SkillsRec: a novel semantic analysis driven learner skills Mmning and filtering approach for personal learning environments based on teacher guidance
Faculty / School
Faculty of Computer Sciences (FCS)
Department
Department of Computer Science
Was this content written or created while at IBA?
Yes
Document Type
Conference Paper
Publication Date
1-1-2015
Conference Name
2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops
Conference Location
Gwangju, Korea (South)
Conference Dates
24-27 March 2015
ISBN/ISSN
84947730426 (Scopus)
First Page
570
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Keywords
Guided PLEs Model, Latent Semantic Analysis, Learner skills, Personal Learning Environment, Teacher guidance
Abstract / Description
This paper presents SkillsRec- A novel teacher guidance based learner skills mining and filtering approach that identifies learner skills for Personal Learning Environment (PLE) based learning scenarios using Latent Semantic Analysis (LSA) technique. Skills Rec is developed on PLE design and development principles of the guided PLEs model [1]. Skills Rec takes teacher competencies/roles [2] and learner interests as input, melds them using LSA, and returns learner skills for the PLE-based learning as output. We compare learner-skill similarity scores of the Skills Rec with those generated through conventional Information Retrieval (IR) and Keywords Matching (KM) techniques. The aim is to report Skills Rec gains over conventional IR techniques. Based on Skills Rec results, this paper also provides top N=8 user-user recommendations most likely to be similar for a given active learner as a testing data.
DOI
https://doi.org/10.1109/WAINA.2015.112
Recommended Citation
Shaikh, Z. A., Gillet, D., & Khoja, S. A. (2015). SkillsRec: a novel semantic analysis driven learner skills Mmning and filtering approach for personal learning environments based on teacher guidance., 570. https://doi.org/10.1109/WAINA.2015.112
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