One of the tasks we’ve commissioned from our informal task group is a case study from Liverpool University on why and how they have implemented a Learning Registry node to enhance access to high-quality visual resources (images, videos, Flash animations, etc.) for use in engineering education. The ENGrich team will be presenting their case study at the JLeRN Final Workshop on Monday 22nd October and completing it based on feedback and discussion there; but we’d be interested in any questions or points you have as well prior to the final version being published here.
So, here is your taster:
Ins and Outs of the Learning Registry: An ENGrich Case Study for JLeRN – draft
A brief summary of the ENGrich project
ENGrich, a project based at the University of Liverpool, is both designing and developing a customised search engine for visual media relevant to engineering education. Using Google Custom Search (with applied filters such as tags, file-types and sites/domains) as a primary search engine for images, videos, presentations and FlashTM movies, this project is then pulling and pushing corresponding metadata / paradata from and to the Learning Registry (LR). A user-interface is also being developed to enable those engaging with the site (principally students and academics) to add further data relating to particular resources, and to how they are being used. This information is published to LR, which is then employed to help order any subsequent searches.
ENGrich process flow
This section will detail the process flow of the proposed service. Effectively, the LR plays a central role in ‘engriching’ visual engineering content, above and beyond the basic data returned by Google Custom Search.
Working with documentation
This section will cover how we worked through the available LR guides and documentation, from the very basic methods (publish, slice etc) to the more customized data services (extract).
The list includes:
Setting up a Learning Registry node at the University of Liverpool
This section will explain the rationale behind the decision to set up an institutional LR node (common node) at the University of Liverpool, and challenges and issues we faced while doing it. This node is to be utilized by the project, as well as providing a means of highlighting the wider potential of the LR to other centres / services across the University.
Summer students identifying learning resources
This section will describe how the project employed undergraduate engineering students over the summer of 2012 to classify visual media available online that are relevant to the University of Liverpool engineering modules. The project relies on their experience as engineering students to provide insights into learning techniques of how to identify resources that will aid future students. 25,000+ records were linked to the appropriate modules, and are ready to be published in the University LR node using the paradata templates described below.
The students blogged every week on their tasks and progress.
Paradata statements templates
This section will report on how we went about creating the required PHP templates to publish the students’ data into our Learning Registry node. We have constructed a set of contextualized usage paradata statements for different types of actions (e.g. recommended, aligned, not aligned) and so far have published a couple of test documents to our University LR node.
Slice, harvest, obtain methods and data services (extract)
This section will summarise our experience with different methods the LR has in place for accessing data from the LR. We report on using slice, obtain, harvest and extract methods, explaining why we have chosen one over the other.
University of Liverpool student portal – using data directly from LR
This section will demonstrate how an iLIKE ‘widget’ (a portative version of ENGrich visual search) could be implemented within the University of Liverpool student portal. The iLIKE prototype gets a unique listing of identifiable University of Liverpool engineering modules (titles and codes) directly from the Learning Registry as the user types into the text field, and then fetches the latest resources relating to that module from the LR. It dynamically generates the thumbnails corresponding to the resources.
Working with Mimas
This section will talk about how we collaborated with the JLeRN team at Mimas to resolve some initial bugs in the slice service; to draw on their experiences in setting up a new LR node at the University of Liverpool; to develop a set of customised extract data services; and also about the possibility of joining the LR nodes at Liverpool and Mimas using the LR distribute service.