In the past years, research on learning analytics has experienced a considerable growth, grabbing more and more attention from scholars and practitioners from many different fields, such as education, computing, statistics, information systems, psychology and sociology, among others. Scientific contributions to the topic are steadily increasing, and it is easy to observe very different approaches to the same problems, as well as identical approaches to very different problems. These advances in research open ways of knowing and discovering, but at the same time they pose new research questions and avenues of research, leading to a spiral of evolution from the interaction between educational research and learning analytics (Conde & Hernández-García, 2015).
In the context of the Technological Ecosystems for Enhancing Multiculturality Conference, the different papers presented in the track on learning analytics in the past two years have been an example of this diversity, despite the focus of the tracks in specific aspects of learning analytics. More interestingly, the subsequent debates with both discussants and the audience that follow the presentations have raised many interesting issues in learning analytics research, confronting visions that could complement but also oppose each other, such as:
Educational vs. Technical approaches
Behaviorist vs. Instructional approaches
Useful vs. Irrelevant information
Automated vs. Semi-automated vs. Non-automated mechanisms
Ethical views on the use of learning analytics
Standard vs. Proprietary systems
Tiny vs. Big data for learning analytics
Consequently, as new advances are made public the boundaries of learning analytics research are becoming blurrier, in what seems to have transformed the scope of learning analytics into an endless horizon. Adding to this, research on learning analytics has focused mainly on the analysis of educational data from learning management systems (and, more recently, MOOCs), but recent innovations in educational technologies (e.g. multimodal learning) and pedagogical approaches (e.g. personal learning environments, game based learning, virtual worlds) that can also benefit from the application of learning analytics, are pushing those boundaries even further.
This track aims therefore to address these concerns in order to integrate the multiple views and aspects of learning analytics, and to discover solid paths that may guide researchers on their journey through this endless horizon. The Call for Papers of this track is then open for submissions covering the following topics:
Discussion on controversial topics on learning analytics:
Educational/pedagogical vs. technical approaches
Tiny data and big data
Behaviorism and learning analytics
Ethical concerns about learning analytics
Development of common frameworks for learning analytics:
Definition of reliable indicators for learning analytics
Scope and boundaries of learning analytics
Integration of academic and learning analytics
Standards for learning analytics
System interoperability in learning analytics
Generalizability of results in learning analytics
Learning analytics for automation of learning processes
“Core” learning analytics
Predictive and early warning systems
Discourse analytics
Integration of learning analytics with new technologies and educational innovation, including:
Multimodal learning
Competence/skill assessment
The different sessions will be held as panels consisting of a maximum of six presenters, and moderated by the co-chairs.
For each session, authors will briefly present an overview of their submission, with a maximum time allowance of five minutes. This presentation shall not be a summary of the submission but rather a general context of their research.
Following the presentations, the moderators will start a debate with the authors addressing the main contributions of each paper, focusing on common and divergent points. This discussion will have a duration of between forty-five and sixty minutes, and may include active participation from the audience in an open round of questions.
Dragan Gasevic, Ph.D. (Athabasca University, Canada)
Félix José Pascual-Miguel, Ph.D. (Universidad Politécnica de Madrid)
Francisco José García-Peñalvo, Ph.D. (Universidad de Salamanca, Spain)
Gustavo Ribeiro Alves, Ph.D. (Instituto Superior de Engenharia do Porto, Portugal)
Inés González-González, Ph.D. (Universitat Pompeu Fabra, Spain)
Javier Alfonso-Cendón, Ph.D. (Universidad de León, Spain)
Julià Minguillón, Ph.D. (Universitat Oberta de Catalunya, Spain)
Luis Panizo-Alonso (Universidad de León, Spain)
Marcus Specht, Ph.D. (Open Universiteit Nederland, Netherlands)
María Arcelina Marques, Ph.D. (Instituto Superior de Engenharia do Porto, Portugal)
Mariluz Guenaga, Ph.D. (Universidad de Deusto, Spain)
Milos Milovanovic, Ph.D. (Univerzitet u Beogradu, Serbia)
Miroslav Minovic, Ph.D. (Univerzitet u Beogradu, Serbia)
Nic Nistor, Ph.D. (Universität der Bundeswehr München and Ludwig Maximilians Universität München, Germany)
Pedro José Muñoz-Merino, Ph.D. (Universidad Carlos III, Spain)
Rebecca Ferguson, Ph.D. (The Open University, United Kingdom, and Learning Analytics Community Exchange (LACE))
Roberto Therón, Ph.D. (Universidad de Salamanca, Spain)
Salvador Ros-Muñoz, Ph.D. (Universidad Nacional de Educación a Distancia, Spain)
Santiago Iglesias-Pradas, Ph.D. (Universidad Politécnica de Madrid, Spain)
Teresa Oliveira, Ph.D. (Universidade Aberta, Portugal )
Teresa Sancho, Ph.D. (Universitat Oberta de Catalunya, Spain)
Vicente Matellán-Olivera, Ph.D. (Universidad de León, Spain)