Dragan Gasevic

The Beginning of a Beautiful Friendship? Intelligent Tutoring Systems and MOOCs

A key challenge in ITS research and development is to support tutoring at scale, for example by embedding tutors in MOOCs. An obstacle to at-scale deployment is that ITS architectures tend to be complex, not easily deployed in browsers without significant server-side processing, and not easily embedded in a learning management system (LMS). We present a case study in which a widely used ITS authoring tool suite, CTAT/TutorShop, was modified so that tutors can be embedded in MOOCs. Specifically, the inner loop (the example-tracing tutor engine) was moved to the client by reimplementing it in JavaScript, and the tutors were made compatible with the LTI e-learning standard. The feasibility of this general approach to ITS/MOOC integration was demonstrated with simple tutors in an edX MOOC “Data Analytics and Learning.”

Current state and future trends: a citation network analysis of the learning analytics field

This paper provides an evaluation of the current state of the field of learning analytics through analysis of articles and citations occurring in the LAK conferences and identified special issue journals. The emerging field of learning analytics is at the intersection of numerous academic disciplines, and therefore draws on a diversity of methodologies, theories and underpinning scientific assumptions. Through citation analysis and structured mapping we aimed to identify the emergence of trends and disciplinary hierarchies that are influencing the development of the field to date. The results suggest that there is some fragmentation in the major disciplines (computer science and education) regarding conference and journal representation. The analyses also indicate that the commonly cited papers are of a more conceptual nature than empirical research reflecting the need for authors to define the learning analytics space. An evaluation of the current state of learning analytics provides numerous benefits for the development of the field, such as a guide for under-represented areas of research and to identify the disciplines that may require more strategic and targeted support and funding opportunities.

Model Driven Engineering and Ontology Development

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Ontology Modeling and MDA.

The paper presents Ontology Definition Metamodel (ODM) that enables using Model Driven Architecture (MDA) standards in ontological engineering. Other similar metamodels are based on ontology representation languages, such as RDF(S), DAML+OIL, etc. However, none of these other solutions uses the recent W3C effort – The Web Ontology Language (OWL). In our approach, we firstly define the ODM place in the context of the MDA four-layer architecture and identify the main OWL concepts. Then, we define ODM using Meta-Object Facility (MOF). The relations between similar MOF and OWL concepts are discussed in order to show their differences (e.g. MOF or UML Class and OWL Class). The proposed ODM is a good starting point for defining an OWL-based UML profile that will enable using the well-known UML notation in ontological engineering more extensively.

Converting UML to OWL ontologies

This paper presents automatic generation of the Web Ontology Language (OWL) from an UML model. The solution is based on an MDA-defined architecture for ontology development and the Ontology UML Profile (OUP). A conversion, that we present here, transforms an ontology from its OUP definition (i.e. XML Metadata Interchange -- XMI) into OWL description. Accordingly, we illustrate how an OUP-developed ontology can be shared with ontological engineering tools (i.e. Protégé).

Assessing the maintainability of software product line feature models using structural metrics

A software product line is a unified representation of a set of conceptually similar software systems that share many common features and satisfy the requirements of a particular domain. Within the context of software product lines, feature models are tree-like structures that are widely used for modeling and representing the inherent commonality and variability of software product lines. Given the fact that many different software systems can be spawned from a single software product line, it can be anticipated that a low-quality design can ripple through to many spawned software systems. Therefore, the need for early indicators of external quality attributes is recognized in order to avoid the implications of defective and low-quality design during the late stages of production. In this paper, we propose a set of structural metrics for software product line feature models and theoretically validate them using valid measurement-theoretic principles. Further, we investigate through controlled experimentation whether these structural metrics can be good predictors (early indicators) of the three main subcharacteristics of maintainability: analyzability, changeability, and understandability. More specifically, a four-step analysis is conducted: (1) investigating whether feature model structural metrics are correlated with feature model maintainability through the employment of classical statistical correlation techniques; (2) understanding how well each of the structural metrics can serve as discriminatory references for maintainability; (3) identifying the sufficient set of structural metrics for evaluating each of the subcharacteristics of maintainability; and (4) evaluating how well different prediction models based on the proposed structural metrics can perform in indicating the maintainability of a feature model. Results obtained from the controlled experiment support the idea that useful prediction models can be built for the purpose of evaluating feature model maintainability using early structural metrics. Some of the structural metrics show significant correlation with the subjective perception of the subjects about the maintainability of the feature models.

Ontology-Based Automatic Annotation of Learning Content

This paper presents an ontology-based approach to automatic annotation of learning objects’ (LOs) content units that we tested in TANGRAM, an integrated learning environment for the domain of Intelligent Information Systems. The approach does not primarily focus on automatic annotation of entire LOs, as other relevant solutions do. Instead, it provides a solution for automatic metadata generation for LOs’ components (i.e., smaller, potentially reusable, content units). Here we mainly report on the content-mining algorithms and heuristics applied for determining values of certain metadata elements used to annotate content units. Specifically, the focus is on the following elements: title, description, unique identifier, subject (based on a domain ontology), and pedagogical role (based on an ontology of pedagogical roles). Additionally, as TANGRAM is grounded on an LO content structure ontology that drives the process of an LO decomposition into its constituent content units, each thus generated content unit is implicitly semantically annotated with its role/position in the LO’s structure. Employing such semantic annotations, TANGRAM allows assembling content units into new LOs personalized to the users’ goals, preferences, and learning styles. In order to provide the evaluation of the proposed solution, we describe our experiences with automatic annotation of slide presentations, one of the most common LO types.

Using Semantic Web Technologies to Analyze Learning Content

The authors demonstrate how to use semantic Web technologies to improve the state-of-the-art in online learning environments and bridge the gap between students on the one hand, and authors or teachers on the other. The ontological framework presented here helps formalize learning object context as a complex interplay of different learning-related elements and shows how we can use semantic annotation to interrelate diverse learning artifacts. On top of this framework, the authors implemented several feedback channels for educators to improve the delivery of future Web-based courses.

“Choose Your Classmates, Your GPA Is at Stake!” The Association of Cross-Class Social Ties and Academic Performance

This article presents results from an investigation of the association between student academic performance and social ties. Based on social capital and networked learning research, we hypothesized that (a) students’ social capital accumulated through their course progression is positively associated with their academic performance and (b) students with more social capital have significantly higher academic performance (operationalized as grade point average). Both hypotheses were supported by results of an empirical study that analyzed 10 years of student course enrolment records (N = 505) in a master’s degree program offered through distance education at a Canadian university. These results are consistent with previous studies that looked at social networks built through student interaction in classrooms or computer-mediated communication environments. The significance of this research lies in the simplicity of the method used to establish student social networks from existing course registration records readily available through an institution’s student information system. Direct implications of this research are that (a) study plans for students should consider investment in building new social ties in each course during degree programs and (b) readily available data about cross-class networks can be used in software systems supporting study planning.

Handbook of Research on Emerging Rule-based Languages and Technologies: Open Solutions and Approaches

Selecting an appropriate rules-based engine requires balancing many different, and often, not well-understood properties such as business rules representation methods, rule history and life cycle management, and interoperability with external data sources. The Handbook of Research on Emerging Rule-Based Languages and Technologies: Open Solutions and Approaches provides a comprehensive collection of state-of-the-art advancements in rule languages, containing methodologies for building rule-based applications, rule interoperability and interchange, and rule-based applications. Developers of rule-based languages and technologies as well as users of these applications will find this Handbook of Research to be a significant resource within the field.