Intervention and strategy analysis for web group-learning

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 a group-learning model to overcome this problem. Teachers first need to organise, manage and monitor the group learning and they must take appropriate actions based on teaching strategies to improve the learning achievements of the students. To perform these tasks effectively, teachers must obtain relevant information by analysing the huge volume of web-access logs or by monitoring web interactions. This paper presents novel methodologies for developing instruments to assist teachers in performing intervention and strategy analysis. The proposed methodologies apply data mining tools provided by existing database management systems. Database techniques, including the multi-dimensional 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 analysing their pedagogical strategies. These tools relieve teachers of tedious data collection and analysis, allowing them to focus on managing the groups to promote students' learning achievement.