This study’s main aim was to focus on the capabilities and potential of educational data mining to sustain both teaching on the faculty side and learning from the students’ perspective. This work addressed the performance achieved by more than 900 students attending the master's degree in engineering and management at Politecnico di Torino. More specifically, the investigation converged on the ‘information systems’ course. The most relevant suggestions to drive future work include the following: • Collect and produce additional data regarding students to extend the attributes set and to bring new knowledge to increase the dataset’s size; interesting attributes concern class attendance, knowledge, skills, and competence obtained by students in their training path; • Pay attention to data quality to exploit the full potential of mining approaches pursuing high-quality data shaped at different granularities; • Make data analysis systemic within high school and higher education, addressing both the institution as a whole and single courses to implement continuous improvement.

Adaptive learning profiles in the education domain / Demartini, CLAUDIO GIOVANNI; Bosso, Andrea; Ciccarelli, Giacomo; Benussi, Lorenzo; Renga, Flavio - In: Artificial Intelligence in STEM Education - The Paradigmatic Shifts in Research, Education, and Technology / Ouyang F., Jiao P., McLaren B. M., Alavi A.H.. - STAMPA. - Boca Raton, Florida : CRC Press, 2023. - ISBN 9781032009216. - pp. 127-148

Adaptive learning profiles in the education domain

Claudio Giovanni Demartini;Andrea Bosso;Giacomo Ciccarelli;Lorenzo Benussi;Flavio Renga
2023

Abstract

This study’s main aim was to focus on the capabilities and potential of educational data mining to sustain both teaching on the faculty side and learning from the students’ perspective. This work addressed the performance achieved by more than 900 students attending the master's degree in engineering and management at Politecnico di Torino. More specifically, the investigation converged on the ‘information systems’ course. The most relevant suggestions to drive future work include the following: • Collect and produce additional data regarding students to extend the attributes set and to bring new knowledge to increase the dataset’s size; interesting attributes concern class attendance, knowledge, skills, and competence obtained by students in their training path; • Pay attention to data quality to exploit the full potential of mining approaches pursuing high-quality data shaped at different granularities; • Make data analysis systemic within high school and higher education, addressing both the institution as a whole and single courses to implement continuous improvement.
9781032009216
Artificial Intelligence in STEM Education - The Paradigmatic Shifts in Research, Education, and Technology
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2972535