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The Future of Adaptive Learning: Does the Crowd Hold the Key?, , , , , , and . Int. J. Artif. Intell. Educ., 26 (2): 615-644 (2016)Incorporating Rich Features into Deep Knowledge Tracing, , , , and . Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale, page 169--172. New York, NY, USA, ACM, (2017)Improving Retention Performance Prediction with Prerequisite Skill Features., , and . EDM, page 375-376. International Educational Data Mining Society (IEDMS), (2014)A Study of Exploring Different Schedules of Spacing and Retrieval Interval on Mathematics Skills in ITS Environment., and . Intelligent Tutoring Systems, volume 8474 of Lecture Notes in Computer Science, page 504-509. Springer, (2014)Going Deeper with Deep Knowledge Tracing., , , and . EDM, page 545-550. International Educational Data Mining Society (IEDMS), (2016)Experimenting Choices of Video and Text Feedback in Authentic Foreign Language Assignments at Scale., , and . L@S, page 335-338. ACM, (2017)A Memory-Augmented Neural Model for Automated Grading., , , , and . L@S, page 189-192. ACM, (2017)Modeling student retention in an environment with delayed testing., , and . EDM, page 328-329. International Educational Data Mining Society, (2013)Will You Get It Right Next Week: Predict Delayed Performance in Enhanced ITS Mastery Cycle., , and . FLAIRS, AAAI Press, (2013)Improving Long-Term Retention Level in an Environment of Personalized Expanding Intervals., and . EDM, page 582-583. International Educational Data Mining Society (IEDMS), (2015)