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Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs.

, , , and . Int. J. Artif. Intell. Educ., 31 (2): 215-233 (2021)

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Serendipitous Gains of Explaining a Classifier - Artificial versus Human Performance and Annotator Support in an Urgent Instructor-Intervention Model for MOOCs., , , and . HUMAN@HT, page 3:1-3:7. ACM, (2023)Forum-based Prediction of Certification in Massive Open Online Courses., , , and . ISD, Universitat Politècnica de València / Association for Information Systems, (2021)On the Need for Fine-Grained Analysis of Gender Versus Commenting Behaviour in MOOCs., , , , , , and . ICIEI, page 73-77. ACM, (2018)How is Learning Fluctuating? FutureLearn MOOCs Fine-Grained Temporal Analysis and Feedback to Teachers., , , , , and . ISD, Lund University / Association for Information Systems, (2018)Can learner characteristics predict their behaviour on MOOCs?, , , , , , and . ICETC, page 119-128. ACM, (2018)The engage taxonomy: SDT-based measurable engagement indicators for MOOCs and their evaluation., , , , , , and . User Model. User Adapt. Interact., 34 (2): 323-374 (April 2024)Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week's Activities., , , , , , and . CoRR, (2020)MOOC Next Week Dropout Prediction: Weekly Assessing Time and Learning Patterns., , , , and . ITS, volume 12677 of Lecture Notes in Computer Science, page 119-130. Springer, (2021)Urgency Analysis of Learners' Comments: An Automated Intervention Priority Model for MOOC., , , and . ITS, volume 12677 of Lecture Notes in Computer Science, page 148-160. Springer, (2021)Predicting Certification in MOOCs Based on Students' Weekly Activities., , and . ITS, volume 12677 of Lecture Notes in Computer Science, page 173-185. Springer, (2021)