PhD Dissertation Defense of CS Scholar, Zaffar Ahmed Shaikh.

Title: Guided Personal Learning Environment Model: Concept, Theory, and Practice
Date: Monday, August 28, 2017
Time: 10:30 a.m.
Venue: Room MAS-1 (Seminar Room), Adamjee Building, IBA Main Campus, Karachi, Pakistan
Advisor: Dr. Shakeel Ahmed Khoja


The inherent complexity of online learning environments and the abundance of resources in these environments cause present-day learners information-overload, knowledge-deficit, and personalisation-related challenges. The concept of online Personal Learning Environment or PLE tackles these issues with its integral user-centred design, recommendation, networking, and persistent online presence features. This thesis presents Guided PLE Model, a teacher competencies-based PLE design and development framework, which contributes to the recommendation feature of the PLE concept. Thus, to overcome the challenges that are brought about by information-overload, knowledge deficit and personalisation-related issues, the Guided PLE Model provides present-day learners with semantic-rich user-user recommendations of people who match them on the basis of their skills. The thesis uses design science research methodology. Thus, to this end, the design science research based theoretical, as well as empirical grounding processes, are applied through conducting critical analysis, structured communication, theory analysis, and user experiment. Therefore, as the proof of concept of the method, the Guided PLE Model contributes teacher competencies based and Latent Semantic Analysis driven recommender system called SkillsRec recommender (Skills based Recommender), as well as a server-side web-browser services’ design-based PLE application called GuidedLearn PLE application. The SkillsRec recommender generates user-user recommendations in ranked order that it develops from the Semantic Analysis of teacher competencies for PLE- xv based pedagogy and learner interests. The GuidedLearn PLE application, a front-end application platform of the SkillsRec recommender, is a prototype PLE application that follows bottom-up social media, systematic, and Human-computer Interaction design principles of the online learning environment designs. The applicability and the effectiveness of the Guided PLE Model have been demonstrated in this thesis through a four experiment-based user study of the SkillsRec recommender, which was conducted on 31 real profiles of users over the GuidedLearn PLE application. Thus, SkillsRec recommender was compared with the ConIR recommender (Conventional Information Retrieval based recommender). The context of the four experiments was strongly tied to the classroom-based collaborative work and learning. This thesis discusses the results of the user evaluation study of two recommenders and provides study conclusions and implications. Thus, for target learner, the significant differences in user-skill similarity scores and in the number of skills returned by the two recommenders (i.e., 28 versus 19) at as high cut-off as 0.9 confirmed that the SkillsRec recommender can perform better than the ConIR recommender. The moderate values of precision and recall tests against the four experiments suggested that the SkillsRec recommender can yield more relevant user-skill similarities and user-user recommendations. Through generating recommendations that are developed from users’ profile statements and not from users’ interaction with the system or search history data, the SkillsRec recommender provides a simplistic solution to the Cold-start problem, the inherent problem of Collaborative Filtering based recommender systems. Thus, considering as base case the unique skill-similarity based user-user recommendation approach of the SkillsRec recommender that is developed on top of the xvi Guided PLE Model, it can be concluded that the Collaborative Filtering recommendation approaches in particular and the learning systems which rely on learners’ interests and skills will benefit greatly.