Learning Dashboards

1984

This post is written for our thesis students. But do feel free to join in on the conversation!

As you may or may not know, learning analytics and learning dashboards, a Quantified-Self take on learning, is an important part of our research here at the HCI research group. Most of you have taken Prof. Duval‘s classes and have had to blog, tweet, read and comment on each others’ work. For your thesis, we’re asking you again to blog and track time with Toggl. But the reasoning behind all this might not be that clear to all of you.

First of all, these activities force you to stand still and think about what you are doing. This is certainly the case for blogs, where you have to do proper research and think before you start writing anything down. This alone will make you understand the subject matter better and already puts you on the path to becoming a better learner.

But what about Toggl? And Twitter? What uses have they except for tracking your every move? Are we really watching you? Is HCI Big Brother?

The data we track has one main goal. To create learning dashboards to improve your learning process. This way we can feed the data back to you to provide you ways of finding patterns in your habits and change your ways to become a better learner.

Let’s explain this with two examples:

You go out drinking quite late. You have an early class next morning. The professor isn’t motivating you much with that book he’s reading from. You have a heavy, greasy lunch. Your afternoon is free so you decide to work on your thesis. Hours go by but you eventually feel like you haven’t accomplished anything.

Quite a depressing example. Here’s another:

You’ve had a great night sleep. You have an early class and it’s super interesting! The professor’s a genius! Your afternoon is free, the weather is awful so you decide to head to the library. You work a few hours on your thesis and feel super productive.

While in these extreme circumstances it’s obvious what affects your motivation and productivity, there are many more factors that influence your learning.  And with dashboards, we can present you that information through interesting visualizations that allow you to find the pieces of data relevant to your learning, so you can figure out what parameters influence you positively, or negatively. You can figure out what works for you, and what doesn’t!

We’re not just talking general guidelines. Too much alcohol will make most people unproductive. But noise? Music? Weather? These things can have a different effects on people.  And there’s a good chance there are factors you never even considered!

To keep the overhead low, we decided we can use Toggl once more. We’d like to define a bunch of categories which would help you log certain personal and environmental information, like mood (happy, sad,…), physical state (hungover, active, awake,…), location (library, home, parents, friends,..), noise (roommates, music, birds), how your time was spent (productive, waste of time,…)… The list hasn’t been set in stone, but you get the idea.

And before we start, we’d like your feedback on this matter. What would be interesting data you wished you’d have a better view on? What do you think affects your motivation and productivity? And what are your general thoughts on learning dashboards anyway?

4 thoughts on “Learning Dashboards

  1. Laurens Van Keer (@LVanKeer) says:

    Personally, I’d just like to get a sense of when I’m most productive. I can then correlate this with my own tracking (sleep, work, diary entries, Internet history, auto screenshots/webcam pics, RescueTime logs and exercise logs). I’m not too shy about sharing this data, but still a bit reluctant :)

    So just an overview of time spent during specific hours would be great (eg. by summing all activities during a specified period on one 24-hour timeline). Also, a rating of satisfaction might be interesting. Ideally this would be provided in Toggl already, eg. a simple 3-point scale or better yet, a like/dislike (with a 3-point scale I’d probably get a lot of 2’s, so only the outliers are actually interesting), but maybe tags could be used for this purpose.

    • api4kul says:

      Yeah, me too actually. If I have to use toggle anyway, I would like to know than just ‘Oh shit I didn’t get my hours this week!’. But maybe not only when but also where I am most productive or what by doing what tasks (or method of working). Also the way I am using it know it is more about the hours I am doing than the work I am accomplishing. And after all we look at the past to predict the future and we are not interested in getting the hours but getting the work finished at the end of this year.

  2. thomasdemoor says:

    I think this kind of tracking can optimize our work efficiency. I’m especially curious about which kind of music, or no music at all, influences my productivity. Toggl is a perfect way to track this kind of data, since all the thesis students already use this. The danger is the overload of data to fill in, so I think the tracking has to be designed in such a way that you don’t lose much time by filling in the different features.

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