Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling

Guleria, Pratiyush; Sood, Manu

doi:10.1007/s10639-022-11221-2

Cited by 178Online Learning and AnalyticsExplainable Artificial Intelligence (XAI)AI and HR Technologies

Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling

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Guleria, Pratiyush, Sood, Manu·2023·Education and Information Technologies

DOI 10.1007/s10639-022-11221-2PMC PMC9287825

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Abstract

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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Integrating ML with XAI supports educational decision-making by providing transparent predictions for student career guidance.
“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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Authors: Guleria, P., & Sood, M.
Affiliations: Himachal Pradesh University (IN)
Corresponding author: Guleria, Pratiyush
ORCID: Guleria, Pratiyush
Sood, Manu
Venue: Education and Information Technologies
Publisher: Springer Science and Business Media LLC
Published: 2022-07-16
Volume: 28
Issue: 1
Pages: 1081-1116
ISSN: 1360-2357 (ISSN-L)
1573-7608
DOI: 10.1007/s10639-022-11221-2
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: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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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Explainable AI and machine learning: performance evaluation and explainability of classifiers on educational data mining inspired career counseling

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