SocialLearn

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.

Simon Buckingham Shum from the SL team will be in Barcelona next month for the co-located Open Education Conference and Mozilla Drumbeat Festival.

Here’s a preview of the article on SocialLearn that we’ll present at OpenEd, reviewing some of the design rationale for SocialLearn, currently in internal testing here at Open U:

Buckingham Shum, S. and Ferguson, R. (2010). Towards a social learning space for open educational resources. OpenEd 2010: Seventh Annual Open Education Conference, 2-4 Nov 2010, Barcelona. Eprint: http://oro.open.ac.uk/23351

Look forward to seeing you there, and look out at Drumbeat for the related demos of Cohere (opening night Science Fair), a social web annotation and knowledge mapping tool tuned for inquiry, sensemaking and learning. This forms part of our thinking on what a Collective Intelligence infrastructure might be not only for social learners, but also as a resilience platform for stakeholders in the open educational resources movement:

Buckingham Shum, S. and De Liddo, A. (2010). Collective intelligence for OER sustainability. OpenEd 2010: Seventh Annual Open Education Conference, 2-4 Nov 2010, Barcelona. Eprint: http://oro.open.ac.uk/23352

YouTube replay

The 1st International Conference Learning Analytics & Knowledge will be held February 27-March 1, 2011 in Banff, Alberta. This is an extremely exciting development, reflecting what in many people’s view is going to be a key dimension to future learning environments, with a strong Open U. presence in the steering committee (join the Learning Analytics Google Group).

From the conference announcement:

The growth of data surpasses the ability of organizations to make sense of it. This concern is particularly pronounced in relation to knowledge, teaching, and learning. Learning institutions and corporations make little use of the data learners “throw off” in the process of accessing learning materials, interacting with educators and peers, and creating new content. In an age where educational institutions are under growing pressure to reduce costs and increase efficiency, analytics promises to be an important lens through which to view and plan for change at course and institutions levels. Corporations face pressure for increased competitiveness and productivity, a challenge that requires important contributions in organizational capacity building from work place and informal learning. Learning analytics can play a role in highlighting the development of employees through their learning activities.

In enterprise settings, information flow and social interactions can yield novel insights into organizational effectiveness and capacity to address new challenges or adapt rapidly when unanticipated event arise.

Thirdly, as we witness the expansion of learning and knowledge work beyond formal institutional boundaries, myriad platforms in the cloud hosting the activity of individuals will be providing/exchanging analytics.

Advances in knowledge modeling and representation, the semantic web, data mining, analytics, and open data form a foundation for new models of knowledge development and analysis. The technical complexity of this nascent field is paralleled by a transition within the full spectrum of learning (education, work place learning, informal learning) to social, networked learning. These technical, pedagogical, and social domains must be brought into dialogue with each other to ensure that interventions and organizational systems serve the needs of all stakeholders.

Learning Analytics 2011 will focus on integrating the technical and the social/pedagogical dimensions of learning analytics.

Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs