2019 IEEE 31st International Conference on Tools With Artificial Intelligence (ICTAI) 2019
DOI: 10.1109/ictai.2019.00217
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Some Improvements of Deep Knowledge Tracing

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Cited by 7 publications
(7 citation statements)
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“…The lack of theories around model creation for data-driven approaches introduces mistrust issues in their outputs and applications [20]. Thus, the integration of machine learning with model-based methods presents a promising research direction [65], [66].…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…The lack of theories around model creation for data-driven approaches introduces mistrust issues in their outputs and applications [20]. Thus, the integration of machine learning with model-based methods presents a promising research direction [65], [66].…”
Section: Discussionmentioning
confidence: 99%
“…In summary, the decisions regarding what data about a student to gather, how to collect, and how it is related to student learning are essential in designing a student model. In view of the advantages and disadvantages of overlay-based and data-driven methods, the hybrid effort of integrating both is envisioned as a future research direction [65], [66].…”
Section: A Findingsmentioning
confidence: 99%
“…Although BKT has been used successfully in many systems, it has some limitations, well summarized by Tato and Nkambou [13]. Specifically, as a starting point of BKT, there is a Bayesian network (BN) [14], which "sometimes implies to manually define apriori probabilities and manually label student interactions with relevant concepts".…”
Section: A Knowledge Tracingmentioning
confidence: 99%
“…[59], [41], [ [20], [74], [34], [36], [75], [37], [38], [39], [76] [27], [43], [44], [52], [45], [68], [54], [37], [47], [46] [69], [55], [56], [57], [58], [59], [60], [61], [62], [50] [34], [63], [59] [20], [42], [77], [79] Hypothesis set [71], [33], [38], [78], [47], [49] [53], [72], [51] [71], [26], [48], [33], [49], [40] [48], [13], [70], [41], [64],…”
Section: Domain Knowledgementioning
confidence: 99%
“…Many researchers employed techniques without manual feature engineering processes to predict dropout. Few previous works explored deep neural network (DNN) model [28], recurrent neural network (RNN) model [27,54], and convolutional neural networks (CNN) followed by RNN [42,53]. Yet, all of these recent models, so far, have given suboptimal performance.…”
Section: Video-clickstreammentioning
confidence: 99%