Mobile learning has become very popular over the last few years. Smaller and more powerful devices that have become much easier to use made it possible to deliver innovative and more flexible services to learners. Especially smart phones which now, apart from cameras and music players, now also contain sensors like gyroscopes, GPS receivers, and a digital compass, cater for a number of innovative applications such as augmented reality.
Mobile technologies enable more flexibility for learners, freeing them from the desktop. Learning with mobile devices particularly serves work-based learning scenarios or field trips. But also the anytime anywhere aspect is most appealing to learners and teachers alike. Delivery of mobile content via e.g. ebooks provide learners with ubiquitous access to quality resources.
Learning has become a lot more social. Whereas previously the focus has been largely on knowledge transfer, more recent pedagogic trends have emerged that recognise that this is not the only way to learn. Human interaction is a vital component in the process of acquiring new skills or knowledge, or for creative processes. Pedagogic theories like socio-constructivism or connectivsm explore and try to explain this and form the basis for a lot of our technology driven teaching and learning approaches.
Especially in the area of professional learning humans almost always act within communities, that is with people or experts with whom we discuss and share our knowledge. These communities of practice (CoP) or communities of interest (fanclubs etc.) are organised in online networks. For the purpose of learning, they are called learning networks. Learning networks show the right flexibility to support ad-hoc learning and other informal methods of knowledge building.
Learning networks, like other networks, are built upon links and connections, but, in learning networks, connections in turn depend largely on two human characteristics: trust and passion. While there is already a good stock of research looking into learning networks, these areas are still mainly unexplored. It is also still unclear what makes such a network successful. Mostly, success is understood in terms of scale, i.e. size of membership or volume of content, but this may actually be not the decisive criteria for a successful learning network. More research into these questions is required.
Being internet-based, learning networks utilise online social platforms, or, sometimes, purpose-built platforms. Twitter, Facebook and other social platforms have entered education and are increasingly used in courses. Because the internet is full of useful tools and technologies, more personal combinations of these are now possible that were not available before. This increasingly open way of providing tools for learning leads away from the traditional provision of tools and platforms to a personal learning enviroment that is centred around the learner, not around the institution.
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?
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.
Re-usable Learning Objects in e-Learning are mainly servicing the creation and sequencing of content artifacts for single self-directed learners. In HE, learning arises from interacting with peers and tutor via multi-learner activities. Although lesson planning is an integrated part of delivery, this still is largely a blind spot in e-learning.
In simple words the relationship between Learning Objects and Learning Activities is one of content and what to do with it, i.e. its instructional application. Suggestions have been made to identify the additional metadata needed for the re-use of Learning Activities and IMS LD is a specification to address this purpose.
What is important to take note of is that teaching strategies can be easier transferred to different educational settings than content. If designed efficiently, content objects can be pulled into a learning activity thus making it applicable in various contexts.
What's more, commercial studies have shown that recycling concepts not content is more cost efficient. Products like the Australian LAMS™ platform and the recognition of the wider possibilities for reuse of Learning Activities over Learning Objects cannot be ignored.
It should be noted that it is possible to build a learning activity sequence without any content (discursive and reflective in nature or a template sequence).
Dalziel (2003): Discussion paper for Learning Activities and Metadata
Feldstein (2003): How to design recyclable learning objects