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The Sequence Matters in Learning -- A Systematic Literature Review (2308.01218v2)

Published 2 Aug 2023 in cs.CY

Abstract: Describing and analysing learner behaviour using sequential data and analysis is becoming more and more popular in Learning Analytics. Nevertheless, we found a variety of definitions of learning sequences, as well as choices regarding data aggregation and the methods implemented for analysis. Furthermore, sequences are used to study different educational settings and serve as a base for various interventions. In this literature review, the authors aim to generate an overview of these aspects to describe the current state of using sequence analysis in educational support and learning analytics. The 74 included articles were selected based on the criteria that they conduct empirical research on an educational environment using sequences of learning actions as the main focus of their analysis. The results enable us to highlight different learning tasks where sequences are analysed, identify data mapping strategies for different types of sequence actions, differentiate techniques based on purpose and scope, and identify educational interventions based on the outcomes of sequence analysis.

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References (104)
  1. Andrew Abbott. 1995. Sequence Analysis: New Methods for Old Ideas. Annual Review of Sociology 21 (1995), 93–113. arXiv:2083405
  2. Kamil Akhuseyinoglu and Peter Brusilovsky. 2021. Data-Driven Modeling of Learners’ Individual Differences for Predicting Engagement and Success in Online Learning. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’21). Association for Computing Machinery, New York, NY, USA, 201–212. https://doi.org/10.1145/3450613.3456834
  3. Kamil Akhuseyinoglu and Peter Brusilovsky. 2022. Exploring Behavioral Patterns for Data-Driven Modeling of Learners’ Individual Differences. Frontiers in Artificial Intelligence 5 (Feb. 2022), 807320. https://doi.org/10.3389/frai.2022.807320
  4. John R. Anderson. 2000. Cognitive Psychology and Its Implications, 5th Ed. Worth Publishers, New York, NY, US. xvi, 531 pages.
  5. Detecting Gaming the System in Constraint-Based Tutors. Lecture Notes in Computer Science 6075 LNCS (2010), 267–278. https://doi.org/10.1007/978-3-642-13470-8_25
  6. Automated Cognitive Analyses for Intelligent Tutoring Systems. In 2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT). Leicester, UK, 171–178. https://doi.org/10.1109/BDCAT50828.2020.00007
  7. A Survey on Educational Process Mining. WIREs Data Mining and Knowledge Discovery 8, 1 (2018), e1230. https://doi.org/10.1002/widm.1230
  8. Identifying Students’ Characteristic Learning Behaviors in an Intelligent Tutoring System Fostering Self-Regulated Learning. In International Educational Data Mining Society. International Educational Data Mining Society, Chania, Greece.
  9. Teaching Data Structures with beSocratic. In Proceedings of the 18th ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE ’13). Association for Computing Machinery, New York, NY, USA, 105–110. https://doi.org/10.1145/2462476.2465583
  10. Towards a Model-Free Estimate of the Limits to Student Modeling Accuracy. In Proceedings of the 11th International Conference on Educational Data Mining, Vol. 11. Raleigh, NC, USA.
  11. Exploring Time-Related Micro-Behavioral Patterns in a Python Programming Online Course. Journal of Information Science & Engineering 38, 6 (2022).
  12. Click-Based Student Performance Prediction: A Clustering Guided Meta-Learning Approach. In 2021 IEEE International Conference on Big Data (Big Data). Orlando, FL, USA, 1389–1398. https://doi.org/10.1109/BigData52589.2021.9671729
  13. Doug Clow. 2012. The Learning Analytics Cycle: Closing the Loop Effectively. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (LAK ’12). Association for Computing Machinery, New York, NY, USA, 134–138. https://doi.org/10.1145/2330601.2330636
  14. Task and Timing: Separating Procedural and Tactical Knowledge in Student Models. In Proceedings of the 10th International Conference on Educational Data Mining. International Educational Data Mining Society, Wuhan, China.
  15. Probabilistic Approaches to Detect Blocking States in Intelligent Tutoring System. In Intelligent Tutoring Systems: 16th International Conference, ITS 2020, Athens, Greece, June 8–12, 2020, Proceedings. Springer-Verlag, Berlin, Heidelberg, 79–88. https://doi.org/10.1007/978-3-030-49663-0_11
  16. Pedagogical Agent Support and Its Relationship to Learners’ Self-Regulated Learning Strategy Use with an Intelligent Tutoring System. In Proceedings of Artificial Intelligence in Education: 23rd International Conference, AIED (Durham, United Kingdom). Springer-Verlag, 332–343. https://doi.org/10.1007/978-3-031-11644-5_27
  17. A Complex Systems Approach to Analyzing Pedagogical Agents’ Scaffolding of Self-Regulated Learning within an Intelligent Tutoring System. Metacognition and Learning (May 2023). https://doi.org/10.1007/s11409-023-09346-x
  18. A Prediction Model Uses the Sequence of Attempts and Hints to Better Predict Knowledge: Better to Attempt the Problem First, Rather than Ask for a Hint. In Proceedings of the 6th International Conference on Educational Data Mining, EDM.
  19. Predicting Individualized Learner Models Across Tutor Lessons. In Proceedings of the 11th International Conference on Educational Data Mining, Vol. 11. Raleigh, NC, USA.
  20. Predicting Learner’s Performance through Video Sequences Viewing Behavior Analysis Using Educational Data-Mining. Education and Information Technologies 26, 5 (2021), 5799–5814. https://doi.org/10.1007/s10639-021-10512-4
  21. Modeling Learner’s Dynamic Knowledge Construction Procedure and Cognitive Item Difficulty for Knowledge Tracing. Applied Intelligence 50, 11 (2020), 3894–3912. https://doi.org/10.1007/s10489-020-01756-7
  22. Predictive Student Modeling in Educational Games with Multi-Task Learning. Proceedings of the AAAI Conference on Artificial Intelligence 34, 01 (April 2020), 654–661. https://doi.org/10.1609/aaai.v34i01.5406
  23. Noise-Filtering Enhanced Deep Cognitive Diagnosis Model for Latent Skill Discovering. Intelligent Automation & Soft Computing 37, 2 (2023), 1311–1324. https://doi.org/10.32604/iasc.2023.038481
  24. Exploring the Affordances of Sequence Mining in Educational Games. In Eighth International Conference on Technological Ecosystems for Enhancing Multiculturality. ACM, Salamanca Spain, 648–654. https://doi.org/10.1145/3434780.3436562
  25. Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game. Sensors 21, 4 (Jan. 2021), 1025. https://doi.org/10.3390/s21041025
  26. Comparing Knowledge Tracing and Performance Factor Analysis by Using Multiple Model Fitting Procedures. In Intelligent Tutoring Systems (Lecture Notes in Computer Science), Vincent Aleven, Judy Kay, and Jack Mostow (Eds.). Springer, Berlin, Heidelberg, 35–44. https://doi.org/10.1007/978-3-642-13388-6_8
  27. How to Classify Tutorial Dialogue? Comparing Feature Vectors vs. Sequences. In EDM 2011 - Proceedings of the 4th International Conference on Educational Data Mining. 169–178. https://dblp.org/rec/conf/edm/Gonzalez-BrenesMD11
  28. Deep Knowledge Tracking Method Based on DKVTMN-DTCN Model. In Computer Science and Education (Communications in Computer and Information Science), Wenxing Hong and Yang Weng (Eds.). Springer Nature, Singapore, 623–636. https://doi.org/10.1007/978-981-99-2446-2_57
  29. Goal Recognition with Markov Logic Networks for Player-Adaptive Games. In Proceedings of the Seventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE’11). AAAI Press, Stanford, California, USA, 32–39.
  30. KT-XL: A Knowledge Tracing Model for Predicting Learning Performance Based on Transformer-XL. In Proceedings of the ACM Turing Celebration Conference - China (ACM TURC ’20). Association for Computing Machinery, New York, NY, USA, 175–179. https://doi.org/10.1145/3393527.3393557
  31. Mining Meaningful Patterns from Students’ Handwritten Coursework. In In Proceedings of the 6th International Conference on Educational Data Mining, EDM 2013.
  32. Network Visualization and Assessment of Student Reasoning About Conditionals. In Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1. ACM, Dublin Ireland, 255–261. https://doi.org/10.1145/3502718.3524793
  33. Visualization of Students’ Solutions as a Sequential Network. In 2022 IEEE Global Engineering Education Conference (EDUCON). 1189–1194. https://doi.org/10.1109/EDUCON52537.2022.9766502
  34. MAKT: Multichannel Attention Networks Based Knowledge Tracing with Representation Learning. In 2022 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE). 292–299. https://doi.org/10.1109/TALE54877.2022.00055
  35. Using Process Mining to Analyze Students’ Quiz-Taking Behavior Patterns in a Learning Management System. Computers in Human Behavior 92 (2019), 496–506. https://doi.org/10.1016/j.chb.2017.12.015
  36. M. Kesselbacher and A. Bollin. 2019. Discriminating Programming Strategies in Scratch: Making the Difference between Novice and Experienced Programmers. In Proceedings of the 14th Workshop in Primary and Secondary Computing Education. https://doi.org/10.1145/3361721.3361727
  37. The Use and Application of Learning Theory in Learning Analytics: A Scoping Review. Journal of Computing in Higher Education (Oct. 2022). https://doi.org/10.1007/s12528-022-09340-3
  38. J.S. Kinnebrew and G. Biswas. 2011. Modeling and Measuring Self-Regulated Learning in Teachable Agent Environments. Journal of E-Learning and Knowledge Society 7, 2 (2011), 19–35.
  39. A Differential Approach for Identifying Important Student Learning Behavior Patterns with Evolving Usage over Time. Lecture Notes in Computer Science 8643 (2014), 281–292. https://doi.org/10.1007/978-3-319-13186-3_27
  40. Paul Kirschner and Jeroen J. G. van Merrienboer. 2008. Ten Steps to Complex Learning: A New Approach to Instruction and Instructional Design. SAGE Publications, Inc., 2455 Teller Road, Thousand Oaks California 91320 United States, I–244–I–253. https://doi.org/10.4135/9781412964012.n26
  41. Time for Change: Why Learning Analytics Needs Temporal Analysis. Journal of Learning Analytics 4, 3 (Dec. 2017). https://doi.org/10.18608/jla.2017.43.2
  42. Modeling Metacognitive Activities in Medical Problem-Solving with Bio World. Intelligent Systems Reference Library 76 (2015), 323–343. https://doi.org/10.1007/978-3-319-11062-2_13
  43. V. Latypova. 2022. Work with Free Response Implementation Process Analysis Based on Sequential Pattern Mining in Engineering Education. In 2022 VI International Conference on Information Technologies in Engineering Education (Inforino). 1–4. https://doi.org/10.1109/Inforino53888.2022.9782969
  44. Temporal Structures and Sequential Patterns of Self-Regulated Learning Behaviors in Problem Solving with an Intelligent Tutoring System. Educational Technology & Society 25 (March 2022), 1–14. https://eric.ed.gov/?id=EJ1366535
  45. Is it a good move? Mining effective tutoring strategies from human-human tutorial dialogues. Future Generation Computer Systems 127 (2022), 194–207. https://doi.org/10.1016/j.future.2021.09.001
  46. Applying Learning Analytics to Deconstruct User Engagement by Using Log Data of MOOCs. Journal of Information Science and Engineering 34, 5 (2018), 1175–1186. https://doi.org/10.6688/JISE.201809_34(5).0004
  47. Enhancing Deep Knowledge Tracing with Auxiliary Tasks. In Proceedings of the ACM Web Conference 2023 (WWW ’23). Association for Computing Machinery, New York, NY, USA, 4178–4187. https://doi.org/10.1145/3543507.3583866
  48. Interpreting Deep Learning Models for Knowledge Tracing. International Journal of Artificial Intelligence in Education 33, 3 (Sept. 2023), 519–542. https://doi.org/10.1007/s40593-022-00297-z
  49. Erik Lundgren. 2022. Latent Program Modeling: Inferring Latent Problem-Solving Strategies from a PISA Problem- Solving Task. Journal of Educational Data Mining 14, 1 (June 2022), 46–80. https://doi.org/10.5281/zenodo.6686443
  50. Evaluating Deep Sequential Knowledge Tracing Models for Predicting Student Performance. In 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE). 1–6. https://doi.org/10.1109/CSDE53843.2021.9718405
  51. Detection of Learning Strategies: A Comparison of Process, Sequence and Network Analytic Approaches. In Transforming Learning with Meaningful Technologies (Lecture Notes in Computer Science), Maren Scheffel, Julien Broisin, Viktoria Pammer-Schindler, Andri Ioannou, and Jan Schneider (Eds.). Springer International Publishing, Cham, 525–540. https://doi.org/10.1007/978-3-030-29736-7_39
  52. Selly Meliana and Dade Nurjanah. 2018. Adopting Good-Learners’ Paths in an Intelligent Tutoring System. In 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). 877–882. https://doi.org/10.1109/TALE.2018.8615176
  53. Inge Molenaar and Alyssa Friend Wise. 2022. Temporal Aspects of Learning Analytics - Grounding Analyses in Concepts of Time. In The Handbook of Learning Analytics (2 ed.). SoLAR, Vancouver, Canada, 66–76.
  54. Progression Trajectory-Based Student Modeling for Novice Block-Based Programming. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, New York, NY, USA, 189–200. https://doi.org/10.1145/3450613.3456833
  55. A LAK of Direction: Misalignment Between the Goals of Learning Analytics and Its Research Scholarship. Journal of Learning Analytics (March 2023), 1–13. https://doi.org/10.18608/jla.2023.7913
  56. Predictive Learning Analytics Using Deep Learning Model in MOOCs’ Courses Videos. Education and Information Technologies 26, 1 (Jan. 2021), 371–392. https://doi.org/10.1007/s10639-020-10273-6
  57. M.H. Myers. 2021. Automatic Detection of a Student’s Affective States for Intelligent Teaching Systems. Brain Sciences 11, 3 (2021), 1–15. https://doi.org/10.3390/brainsci11030331
  58. Systematic Literature Review for the Use of AI Based Techniques in Adaptive Learning Management Systems. In Proceedings of the 5th European Conference on Software Engineering Education. ACM, Seeon/Bavaria Germany, 83–92. https://doi.org/10.1145/3593663.3593681
  59. Advancing Log Analysis of Student Interactions with Cognitive Tools. In 2th Biennial Conference of the European Association for Research on Learning and Instruction (EARLI). 2–20.
  60. Understanding Student Interactions in Capstone Courses to Improve Learning Experiences. In Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education. ACM, Seattle Washington USA, 423–428. https://doi.org/10.1145/3017680.3017716
  61. HHSKT: A Learner–Question Interactions Based Heterogeneous Graph Neural Network Model for Knowledge Tracing. Expert Systems with Applications 215 (April 2023), 119334. https://doi.org/10.1016/j.eswa.2022.119334
  62. Problem Solving and Human Expertise. In International Encyclopedia of Education (Third Edition), Penelope Peterson, Eva Baker, and Barry McGaw (Eds.). Elsevier, Oxford, 265–272. https://doi.org/10.1016/B978-0-08-044894-7.00486-3
  63. How Well Do Teachers Predict Students’ Actions in Solving an Ill-Defined Problem in STEM Education: A Solution Using Sequential Pattern Mining. IEEE access : practical innovations, open solutions 8 (2020), 134976–134986. https://doi.org/10.1109/ACCESS.2020.3010168
  64. Data Mining for Discovering Effective Time-Series Transition of Learning Strategies on Mutual Viewing-Based Learning. Journal of Advanced Computational Intelligence and Intelligent Informatics 22, 7 (2018), 1046–1055. https://doi.org/10.20965/jaciii.2018.p1046
  65. Modeling User Exploration and Boundary Testing in Digital Learning Games. In UMAP 2016 - Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization. 301–302. https://doi.org/10.1145/2930238.2930271
  66. Gustavo Padrón-Rivera and Genaro Rebolledo-Mendez. 2015. Identifying Affective Trajectories in Relation to Learning Gains During the Interaction with a Tutoring System. In Artificial Intelligence in Education (Lecture Notes in Computer Science), Cristina Conati, Neil Heffernan, Antonija Mitrovic, and M. Felisa Verdejo (Eds.). Springer International Publishing, Cham, 756–759. https://doi.org/10.1007/978-3-319-19773-9_109
  67. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. Systematic Reviews 10, 1 (March 2021), 89. https://doi.org/10.1186/s13643-021-01626-4
  68. Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs. Applied Sciences (Switzerland) 9, 24 (2019). https://doi.org/10.3390/app9245539
  69. A Temporal Model of Learner Behaviors in OELEs Using Process Mining. In ICCE 2018 - 26th International Conference on Computers in Education, Main Conference Proceedings. 276–285. https://apsce.net/icce/icce2018/wp-content/uploads/2018/12/C3-07.pdf
  70. A Virtual Reality Simulator for Teaching and Evaluating Dental Procedures. Methods of Information in Medicine 49, 4 (2010), 396–405. https://doi.org/10.3414/ME9310
  71. Implementing a Learning Analytics Intervention and Evaluation Framework: What Works? In Big Data and Learning Analytics in Higher Education: Current Theory and Practice, Ben Kei Daniel (Ed.). Springer International Publishing, Cham, 147–166. https://doi.org/10.1007/978-3-319-06520-5_10
  72. Diego Riofrío-Luzcando and Jaime Ramírez. 2016. Student Action Prediction for Automatic Tutoring for Procedural Training in 3D Virtual Environments. In E-Learning, E-Education, and Online Training (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering), Giovanni Vincenti, Alberto Bucciero, and Carlos Vaz de Carvalho (Eds.). Springer International Publishing, Cham, 200–207. https://doi.org/10.1007/978-3-319-28883-3_25
  73. Predicting Student Actions in a Procedural Training Environment. IEEE Transactions on Learning Technologies 10, 4 (2017), 463–474. https://doi.org/10.1109/TLT.2017.2658569
  74. Predicting Student Performance in Solving Parameterized Exercises. Lecture Notes in Computer Science 8474 LNCS (2014), 496–503. https://doi.org/10.1007/978-3-319-07221-0_62
  75. Tensor Factorization for Student Modeling and Performance Prediction in Unstructured Domain. In Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016. 502–506.
  76. Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learning. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. ACM, Frankfurt Germany, 402–411. https://doi.org/10.1145/3375462.3375487
  77. Margarete Sandelowski and Julie Barroso. 2007. Handbook for Synthesizing Qualitative Research. Springer Publishing Company, New York, NY.
  78. Carlotta Schatten and Lars Schmidt-Thieme. 2016. Student Progress Modeling with Skills Deficiency Aware Kalman Filters:. In Proceedings of the 8th International Conference on Computer Supported Education. SCITEPRESS - Science and and Technology Publications, Rome, Italy, 31–42. https://doi.org/10.5220/0005737200310042
  79. Robin Schmucker and Tom M Mitchell. 2022. Transferable Student Performance Modeling for Intelligent Tutoring Systems. In Proceedings of the 30th International Conference on Computers in Education. Asia-Pacific Society for Computers in Education. The Asia-Pacific Society for Computers in Education, Kuala Lumpur, Malaysia.
  80. Linking Learning Behavior Analytics and Learning Science Concepts: Designing a Learning Analytics Dashboard for Feedback to Support Learning Regulation. Computers in Human Behavior 107 (June 2020), 105512. https://doi.org/10.1016/j.chb.2018.05.004
  81. Diagrammatic Student Models: Modeling Student Drawing Performance with Deep Learning. In User Modeling, Adaptation and Personalization, Francesco Ricci, Kalina Bontcheva, Owen Conlan, and Séamus Lawless (Eds.). Springer International Publishing, Cham, 216–227. https://doi.org/10.1007/978-3-319-20267-9_18
  82. A Survey on Deep Learning Based Knowledge Tracing. Knowledge-Based Systems 258 (Dec. 2022), 110036. https://doi.org/10.1016/j.knosys.2022.110036
  83. Effects of Intelligent Feedback on Online Learners’ Engagement and Cognitive Load: The Case of Research Ethics Education. Educational Psychology 39, 10 (2019), 1293–1310. https://doi.org/10.1080/01443410.2018.1527291
  84. A.A. Supianto and M. Hafis. 2019. GTRAS: Graphical Tracking Activity System for Problem-Posing Learning Process Insights. In 2018 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018. IEEE, 231–235. https://doi.org/10.1109/ICACSIS.2018.8618216
  85. An Exploratory Analysis of the Latent Structure of Process Data via Action Sequence Autoencoders. Brit. J. Math. Statist. Psych. 74, 1 (2021), 1–33. https://doi.org/10.1111/bmsp.12203
  86. Modeling Learning Behaviors and Predicting Performance in an Intelligent Tutoring System: A Two-Layer Hidden Markov Modeling Approach. Interactive Learning Environments 0, 0 (Dec. 2021), 1–13. https://doi.org/10.1080/10494820.2021.2010100
  87. Michelle Taub and Roger Azevedo. 2019. How Does Prior Knowledge Influence Eye Fixations and Sequences of Cognitive and Metacognitive SRL Processes during Learning with an Intelligent Tutoring System? International Journal of Artificial Intelligence in Education 29, 1 (March 2019), 1–28. https://doi.org/10.1007/s40593-018-0165-4
  88. Building a Learner Model for a Smartphone-Based Clinical Training Intervention in a Low-Income Context: A Pilot Study. In Transforming Learning with Meaningful Technologies (Lecture Notes in Computer Science), Maren Scheffel, Julien Broisin, Viktoria Pammer-Schindler, Andri Ioannou, and Jan Schneider (Eds.). Springer International Publishing, Cham, 55–68. https://doi.org/10.1007/978-3-030-29736-7_5
  89. Coded Dataset of a Literature Review on Learning Sequence Analysis. https://doi.org/10.4121/10FC3A10-37AA-4292-818D-468B940B2BB7
  90. edX Log Data Analysis Made Easy: Introducing ELAT: An Open-Source, Privacy-Aware and Browser-Based edX Log Data Analysis Tool. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge (LAK ’20). Association for Computing Machinery, New York, NY, USA, 502–511. https://doi.org/10.1145/3375462.3375510
  91. Using Sequential Pattern Mining to Explore Learners’ Behaviors and Evaluate Their Correlation with Performance in Inquiry-Based Learning. Lecture Notes in Computer Science 10474 LNCS (2017), 286–299. https://doi.org/10.1007/978-3-319-66610-5_21
  92. Jill-Jenn Vie and Hisashi Kashima. 2019. Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing. Proceedings of the AAAI Conference on Artificial Intelligence 33, 01 (July 2019), 750–757. https://doi.org/10.1609/aaai.v33i01.3301750
  93. A Systematic Review of Empirical Studies Using Log Data from Open-Ended Learning Environments to Measure Science and Engineering Practices. British Journal of Educational Technology 54, 1 (2023), 192–221. https://doi.org/10.1111/bjet.13289
  94. Evaluating Student Learning Effect Based on Process Mining. In Applied Informatics (Communications in Computer and Information Science), Hector Florez, Marcelo Leon, Jose Maria Diaz-Nafria, and Simone Belli (Eds.). Springer International Publishing, Cham, 59–72. https://doi.org/10.1007/978-3-030-32475-9_5
  95. Philip H. Winne. 2010. Improving Measurements of Self-Regulated Learning. Educational Psychologist 45, 4 (Oct. 2010), 267–276. https://doi.org/10.1080/00461520.2010.517150
  96. Alyssa Friend Wise and David Williamson Shaffer. 2015. Why Theory Matters More than Ever in the Age of Big Data. Journal of Learning Analytics 2, 2 (Dec. 2015), 5–13. https://doi.org/10.18608/jla.2015.22.2
  97. Exploring Sequences of Learner Activities in Relation to Self-Regulated Learning in a Massive Open Online Course. Computers & Education 140 (Oct. 2019). https://doi.org/10.1016/j.compedu.2019.103595
  98. Tangjie Wu and Qiang Ling. 2023. Fusing Hybrid Attentive Network with Self-Supervised Dual-Channel Heterogeneous Graph for Knowledge Tracing. Expert Systems with Applications 225 (Sept. 2023), 120212. https://doi.org/10.1016/j.eswa.2023.120212
  99. Multi-Variate Knowledge Tracking Based on Graph Neural Network in ASSISTments. IEEE Transactions on Learning Technologies (2023), 1–12. https://doi.org/10.1109/TLT.2023.3301011
  100. A Survey on Educational Data Mining Methods Used for Predicting Students’ Performance. Engineering Reports 4, 5 (2022), e12482. https://doi.org/10.1002/eng2.12482
  101. Going Deeper with Deep Knowledge Tracing (Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016). 545–550.
  102. Skill-Oriented Hierarchical Structure for Deep Knowledge Tracing. In 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI). 425–432. https://doi.org/10.1109/ICTAI56018.2022.00069
  103. Dynamic Multi-Objective Sequence-Wise Recommendation Framework via Deep Reinforcement Learning. Complex & Intelligent Systems 9, 2 (April 2023), 1891–1911. https://doi.org/10.1007/s40747-022-00871-x
  104. What Do Students’ Interactions with Online Lecture Videos Reveal about Their Learning?. In Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’22). Association for Computing Machinery, New York, NY, USA, 295–305. https://doi.org/10.1145/3503252.3531315
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