Introducing Hunch

One of the activities for the first week of the lak11 MOOC is to get started with using Hunch and reflect on it as a model for learning.

What is Hunch?

From the Hunch about page it is an application of machine learning to provide recommendations to users about what might be of interest to them on the web. It’s the work of a bunch of self-confessed MIT “nerds”.

Using Hunch

Creating an account on Hunch starts with logging in with either a Facebook or Twitter account. Went with Twitter. Some of the other LAK11 participants have queried the privacy question with this and then answering the questions.

The site now asks a range of questions using a fun (ish) approach using photos, increasing interest somewhat. It also provides feedback on what others have answered.

As others have noted there is a North American cultural bias to the questions.

Interesting, only 4% of respondents said they didn’t have a Facebook account.

After answering a few more than the minimum 20, Hunch presents a selection of recommendations. In this case five recommendations each for magazines, TV shows and books. I’m assuming that the categories of answers were also based on my answers. the recommendations are all good or close matches. All three categories included examples I had read/watched and enjoyed.

So, it appears that Hunch is designed with badges to earn as you use the site more, provide more information. There are other features sought to encourage connections and feedback between users. After all that would appear to be the currency that Hunch needs to generate its recommendations. The more connections, the better the math, the better the recommendations.

And perhaps that is the problem. I don’t feel the need for a site like Hunch to get the recommendations I want. I already have strategies, social networks and information sources that I use. I can’t see myself expending the effort on this sort of site. The question that is how many others might be bothered to provide this information?

That said, it does appear to be working fairly well already.


After using Hunch, the LAK11 syllabus asks

What are your reactions? How can this model be used for teaching/learning?

and suggests sharing views in the discussion forum. I’m going to reflect here first and then check the discussion forum. Mainly because the following will be more stream of consciousness dumping than well-considered insight.

The obvious academic question to ask is what is meant by teaching/learning. Most of my experience has been/will be with more formal areas of learning and teaching and thus my reflections are likely to be coloured/biased by that experience.

My first observation (taking the viewpoint of a teacher) would be that any additional information about my students would be useful. Especially if a system like Hunch was able to provide useful recommendations. Such recommendations would be useful to the students as well, but I wonder how much freedom they would have to take up those recommendations within a formal educational setting. It would seem that what freedom does exist, lays with the teaching staff.

Such information in a L&T situation might feel somewhat similar to some of the learning style surveys that are around. Similarly, I wonder how much these type of things would reinforce existing categories/beliefs, rather than offering new paths or opportunities.

Am feeling that I’m somewhat ill-informed about the nature and capabilities of Hunch and thus somewhat ill-informed to reflect on its applicability to learning and teaching. Drawing some conclusions from the little I know means that they are building models based on answers to the questions. Then comparing that with models of the items/recommendations to come up with matches.

I wonder how difficult building these models would be for learning and teaching. It’s my understanding that disciplines such as physics have built fairly complex conceptual models of the domain, in particular for undergraduate studies. But it’s also my belief that the construction of such models was a fairly resource intensive task. Will the resource intensive nature make it difficult to implement a L&T focused Hunch? Then making the connections between other models would seem difficult. Hunch after all hasn’t handled the cross-cultural aspects all that well (probably was designed to retain the North American emphasis) and operates in an area (commercial products and services) in which there has been a lot of research and a lot of commercial interest/resources.

From the perspective of an motivated learner, a L&T flavoured Hunch could be very useful. But what percentage of learners would use such a system? e.g. given my reservations about using the current Hunch. Especially given that Hunch relies somewhat on the contributions the users make to the system. Given the limited percentage of folk that contribute content to social networking sites this is likely to limit a L&T flavoured Hunch even further.

This perhaps sums up my cynical view of the difficulty of effectively and appropriately applying analytics in L&T.

Let’s see if the Moodle discussion forum has more positive contributions.