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ERIC Number: ED615582
Record Type: Non-Journal
Publication Date: 2021
Pages: 8
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: N/A
Available Date: N/A
Knowledge Transfer by Discriminative Pre-Training for Academic Performance Prediction
Kim, Byungsoo; Yu, Hangyeol; Shin, Dongmin; Choi, Youngduck
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (14th, Online, Jun 29-Jul 2, 2021)
The needs for precisely estimating a student's academic performance have been emphasized with an increasing amount of attention paid to Intelligent Tutoring System (ITS). However, since labels for academic performance, such as test scores, are collected from outside of ITS, obtaining the labels is costly, leading to label-scarcity problem which brings challenge in taking machine learning approaches for academic performance prediction. To this end, inspired by the recent advancement of pre-training method in natural language processing community, we propose DPA, a transfer learning framework with Discriminative Pre-training tasks for Academic performance prediction. DPA pre-trains two models, a generator and a discriminator, and fine-tunes the discriminator on academic performance prediction. In DPA's pre-training phase, a sequence of interactions where some tokens are masked is provided to the generator which is trained to reconstruct the original sequence. Then, the discriminator takes an interaction sequence where the masked tokens are replaced by the generator's outputs, and is trained to predict the originalities of all tokens in the sequence. We conduct extensive experimental studies on a real-world dataset obtained from a multi-platform ITS application and show that DPA outperforms the previous state-of-the-art generative pre-training method with a reduction of 4.05% in mean absolute error and more robust to increased label-scarcity. [For the full proceedings, see ED615472.]
International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: South Korea
Identifiers - Assessments and Surveys: Test of English for International Communication
Grant or Contract Numbers: N/A
Author Affiliations: N/A