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 - [email protected] –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).


http://ascilite.org.au/ajet/submission/index.php/AJET/article/view/324/936

http://zaffarahmedshaikh.com/

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.


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[2] Shaikh, Z.A., Khoja, S.A.: Personal Learning Environments and University Teacher Roles Explored using Delphi. Australasian J. Educ. Tech. 30, 202-226 (2014).

[3] Moore, M.G.: Transforming e-learning. Keynote Address to The 3rd International Conference on e-Learning and Distance Learning, Riyadh, Saudi Arabia (2013).

[4] Gillet, D.: Personal Learning Environments as Enablers for Connectivist MOOCs. In: IEEE International Conference on Information Technology Based Higher Education and Training, pp. 15. IEEE Press, New York (2013).

[5] El Helou, S., Gillet, D., Salzmann, C.: The 3A Ranking System: Contextual, Personalized & Simultaneous Recommendation of Actors, Activities & Assets. J. Universal Comput. Sci., Special Issue on Context-Aware Recommendations, (2010).

[6] Bogdanov, E., Ullrich, C., Isaksson, E., Palmér, M., Gillet, D.: Towards PLEs through Widget Spaces in Moodle. Comput. Sci. Inf. Syst. 11, 443-460 (2014).


[7] Landauer, T.K., McNamara, D.S., Dennis, S., Kintsch, W.: Handbook of Latent Semantic Analysis, Psychology Press, (2013).

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[10] Verbert, K., Duval, E., Lindstaedt, S.N., Gillet, D.: Context aware Recommender Systems. J. Uni. Comp. Sci. 16, 2175-2178, (2010).

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