Technology-enhanced learning


Technology-enhanced Learning

Modeling of learners, users and environment

My long-term research goal in the area of Technology-enhanced Learning is to improve stduents' or workers' learning through developing models of learners, learning content, and learning context and applying pedagogies in the form of reasoning on these models. Over the years, with my stduents, we focused on different aspects of this broad area. For example, using Self-Regulated Learning (SLR) paradigm, we studied how to support development of learning skill for students studying programming. We studied and proposed approaches for utilization of the social web to overcome shortcoming of the semantic web techniques. In turn, we studied how educators can utilize current semantic web tools to develop basic structures for envisioned semantic-web based learning environments.

Our research contributed to establishing an area of Learning Analytics (see separate page on this current research area). We developed a tool LOCO-Analyst that analyzes and visualizes the user activity data from learning environments to help educators to understand learner’s interactions and see their studying patterns in a context-specific manner. We extended LOCO-Analyst with student-generated collaborative tags to facilitate ontology maintenance used by the tool. This previous work culminated, through work of my PhD student Liaqat Ali, in proposing a Learning Analytics Acceptance Model (link to paper). We also developed and empirically validated coding schemes for quantitative content analysis of educators’ observations about learning analytics tools.

In period of 2007-2010 my PhD student Melody Siadaty participated in the EU-funded IntelLEO project (Intelligent Learning Extended Organisation), where we developed the social-analytics enabled tool Learn-B for supporting workplace learning. We have developed a theoretical framework based on Self-Regulated Learning (SRL) paradigm and studied how introducing social networking type of interventions motivates learners towards defining higher quality learning goals, increased participation, and improved SRL practices. We found recommendations about competences, learning paths through curricula and social streams of activities significantly enhanced SRL practices in the workplace. My current work in the area of Open Learner Modeling (separate page) builds on this research.

Open infrastructures for knowledge sharing

My work on infrastructures for sharing knowledge, learning objects in particular, started with two large scale Canarie Inc. funded projects developing the pan-Canadian learning object repository (LOR). In the POOL project (2002-2003) we have rejected the idea of centralized repository model and developed a peer-to-peer based infrastructure for connecting Learning Object Repositories. In the follow up project called eduSource (2003-2004) I led the development of Service-oriented Architecture for open learning repositories networks that supported interoperability across protocols and exchanged data. In the NSERC Research Network LORNET (2003-2008), we have continued our work on interoperability, with particular contributions in the area of metadata interoperability and vocabulary (ontologies) via ontology mappings.

In the context of learning networks we have studied the security mechanisms suitable for Service-oriented Architectures for supporting security and trust in open infrastructure scenarios. In the LionShare project (2003-2005) we have developed security layer for the web services that was compatible with the Shibboleth trust federation. Our work on security profiles (in collaboration with Internet 2 Middleware Group) contributed to the WS-Security standardization effort (especially SAML2.0). This work was disseminated in the infrastructure used by CampusCanada for secure exchange of student credentials between institutions.

After achieving interoperability at the secure communication level for web services we shifted our attention to the policy interoperability. We have dissected both description logic based and computational logic based policy languages to develop deep metamodels of policy languages. For the transformation of domain concepts (vocabularies) used by policies we use methods we developed for ontology mappings. This work led to our further work in the area of Software Engineering were we worked on integration of this level of (meta-) modeling across software engineering lifecycle and specifically for service oriented software product lines.