Limits...
Study Partners Recommendation for xMOOCs learners.

Xu B, Yang D - Comput Intell Neurosci (2015)

Bottom Line: Then we proposed a topic model to measure learners' course knowledge awareness.Finally, a social network was constructed based on their activities in the course forum, and the relationship in the network was then employed to recommend study partners for target learner combined with their behavior features and course knowledge awareness.The experiment results show that our method achieves better performance than recommending method only based on content information.

View Article: PubMed Central - PubMed

Affiliation: Computing Center, Northeastern University, Shenyang 110819, China.

ABSTRACT
Massive open online courses (MOOCs) provide an opportunity for people to access free courses offered by top universities in the world and therefore attracted great attention and engagement from college teachers and students. However, with contrast to large scale enrollment, the completion rate of these courses is really low. One of the reasons for students to quit learning process is problems which they face that could not be solved by discussing them with classmates. In order to keep them staying in the course, thereby further improving the completion rate, we address the task of study partner recommendation for students based on both content information and social network information. By analyzing the content of messages posted by learners in course discussion forum, we investigated the learners' behavior features to classify the learners into three groups. Then we proposed a topic model to measure learners' course knowledge awareness. Finally, a social network was constructed based on their activities in the course forum, and the relationship in the network was then employed to recommend study partners for target learner combined with their behavior features and course knowledge awareness. The experiment results show that our method achieves better performance than recommending method only based on content information.

Show MeSH
Classical LDA topic model and proposed topic model for recommendation study buddies.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4309214&req=5

fig4: Classical LDA topic model and proposed topic model for recommendation study buddies.

Mentions: latent Dirichlet allocation (LDA) is a widely used generative model to infer the latent topic distribution in a large corpus. All documents are represented as a “document-word” matrix and then are clustered into several different topics, and the distribution of each document over topics and the distribution of each topic over words are calculated finally. Figure 4(a) is a graphical representation of LDA model.


Study Partners Recommendation for xMOOCs learners.

Xu B, Yang D - Comput Intell Neurosci (2015)

Classical LDA topic model and proposed topic model for recommendation study buddies.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4309214&req=5

fig4: Classical LDA topic model and proposed topic model for recommendation study buddies.
Mentions: latent Dirichlet allocation (LDA) is a widely used generative model to infer the latent topic distribution in a large corpus. All documents are represented as a “document-word” matrix and then are clustered into several different topics, and the distribution of each document over topics and the distribution of each topic over words are calculated finally. Figure 4(a) is a graphical representation of LDA model.

Bottom Line: Then we proposed a topic model to measure learners' course knowledge awareness.Finally, a social network was constructed based on their activities in the course forum, and the relationship in the network was then employed to recommend study partners for target learner combined with their behavior features and course knowledge awareness.The experiment results show that our method achieves better performance than recommending method only based on content information.

View Article: PubMed Central - PubMed

Affiliation: Computing Center, Northeastern University, Shenyang 110819, China.

ABSTRACT
Massive open online courses (MOOCs) provide an opportunity for people to access free courses offered by top universities in the world and therefore attracted great attention and engagement from college teachers and students. However, with contrast to large scale enrollment, the completion rate of these courses is really low. One of the reasons for students to quit learning process is problems which they face that could not be solved by discussing them with classmates. In order to keep them staying in the course, thereby further improving the completion rate, we address the task of study partner recommendation for students based on both content information and social network information. By analyzing the content of messages posted by learners in course discussion forum, we investigated the learners' behavior features to classify the learners into three groups. Then we proposed a topic model to measure learners' course knowledge awareness. Finally, a social network was constructed based on their activities in the course forum, and the relationship in the network was then employed to recommend study partners for target learner combined with their behavior features and course knowledge awareness. The experiment results show that our method achieves better performance than recommending method only based on content information.

Show MeSH