Using a Learner-Topic Model for Mining Learner Interests in Open Learning Environments

The present study uses a text data mining approach to automatically discover learner interests in open learning environments. We propose a method to construct learner interests automatically from the combination of learner generated content and their dynamic interactions with other learning resources. We develop a learner-topic model to discover not only the learner’s knowledge interests (interest in generating content), but also the learner’s collection interests (interest in collecting content generated by others). Then we combine the extracted knowledge interests and collection interests to yield a set of interest words for each learner. Experiments using a dataset from the Learning Cell Knowledge Community demonstrate that this method is able to discover learners’ interests effectively. In addition, we find that knowledge interests and collection interests are related and consistent in their subject matter. We further show that learner interest words discovered by the learner-topic model method include learner self-defined interest tags, but reflect a broader range of interests.

[1]  Tsunenori Mine,et al.  Analysis of Students' Learning Activities through Quantifying Time-Series Comments , 2011, KES.

[2]  Gwo-Jen Hwang,et al.  A Data Mining Approach to Diagnosing Student Learning Problems in Sciences Courses , 2005, Int. J. Distance Educ. Technol..

[3]  Rong Gu,et al.  Interest mining in virtual learning environments , 2008, Online Inf. Rev..

[4]  Shengquan Yu,et al.  Designing a trust evaluation model for open-knowledge communities , 2014, Br. J. Educ. Technol..

[5]  Ana-Maria Popescu,et al.  A Machine Learning Approach to Twitter User Classification , 2011, ICWSM.

[6]  Jiebo Luo,et al.  A picture tells a thousand words - About you! User interest profiling from user generated visual content , 2015, Signal Process..

[7]  Takatoshi Ishii,et al.  A Topic Model for Clustering Learners Based on Contents in Educational Counseling , 2015, HCI.

[8]  Tsunenori Mine,et al.  Comment Data Mining to Estimate Student Performance Considering Consecutive Lessons , 2017, J. Educ. Technol. Soc..

[9]  Qing Yang,et al.  Discovering User Interest on Twitter with a Modified Author-Topic Model , 2011, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[10]  Shengquan Yu,et al.  Design of a Novel Intelligent Framework for Finding Experts and Learning Peers in Open Knowledge Communities , 2015, EAI Endorsed Trans. Future Intell. Educ. Environ..

[11]  Kazunori Yamaguchi,et al.  Development of a curriculum analysis tool , 2010, 2010 9th International Conference on Information Technology Based Higher Education and Training (ITHET).

[12]  Hui Xiong,et al.  Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Allan Wigfield,et al.  Students' achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes , 2010 .

[14]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[15]  Tsunenori Mine,et al.  Evaluation of Effectiveness of Time-Series Comments by Using Machine Learning Techniques , 2015, J. Inf. Process..

[16]  Yao Lu,et al.  User interest modeling and its application for question recommendation in user-interactive question answering systems , 2012, Inf. Process. Manag..

[17]  Zhe Zhao,et al.  Improving User Topic Interest Profiles by Behavior Factorization , 2015, WWW.

[18]  Toshiro Minami,et al.  Analysis towards to Know How the Students ’ Attitudes Affect to their Evaluations , 2013 .

[19]  Ming Li,et al.  A contextualized and personalized model to predict user interest using location-based social networks , 2016, Comput. Environ. Urban Syst..

[20]  Matthew Michelson,et al.  Tweet Disambiguate Entities Retrieve Folksonomy SubTree Step 1 : Discover Categories Generate Topic Profile from SubTrees Step 2 : Discover Profile Topic Profile : “ English Football ” “ World Cup ” , 2010 .

[21]  Debbie Richards,et al.  Realising the Potential of Web 2.0 for Collaborative Learning Using Affordances , 2011, J. Univers. Comput. Sci..

[22]  Toshiro Minami,et al.  How Student's Attitude Influences on Learning Achievement? --An Analysis of Attitude-Representing Words Appearing in Looking-Back Evaluation Texts-- , 2015 .

[23]  Wu He,et al.  Examining students' online interaction in a live video streaming environment using data mining and text mining , 2013, Comput. Hum. Behav..

[24]  Wu He,et al.  Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade , 2012, J. Educ. Technol. Soc..

[25]  Songjie Gong,et al.  Learning User Interest Model for Content-based Filtering in Personalized Recommendation System , 2012 .

[26]  Peter Brusilovsky,et al.  User Models for Adaptive Hypermedia and Adaptive Educational Systems , 2007, The Adaptive Web.

[27]  Minjuan Wang,et al.  From Learning Object to Learning Cell: A Resource Organization Model for Ubiquitous Learning , 2013, J. Educ. Technol. Soc..

[28]  Shourya Roy,et al.  Analytics for Noisy Unstructured Text Data I , 2009, Encyclopedia of Artificial Intelligence.

[29]  Agustín C. Caminero,et al.  Analyzing the students' behavior and relevant topics in virtual learning communities , 2014, Comput. Hum. Behav..