Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/165580
Title: Toward better grade prediction via A2GP - an academic achievement inspired predictive model
Authors: Qiu, Wei
Supraja, S.
Khong, Andy Wai Hoong
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Source: Qiu, W., Supraja, S. & Khong, A. W. H. (2022). Toward better grade prediction via A2GP - an academic achievement inspired predictive model. 15th International Conference on Educational Data Mining (EDM 2022), 195-205. https://dx.doi.org/10.5281/ZENODO.6852984
Conference: 15th International Conference on Educational Data Mining (EDM 2022)
Abstract: Predicting student performance in an academic institution is important for detecting at-risk students and administering early-intervention strategies. We propose a new grade prediction model that considers three factors: temporal dynamics of prior courses across previous semesters, short-term performance consistency, and relative performance against peers. The proposed architecture comprises modules that incorporate the attention mechanism, a new short-term gated long short-term memory network, and a graph convolutional network to address limitations of existing works that fail to consider the above factors jointly. A weighted fusion layer is used to fuse learned representations of the above three modules—course importance, performance consistency, and relative performance. The aggregated representations are then used for grade prediction which, in turn, is used to classify at-risk students. Experiment results using three datasets obtained from over twenty thousand students across seventeen undergraduate courses show that the proposed model achieves low prediction errors and high F1 scores compared to existing models that predict grades and thereafter identifies at-risk students via a pre-defined threshold.
URI: https://hdl.handle.net/10356/165580
URL: https://educationaldatamining.org/edm2022/proceedings/
ISBN: 9781733673631
DOI: 10.5281/ZENODO.6852984
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 The author(s). This work is distributed under the Creative Commons Attribution NonCommercial NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. https://doi.org/10.5281/zenodo.6852984
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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