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 language | English |
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Pages (from-to) | 14-28 |
Number of pages | 15 |
Journal | Neurocomputing |
Volume | 192 |
DOIs | |
Publication status | Published - 5 Jun 2016 |
Funding
This work was supported in part by the Erasmus Mundus Joint Doctorate in Interactive and Cognitive Environments, which is funded by the EACEA Agency of the European Commission under EMJD ICE FPA n 2010-0012 . Also, we thank the Professors Giuliano Donzellini, Carla Gambaro, Franco Parodi, Domenico Ponta, Marco Storace, and Eugenia Torello of the University of Genoa who provided their time for running the experiments during their classes, and Remi Brochenin, Emanuele Fumeo, Alessandro Ghio, Ilenia Orlandi, and Jorge Luis Reyes Ortiz for providing their support to our experiment. Finally we thank the authors of [57] and [78] for providing their datasets.
Keywords
- Algorithmic stability
- Exploratory experiments on students
- Human learning
- Machine learning
- Rademacher Complexity