The DOI matches its Crossref record; 73 of the 77 references it cites were found in a registry or an index.
Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling
Open accessAbstract
Machine Learning concept learns from experiences, inferences and conceives complex queries. Machine learning techniques can be used to develop the educational framework which understands the inputs from students, parents and with intelligence generates the result. The framework integrates the features of Machine Learning (ML), Explainable AI (XAI) to analyze the educational factors which are helpful to students in achieving career placements and help students to opt for the right decision for their career growth. It is supposed to work like an expert system with decision support to figure out the problems, the way humans solve the problems by understanding, analyzing, and remembering. In this paper, the authors have proposed a framework for career counseling of students using ML and AI techniques. ML-based White and Black Box models analyze the educational dataset comprising of academic and employability attributes that are important for the job placements and skilling of the students. In the proposed framework, White Box and Black Box models get trained over an educational dataset taken in the study. The Recall and F-Measure score achieved by the Naive Bayes for performing predictions is 91.2% and 90.7% that is best compared to the score of Logistic Regression, Decision Tree, SVM, KNN, and Ensemble models taken in the study.
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“Guleria and Sood [20] showed that integrating ML with XAI supports educational decision-making by providing transparent predictions for student career guidance.”
From Multinational machine learning investigation of whether generative AI interactions augment or substitute cognitive skills and study efficiency in global higher education (2026) · cites this paper as Guleria and Sood [20]
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Cited by
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Dimitriadou, E., & Lanitis, A. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00231-3 · Cited by 370
XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques
Guleria, P., Srinivasu, P. N., Ahmed, S., Almusallam, N., & Alarfaj, F. K. (2022) · Electronics · doi:10.3390/electronics11244086 · Cited by 157
Self-citationExplainable Artificial Intelligence-Based Decision Support Systems: A Recent Review
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Metadata
- Authors
- Guleria, P., & Sood, M.
- Affiliations
- Himachal Pradesh University (IN)
- Corresponding author
- Guleria, Pratiyush
- Venue
- Education and Information Technologies
- Published
- 2022-07-16
- Volume
- 28
- Issue
- 1
- Pages
- 1081-1116
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- PMC
- PMC9287825
- OpenAlex
- W4285586905
- Cited by
- 178
- References
- 77
- Citations by year
- 2026: 43 · 2025: 63 · 2024: 54 · 2023: 17 · 2022: 1
- FWCI
- 26.05
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Education and Information Technologies (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- explainable AI, career guidance, educational data mining, classifier evaluation, decision support system, naive Bayes
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