Collaborative activities recommendation based on students’ collaborative learning styles using ANN and WSM

Abstract

ABSTRACT Computer-Supported Collaborative Learning is a promising innovation that ameliorates tutoring through modern technologies. However, the way of recommending collaborative activities to learners, by taking into account their learning needs and preferences, is an important issue of increasing interest. In this context, this paper presents a framework for providing recommendations of collaborative activities to learners by employing an Artificial Neural Network (ANN) and the Weighted Sum Model (WSM). In our approach, the Gardner and Korth framework is employed to identify the dimensions of the learners’ collaborative learning styles. For each dimension, different weights are assigned and used as input data for the ANN. Then, the WSM and the activation function determine the output vector by mapping the resulting values. The final output of the ANN is a different percentage of collaborative activities in which learners can participate. This output is used to recommend the activities to the learners. As a testbed for our research, we have developed an intelligent tutoring system for the programming language “Java”. Our system was evaluated using an established framework and the statistical hypothesis test. The results show a high degree of pedagogical affordance and usefulness of our approach as well as its positive impact on learning.