Multimodal Data for Learning
I am on the review board for this special call for papers on "Multimodal Data for Learning" for the Journal of Computer Assisted Learning (JCAL). The special issue deals with new data sources coming from the Internet of Things (IoT), wearables, eye-trackers and other camera systems, self-programmable microcomputers such as Raspberry Pi and Arduino. How can these multimodal datasets that combine traditional learning data with different data from physical activity, physiological responses or contextual information be exploited for learning?
On these pages you find information about my personal and professional background as well as some features about my interests in technology-enhanced learning, knowledge creation, knowledge transfer, and networked universities.
Learning technology has come a long way, and provides organisations, learners, and teachers with enormous opportunities to innovate not only their technical environment, but also the teaching and learning methodologies as well as their business processes. Most of all, the introduction of new media technologies leads to reflections about inherited traditional systems versus new approaches. My work contributes to these reflections and pursues not only innovation but also the effects that this innovation has on education, learning, and people.
This is a fast moving area and research focus changes quickly and often unexpectedly. My publications page contains a list of works in the field over the years. To a great extent they too reflect the changing nature of education.
My views on technology-enhanced learning
I am a passionate believer in the opportunities that technology has to offer to the knowledge society, both in terms of enhancement of learning and in reaching out to new learners. In remote and rural communities it is often the only way for people to access higher education. However, I am also of the opinion that technology alone does not produce new knowledge or learning and that new developments need to have a pedagogic and learner-centred approach.
Experience in commerce and education has shown that online solutions are at their best when built upon a traditional well-established structure. The pedagogic concept of Blended Learning is increasingly supported by universities and governments who realise that it provides a more sustainable approach than purely online offerings. This goes some way towards recognising that we cannot ignore pedagogic concepts that have been successful for decades before the internet arrived and still are.
Areas of interest
Technology enhanced learning has made giant leaps forward over the past few years. In my work, I try to keep up-to-date with latest developments and newest technologies. My current research interests focus especially on Learning and Knowledge Analytics, Language Technologies for learning support, Learning Networks, and Mobile Learning, but I also have a keen interest in other topics, including open education, game mechanics, or the most recent debate about connectivism.
Read more about my views on e-learning developments.
Learning Analytics and Knowledge
The Horizon 2011 report predicted Learning Analytics to be adopted over the next five years. The field of Learning Analytics itself is hardly new. It has its origins in traditional data collection and statistical methods. The reason why it attracts so much attention now is that we live in a world where data exists in abundance and data collection has become cheap. Furthermore, the data economy is still massively growing since the volume of data created by new technologies and people doubles in shorter and shorter time intervals.
My interest in Learning and Knowledge Analytics is mainly directed towards two perspectives. Firstly, there is benefits to be had from harvesting learner data better, looking at the messages they contain, and presenting this information back to the learner (or teacher) to allow them to reflect upon their activities or to compare themselves with peers or ideals. This can reveal insights that were largely invisible before.
Secondly, the field of educational data mining and learning analytics has substantial ethical implications that need to be investigated. First and foremost is the question of who owns the data about a person's (online) behaviour. Then there is the issue of the analytics design and what it actually reflects. And, finally, there is the competence issue of what we can expect users to do when they are confronted with the analytics' results.
As the field develops, a diversity of challenges and research questions arise. In addition to programming and hardcore algorithmic debates around data models, metrics, weightings and indicators, there are questions about the pedagogic application of learning analytics, and the contribution to theory-based learning sciences. What does data tell us about the theories that we hold about learning. Can learning analytics help us understand better the learning processes and validity of theoretical beliefs? Can LA give us insights into hitherto hidden patterns and dependencies?