TY - CONF
T1 - Deep Embeddings of Contextual Assessment Data for Improving Performance Prediction.
AU - Clavié, Benjamin
AU - Gal, Kobi
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2020/7
Y1 - 2020/7
N2 - We introduce DeepPerfEmb, or DPE, a new deep-learning model that captures dense representations of students’ online behaviour and meta-data about students and educational content. The model uses these representations to predict student performance. We evaluate DPE on standard datasets from the literature, showing superior performance to the state-of-the-art systems in predicting whether or not students will answer a given question correctly. In particular, DPE is unaffected by the cold-start problem which arises when new students come to the system with little to no data available. We also show strong performance of the model when removing students’ histories altogether, relying in part on contextual information about the questions. This strong performance without any information about the learners’ histories demonstrates the high potential of using deep embedded representations of contextual information ineducational data mining.
AB - We introduce DeepPerfEmb, or DPE, a new deep-learning model that captures dense representations of students’ online behaviour and meta-data about students and educational content. The model uses these representations to predict student performance. We evaluate DPE on standard datasets from the literature, showing superior performance to the state-of-the-art systems in predicting whether or not students will answer a given question correctly. In particular, DPE is unaffected by the cold-start problem which arises when new students come to the system with little to no data available. We also show strong performance of the model when removing students’ histories altogether, relying in part on contextual information about the questions. This strong performance without any information about the learners’ histories demonstrates the high potential of using deep embedded representations of contextual information ineducational data mining.
M3 - Paper
T2 - International Conference on Educational Data Mining (EDM)<br/>
Y2 - 10 July 2020 through 13 July 2020
ER -