Adaptive e-learning interactions using dynamic clustering of learners' characteristics

The proliferation of Internet technologies has rendered education available to a vast majority of people, irrespective of their place, giving birth to e-learning. As such, learners, sharing different characteristics, have access to the learning material. In the light of recent developments, educational software should offer a student-centered learning experience. In view of the above, this paper presents artificial intelligence dynamic clustering of learners' characteristics for preserving the learning pace of each student. As a testbed of our research, we have designed and implemented an adaptive system for providing individualized tutoring of mathematics to elementary school students. Dynamic clustering takes as input several students' characteristics, namely pre-existing knowledge, current and previous knowledge level, etc., in order to construct homogeneous student clusters. Through dynamic clustering, the system provides individualized hints to students for improving knowledge acquisition, recommendation for group collaboration, domain knowledge delivery and trophies. The system was evaluated using an established framework and the results show that its incorporated intelligent techniques can offer individualized and adaptive learning while retaining a high level of pedagogical affordance.