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Incorporating Rich Features into Deep Knowledge Tracing.

, , , , и . L@S, стр. 169-172. ACM, (2017)

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Xiaolu Wang

Xianjun Xiong

Xiong Qi

Guangwei Xiong

Lixiang Xiong

 

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