An interactive learning analytics tool to support higher education stakeholder to more explain and interpret predictive student’s failure or success

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Introduction State of Art Introduction Learning analytics LA life cycle Higher education stakeholder Stakeholders in education Data mining KDD process The functions of data mining Educational data mining The main Goals of educational data mining Data mining work methodology Machine learning (ML) Supervised learning Classification method Decision tree The goal of a decision tree Algorithms of decision tree Explainability and interpretability Explainability Interpretability The goals of interpretability Relation between explainability and interpretability Related work Main results of related work conclusion Design Introduction Decision tree setbacks Data collection Data Description Architectural design Conclusion Realization Environment and tools IDEs Language Technologies Libraries Library Desktop application Conclusion General conclusion Bibliographie

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📥 تحميل الملف

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