A latent class approach for advising in learning statistics: Implementation in the ALEAS system

Over the years, several instructional methods have been employed to support students in learning Statistics. Among them, intelligent tutoring systems deserve particular interest, personalizing the learning activities according to individual characteristics. Generally, students enrolled in human and social sciences degrees exhibit a high level of Statistical anxiety and severe math shortcomings that negatively affect their success in Statistics. In this framework, the present paper reports the description of ALEAS (Adaptive LEArning in Statistics) ERASMUS+ Project, which is aimed to implement an adaptive system for advising these students in learning Statistics. The ALEAS system includes a knowledge structure for the introductory statistics course, accounting for three of the five Dublin descriptors (i.e. knowledge, application, judgement). Dublin descriptors refer to the dimensions defining students’ ability, they allow to detect homogeneous sub-populations of students and to select the most appropriate learning path accordingly. Students’ learning process is also supported by learning materials, cartoons, and formative and motivational feedback. Some examples of how students can interact with the ALEAS system are illustrated in the last section of the paper.