What is the need?

Imagine what could happen if we had a really good sets of usable open learning outcomes, across academic subjects, occupations and professions. It would be easy to express and then trace the relationships between any learning outcomes. To start with, it would be easy to find out which higher-level learning outcomes are composed, in a general consensus view, of which lower-level outcomes.

Some examples … In academic study, for example around a more complex topic from calculus, perhaps it would be made clear what other mathematics needs to be mastered first (see this recent example which lists, but does not structure). In management, it would be made clear, for instance, what needs to be mastered in order to be able to advise on intellectual property rights. In medicine, to pluck another example out of the air, it would be clarified what the necessary components of competent dementia care are. Imagine this is all done, and each learning outcome or competence definition, at each level, is given a clear and unambiguous identifier. Further, imagine all these identifiers are in HTTP IRI/URI/URL format, as is envisaged for Linked Data and the Semantic Web. Imagine that putting in the URL into your browser leads you straight to results giving information about that learning outcome. And in time it would become possible to trace not just what is composed of what, but other relationships between outcomes: equivalence, similarity, origin, etc.

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Learning to learn – the ultimate life-skill

You can turn learning in on itself and use your learning skills to help you learn how to become an increasingly effective learner. Learning to learn is the key to enhancing all the above.

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Self-disclosure is something that we would probably all like to control. There’s a lot of literature on self-disclosure in many settings, and it is clearly recognised as important in several ways. I like the concept of self-disclosure, because it is a positive concept, in contrast to the rather negative idea of privacy. Privacy is, as its name suggests, a “privative” concept. Though definitions vary greatly, one common factor is that definitions of privacy tend to be in terms of the absence of something undesirable, rather than directly as the presence of something valuable.

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InLOC — Integrating Learning Outcomes and Competences — is all about devising a good way of representing and communicating structures of learning outcomes, competence, skills, competencies, etc. that can be defined by framework owners, and used by many kinds of ICT tools, including those supporting: specifying learning outcomes of courses; claiming skills and competences in portfolios; recruitment and specifying job requirements; learning objectives relevant to resources; and possibly many more.

Project outcomes

We have produced three CEN Workshop Agreements, two formally approved and awaiting publication (Information Model, and Guidelines), and one where a workshop vote will be concluded in the coming days (Application Profile: we don’t expect any problems). Further work includes technical bindings, and two demo prototypes kindly contributed.

The Information Model

There are a number of key advances made in the InLOC Information Model, with respect to other and previous work. “LOC” here stands for “Learning Outcome or Competence”.

1. A clear distinction is made between a LOCdefinition and a LOCstructure.
   • A LOCdefinition is similar in some ways to IMS RDCEO or IEEE RCD. Any idea of structure is kept separate from this, so that the definition can potentially be reused in different structures. Thus, a LOC definition is like the expression of just one concept about learning outcome, competence, etc.
   • A LOCstructure is the information about the structure and compound properties, but this is kept separate from any particular single definition. While it is recognised that in practice the two are often mixed, the InLOC specification separates them for clarity and for effective implementation.
2. A clear distinction is made between defining levels with level definitions, and attributing levels (from another scheme) to definitions. This is explained in InLOC treatment of levels. This is necessary for logical clarity, and therefore at some point for applications. A decimal number is introduced as a key part of the model, to allow level information to be automatically processed.
3. A single structural form, the LOCassociation, is used both to represent relationships between LOC structures and definitions, and to represent several different kinds of compound properties, each with more than one part. This results in structures that are easier to process, with fewer distinct information model components. It also is responsible for the relative ease of representing InLOC naturally in RDF, with minor changes to the model.

Within InLOC in general, a recurrent pattern is of one identifier together with a set of multilingual titles or labels. This is a common pattern elsewhere, and ensures that InLOC representations can naturally work multilingually.

There is a diagrammatic illustration of the Information Model structure as a UML diagram, and many more illustrative diagrams in the Guidelines section on InLOC explained through example.

The Guidelines

The central feature of the Guidelines is a detailed examination of a cross-section of the European e-Competence Framework, given as a good example of the power and flexibility of InLOC in a case from real life. The e-CF is a useful example for InLOC as it identifies 5 levels of competence. The e-CF is analysed in the section on InLOC explained through example. In this section, there are more diagrams illustrating the Information Model and how it is applied in this case.

The e-CF is fully expressed here in InLOC XML format.

Application Profile of Europass CV and Language Passport

The most used Europass instrument is the Europass CV, and Cedefop have recently been revamping it. It is a kind of simple e-portfolio, and the challenge here is to allow it to refer effectively to InLOC structures, so that the end users — the people who have the skills and competences they want to show off — can refer directly to InLOC identifiers, and so have better hope of having them accurately recognised and found in relevant searches. For the Europass CV, the InLOC team have proposed a modification of their XML Schema, and it looks like several if not all of our proposals will be taken on by Cedefop, paving the way for the Europass CV being a leading example of the use of InLOC structures in practice.

Technical bindings

No information model is complete without suggestions for how to bind it to currently relevant technologies. The ones chosen by InLOC were:

• XML
• RDF
• JSON

We hope that they are reasonably clear and self-explanatory.

While the project found no great motivation for developing other bindings, I personally believe that it would be very valuable in the future to develop something with RDFa and schema.org.

Prototypes

We have been really lucky to have two initiatives filling in where the project was not funded to deliver. There is a Viewer-editors page on the project wiki with access details.

• A team from Eummena, led by one of the team, have produced an InLOC viewer/editor based on PHP and MySQL. Here is their login page.
• Henk Vos from Rapasso has produced a prototype using Python and Django. The interface starts here. The code is open source, GPL v3, available on GitHub.

Challenges

The main challenge in this project has been trying to generate interest and contributions from interested parties. It’s not that the topic isn’t important, just that, as usual, busy people need a pressing reason to engage with this kind of activity. This challenge is endemic to all “anticipatory” standardization work. Before either policy mandation or clear economic interest, it takes some spare effort and a clear vision before people are willing to engage.

I’m intending to write more about what this means for my own personal view of what standardization could best be, or perhaps “should” be.

Recommendations

It seems to me good practice to make some recommendations at the end of the project — after all, if one has been engaged in some good work, there should be some ways forward that are clearer at the end than at the beginning. The recommendations that the team agreed included:

• focusing on trying to get people to publish frameworks in InLOC, as this will in turn motivate tool builders;
• ensuring that when people are ready to adopt InLOC, they can find resources and expertise;
• persuading developers to make it easy for users to refer to definitions within InLOC structures;
• get other Workshop, and other European, projects to use InLOC where possible;
• work on APIs, and on automatic configuration of key domain terms within user interfaces.

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The first official Scottish government data jam, facilitated by Snook and supported by TechCube, is part of a wider project to help people navigate the various education and employment options in life, particularly post 16. The jam was meant to provide a way to quickly prototype a wide range of ideas around the learner journey theme.

While many other teams at the jam built things like a prototype social network, or great visualisations to help guide learners through their options, we decided to use the data that was provided to help see what an infrastructure could look like that supported the apps the others were building.

In a nutshell, I wanted to see whether a mash-up of open data in open standard formats could help answer questions like:

• Where is the learner in their journey?
• Where can we suggest they go next?
• What can help them get there?
• Who can help or inspire them?

Here’s a slide deck that outlines the results. For those interested in the nuts and bolts read on to learn more about how we got there.

Where is the learner?

To show how you can map where someone is on their learning journey, I made up an e-portfolio. Following an excellent suggestion by Lizzy Brotherstone of the Scottish Government, I nicked a story about ‘Ryan’ from an Education Scotland website on learner journeys. I recorded his journey in a Mahara e-portfolio, because it outputs data in the standard LEAP2a format- I could have used PebblePad as well for the same reason.

I then transformed the LEAP2a XML into very rough but usable RDF using a basic stylesheet I made earlier. Why RDF? Because it makes it easy for me to mash up the portfolios with other datasets; other data formats would also work. The made-up curriculum identifiers were added manually to the RDF, but could easily have been taken from the LEAP2a XML with a bit more time.

Where can we suggest they go next?

I expected that the Curriculum for Excellence would provide the basic structure to guide Ryan from his school qualifications to a college course. Not so, or at least, not entirely. The Scottish Qualifications Framework gives a good idea of how courses relate in terms of levels (i.e. from basic to a PhD and everything in between), but there’s little to join subjects. After a day of head scratching, I decided to match courses to Ryan’s qualifications by level and comparing the text of titles. We ought to be able to do better than that!

The course data set was provided to us was a mixture of course descriptions from the Scottish Qualifications Authority, and actual running courses offered by Scottish colleges all in one CSV file. During the jam, Devon Walshe of TechCube made a very comprehensive data set of all courses that you should check out, but too late for me. I had a brief look at using XCRI feeds like the ones from Adam Smith college too, but went with the original CSV in the end. I tried using LOD Refine to convert the CSV to RDF, but it got stuck on editing the RDF harness for some reason. Fortunately, the main OpenRefine version of the same tool worked its usual magic, and four made-up SQA URIs later, we were in business.

This query takes the email of Ryan as a unique identifier, then finds his qualification subjects and level. That’s compared to all courses from the data jam course data set, and whittled down to those courses that match Ryan’s qualifications and are above the level he already has.

The result: too many hits, including ones that are in subjects that he’s unlikely to be interested in.

So let’s throw in his interests as well. Result: two courses that are ideal for Ryan’s skills, but are a little above his level. So we find out all the sensible courses that can take him to his goal.

What can help them get there?

One other quirk about the curriculum for excellence appears to be that there are subject taxonomies, but they differ per level. Intralect implemented a very nice one that can be used to tag resources up to level 3 (we think). So Intralect’s Janek exported the vocabulary in two CSV files, which I imported in my triple store. He then built a little web service in a few hours that takes the outcome of this query, and returns a list of all relevant resources in the Intralibrary digital repository for stuff that Ryan has already learned, but may want to revisit.

Who can help or inspire them?

It’s always easier to have someone along for the journey, or to ask someone who’s been before you. That’s why I made a second e-portfolio for Paula. Paula is a year older than Ryan, is from a different, but nearby school, and has done the same qualifications. She’s picked the same qualification as a goal that we suggested to Ryan, and has entered it as a goal on her e-portfolio. Ryan can get it touch with her over email.

This query takes the course suggested to Ryan, and matches it someone else’s stated academic goal, and reports on what she’s done, what school she’s from, and her contact details.

Conclusion

For those parts of the Curriculum for Excellence for which experiences and outcomes have been defined, it’d be very easy to be very precise about progression, future options, and what resources would be particularly helpful for a particular learner at a particular part of the journey. For the crucial post 16 years, this is not really possible in the same way right now, though it’s arguable that its all the more important to have solid guidance at that stage.

Some judicious information architecture would make a lot more possible without necessarily changing the syllabus across the board. Just a model that connects subject areas across the levels, and school and college tracks would make more robust learner journey guidance possible. Statements that clarify which course is an absolute pre-requisite for another, and which are suggested as likely or preferable would make it better still.

We have the beginnings of a map for learner journeys, but we’re not there yet.

Other than that, I think agreed identifiers and data formats for curriculum parts, electronic portfolios or transcripts and course offerings can enable a whole range of powerful apps of the type that others at the data jam built, and more. Thanks to standards, we can do that without having to rely on a single source of truth or a massive system that is a single point of failure.

Find out all about the other great hacks on the learner journey data jam website.

All the data and bits of code I used are available on github

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