An Instructional and Collaborative Learning System with Content Recommendation

Introduction

With the rapid development of Internet in various application domains, Internet has changed people’s life-style like shopping, reading, learning, entertainment and etc. Among the above, e-learning has also become more and more realistic and popular (Jin, 2009). E-learning provides facilities to people in different times and geographical locations to collaborate, learn and work together by using various educational services and saves time for instructors and students (Dillenbourg, Jarvela, & Fischer, 2009).

Although e-learning has numerous advantages, instructors and educational researchers are experiencing both unprecedented opportunities and challenges to adapt information age. By now, lots of e-learning applications that have been deployed are just electronic learning materials with very limited interactivity and diagnostic capability (Romoero & Ventura, 2007; Romoero & Ventura, 2010; Shishehchi, Banihashem, & Zin, 2010). Few of them were employed in daily instruction and learning processes among learners and instructors (Kumar & Chadha, 2011). Therefore, integrated environments for online instruction, collaborative learning, evaluation and analysis, and content recommendation are solicited and will be very beneficial to learners and instructors. Our study was motivated by the above facts and we developed an instructional and Collaborative Learning System with content Recommendation named iCLSR to promote e-learning.

This paper details iCLSR as follows. Firstly, the teaching and learning environment is presented for instruction and collaborative learning provided by iCLSR, where instructors can give online speech and upload some video clips of some courses and then students can download them at anytime and anywhere. Furthermore, students can join or create interest groups after classes where group members can freely discuss questions and exchange ideas with or without instructors online. Secondly, online evaluation on instructors and learners can be conducted by iCLSR. Instructors and learners can give their marks based on many evaluation indices. The collected data will be clustered based on an improved PSK-means clustering algorithm so as to analyze the teaching and learning results. Lastly, content recommendation lists will be generated based on evaluation results and their learning history for learners so as to improve their learning processes and styles.

The remainder of this paper is organized as follows. The second section reviews the related work on recent research on instructional, collaborative learning and recommendation systems. The third section details an Internet-based instruction and collaborative system and the fourth section presents the online evaluation and analysis based on PSK-means clustering. The fifth section describes learning content recommendation based on evaluation data and student model. The sixth section introduces system development of iCLSR and the seventh section concludes the paper and points out the future work.