Over the years, several studies have shown the relevance of one-to-one compared to one-to-many tutoring, shedding light on the need for technology-based platforms to assist traditional learning methodologies. Therefore, in recent years, tutoring systems that collect and analyse responses during the user interaction for an automated assessment and profiling were developed as a new standard to improve the learning out- come. In this framework, the tutoring system Adaptive LEArning system for Statistics (ALEAS) is aimed at providing an adaptive assessment of undergraduate students’ statistical abilities enrolled in social and human sciences courses. ALEAS is developed in the contest of the ERAS- MUS+ Project (KA+ 2018-1-IT02-KA203-048519). The article describes the ALEAS workflow; in particular, it focuses on the students’ categorisation according to their abilities. The student follows a learning process defined according to the Knowledge Space Theory, and she/he is classified at the end of each learning unit. The proposed classification method is based on the multidimensional latent class item response theory, where the dimensions are defined according to the Dublin learning dimensions. In this work, results from a simulation study support our approach’s effectiveness and encourage its future use with students.

ALEAS: a tutoring system for teaching and assessing statistical knowledge

Cristina Davino;Daniela Pacella;Domenico Vistocco
;
Francesco Palumbo
2020

Abstract

Over the years, several studies have shown the relevance of one-to-one compared to one-to-many tutoring, shedding light on the need for technology-based platforms to assist traditional learning methodologies. Therefore, in recent years, tutoring systems that collect and analyse responses during the user interaction for an automated assessment and profiling were developed as a new standard to improve the learning out- come. In this framework, the tutoring system Adaptive LEArning system for Statistics (ALEAS) is aimed at providing an adaptive assessment of undergraduate students’ statistical abilities enrolled in social and human sciences courses. ALEAS is developed in the contest of the ERAS- MUS+ Project (KA+ 2018-1-IT02-KA203-048519). The article describes the ALEAS workflow; in particular, it focuses on the students’ categorisation according to their abilities. The student follows a learning process defined according to the Knowledge Space Theory, and she/he is classified at the end of each learning unit. The proposed classification method is based on the multidimensional latent class item response theory, where the dimensions are defined according to the Dublin learning dimensions. In this work, results from a simulation study support our approach’s effectiveness and encourage its future use with students.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/822539
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