In the context of formal education, advice can be straightforward: to achieve this qualification, start here, go there, do this, do that. In a highly structured career it might also be straightforward. But when we start to talk about informal learning, it becomes very hard to find anyone able and willing to give reliable relevant advice. As we believe this is an important aspect of an informal learning and careers ecosystem, we need to suggest useful possible answers for the MyLK project.

Other parts of the MyLK project deal with recording and classifying informal learning episodes. From there, the question arises, what can we add to that, how can we build onto the rest of the project, to provide useful advice for informal learners? – and providing useful advice may be one of the key motivators to bring people into using the MyLK toolset.

What kinds of advice?

We see two kinds of advice that are useful and feasible for MyLK to offer. The first is to recommend appropriate learning resources, courses, or sets of resources, in the contexts both of education, and of careers. This fits in well with the other parts of the MyLK project that are under active development. This will be the topic of this post. The second is to do with experience, and that is more to do with careers – not just the formally recognised career paths, but, vitally, less well-known career paths. We will address this in the following post.

In the rest of this post, we will outline what we believe is necessary to make the advice to be useful and welcome, and how we understand the practicalities of implementing an advice generator in this area.

Formal and informal learning

It has always been normal for learners engaged in formal learning to be given lists of relevant resources by their educators, trainers, guides, mentors, or whoever is helping them in their learning path. In the past, these were often reading lists, of books and articles. More recently, they have included Wikipedia pages, YouTube and other videos, “podcasts”, and selections from highly regarded blog posts or other resources on the Internet. These kinds of resources, that are openly and freely available on the Web, also are highly suitable for informal learning. But away from the context of formal learning, there are no appointed advisors whose advice will necessarily be trusted. This is where the MyLK advice generator will be particularly helpful.

Resources: both separately, and bundled into courses

We are not only referring here to individual, separate resources. Many people are now familiar with open courses, or “MOOCs” as they are often called, after the fact of their being open and online allows many people to access them at once. These MOOCs may link together many individual open resources on the web, and like those individual resources, they can be also be used both formally and informally. A MOOC could thus be thought of as a higher-level resource. People often recommend MOOCs to colleagues and friends, and MyLK could also recommend MOOCs to MyLK learners, in just the same ways (to be discussed below) as it recommends individual resources.

MOOC content is still mainly put together by providers of education or training – often universities – using platforms that may or may not be institutionally based. But there is another kind of open “course” that we can imagine: one put together, whether deliberately or not, by a self-directed learner browsing through open resources. This may not be thought of as a “course” in the usual sense, but it is like a course in that it includes a set of related resources, and could have other information alongside them. MyLK users could, in effect, say: “This is the set of resources I found helpful in moving from where I was to where I am.”  People are already familiar with sets of resources in more limited contexts: playlists on YouTube or albums in photograph applications. This wider concept of a user-generated “course” could include not just videos and photos, but any resources.

Getting the level right – the Goldilocks principle

If the MyLK advice generator is to give advice that is useful, and welcomed by learners, it needs to assess which resources are likely to be useful to each specific learner, because the usefulness of resources obviously differs between learners. No one wants to have a learning resource recommended if they already have studied it. They do not want resources that are at too low a level – these will be seen as a waste of time. Learners will not be able to learn from resources that are beyond their current level of comprehension. Like Goldilocks (“Boucles d’or”, “Goudhaartje”, “Goldlöckchen”, “Riccioli d’Oro”) they want their advice at a level that is “just right”, as well as being relevant to their career or learning goals.

Internet retailers like Amazon have been tackling related challenges for many years. Messages like “People who bought products in your basket also bought…”, as seen in interactions with Internet retailers, could be transformed to “People who highly rated the resources you highly rated also highly rated …” This is one clear way in which advice could be tailored, on the basis of how MyLK users rate the resources that they see for usefulness. Another approach would be possible on the basis of self-assessment. If users could assess their own abilities, on a range of skills and competences, then the resources that they found useful could be recommended to other learners, on the basis that their self-assessed abilities were similar. Two kinds of abilities could be relevant: first, in the area of their career; and second, in the area of general skills that are necessary to benefit from particular resources. One obvious set of abilities that are both highly relevant and relatively easy to self-assess is competence in languages. Whether it is one’s own native language, or another one, learners need to have an appropriate level of competence in the language of the resource.

Feedback, motivation and more

Even if a resource is unsuitable for a particular user at a particular time, the MyLK system could still ask the user why the resource was unsuitable. Is it too advanced, too simple, or simply the wrong topic? Is the learning style unsuitable, or are the accessibility problems? All this information would be useful to store, as it could potentially be used to help give advice to others, and to fine tune the advice generator. To gather this information from users would need a more detailed review process than is common with Internet retailers, and MyLK would need to motivate the user, perhaps by appealing to their altruism and reciprocity: I learn from it, and in return I contribute to it. These are the kind of motives that are apparent also in Wikipedia: given the right environment and culture, people enjoy contributing to others, and to the common good.

A further way to recommend useful resources appears if the MyLK system has information about what the learner’s learning goals are – whether in terms of knowledge, skill, competence, qualification, job or whatever else. But what happens when learners themselves are unclear about what they goals are? Maybe they do not know where they want to go? In the slower-changing world of the past, that is what careers advisors used to be good at. Even now, a good careers advisor might be able to help learners clarify their goals. An automatic system like MyLK cannot emulate a highly experienced careers advisor, but learners can potentially help themselves and each other, if prompted in helpful ways.

Conclusion

Learning resources can be recommended simply through mechanisms similar to Internet retail recommendations. But we believe that MyLK can offer much more helpful and accurate advice if we understand where each user is on his or her career path, and if we create a map to contain those paths and allow each user to locate themselves.

This will be the topic of the following post.

 

Simon Grant and the Endurance team.