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

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

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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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Research works printed but not linked: 11

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Metadata

Authors
Guleria, P., & Sood, M.
Affiliations
Himachal Pradesh University (IN)
Corresponding author
Guleria, Pratiyush
ORCID
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
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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