SocialLearn Research Blog

Announcing a new technical report, The State of Learning Analytics in 2012: A Review and Future Challenges.

‘The State of Learning Analytics’ reviews the emergence of the field over the past decade, tracing its roots in other disciplines and setting out current trends and future lines of work. In the process, it identifies the focus of and drivers behind related types of analytic – particularly academic analytics and action analytics.

The development of the field is set out in a broadly chronological structure, which demonstrates the increasingly rapid pattern of development as new drivers emerge, new fields are appropriated and new tools developed. Tracing the development of learning analytics over time highlights a gradual shift away from a technological focus towards an educational focus, and the introduction of tools, initiatives and methods that are significant in the field today.

The report draws on two main sources. The first is the list of resources provided by the online course associated with the LAK 2012 conference. The second is the literature cited in over 70 submissions to that conference. The 20 most-cited works and the 20 most‐cited authors are all included in the report.

Reference:
Ferguson, R. (2012). The State Of Learning Analytics in 2012: A Review and Future Challenges. Technical Report KMI-12-01, Knowledge Media Institute, The Open University, UK. http://kmi.open.ac.uk/publications/techreport/kmi-12-01

In our report on Social Learning Analytics, we discuss social content indexing technologies, including image analysis. In this blog post, Suzanne Little provides a bit more insight into the rationale for exploring this…

It’s almost a truism that educational content these days is more than just text or spoken word. Exciting and effective learning materials contain diagrams, illustrations, photographs, presentations, audio and video. Courses are delivered via broadcasts, streaming video, online slide sharing, interactive games and collaborative forums. The Open University, in particular, has a very rich archive of multimedia educational resources to offer including videos, photographs, slideshow-based presentations, bundled educational archives and web pages.

Traditionally you would discover this type of material through a curated index built by librarians and educators who would guide you to useful resources depending on your question or learning goal. This might be through formal metadata in a library system or specific links given in a course outline. The information age opened up resources by indexing text (the content) that could then be searched by supplying a keyword or phrase (a la Google) that the learner thinks best describes what they are looking for.

Of course this puts a burden on the learner to have enough understanding of both the topic and the type of available material to craft a good search term. With the ever-increasing volumes of educational resources being made available, it is a challenge to find new material and forge appropriate learning pathways. The SocialLearn project is helping learners by developing tools to support the building and exploration of personal learning networks created with help from a learners peers. But how can we make it easier for learners (and educators developing course material) to find resources that aren’t well described using text – images, audio, video? Particularly where material is reused in other contexts.

Visual search (or content-based multimedia indexing) can help when it is difficult to describe your interests in words (“search terms”) or when you want to browse for inspiration without a specific result in mind. Users can then find reuse of material in different contexts with different supporting materials, discover the source of a screenshot or find items that share visual features and may provide new ways of understanding a concept. For example, using slides a visual search can identify a video of the lecture where slides are displayed or using a screenshot from a document the original source video can be identified. The integration of visual search with traditional search methods and social network based learning support provides exciting new ways to develop and explore learning pathways.

In the Multimedia Information Retrieval Group at the OU’s Knowledge Media Institute, we have been researching multimedia information retrieval and visual search for educational resources and started to integrate this work with the SocialLearn platform. Suzanne Little will be presenting this work at the World Conference on Educational Multimedia (EdMedia) in Lisbon, Portugal next week (June 30th, 2pm) based on the paper “Navigating and Discovering Educational Materials through Visual Similarity Search”.

Image search in SocialLearn (interface mockup)

We’ve laid the plumbing which connects the image indexing and search technology with SocialLearn, and have some proof of concept demos. This interface mockup shows the rendering of this indexing technology with the SocialLearn Backpack, the toolbar that can be activated while browsing the web, to access SocialLearn facilities. Images on the website have been extracted, and the user can select one of them to initiate a search for related images. A social learning dimension kicks in when, for instance, a learner’s social network is used to prioritise indexing, linked data from other OU datasets is used to infer potentially relevant sources, the learner’s own navigation history is mined to remind them of where they have encountered the image before, or discourse analytics are used to present that image from a different perspective to the learner’s.

We’re hiring! Shortly to follow this technical post, will be another Research Associate position, who will work closely with this one — focused on the learner experience, and theoretical dimensions of learning analytics…

Research Associate: Social Learning Analytics & Recommender Services

£29,972 – £35,788, Closing date : 13/07/2011

This morning on the opening day of the CALRG 2011 Conference, we presented some of the recent thinking we’ve been doing on learning analytics, specifically in a social learning context.

A technical report setting out the line of argument in more detail…

Buckingham Shum, S. and Ferguson, R. (2011). Social Learning Analytics. Available as: Technical Report KMI-11-01, Knowledge Media Institute, The Open University, UK. http://kmi.open.ac.uk/publications/pdf/kmi-11-01.pdf

Abstract: We propose that the design and implementation of effective Social Learning Analytics presents significant challenges and opportunities for both research and enterprise, in three important respects. The first is the challenge of implementing analytics that have pedagogical and ethical integrity, in a context where power and control over data is now of primary importance. The second challenge is that the educational landscape is extraordinarily turbulent at present, in no small part due to technological drivers. Online social learning is emerging as a significant phenomenon for a variety of reasons, which we review, in order to motivate the concept of social learning, and ways of conceiving social learning environments as distinct from other social platforms. This sets the context for the third challenge, namely, to understand different types of Social Learning Analytic, each of which has specific technical and pedagogical challenges. We propose an initial taxonomy of five types. We conclude by considering potential futures for Social Learning Analytics, if the drivers and trends reviewed continue, and the prospect of solutions to some of the concerns that institution-centric learning analytics may provoke.

I recently  attended Alpine Rendez-Vous 2011, meeting people from all over Europe with an interest in technology-enhanced learning (TEL). For most of the time, we were split into eight workshops – my group was exploring ‘Methods and models of next-generation technology enhanced learning‘, examining the roles of assessment and evaluation in learning.

A focus for the workshop was to identify the ‘Grand Challenges’ for future research – constructing a framework for the development of TEL over the next ten years. The ideas were wide-ranging and exciting, for example:  Develop new technology to harness the power of emotions for learning. One challenge was closely related to our work at SocialLearn on learning analytics and recommendations engines: ‘How can learning be assessed in an open, social TEL environment?’

Our current model for the assessment of learning is primarily summative and individual, firmly bound to hierarchical education structures. It is a model that was developed for use when educational technology had only just moved beyond the horn book, knowledge was not abundantly available, groups of learners were taught and examined at the same time in the same physical location, teachers and learners were clearly differentiated and online collaboration and publication were undreamed-of possibilities. As new models of learning have been widely adopted, this model of assessment is no longer fit for purpose. We need new approaches and new models.

Open, social TEL environments have made new models of learning possible. Learners now draw upon many different people and resources, knowledge is dispersed and distributed, individuals may move rapidly between the roles of teacher and learner, and their collaborations extend across time and space. Despite these changes, the group work, the growing archives of activity and the available data about learning networks, there is still a focus on summative assessment of individuals.

TEL environments offer learners and educators a wealth of new resources in the form of the data they record – learners’ demographics, activities, interactions, participation and engagement – but little of this is currently harnessed to support assessment and even less is used to provide formative feedback and to help learners develop their metacognitive skills, their learning dialogue, their skill-sets or their knowledge.

Colleges and universities are beginning to make use of this data, but educational data mining and analytics are often viewed from an institutional point of view – how can we recruit more students? how can we retain students for longer? how can this institution maximise its income? A wordsearch for ‘teaching’ or ‘learning’ in the current literature produces woefully limited results.

Our challenge is to make use of new resources and technologies to develop and build on learning analytics – analytics that can make a real difference for learners, producing measurable improvements in areas such as:

• Engagement with learning – supported by appropriate and personalised feedback
• Quality of online learning dialogue
• Engagement with online learning networks
• Enjoyment – due, in part, to development of a students-in-trouble alerting system
• Learners’ and teachers’ awareness of the value of learning analytics and recommendations.