Between the clicks
Student learning paths when interacting with an adaptive learning resource in 4th grade mathematics
DOI:
https://doi.org/10.7146/lt.v9i14.137137Abstract
Adaptive learning technology has the potential to tailor learning to suit individual students’ needs, desires and competence level. However, qualitative close up studies of students’ interaction with adaptive technology are rare. This study explores 4th grade mathematics students’ use of adaptive learning technology through screen recordings supplemented with analysis of data generated and tagged by the adaptive engine. The study explores how different types of students, i.e. students with varying mathematical competence levels, motivation and self-efficacy towards mathematics, interact with an adaptive learning material, what learning paths emerge for different types of students in their interaction with the adaptive learning resource and how student self-efficacy is affected by the interaction. Implications for both future design and teacher use of adaptive learning materials are discussed.
References
Apoki, U.C., Hussein, A.M.A., Al-Chalabi, H.K.M., Badica, C. & Mocanu, M.L. (2022). The Role of Pedagogical Agents in Personalised Adaptive Learning: A Review. Sustainability, 14(11), 6442. https://doi.org/10.3390/su14116442
Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191-215.
Bulman, G. & Fairlie, R. W. (2016). Technology and Education: Computers, Software, and the Internet. In: Hanushek, E.A., Machin, S. & Woessmann, L. (Eds.) Handbook of the Economics of Education, vol 5, 239-280. Elsevier. https://doi.org/10.1016/B978-0-444-63459-7.00005-1
Conejo, R., Guzmán, E., Millán, E., Trella, M., Pérez-De-La-Cruz, J. L. & Ríos, A. (2004). SIETTE: A Web-Based Tool for Adaptive Testing. International Journal of Artificial Intelligence in Education, 14(1), 29-61.
Cuban, L. (2015). The Lack of Evidence-Based Practice: The Case of Classroom Technology. Retrieved from: https://www.larrycuban.wordpress. com/2015/02/05/the-lack-of-evidence-based-practice-the-case-of-classroomtechnology-
part-1/
Demšar, U. & Çöltekin, A. (2017). Quantifying gaze and mouse interactions on spatial visual interfaces with a new movement analytics methodology. PloS one, 12(8), e0181818. https://doi.org/10.1371/journal.pone.0181818
Du Boulay, B. (2016). Artificial intelligence as an effective classroom assistant. IEEE Intelligent Systems, 31(6), 76-81.
Flyvbjerg, B. (2006). Five misunderstandings about case-study research. Qualitative inquiry, 12(2), 219-245.
Gerick, J., Eickelmann, B., Vennemann, M. (2014). Zum Wirkungsbereich digitaler Medien in Schule und Unterricht. Jahrbuch der Schulentwicklung, 18, 206-238.
Gissel, S. T., Gynther, K., Hansen, T.I., Højgaard, T., Jørnø, R.V.L., Nortvig, A.-M. & Pettersson, M. (2020). Rhapsode – design, brug og virkning. Rapport, Læremiddel.dk. https://laeremiddel.dk/wp-content/uploads/2021/06/Rhapsode_Rapport_L%C3%A6remiddel.dk_080621.pdf
Gissel, S. T. & Skovmand, K. (2018). Kategorisering af digitale læremidler: En undersøgelse af didaktiske, digitale læremidlers karakteristika. (2 udg.) Læremiddel.dk. https://laeremiddel.dk/wp-content/uploads/2018/05/Kategorisering-af-digitale-l%C3%A6remidler.pdf
Guzmán, E., Conejo, R. & Pérez-de-la-Cruz, J. L. (2007). Improving Student Performance using Self-Assessment Tests. IEEE Intelligent Systems, 22(4), 46-52. DOI: 10.1109/MIS.2007.71.
Gyldendal (n.d.a). Om Matematikprofilen. Retrieved December 11, 2020, from https://matematikprofilen.gyldendal.dk/
Gyldendal (n.d.b). Om Matematikprofilen – Uddybende beskrivelse. Retrieved December 11, 2020, from https://portalmotor.gyldendal.dk//-/media/Home/matematik/matematikprofilen/dokumenter/Kategoribeskrivelser/Kategoribeskrivelser---Matematikprofilen-3.ashx
Gyldendal (n.d.c). Om Matematikprofilen – Resultater. Retrived December 11, 2020, from https://matematikprofilen.gyldendal.dk/3-klasse/resultater
Holmes, W., Anastopoulou, S., Schaumburg, H. & Mavrikis, M. (2018). Technology-enhanced personalised learning: untangling the evidence. Robert Bosch Stiftung. Available at: http://oro.open.ac.uk/56692/
Johnson, D., & Samora, D. (2016). The potential transformation of higher education through computer-based adaptive learning systems. Global Education Journal, 2016(1), 1-17.
Kerr, P. (2015). Adaptive learning. ELT Journal, 70(1), 88-93. https://doi.org/10.1093/elt/ccv055
Kulik, J. & Fletcher, J. D. (2015). Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review. Review of Educational Research, 86(1), 42-78. https://doi.org/10.3102/0034654315581420.
Liu, M., McKelroy, E., Corliss, S. B. & Carrigan, J. (2017). Investigating the effect of an adaptive learning intervention on students’ learning. Education Tech Research and Development, 65, 1605-1625. DOI:10.1007/s11423-017-9542-1
Martin, F., Chen, Y., Moore, R. L. & Westine, C. D. (2020). Systematic review of adaptive learning research designs, context, strategies, and technologies from 2009 to 2018. Education Technology Research and Development, 68, 1903-1929. https://doi.org/10.1007/s11423-020-09793-2
Martin, M. O., Mullis, I. V. S., Hooper, M., Yin L., Foy, P. & Palazzo, L. (2016). Creating and Interpreting the TIMSS 2015 Context Questionnaire Scales. In: Martin, M.O, Mullis, I.V.S, Hooper, M. (Ed.), Methods and Procedures in TIMSS 2015. TIMSS & PIRLS International Study Center, Boston College.
Modén, M. U. (2021). Teaching with digital mathematics textbooks. Activity theoretical studies of data-driven technology in classroom practices. Doctoral Dissertation. Department of Applied Information Technology, University of Gothenburg. http://hdl.handle.net/2077/69472
Mozahem, N. A., Boulad, F. M. & Ghanem, C. M. (2021). Secondary school students and self-efficacy in mathematics: Gender and age differences. International Journal of School and Educational Psychology, 9(1), 142-152.
Nakic, J., Granić, A. & Glavinić, V. (2015). Anatomy of student models in adaptive learning systems: A systematic literature review of individual differences from 2001 to 2013. Journal of Educational Computing Research, 51(4), 459-489. doi:10.2190/EC.51.4.e.
Nguyen, L. (2015). A User Modeling System for Adaptive Learning. The 17th International Conference on Interactive Computer aided Learning (ICL2014). Doi: 10.1109/ICL.2014.7017887
OECD (2015). Students, Computers and Learning: Making the Connection. Retrieved from https://www.oecd.org/publications/students computers-and-learning-9789264239555-en.htm
Peng, H., Ma, S. & Spector, J. M. (2019). Personalized Adaptive Learning: An Emerging Pedagogical Approach Enabled by a Smart Learning Environment. In: M. Chang, E. Popescu, Kinshuk, N-S. Chen, M. Jemni, R. Huang,
J. M. Spector & D. G. Sampson (red.), Foundations and Trends in Smart Learning. Lecture Notes in Educational Technology (p. 171-176). Springer. DOI:10.1007/978-981-13-6908-7_24
Pollard, A. & James, M. (eds) (2004). Personalised Learning: A Commentary by the Teaching and Learning Research Programme, TLRP/ESRC, Swindon, UK. DOI: 10.1080/0305764X.2017. 1375458.
Rodden, K., Fu, X., Aula, A. & Spiro, I. (2008). Eye-mouse coordination patterns on web search pages. Conference on Human Factors in Computing Systems – Proceedings. 2997-3002. 10.1145/1358628.1358797.
Schunk, D. H. (1991). Self-Efficacy and Academic Motivation, Educational Psychologist, (26)3-4, 207-231. DOI:10.1080/00461520.1991.9653133
Somyürek, S. (2015). The new trends in adaptive educational hypermedia systems. International Review of Research in Open and Distributed Learning, 16(1), 221-241.
Tai, D. W-S., Tsai, T-A. & Chen, F. M-C. (2001). Performance Study on Learning Chinese Keyboarding Skills Using the Adaptive Learning System. Global Journal of Engineering Education, 5(2), 153-161.
Tamim, R., Bernard, R. M., Borokhovski, E. & Abrami, P. C. (2011): What Forty Years of Research Says About the Impact of Technology on Learning. Review of Educational Research, 81(1), 4-28.
Taylor, R. P. (1980). Introduction. In: R. P. Taylor (Ed.), The computer in school: Tutor, tool, tutee, 1-10. Teachers College Press. U.S. Department of Education, Office of Educational Technology. (2017). Reimagining the Role of Technology in Education: 2017 National Education Technology Plan Update. https://tech.ed.gov/files/2017/01/NETP17.pdf
Verdu, E., Regueras, L. M., Verdu, M. J., De Castro, J. P. & Pérez, M. A. (2008). Is adaptive learning effective? A review of the research. WSEAS International Conference. Mathematics and Computers in Science and Engineering, 7.
Walkington, C. (2013). Using Adaptive Learning Technologies to Personalize Instruction to Student Interests: The Impact of Relevant Contexts on Performance and Learning Outcomes. Journal of Educational Psychology, 105(4),932-945. https://doi.org/10.1037/a0031882.
Wang, S., Christensen, C., Cui, W., Tong, R., Yarnall, L., Shear, L. & Feng, M. (2020). When adaptive learning is effective learning: comparison of an adaptive learning system to teacher-led instruction. Interactive Learning Environments. https://doi.org/10.1080/10494820.2020.1808794.
Xie, H., Chu, H.-C., Hwang, G.-J. & Wang, C.-C. (2019). Trends and development in technology-enhanced adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017. Computers & Education, 140, 1-16.
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