Daniel Schön

Situation-aware choice of the most accurate positioning system

With the increasing proliferation of powerful mobile devices, the use of location-based services has gained noticeable momentum. However, new challenges arose with the higher user mobility. Especially in the context of location-based services, one of these challenges is the problem of selecting the most suitable positioning technology. This paper gives a brief overview of the three most common techniques used by location-based services today. We introduce various schemes on how to select the most suitable system, based on the systems estimation of the positioning error. The schemes are thoroughly evaluated by using an emulation with real-world data, recorded within and around an office building of the University of Mannheim.

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.

Prompting in Pseudonymised Learning Analytics - Implementing Learner Centric Prompts in Legacy Systems with High Privacy Requirements.

Learning analytics have become a well-considered aspect of modern e-learning environments. One opportunity of learning analytics is the use of learning process data enabling lecturers to analyse students’ learning progression as well as to identify obstacles and risks. With this analytics knowledge, lecturers may want to scaffold students’ learning activities to improve the learning progress and overcome obstacles or risks. Prompts are known to be a possible solution for such scaffolding mechanics. However, implementing prompts into existing legacy systems in learning environments with high data privacy concerns is quite a challenge. This research shows how a prompting application has been implemented into an existing university environment by adding a plugin to the local e-learning platform which injects user centric prompts to specific objects within their e-learning environment. The prompts are dynamically loaded from a separate learning analytics application which also collects the students’ learning trails and progress. The system is evaluated in two courses in the fall semester 2017 with more than 400 students altogether. The system collects up to two thousand student events per day. An in-depth empirical investigation on how various prompts influence students’ learning behaviours and outcomes is currently conducted.

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.