LearningLab er på konferanse i Berlin med testindustrien, først og…
I just came home from a two-day conference in a Dutch forest on Computer Assisted Assessment, happy and satisfied, thinking that this was a really good conference. But what made it so good? It couldn’t have been only the relaxed atmosphere in the forest, surrounded by wild rabbits and greenery, could it?
The conference spanned from online feedback and games to large halls for sitting 500 students for a multiple choice test – with computers. The conference also discussed the competence of teachers, the future challenges of the field, and the relationship with learning analytics. A lot of pilots, especially from the Dutch context, were presented, and the common goal seems to be to use technology to enhance both efficiency and quality of feedback and learning.
I think what I enjoyed most about the conference was that I could go from a presentation of gamification (without it being said so) to a presentation on feedback and large scale summative assessment, and it helped me think about the connections between these. With feedback there is a challenge to get the students to take the feedback and use it for further learning, and it is also a challenge to make the students do formative tasks for learning. They are strategic and exam-driven. But – by implementing knowledge from adaptive testing, learning analytics and gamification we can counter those challenges and maybe be able to build systems, like the math garden, or the statistics factory, that in itself is motivating and that makes the students solve more tasks, and get better at mathematics and statistics even if they are candidates for failing the course.
I’m not sure if the idea works for other kind of knowledge than procedural. And how it can be combined with for example a flipped classroom approach for those subjects. I also think that if we were to adopt such a approach to learning mathematics and statistics it would be necessary to carefully design the gamification principles and to adjust them along the way to make an optimal system. The result will be to help students train more, and build the feedback in as a game principle, and hence something you learn by trial and error, and is motivated to uncover by increasing your rating for example.