7.6.645
  34. Stokes, T. A., Gillan, D. J., & Braden, G., J. P. (2016). Establishing the Link between Usability and Student Satisfaction in Adaptive Online Learning. Proceedings of the Human Factors and Ergonomics Society (pp.1969-1973). September 19-23, Washington, DC. US.
  35. Sullins, J., Meister, R., Craig, S. D., Wilson, W. M., Bargagliotti, A., & Hu, X. (2013). The Impact of a Mathematical Intelligent Tutoring System on Students' Performance on Standardized High-Stake Tests. In J. Falmagne, D. Albert, C. Doble, D. Eppstein, & X. Hu (Eds.), Knowledge Spaces: Applications in Education (pp.69-78), Berlin, Heidelberg, Germany: Springer.
  36. Sun, Q., Abdourzazkou, Y., & Norman, T. J. (2017). LearnSmart, Adaptive Teaching, and Student Learning Effectiveness: An Empirical Investigation. Journal of Education for Business, 92(1), 36-43. https://doi.org/10.1080/08832323.2016.1274711
  37. Taylor, J. M. (2006). The Effects of a Computerized-Algebra P rogram on Mathematics Achievement of College and University Freshmen Enrolled in a Developmental Mathematics Course. Texas, U.S.: Doctoral Dissertation, Texas A&M University, College Station, Texas, U.S.
  38. Taylor, D., L., Yeung, M., & Bashet, A. Z. (2021). Personalized and Adaptive Learning. In J. Ryoo, & K. Winkelmann (Eds.), Innovative Learning Environments in STEM Higher Education: Opportunities, Challenges, and Looking Forward (pp.17-34), New York, NY: Springer Publishing.
  39. Verdu, E., Regueras, L. M., Verdu, M. L., De Castro, J. P., & Perez, M. A. (2014). An Analysis of the Research on Adaptive Learning: The Next Generation of E-Learning. WSEAS Transactions on Information Science & Applications, 6(5), 859-868.
  40. Webley, K. (2013). The Adaptive Learning Revolution. Time Magazine, June 6, http://nation.time.com/2013.
  41. Xu, Y. J., Meyer, J. A., & Morgan, D. D. (2009). A Mixed-Methods Assessment of Using an Online Commercial Tutoring System to Teach Introductory Statistics. Journal of Statistics Education, 17(2), 1-16.
  42. Yang, J., Huang, R., & Li, Y. (2013). Optimizing classroom environment to support technology enhanced learning. In A. Holzinger & G. Pasi (Eds.), Human-computer interaction and knowledge discovery in complex (pp. 275-284), Berlin, Germay: Springer
  43. https://www.mheducation.com
  44. https://www.aleks.com