SkillsRec: A Novel Semantic Analysis Driven Learner Skills Mining and Filtering Approach for Personal Learning Environments based on Teacher Guidance

SkillsRec: A Novel Semantic Analysis Driven Learner Skills
Mining and Filtering Approach for Personal Learning
Environments based on Teacher Guidance
Authors: Zaffar Ahmed Shaikh, Denis Gillet, Shakeel Ahmed Khoja
Presenter: Zaffar Ahmed Shaikh

Agenda
• The Problem
• Our Solution
• Abstract
• Introduction
• Related Work
• Results
• Conclusions
Zaffar Ahmed Shaikh – EPFL /IBA Karachi - zaffarahmedshaikh@gmail.com –AINA 2015 (MAW’15) – March 25 2015 2Friday, March 27, 2015

Teacher guidance
Problem Solution

Abstract
• SkillsRec is a novel teacher guidance based learner skills mining and
filtering approach that identifies learner skills for PLE based learning
scenarios using Latent Semantic Analysis (LSA) technique.
• SkillsRec is developed on PLE design and development principles of
the guided PLEs model [1].
• This paper compares learner-skill similarity scores generated through
the SkillsRec with those generated through conventional IR and KM
techniques.
• We provide top N=8 user-user recommendations most likely to be
similar for a given active learner.

Introduction / PLE
• Online PLE is a modern day personalized learning based
teaching/learning environment.
• PLE can be defined as “a highly flexible ‘one-size-fits-all’ solution to
online learning that provides personalized, collaborative, inquiry-based
and guided learning experiences to Internet users [4]”.
• PLE takes care of learner personality, mood, interests, and needs during
her interaction with online environments [3].
• In addition, PLE concept addresses information overload problem
through recommender technology [5,6].

Introduction / gPLEs model
• The gPLEs model incorporates teacher-based guidance mechanism into
the PLE concept through learner skills mining and filtering based
recommendation mechanism [1].
• It identifies/develops learner skills through semantically analyzing teacher
competencies [2] and learner interests.
• Using those skills of a learner, it provides her with skill-similarity based
user-user recommendations.
• The model has been / can be implemented in online PLE(s).

Introduction / LSA
• LSA is a model-based natural language processing and data/document
retrieval technique that improves retrieval process through developing
measures of semantic similarities between user and text [7].
• LSA performs various statistical computations to search items that
match with user query based on contextual usage meaning of words in
user query and item descriptions [7,8].

Introduction / SkillsRec
• SkillsRec is a model-based learner skills identification and assessment technique for CF based
recommendation systems. It works on descriptive/unstructured data.
Mines user data to
identify user skills
Filters user skills matching
with teacher roles
Generates user-user
recommendations in ranked
order

Related Work
• Model-based techniques (Bayesian models or LSA) have been used
before in modeling user profile and her context [10,11,12].
• In existing literature there is no evidence about finding learner skills
through analyzing learner interests against teacher roles.
• There is also a lack of information in literature about exploiting learner
interests-related data for generating similarity recommendations.
• Hence, the main mission of this work was to develop a CF based
recommendations system which employs natural language processing
tool (LSA) to identify learner skills which are based on teacher guidance.

Data Organization

Data Analysis

Results

Conclusions
• We have presented here the SkillsRec–a novel semantic analysis based
recommender model for PLEs.
• This provides the solution to how to overcome the massive, exponentially
increasing [9], information overload problem.
• SkillsRec provided results have been compared with conventional IR and KM
based similarity techniques.
• It can be concluded from the presented details and results that semantic
analysis based data mining and filtering approaches provide promising
results; thus, they need to be further explored and tested.

References
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Australasian J. Educ. Tech. 30, 202-226 (2014).
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and Distance Learning, Riyadh, Saudi Arabia (2013).
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Conference on Information Technology Based Higher Education and Training, pp. 15. IEEE Press, New York
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Psychology Press, (2013).
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support using semantic similarity. Educational Technology & Society, 17 (3), p. 54-64 (2014).
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