Elicitation of latent learning needs through learning goals recommendation

Highlights

• We designed a hybrid recommender system able to suggest learning goals to learners.

• We defined a methodology for providing recommendations.

• The learning goals are suggested considering learners’ knowledge.

• We integrated the recommender system into an existing learning platform (IWT).

• The integration allow us to improve the IWT adaptive learning courses generation.

Abstract

The aim of a recommender system is to estimate the relevance of a set of objects belonging to a given domain, starting from the information available about users and objects. Adaptive e-learning systems are able to automatically generate personalized learning experiences starting from a learner profile and a set of target learning goals. Starting form research results of these fields we defined a methodology and developed a software prototype able to recommend learning goals and to generate learning experiences for learners using an adaptive e-learning system. The prototype has been integrated within IWT: an existing commercial solution for personalized e-learning and experimented in a graduate computer science course.

Introduction

A significant educational action able to guide the learner in a comprehensive learning process is not only focused on learning (cognition level) but also on fostering a correct learning behavior that empowers learners to achieve their learning goals in a controlled and directed way (metacognition level) (Mangione, Gaeta, Orciuoli, & Salerno, 2010).

Starting from this principle we defined and developed an e-learning system able to build personalized learning experiences starting from a set of target concepts selected on an ontology-based domain model (Capuano, Gaeta, Miranda, Orciuoli, & Ritrovato, 2008). We then extended such system in order to allow course generation from an explicit request in terms of needs to be satisfied and expressed by the learner in natural language (CapuaCapuano, Gaeta, Orciuoli, & Ritrovato, 2009).

The work presented in this paper deals with the definition of a further process of course building starting from an implicit request rather than from an explicit one. In other words, a methodology to recommend learning goals based on the analysis of a learner’ profile (including known topics) and on the comparison of this profile with profiles of similar learners is defined.

The proposed methodology upholds the social presence (Acampora et al., 2010, Capuano et al., 2010), while supporting the development of self-regulated learning. Educational recommendations serves as a pedagogical advance organizer for the learners, as it anticipates and spreads needs, knowledge and learning paths. Furthermore the proposed solution also supports help seeking processes improving the students’ control over learning. This makes the solution adequate nononly for educational settings but also for enterprise training (Capuano, Gaeta, Ritrovato, & Salerno, 2008).

The paper is organized in this way: Section 2 introduces some background about recommender systems; Section 3 briefly introduces the starting point of our research and then describes the proposed methodology; Section 4 introduces the developed prototype and presents some example of use; Section 5 compares our approach with some existing recommender systems for e-learning; eventually Section 6 presents conclusions and planned future work.