Publication2025
Machine Learning Based Student Performance Analysis Using XAI
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- National
- Journal
- Lecture Notes in Networks and Systems
- Volume
- 1241
- Pages
- 451-459
- Published
- 1 January 2025
- DOI
- 10.1007/978-981-96-1267-3_38
- Scopus ID
- 105002387726
- ISSN
- 23673370
- Authors
- 6
- Open access
- No
- Citations
- 1
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
4.3 Ensure equal access for all women and men to affordable quality technical, vocational and tertiary education, including university
Machine learning models predict academic achievement and identify at-risk students, enabling targeted educational interventions and improved outcomes.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Feature importance analysis and explainable AI methodology advance machine learning application to educational systems.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-1.1 Education and lifelong learning
Student performance prediction enables proactive educational support and personalized learning pathways.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Machine Learning Based Student Performance Analysis Using XAI”. Kortex.