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 of Computer Science

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Document Type

Conference Paper

Publication Date


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


84947730426 (Scopus)

First Page



Institute of Electrical and Electronics Engineers (IEEE)

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.