Explainable AI and Machine Learning: Performance Evaluation and Explainability of Classifiers on Educational Data Mining Inspired Career Counseling

Autor/inn/en	Guleria, Pratiyush; Sood, Manu
Titel	Explainable AI and Machine Learning: Performance Evaluation and Explainability of Classifiers on Educational Data Mining Inspired Career Counseling
Quelle	In: Education and Information Technologies, 28 (2023) 1, S.1081-1116 (36 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Guleria, Pratiyush)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-022-11221-2
Schlagwörter	Artificial Intelligence; Career Counseling; Data Analysis; Employment Potential; Job Placement; Job Skills; Models; Scores; Bayesian Statistics; Prediction; Regression (Statistics) + Suchen Sie Ihr Suchwort? Künstliche Intelligenz; Auswertung; Arbeitsmarktbezogene Qualifikation; Beschäftigungsfähigkeit; Employment service; Employment services; Arbeitsvermittlung; Produktive Fertigkeit; Analogiemodell; Vorhersage; Regression; Regressionsanalyse
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01