I think that a learning style is something to keep in mind; however looking at courses at the university level where you have 20-30 unique individuals taking the course, what do you do? create a specialized course for each individual based on their learning style? It's a waste of time! If I were tutoring one person, learning styles might be more useful.
I do think that it's a narrow way of defining how one learns. Much more goes into learning that just learning styles.
I've seen learning styles - or some similar learner classification model - used as part of forming learner profiles. Through an initial profile survey (such as Hunch questions), learners are grouped into established categories. I see the current necessity of some classification - our systems for personalization at the higher education level are not developed sufficiently to abandon classifications yet. However, I imagine - and we'll get into this next week - as we start accessing a large enough pool of data, we'll be able to do away with classifying individuals by learning styles/preferences/intelligences and instead base recommendations and personalizations on the profile of learners. In this case, the learner profile (past learning habits, state of knowledge, evolving context) serves as the basis for adapting the content and the learning path. Our profile - unlike categorization in terms of learning styles - changes constantly.
I hope we get a chance to push on this concept of learner profiles during the course. I agree that simplistic classification of individuals may be of questionable merit, and I also agree with George's statement that we probably can't get away from classification altogether. My hunch -- pardon the pun -- is that we may see learners employ a variety of strategies and tactics. Lumping those choices into big, immutable categories seems to be the way we've dealt with learner styles/profiles/etc. in the past but I wonder if there is merit in thinking about the effects of, say, learners switching to a very different "profile" depending on context. I wonder if there's some middle ground between big categories and continuously changing profiles.
Hunch (the web-based recommendation service) seems to make the assumption that I have one, persistent and more or less predictable set of preferences. That's just not true for me, and I suspect it's not true for a lot of other people too. I don't work the same way as I play. Or do I? I remember reading in another thread in this forum that it's a bit unnerving to see a machine be able to predict what I like or dislike. It's certainly worth thinking more about this sentiment!
Hunch (the web-based recommendation service) seems to make the assumption that I have one, persistent and more or less predictable set of preferences. That's just not true for me, and I suspect it's not true for a lot of other people too. I don't work the same way as I play. Or do I? I remember reading in another thread in this forum that it's a bit unnerving to see a machine be able to predict what I like or dislike. It's certainly worth thinking more about this sentiment!
Hi Christopher - good to see you contributing given your chaotic schedule with moving!
Great points here: "I wonder if there is merit in thinking about the effects of, say, learners switching to a very different "profile" depending on context. I wonder if there's some middle ground between big categories and continuously changing profiles."
My colleague at TEKRI - Jon Dron - has been working on what he calls a "context switcher" for allowing individuals to change their information flows and social interactions based on their context. We are running an elgg install (called the Landing). We're using this as a social network site to create a virtual learning/social commons. As more of our social identities bleed into each other, we need a means to filter and share information and activity based on context. Jon's work is quite forward thinking in this regard...and your comments of balancing fluidity (profiles) and rigidity (categories) fits nicely in the discussion of context switching.
Great points here: "I wonder if there is merit in thinking about the effects of, say, learners switching to a very different "profile" depending on context. I wonder if there's some middle ground between big categories and continuously changing profiles."
My colleague at TEKRI - Jon Dron - has been working on what he calls a "context switcher" for allowing individuals to change their information flows and social interactions based on their context. We are running an elgg install (called the Landing). We're using this as a social network site to create a virtual learning/social commons. As more of our social identities bleed into each other, we need a means to filter and share information and activity based on context. Jon's work is quite forward thinking in this regard...and your comments of balancing fluidity (profiles) and rigidity (categories) fits nicely in the discussion of context switching.
I think the big problem with recommender systems for this kind of thing is that to learn is to change, so past tastes are a very poor predictor of future needs.
RE context switching, absolutely: we are different personas with different needs at different times when we have different intentions and different contexts. To assume that we are a single kind of thing, socially and personally, is just wrong. Quite a bit of the work I have done on recommendation strategies is to try to provide recommendations which do not rely so much on a mined model, but which allow people to create their own disembodied user models. A very simple example would be the use of tags to describe what it is that we value in a resource. Once we have done that, others who value the same things can explicitly choose that facet of their interests to find stuff that helps. Combining this with implicit and explicit ratings in the context of such a tag can lead to recommendations that relate to a particular need in a particular context, rather than an assumption that we are a simple flat set of attributes.
Another approach is to use scrutable learner models, as Judy Kay and colleagues have done. Scrutable systems let you vary the parameters that are used to define you. I quite like that but it runs into difficulties on implementation because it requires people to understand how the user model is being used to provide recommendations, which adds layers of complexity most people are not keen to deal with.
Using clusters such as learning styles might be one way to help simplify that though I am *very* wary of stereotyping of this nature, especially in the light of Coffield et al's wonderful and thorough demolition job on learning style theories a few year's back. Essentially, learning styles are mostly stuff and nonsense and, even when they can reliably identify sets of related learners, offer no reliable means to apply that knowledge to achieve better learning. However, maybe there is scope for emergent, bottom up clusters to be used, identifying similarities through statistical/network analysis techniques like k-means, though it would be hard to give labels that people could reliably identify with using such a technique. Interesting possibility though.
RE context switching, absolutely: we are different personas with different needs at different times when we have different intentions and different contexts. To assume that we are a single kind of thing, socially and personally, is just wrong. Quite a bit of the work I have done on recommendation strategies is to try to provide recommendations which do not rely so much on a mined model, but which allow people to create their own disembodied user models. A very simple example would be the use of tags to describe what it is that we value in a resource. Once we have done that, others who value the same things can explicitly choose that facet of their interests to find stuff that helps. Combining this with implicit and explicit ratings in the context of such a tag can lead to recommendations that relate to a particular need in a particular context, rather than an assumption that we are a simple flat set of attributes.
Another approach is to use scrutable learner models, as Judy Kay and colleagues have done. Scrutable systems let you vary the parameters that are used to define you. I quite like that but it runs into difficulties on implementation because it requires people to understand how the user model is being used to provide recommendations, which adds layers of complexity most people are not keen to deal with.
Using clusters such as learning styles might be one way to help simplify that though I am *very* wary of stereotyping of this nature, especially in the light of Coffield et al's wonderful and thorough demolition job on learning style theories a few year's back. Essentially, learning styles are mostly stuff and nonsense and, even when they can reliably identify sets of related learners, offer no reliable means to apply that knowledge to achieve better learning. However, maybe there is scope for emergent, bottom up clusters to be used, identifying similarities through statistical/network analysis techniques like k-means, though it would be hard to give labels that people could reliably identify with using such a technique. Interesting possibility though.
Funny you should mention scrutable learner models: In Denmark I will be working with a large team that includes Susan Bull (a close colleague of Judy Kay's -- with luck we'll manage to get some of her attention too) and we will be focusing on Open Learner Modelling. My contribution to the project will be the implementation of a communication and negotiation layer to communicate the learner models to the interested parties (students, teachers, administrators, parents, etc.). The big challenge, as you rightly point out, is how to communicate the model of a complex thing (the learner model in this case) without using a complex representation. I hope we'll take up some issues around representations of complex entities in Week 4 with tools like Gephi.
The error of this models can be derived of that takes into account the interests of a group and not the individual interests.