Grade prediction via prior grades and text mining on course descriptions: course outlines and intended learning outcomes
Academic grades in assessments are predicted to determine if a student is at risk of failing a course. Sequential models or graph neural networks that have been employed for grade prediction do not consider relationships between course descriptions. We propose the use of text mining to extract seman...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/175885 |
| _version_ | 1826119568594042880 |
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| author | Li, Jiawei Supraja, S. Qiu, Wei Khong, Andy Wai Hoong |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Li, Jiawei Supraja, S. Qiu, Wei Khong, Andy Wai Hoong |
| author_sort | Li, Jiawei |
| collection | NTU |
| description | Academic grades in assessments are predicted to determine if a student is at risk of failing a course. Sequential models or graph neural networks that have been employed for grade prediction do not consider relationships between course descriptions. We propose the use of text mining to extract semantic, syntactic, and frequency-based features from course content. In addition, we classify intended learning outcomes according to their higher- or lower-order thinking skills. A learning parameter is then formulated to model the impact of these cognitive levels (that are expected for each course) on student performance. These features are then embedded and represented as graphs. Past academic achievements are then fused with the above features for grade prediction. We validate the performance of the above approach via datasets corresponding to three engineering departments collected from a university. Results obtained highlight that the proposed technique generates meaningful feature representations and outperforms existing methods for grade prediction. |
| first_indexed | 2024-10-01T05:02:11Z |
| format | Conference Paper |
| id | ntu-10356/175885 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:02:11Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1758852024-05-10T15:43:30Z Grade prediction via prior grades and text mining on course descriptions: course outlines and intended learning outcomes Li, Jiawei Supraja, S. Qiu, Wei Khong, Andy Wai Hoong School of Electrical and Electronic Engineering 15th International Conference on Educational Data Mining (EDM) Computer and Information Science Grade prediction Graph networks Course descriptions Semantic similarities Cognitive levels Academic grades in assessments are predicted to determine if a student is at risk of failing a course. Sequential models or graph neural networks that have been employed for grade prediction do not consider relationships between course descriptions. We propose the use of text mining to extract semantic, syntactic, and frequency-based features from course content. In addition, we classify intended learning outcomes according to their higher- or lower-order thinking skills. A learning parameter is then formulated to model the impact of these cognitive levels (that are expected for each course) on student performance. These features are then embedded and represented as graphs. Past academic achievements are then fused with the above features for grade prediction. We validate the performance of the above approach via datasets corresponding to three engineering departments collected from a university. Results obtained highlight that the proposed technique generates meaningful feature representations and outperforms existing methods for grade prediction. Published version 2024-05-09T01:17:57Z 2024-05-09T01:17:57Z 2022 Conference Paper Li, J., Supraja, S., Qiu, W. & Khong, A. W. H. (2022). Grade prediction via prior grades and text mining on course descriptions: course outlines and intended learning outcomes. 15th International Conference on Educational Data Mining (EDM), July 2022, 446-453. https://dx.doi.org/10.5281/zenodo.6853171 9781733673631 https://hdl.handle.net/10356/175885 10.5281/zenodo.6853171 2-s2.0-85160842236 July 2022 446 453 en © 2022 Copyright is held by the author(s). This work is distributed under the Creative Commons Attribution NonCommercial NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. application/pdf |
| spellingShingle | Computer and Information Science Grade prediction Graph networks Course descriptions Semantic similarities Cognitive levels Li, Jiawei Supraja, S. Qiu, Wei Khong, Andy Wai Hoong Grade prediction via prior grades and text mining on course descriptions: course outlines and intended learning outcomes |
| title | Grade prediction via prior grades and text mining on course descriptions: course outlines and intended learning outcomes |
| title_full | Grade prediction via prior grades and text mining on course descriptions: course outlines and intended learning outcomes |
| title_fullStr | Grade prediction via prior grades and text mining on course descriptions: course outlines and intended learning outcomes |
| title_full_unstemmed | Grade prediction via prior grades and text mining on course descriptions: course outlines and intended learning outcomes |
| title_short | Grade prediction via prior grades and text mining on course descriptions: course outlines and intended learning outcomes |
| title_sort | grade prediction via prior grades and text mining on course descriptions course outlines and intended learning outcomes |
| topic | Computer and Information Science Grade prediction Graph networks Course descriptions Semantic similarities Cognitive levels |
| url | https://hdl.handle.net/10356/175885 |
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