Using collaborative filtering to support college students' use of online forum for English learning

This study examined the impact of collaborative filtering (the so-called recommender) on college students' use of an online forum for English learning. The forum was created with an open-source software, Drupal, and its extended recommender module. This study was guided by three main questions: 1) Is there any difference in online behaviors between students who use a traditional forum and students who use a forum with a recommender?; 2) Is there any difference in learning motivation between students who use a traditional forum and students who use a forum with a recommender?; 3) Is there any difference in learning achievement between students who use a traditional forum and students who use a forum with a recommender?. This study was a one-way quasi-experimental design where the independent variable was the type of forum (two levels: traditional forum and forum with recommender). Students registering in four sessions of a college English course participated in the study and were randomly assigned into two groups. The total sample number was 144. The whole experiment lasted eight weeks. All students took a diagnostic test as a pre-test in Week One. From Week Two to Week Seven, students joined the class and wrote summaries, reflections and comments on the online forum. Students in different groups went on different forums. All students were asked to participate in a midterm exam in Week Four, and a final exam and online survey in Week Eight. Data collected in this study included pre-test scores, midterm exam scores (receptive and productive language test scores), final exam scores (receptive and productive language test scores), online survey (motivation and recommender perception), and Weblog data. The data were analyzed by using ANOVA procedure and Wilcoxon-Mann-Whitney U-test. The findings were as follows: 1) Students in the group with the forum recommender read online posts more frequently than the control group, and 2) students with the forum recommender outperformed their counterparts in their productive language test scores. However, there was no significant difference in learning motivation between the two groups. To enhance motivation for using the recommender, students offered their comments on how to revise the recommender, such as making the recommendation rating more personalized and explicit. This study is expected to provide empirical evidence to recommender research in education as well as broaden innovative insights into instructional recommender design.

[1]  L. Vygotsky Mind in Society: The Development of Higher Psychological Processes: Harvard University Press , 1978 .

[2]  Peter Brusilovsky,et al.  Social Navigation Support in a Course Recommendation System , 2006, AH.

[3]  Jon Dron,et al.  CoFIND - An experiment in N-dimensional collaborative filtering , 2000, J. Netw. Comput. Appl..

[4]  Sidney C. Sufrin,et al.  The Logic of Collective Action: Public Goods and the Theory of Groups. , 1966 .

[5]  Rob Nadolski,et al.  Effects of the ISIS Recommender System for Navigation Support in self-organised Learning Networks , 2009, J. Educ. Technol. Soc..

[6]  Sheizaf Rafaeli,et al.  QSIA - a Web-based environment for learning, assessing and knowledge sharing in communities , 2004, Comput. Educ..

[7]  Mimi Recker,et al.  What do you recommend? Implementation and analyses of collaborative information filtering of web resources for education , 2003 .

[8]  Mohamed Jemni,et al.  Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval , 2008, 2008 Eighth IEEE International Conference on Advanced Learning Technologies.

[9]  Rob Nadolski,et al.  Combining social-based and information-based approaches for personalised recommendation on sequencing learning activities , 2007, Int. J. Learn. Technol..

[10]  Georgia Koutrika,et al.  CourseRank: A Closed-Community Social System through the Magnifying Glass , 2009, ICWSM.

[11]  Umberto Straccia,et al.  User recommendation for collaborative and personalised digital archives , 2005, Int. J. Web Based Communities.

[12]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[13]  Ruimin Shen,et al.  Learning Content Recommendation Service Based-On Simple Sequencing Specification , 2004, ICWL.

[14]  Mimi Recker,et al.  Supporting ???Word-of-Mouth??? Social Networks Through Collaborative Information Filtering , 2003 .

[15]  Michael Hahsler,et al.  Educational and scientific recommender systems: Designing the information channels of the virtual university , 2001 .

[16]  L. Festinger A Theory of Social Comparison Processes , 1954 .

[17]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[18]  Hendrik Drachsler,et al.  Recommender Systems in Technology Enhanced Learning , 2011, Recommender Systems Handbook.

[19]  Harold Boley,et al.  RACOFI: A Rule-Applying Collaborative Filtering System , 2003 .