Instructional instruments for Web group learning systems

Abstract

Owing to the lack of face-to-face interactions, students using a web-based learning system are likely to study alone and with relatively little classmate support and pressure. Teachers in a web-based learning system may apply the group-learning model to overcome this problem. Therefore, teachers first need to organize, manage, and monitor the group learning. Additionally, they must take appropriate actions based on teaching strategies to improve the learning achievements of the students. To perform these tasks effectively, the teachers must obtain relevant information by searching and analyzing the huge amount of web-access logs or by monitoring web interactions. This will be burdensome and difficult to do well for the teachers. This work presents novel methodologies for developing instruments to assist teachers in performing grouping, intervention and strategy analysis. The proposed methodologies apply data mining tools provided by existing database management systems. A tool is initially R developed to assist in organizing learning groups according to teacher specifications. Database techniques, including multidimensional cube, are then applied to make student web logs meaningful and helpful to teachers in managing group learning. The associate rule mining tool is finally employed to assist teachers in analyzing their pedagogical strategies. These tools relieve the teacher of tedious data collection and analysis, and thus can focus on managing the groups to promote student learning achievement.