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Permanent URI for this collectionhttps://repositorio.grial.eu/handle/123456789/34

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Author: search.filters.author.Cruz-Benito, J.Subject: search.filters.subject.Artificial Intelligence

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    Assessed by Machines: Development of a TAM-Based Tool to Measure AI-based Assessment Acceptance Among Students
    (2020-12-05) Sánchez-Prieto, J. C.; Cruz-Benito, J.; Therón, R.; García-Peñalvo, F. J.
    In recent years, the use of more and more technology in education has been a trend. The shift of traditional learning procedures into more online and tech-ish approaches has contributed to a context that can favor integrating Artificial-Intelligence-based or algorithm-based assessment of learning. Even more, with the current acceleration because of the COVID-19 pandemic, more and more learning processes are becoming online and are incorporating technologies related to automatize assessment or help instructors in the process. While we are in an initial stage of that integration, it is the moment to reflect on the students' perceptions of being assessed by a non-conscious software entity like a machine learning model or any other artificial intelligence application. As a result of the paper, we present a TAM-based model and a ready-to-use instrument based on five aspects concerning understanding technology adoption like the AI-based assessment on education. These aspects are perceived usefulness, perceived ease of use, attitude towards use, behavioral intention, and actual use. The paper's outcomes can be relevant to the research community since there is a lack of this kind of proposal in the literature.
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    Proposing a machine learning approach to analyze and predict employment and its factors
    (2018-08-28) García-Peñalvo, F. J.; Cruz-Benito, J.; Martín-González, M.; Vázquez-Ingelmo, A.; Sánchez-Prieto, J. C.; Therón
    This paper presents an original study with the aim of propose and test a machine learning approach to research about employability and employment. To understand how the graduates get employed, researchers propose to build predictive models using machine learning algorithms, extracting after that the most relevant factors that describe the model and employing further analysis techniques like clustering to get deeper insights. To test the proposal, is presented a case study that involves data from the Spanish Observatory for Employability and Employment (OEEU). Using data from this project (information about 3000 students), has been built predictive models that define how these students get a job after finalizing their degrees. The results obtained in this case study are very promising, and encourage authors to refine the process and validate it in further research.