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.
Language Technologies for Learning
When people hear language technologies many immediately think about technologies for language learning. This is not correct. Language technologies are looking at interpreting human language and making sense of it. This is also different to the Semantic Web, a difference that I explained in this blog post a while ago.
Language technologies include, for example, sentiment analysis and opinion mining techniques which aim at categorising human expression into moods of expression. Natural language processing or latent semantic analysis (LSA) also try to "understand" human texts and compare them with each other to detect closeness or quality.
My own interest is based on a three year international research project that I managed. It focused on language technologies to support learners and tutors to make sense of large amounts of data, ways that helps them to save time or that lower the cognitive load. Among other things, language technologies can help analyse large multi-participant discussions (like chat and forums). With these analytics techniques, intertwined threads of chat utterances can be untangled and interaction between participants can be detected. This makes it easier to look at a long confused discussions and detect cross-links between individual participants.
Language technologies hold many promises for learning and knowledge construction. Similarly to Learning Analytics, they can help people to better understand their learning. What is best is that these are qualitative analyses that directly look at the language artefacts that learners have produced. In this way, they are unobtrusively observing learners while they learn and can provide feedback on demand at any time and independent of tutor availability.