A Recommendation System based on Knowledge Gap Identification in MOOCs Ecosystems

In recent years, students face difficulties in choosing the best content from the online distance learning of MOOCs (Massive Open Online Courses). The emerged recommendations systems to solve this problem do not identify the student's prior knowledge broadly. From this problem, the main contribution of this work is the identification and reduction of the students' knowledge gap in MOOCs. As such, in this Master's thesis, we model and analyze the MOOCs ecosystems and propose a solution for recommending parts of courses. Based on a set of three experiments, we verify that our recommendations are accurate, useful and reliable. We also present new content to fill the knowledge gap of users as the main contribution of this work to the state of the art.

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Knowledge Gap Exists on Best Practices for PTSD

Clinical Psychiatry News ◽

10.1016/s0270-6644(08)70800-2 ◽

2008 ◽

Vol 36 (12) ◽

pp. 10

Author(s):

PATRICE WENDLING

Keyword(s):

Best Practices ◽

Knowledge Gap

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Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207330 ◽

2020 ◽

pp. 120-127

Author(s):

Htay Htay Win ◽

Aye Thida Myint ◽

Mi Cho Cho

Keyword(s):

Social Network ◽

Dimensionality Reduction ◽

Correspondence Analysis ◽

Recommendation System ◽

Research Paper ◽

Topic Modelling ◽

Research Papers ◽

The Right ◽

Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

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