Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
TechTrends
Published in:

11-04-2023 | Original Paper

Discovering Unproductive Learning Patterns of Wheel-spinning Students in Intelligent Tutors Using Cluster Analysis

Author: Seoyeon Park

Published in: TechTrends | Issue 3/2023

Login to get access

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Wheel-spinning is unproductive persistence without the mastery of skills. Understanding wheel-spinning during the use of intelligent tutoring systems (ITSs) is crucial to help improve productivity and learning. In this study, following Beck and Gong (2013), we defined wheel-spinning students (unsuccessful students in ITSs) as those who practiced the same skill set over 10 times but failed to submit correct answers three times in a row. The t-SNE and K-means clustering algorithms were used to probe wheel-spinning learning patterns. Our results showed three types of wheel-spinning patterns when using ASSISTments, an online mathematics tutoring system. The findings indicate that a lack of motivation, math knowledge, or metacognitive ability can cause the failure to learn math with ITSs, which provides us with a deeper understanding of students' failure in ITSs and clues about how we can help these unsuccessful students in ITSs.
previous article Students' Perception of Computer-Based Tests in the Use of English Programme in Nigerian Universities
next article Effects of Two Modes of Digital Storytelling Instructional Strategy on Pupils' Achievement in Social Studies
Literature
Aggarwal, S., & Sharma, N. (2019). Laplace transform for the solution of first kind linear Volterra integral equation. Journal of Advanced Research in Applied Mathematics and Statistics, 4(3&4), 16–23.
Aleven, V., & Koedinger, K. R. (2000, June). Limitations of student control: Do students know when they need help? In International Conference on Intelligent Tutoring Systems (pp. 292–303). Springer.
Anderson, J. R. (1996b). ACT: A simple theory of complex cognition. American Psychologist, 51(4), 355.CrossRef
Anderson, J. R., Conrad, F. G., & Corbett, A. T. (1989). Skill acquisition and the LISP tutor. Cognitive Science, 13(4), 467–505.CrossRef
Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167–207.CrossRef
Anderson, J. R. (1996a). The architecture of cognition (Vol. 5). Psychology Press.
Antonenko, P. D., Toy, S., & Niederhauser, D. S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60(3), 383–398.CrossRef
Baker, R. S., Gowda, S. M., & Corbett, A. T. (2011, June). Towards predicting future transfer of learning. In International Conference on Artificial Intelligence in Education (pp. 23–30). Springer.
Beck, J. E., & Gong, Y. (2013, July). Wheel-spinning: Students who fail to master a skill. In International Conference on Artificial Intelligence in Education (pp. 431–440). Springer.
Beck, J. E., Chang, K. M., Mostow, J., & Corbett, A. (2008, June). Does help help? Introducing the Bayesian evaluation and assessment methodology. In International Conference on Intelligent Tutoring Systems (pp. 383–394). Springer.
Beck, J., Rodrigo, M., & Mercedes, T. (2014, June). Understanding wheel spinning in the context of affective factors. In International Conference on Intelligent Tutoring Systems (pp. 162–167). Springer.
Bholowalia, P., & Kumar, A. (2014). EBK-means: A clustering technique based on elbow method and K-means in WSN. International Journal of Computer Applications, 105(9), 17–24.
Bloom, H. (1971). The visionary company: A reading of English romantic poetry (Vol. 117). Cornell University Press.
Bloom, B. S. (1976). Human characteristics and school learning. McGraw-Hill.
Cen, H., Koedinger, K., Junker, B. (2007). Is over practice necessary? - Improving learning efficiency with the cognitive tutor through educational data mining. In Proceedings of the 13th international conference on computers in education, 511–518.
Clearinghouse, W. W. (2010). Scott Foresman-Addison Wesley Elementary Mathematics. What Works Clearinghouse Intervention Report. ERIC Clearinghouse.
Feng, M., Heffernan, N., & Koedinger, K. (2009). Addressing the assessment challenge with an online system that tutors as it assesses. User Modeling and User-Adapted Interaction, 19(3), 243–266.CrossRef
Freund, Y., & Schapire, R. E. (1996). Experiments with a new boosting algorithm. In ICML, 96, 148–156.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 1189–1232.
Goldin, P. R., Ziv, M., Jazaieri, H., Werner, K., Kraemer, H., Heimberg, R. G., & Gross, J. J. (2012). Cognitive reappraisal self-efficacy mediates the effects of individual cognitive-behavioral therapy for social anxiety disorder. Journal of Consulting and Clinical Psychology, 80(6), 1034–1040.CrossRef
Gong, Y., & Beck, J. E. (2015). Towards detecting wheel-spinning: Future failure in mastery learning. In Proceedings of the Second (2015) ACM Conference on Learning@Scale. ACM, 67–74.
Gong, Y., Wang, Y., & Beck, J. (2016). How long must we spin our wheels? Analysis of student time and classifier inaccuracy. Student modeling from different aspects, The 9th International Conference on Educational Data Mining. ACM, 32–38.
Guskey, T. R. (2008). Practical solutions for serious problems in standards-based grading. Corwin Press.
Kai, S., Almeda, M. V., Baker, R. S., Heffernan, C., & Heffernan, N. (2018). Decision tree modeling of wheel-spinning and productive persistence in skill builders. JEDM| Journal of Educational Data Mining, 10(1), 36–71.
Kaufman, L., & Rousseeuw, P. J. (1987). Clustering by means of medoids, in statistical data analysis based on the L-norm and related methods, Y. Dodge (Ed.), North-Holland, 405–416.
Kaufmann, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. John Wiley.CrossRef
Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41, 75–86.CrossRef
Koedinger, K. R., Anderson, J. R., Hadley, W. H., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8(1), 30–43.
Koedinger, K., Yudelson, M., & Pavlik, P. (2016). Testing Theories of Transfer Using Error Rate Learning Curves. Topics in Cognitive Science, 8(3), 589–609.
Kozikoglu, I. (2019). Investigating critical thinking in prospective teachers: Metacognitive skills, problem solving skills and academic self-efficacy. Journal of Social Studies Education Research, 10(2), 111–130.
Maaten, L. V. D., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579–2605.
Maclin, R., & Opitz, D. (1997). An empirical evaluation of bagging and boosting. AAAI/IAAI, 546–551.
Matsuda, N., Chandrasekaran, S., & Stamper, J. C. (2016). How quickly can wheel spinning be detected? In EDM (pp. 607–608).
Oyelade, O. J., Oladipupo, O. O., & Obagbuwa, I. C. (2010). Application of k means clustering algorithm for prediction of students’ academic performance. arXiv preprint arXiv:1002.2425.
Park, S., & Matsuda, N. (2018, May). Predicting students’ unproductive failure on intelligent tutors in adaptive online courseware. In Proceedings of the Sixth Annual GIFT Users Symposium, 6, p. 131–138. US Army Research Laboratory.
Park, S. (2019). Who fails to achieve mastery in computer-based learning environments? In G. Marks (Ed.), Proceedings of Global Learn 2019–Global Conference on Learning and Technology (pp. 201–207). Princeton-Mercer, NJ: Association for the Advancement of Computing in Education (AACE). Retrieved December 30, 2021, from https://​www.​learntechlib.​org/​primary/​p/​210306/​
Pedro, M. O., Baker, R., Bowers, A., & Heffernan, N. (2013, July). Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School. In S. D'Mello, R. Calvo & A. Olney (Eds.), Proceedings of the 6th International Conference on Educational Data Mining (EDM 2013), 177–184. International Educational Data Mining Society.
Razzaq, L., Patvarczki, J., Almeida, S. F., Vartak, M., Feng, M., Heffernan, N. T., & Koedinger, K. R. (2009). The ASSISTment builder: Supporting the life cycle of tutoring system content creation. IEEE Transactions on Learning Technologies, 2(2), 157–166.CrossRef
Ritter, S., Anderson, J. R., Koedinger, K. R., & Corbett, A. (2007). The cognitive tutor: Applied research in mathematics education. Psychonomics Bulletin & Review, 14(2), 249–255.
Schank, R. (1990). Case‐Based Teaching: Four Experiences in Educational Software Design. Interactive Learning Environments, 1(4), 231–253.
Schank, R. C., Berman, T. R., & Macpherson, K. A. (1999). Learning by doing. Instructional-Design Theories and Models: A New Paradigm of Instructional Theory, 2(2), 161–181.
Schapire, R. E. (2003). The boosting approach to machine learning: An overview. Nonlinear Estimation and Classification (pp. 149–171). Springer.CrossRef
Self, J. (1999). The defining characteristics of intelligent tutoring systems research: ITSs care, precisely. International Journal of Artificial Intelligence in Education, 10(3–4), 350–364.
Seymour, E., & Hewitt, N. M. (1997). Talking about leaving (p. 134). Westview Press.
Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (statistical Methodology), 63(2), 411–423.CrossRef
Tomlinson, C. A., Brimijoin, K., & Narvaez, L. (2008). The differentiated school: Making revolutionary changes in teaching and learning. Association for Supervision and Curriculum Development.
Wang, Y. (2016). Student Modeling From Different Aspects [Master's Thesis, Worcester Polytechnic Institute].
Watkins, J., & Mazur, E. (2013). Retaining students in science, technology, engineering, and mathematics (STEM) majors. Journal of College Science Teaching, 42(5), 36–41.
Metadata
Title
Discovering Unproductive Learning Patterns of Wheel-spinning Students in Intelligent Tutors Using Cluster Analysis
Author
Seoyeon Park
Publication date
11-04-2023
Publisher
Springer US
Published in
TechTrends / Issue 3/2023
Print ISSN: 8756-3894
Electronic ISSN: 1559-7075
DOI
https://doi.org/10.1007/s11528-023-00847-9

Other articles of this Issue 3/2023

Microlearning through the Lens of Gagne’s Nine Events of Instruction: A Qualitative Study

  • Original Paper

Expanding Theories of Sociopolitical Development: Centering the Intersectional Experiences of Girls of Color in an Informal STEM Program

  • Original Paper

Youth Hackathons in Computing for the Community: A Design Case

  • Original Paper

Racial Justice Amidst the Dangers of Computing Creep: A Dialogue

  • Original Paper

Ancestral Computing for Sustainability: Centering Indigenous Epistemologies in Researching Computer Science Education

  • Original Paper

Informal Learning in United States Libraries: A Systematic Review

  • Original Paper

Premium Partner

    Image Credits