Time and traffic lights at #lak12

John Fritz's video shows a perfect confirmation of one of the basic pieces of knowledge in learning psychology: if you spend more time on a learning task you'll do better (upto a certain maximum). Why is it so difficult to convince students of this obvious truth? Not so difficult: students choose between study and sport or the pub every day. Some students make wise choices, others succumb to temptations. What interests me most in this context is: how do we seduce students to spend more time? The classic solution was to use force: force them to sit in a classroom for hours, force them to hand in products or to do exams... We all agree that we would prefer not to use force; can force be replaced by a tool that allows students to inspect how much time they spend?
Possible..... It sounds too simple to me, but who knows? 
I think I have higher expectations of something like the traffic lights at Purdue, though, where teachers are involved in specifying what kind of behaviour and performance they expect from students in the course of there unit.

#lak12 OLA o yeah!?

I have enjoyed this week's talk by Simon Buckingham. It is refreshing to hear people talk optimistically about improving education, collaborating over borders, and -very important- empowering learners and teachers. None of that moaning of students are lazy and stupified by games, teachers aren't educated well enough and are only interested in lots of holidays, or there's no money for innovation anyway.....

Inspiring enthusiasm. Thanks!

Last night, awake in bed, I was thinking about using OLA communities to improve education. What is puzzling me is the role of local context.... My fundamental problem is that I just do not think that one way of learning or teaching is better than another, what works for us will not necessarily work for you and vice versa and there are many possible reasons: content, student population, capacity of teachers, available resources, you name it. We do PBL in Maastricht, but I would be the last to say that PBL is the best option everywhere. Taking that thought further I am wondering whether anyone can sensibly interpret an analysis of data without knowing the local context. One of the difficulties of educational research is that research articles almost never contain enough information about the context or concrete implementation of an educational design. Lack of space, but also lack of awareness, probably. Over the last 10 years I have heard quite a number of people trying to bridge that gap by developing educational ontologies.... As far as I know, they've all failed. We just can't get a grip on this. More and more I am inclined to think that it is qualitative research that we need to solve this and not quantitative... Not to say that no quantitative research can be useful, of course.

Will OLA also work for qualitative research data?

#lak12 Side effect or main effect?

I'm enjoying the examples of how visualisation of student data is used to let teachers improve their course...
The software is good at presenting data in a way that makes it easy to get an overview, and the teachers are good at understanding their course content, their students, their context and, thus, good at coming up with measures to improve their education.

A positive side effect is that visualizing student data lures the teachers into thinking more carefully about questions like: what am I trying to achieve? what kind of student behaviour would I like to see? and how can I make that happen? It lures teachers into spending more time at carefully designing their education...

Thinking on further, I'm starting to wonder whether this is a side effect or a main effect. When I teach instructional design this is the behaviour that I would like to see in my students: that they spend time and effort (over and over again) on carefully thinking about what they are trying to achieve, monotoring whether these goals are reached, and keep on trying to improve their education....

In practice, a teacher can never pay attention to all problems and never try out all solutions. I am satisfied with teachers that just pick out one or two points and work on those, and pick one or two other points next time. My experience is, that it is often not that relevant which problems you start with, as long as you keep on trying to improve something.

So once I have lured teachers into spending that time and effort, I don't mind if we switch the software off :-)?

#lak12 contradicting trends...

I have just realized that there are two contradicting trends in educational research:
- analzing big data in pursue of Evidence-Based Education
- small-scale qualitative studies where researchers consciously abolish striving for objectivity and generalizability in favour of deep understanding.

Interesting...

#lak 12 week 5 - part 2

I'm splitting this up, so as not to confuse myself :-)

Six provocations of big data (Boyd & Crawford):
1. Automating Research Changes the Definition of Knowledge.
2. Claims to Objectivity and Accuracy are Misleading - all analysis means interpretation
3. Bigger Data are Not Always Better Data - they can be incomplete, biased, noisy
4. Not All Data Are Equivalent - Context matters!
5. Just Because it is Accessible Doesn’t Make it Ethical
6. Limited Access to Big Data Creates New Digital Divides

This summarizes about all my concerns, I think.
Why didn't we read this earlier on?

#lak12 Week 5

I'm catching up on the readings of week 5 and started of with the big Aspen report.

Nice and down to earth.... in some cases correlations can be useful, but be careful: correlations are not causal relations and they have limited value as predictors. If you want more, you still need to develop theories and test them purposefully. Yep.

In many cases you need to think carefully about what data you need, instead of just collecting everything just because you can... because otherwise you create more confusion and distractions.

I hadn't thought so much about the value of visualization of data (but good to be remembered that visualization techniques contain embedded judgements).

The privacy issues confuse me.... on the one hand I do feel powerless, because I don't know which data about me are collected by whom... and I dread the consequences for the less fortunate people: if you are illegal, you can already not go to a hospital any longer, but in future the police will track you down because of your mobile phone or your Facebook account?

On the other hand, I tend to think this is nothing new. Didn't we hate it when big companies were buying sets of addresses to send (paper) advertisements? well, nowadays I have the right to tell them to stop... And don't we already know much more about our risks to get diseases than 50 years ago? And we still have a reasonable insurance system (at least here in the Netherlands). So, we can expect new legislation to develop. Maybe along the lines of the 'information audit' proposed by Stefaan Verhulst.

#LAK12 Running a week behind...

I'm listening to the recording of the talk by Dragan Gasevic that I missed last week (when I'd missed the fact that it was at a different time :-))

I've been struggling to follow until he started talking about the concrete example of what they do in their own place. I can see how this is useful there, but I cannot link it to my own teaching... I've been thinking why.
It has something to do with the kind of course, which might have to be:
- large (with so many students that the teacher doesn't know them all)
- most interaction with learning material & other students online,
- a domain that can be modelled well (how else can we analyze the content of students' messages or contributions)
- a certain kind of content; some set of concepts or knowledge that is explained or 'delivered' in web resources...
The latter seems in contradiction with Dragan's claim that he does not believe in 'pizza delivery'.

My discovery of the week: I have realized that I do believe that learner analytics can be immensely useful to enable students to self-direct their learning. Big problem with that is that  -even though we know that self-directed learning is good for retention and transfer- most students aren't interested in investing all that time and effort, they are interested in doing as little as possible to make their exam. Of course, there are exceptions... among which the participants of this course :-)

Some things I'm allergic to, though. Evidence-based education... measure whether some treatment works... hey guys, education and medicine cannot be compared so easily. For one thing, in medicine it doesn't matter which doctor prescribes the pills, in education that is not true. And if we know one thing from research it is that the teacher is an important success (or failure) factor. It would be more fair to compare education to psychotherapy really, where the bond between client and therapist has been identiefied as the main success factor. I'll stop moaning about one of my hobby horses now..

#LAK12 listening to John Campbell

I am enjoying this talk a lot more. It is very concrete... using learner analytics to support something that we know that works: a school that cares how their students are doing :-)
Can this help us to make a mentoring system financially feasible?

Half an hour later and John confirmed my ideas: students think that faculty care more about them...
I understand that he is dealing with large numbers of students in a course. I can see this making sense then, because the staff wouldn't know all the students anymore. I would expect that small-scale education where students are not anonymous will do better, both in performance and student satisfaction.

P.S. Relating to my previous post: I particularly like the fact that faculty can change the system's decision. I am sure that this has been an important success factor with regards to acceptance.


Given my previous concerns, I did appreciate one of the last slides :-)

#LAK12 Preparing data for human inspection

This is the sentence that stuck with me from last week, and something that I could see a lot of value in.
We are notoriously bad at keeping an overview over large amounts of data, or for that matter, in filtering data in a sensible way. The latter, of course, requires some careful consideration of which data are important.

Had a long discussion about this last week with one of my colleagues. He advocated the view that we should allow the data to come up with unexpected conclusions and that we would miss those if we always decide beforehand what we want to know. My thoughts: true.... but only adviseable for people who have enough time to do this & look at the stuff that we know is important :-)

#LAK12 Am I the only instructional designer on this course?

I have a background in learning psychology and ITS, but I guess I mostly feel that I am an instructional designer...

As an instructional designer I struggle with the atomistic, almost mechanical view of learning presented today, like learning can be split up into little pieces that can be combined like lego blocks by some system that can take over the role of the teacher. Haven't we tried this in ITS & learning object times? In my world we have come to the conclusion that this view too simplistic, it might work for (non-complex) STEP domains or some part-tasks but it doesn't apply to learning complex tasks. There are fundamental problems here, that cannot be solved with big data or powerful computing.

Not that I think that EDM or LAC is useless..... but I think we should be using it for things that human teachers are not good at (and he, we know why some teachers are more effective than others, ask the principal or the students and they'll tell you :-))

For example, keeping an overview on lots of information from different sources...
- so yes, if you can help me to identify students who are 'bad writers' early on (start looking at writing assignments in different courses, but there might be other predictors too)
- so yes, if you can help me to collect and synthesize information about my communication skills from different courses, exams, people....






I'll think about other areas this week.
Daniëlle

#lak12 - Big data - small questions?

Big data ..... big assumptions?

A large part of big data are things that I do not want or need to know, and a large part of what I want to know is not in the data (or not analyzable). The number of messages between team members doesn't say anything about how well they fuction as a team, and we don't measure the messages that should have been sent :-)
Looking backwards on n Google's story I also wonder: analyzing which colour makes people interact more. So why do we think that more interaction is always better?

Big data..... wrong questions?

Big data make us ask questions about things that can be measured. These questions are not always the most important questions. Can't hurt?
In my world teachers only spend a limited amount of time and attention on improving their course when they get the evaluations. If they spend time on examining which learning resources were used by students, they won't spend it on thinking about the depth of the discussions.


I suppose my preliminary conclusion could be that it is all about asking the right questions.
It is also, howver, very much about not asking the wrong qeustions :-)

Starting with my first MOOC #LAK12

Just a brief introduction... My name is Daniëlle Verstegen and I am an assistant professor at at the department of educational development and research of the faculty of health, medicine and life sciences of Maastricht University in the Netherlands (a direct colleague of Jeroen Donkers).

I have a background in learning psychology and cognitive scienc. I have worked mostly in the field of instructional design and e-learning. It is probably the instructional designer in my that always wants to start with the question: what's the problem? and what's the goal? Analyzing learner behaviour, but what for?

My second comment is probably a frustration from knowledge-management-hype days: please let's not try to share too much information, because nobody will use it (or even read it).

Enough for now, I am curious how my views will develop over the weeks. We are following this course with a few colleagues and we intend to pull the discussion into our workspace (face-to-face as well). Very old-fashioned :-)

Daniëlle