Learning Analytics Community Exchange: Evidence Hub

This poster sets out the background and development of the LACE Evidence Hub, a site that gathers evidence about learning analytics in an accessible form. The poster also describes the functionality of the site, summarises its quantitative and thematic content to date and the state of evidence. In addition, it encourages people to add to and make use of the Hub

Using Linked Data in Learning Analytics

Learning Analytics has a lot to do with data, and the way to make sense of raw data in terms of the learner’s experience, behaviour and knowledge. In this article, we argue about the need for a closer relationship between the field of Learning Analytics and the one of Linked Data, which in our view constitutes an ideal data management layer for Learning Analytics. Based on our experience with organising the “Using Linked Data in Learning Analytics” tutorial at the Learning Analytics and Knowledge conference, we discuss the existing trends in the use of linked data and semantic web technologies, in general in education and in learning analytics specifically. We find that the emerging connections between the two fields are still, at the time of writing, much less prominent than one would expect considering the complementary nature of the considered technologies and practices. We therefore argue that specific efforts, somehow materialised through the tutorial and the work in the LinkedUp support action, are needed to ensure the realisation of the potential cross-benefits that combining Learning Analytics and Linked Data research could bring.LinkedU

Spiral me to the core: Getting a visual grasp on text corpora through clusters and keywords

The amount of literature within a research domain is ever growing, thus making it difficult to stay on top of everything. Getting a grasp on the important topics of and areas within a domain or even knowing where to start is often tough and tedious. This paper therefore presents a visualization, that is a cluster spiral, that offers a fast but plain and simple way of exploring the content of large text collections

Towards Ontology-Based Design Science Research for Knowledge Accumulation and Evolution

The potential of design science research (DSR) to contribute to real-world problems solving and innovation has been considered as an opportunity for IS researchers to demonstrate the relevance and significance of DSR paradigm. While most DSR studies have been informed on single design and development projects, future research needs to consider knowledge sharing and accumulation across multiple projects. This paper argues for combining the forces of design science research and ontology studies to foster knowledge creation and evolution. We propose a new approach to DSR by adopting ontology engineering as a knowledge sharing mechanism in which researchers assemble knowledge parts throughout the study. We develop a framework for understanding, conducting and evaluating ontology-based design science research, then present the roadmap and guidelines for its conduct and evaluation. This paper concludes with a call for a more collaborative endeavor to design studies in IS research

Serious Gaming Analytics: What Students´ Log Files Tell Us about Gaming and Learning

In this paper we explore existing log files of the VIBOA environmental policy game. Our aim is to identify relevant player behaviours and performance patterns. The VIBOA game is a 50 hours master level serious game that supports inquiry-based learning: students adopt the role of an environmental consultant in the (fictitious) consultancy agency VIBOA, and have to deal with complex, multi-faceted environmental problems in an academic and methodologically sound way. A sample of 118 master students played the game. We used learning analytics to extract relevant data from the logging and find meaningful patterns and relationships. We observed substantial behavioural variability across students. Correlation analysis suggest a behavioural trade that reflects the rate of “switching” between different game objects or activities. We were able to establish a model that uses switching indicators as predictors for the efficiency of learning. Also we found slight evidence that students who display increased switching behaviours need more time to complete the games. We conclude the paper by critically evaluating our findings, making explicit the limitations of our study and making suggestions for future research that links together learning analytics and serious gaming

Data Analytics in Higher Education: An Integrated View

Data analytics in higher education provides unique opportunities to examine, understand, and model pedagogical processes. Consequently, the methodologies and processes underpinning data analytics in higher education have led to distinguishing, highly correlative terms such as Learning Analytics (LA), Academic Analytics (AA), and Educational Data Mining (EDM), where the outcome of one may become the input of another. The purpose of this paper is to offer IS educators and researchers an overview of the current status of the research and theoretical perspectives on educational data analytics. The paper proposes a set of unified definitions and an integrated framework for data analytics in higher education. By considering the framework, researchers may discover new contexts as well as areas of inquiry. As a Gestalt-like exercise, the framework (whole) and the articulation of data analytics (parts) may be useful for educational stakeholders in decision-making at the level of individual students, classes of students, the curriculum, schools, and educational systems

Ontology Learning to Analyze Research Trends in Learning Analytics Publications

In this paper, we show how ontology learning tools can be used to reveal (i) the central research topics that are tackled in the published literature on learning analytics and educational data mining; and (ii)relationships between these research topics and iii) (dis)similarities between learning analytics and educational data mining. Categories and Subject Descriptor