Can machine learning explain human learning?

M. Vahdat, L. Oneto, D. Anguita, M. Funk, G.W.M. Rauterberg

    Research output: Contribution to journalArticleAcademicpeer-review

    12 Citations (Scopus)
    7 Downloads (Pure)

    Abstract

    Learning Analytics (LA) has a major interest in exploring and understanding the learning process of humans and, for this purpose, benefits from both Cognitive Science, which studies how humans learn, and Machine Learning, which studies how algorithms learn from data. Usually, Machine Learning is exploited as a tool for analyzing data coming from experimental studies, but it has been recently applied to humans as if they were algorithms that learn from data. One example is the application of Rademacher Complexity, which measures the capacity of a learning machine, to human learning, which led to the formulation of Human Rademacher Complexity (HRC). In this line of research, we propose here a more powerful measure of complexity, the Human Algorithmic Stability (HAS), as a tool to better understand the learning process of humans. The experimental results from three different empirical studies, on more than 600 engineering students from the University of Genoa, showed that HAS (i) can be measured without the assumptions required by HRC, (ii) depends not only on the knowledge domain, as HRC, but also on the complexity of the problem, and (iii) can be exploited for better understanding of the human learning process.

    Original languageEnglish
    Pages (from-to)14-28
    Number of pages15
    JournalNeurocomputing
    Volume192
    DOIs
    Publication statusPublished - 5 Jun 2016

    Keywords

    • Algorithmic stability
    • Exploratory experiments on students
    • Human learning
    • Machine learning
    • Rademacher Complexity

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