Title Analysis of student achievement scores via cluster analysis
Authors Chaves V.E.J. , García-Torres M. , BECERRA ALONSO, DAVID, Gómez-Vela F. , Divina F. , Vázquez-Noguera J.L.
External publication No
Means Adv. Intell. Sys. Comput.
Scope Conference Paper
Nature Científica
SJR Quartile 4
SJR Impact 0.21500
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090038293&doi=10.1007%2f978-3-030-57799-5_41&partnerID=40&md5=34b6b890cafe2eced21945e031d43099
Publication date 01/01/2021
Scopus Id 2-s2.0-85090038293
DOI 10.1007/978-3-030-57799-5_41
Abstract In education, the overall performance of every student is an important issue when assessing the quality of teaching. However, in the traditional educational system not all students have the same opportunity to develop their academic skills in an efficient way. Different teaching techniques have been proposed to adapt the learning process to the student profile. In this work, we analyze the profile of students according to their performance on academic activities and taking into account two different evaluation systems: work-based assessment and knowledge-based assessment. To this aim, data was collected during the fall semester of 2019 from a physics course at Universidad Loyola Andalucía, in Seville, Spain. In order to study the student profiles, a clustering approach combined with supervised feature selection was applied. Results suggest that two student profiles are clearly distinguished according to their performance in the course in both evaluation approaches. These two profiles correspond to students that pass and fail the course. The output of the analysis also indicates that there are redundant and/or irrelevant features. Therefore, machine learning techniques may be helpful for the design of effective activities to enhance the student learning process in this physics course. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.
Keywords Cluster analysis; Curricula; Knowledge based systems; Learning systems; Teaching; Academic activities; Clustering approach; Educational systems; Evaluation approach; Machine learning techniques; Quali
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