Alma: An adaptive learning models environment from texts and activities that improves students’ science comprehension

Abstract

This paper presents the Adaptive Learning Models from texts and Activities (ALMA) environment and the interaction of undergraduate students in Computer Science with it, while studying the learning goal “Computer Networks’ Principles”. The learning design of ALMA is based on Kintsch's Construction-Integration model for text comprehension and takes into account the learner's background knowledge in order to propose a text of appropriate cohesion from four versions of a text, orthogonally varying local and global cohesion. Learners’ comprehension is also supported and assessed through a series of activities such as a free text recall measure, case studies, activities of active experimentation, text-based, bridging-inference, elaborative-inference and problem-solving questions and a sorting task. Moreover, ALMA proposes a learning sequence according to students’ learning style; however students can set their own. Students interacted with the system and expressed their opinion about the course designed via ALMA which was very satisfactory.