Melanie Klinger

Customizable Learning Scenarios for Students’ Mobile Devices in Large University Lectures: A Next Generation Audience Response System

Audience Response Systems are a common sight in today’s university lectures. They are used to increase student’s motivation, self reflection and activation during large lectures with up to hundreds of students. The wide distribution of mobile devices supports this development, as teachers no longer have to hand out special hardware clickers, but can build on a variety of mobile web applications, which are able to perform classroom activities, like quizzes, lecture feedback and brainstorming. Therefore, most systems do either support a reasonable amount of basic scenarios or are very specialized in a few less common scenarios. But teachers often want to customize an established scenario for their own needs. This could be a small variation like an additional information text to a multiple choice question up to a more complex background logic to perform live experiments. Therefore, we developed a model and wrote a prototype which enables the users to customize their established learning scenarios or even build novel ones. Our teachers are now able to design and customize their own in-class learning scenarios, fill them with their content, use them in their lectures and reflect the results with their students. But besides the technological capabilities, designing a well-founded learning scenario still needs a certain amount of time and didactic experience.

A Model for Customized In-class Learning Scenarios

Audience Response Systems (ARS) are quite common nowadays. With a very high smart phone availability among students, the usage of ARS within classroom settings has become quite easy. Together with the trend for developing web applications, the number of ARS implementations grew rapidly in recent years. Many of these applications are quite similar to each other, and fit into many classroom learning scenarios like test questions, self-assessment and audience feedback. But they are mostly limited to their original purpose. Using another question types or little differences in the existing quiz logic cause considerable effort to develop as they have to be implemented separately. We searched for similarities between the different ARS implementations, matched them to a universal process and present a generalized model for all of them. We implemented a prototype that serves many known scenarios ranging from simple knowledge feedback questions up to complex marketplace simulations. A first evaluation in different course types with up to fifty students showed, that the model satisfies our expectations and offers a lot of new opportunities for classroom learning scenarios.