28.05.2024
Statusvortrag von Herrn Sebastian Kucharski
The combination of Learning Management Systems (LMS) and Adaptive Learning Systems (ALS) can be used to address the one-size-fits-all problem of most LMS. The goal is to improve overall learning gains by taking into account the learners´ heterogeneity, e.g. in terms of their prior knowledge or motivation, instead of presenting the same learning content to all learners in the same way without considering or supporting the individual. At the same time, with this combination the disadvantages of ALS, such as the typically high cost of creating adaptive means, can be partially reduced. In this context there are two relevant user groups in terms of authoring Adaptive Learning
Mechanisms (ALM) that are applied by ALS - i.e. instructors who want to take advantage of the mechanisms and researchers who want to investigate which mechanisms are beneficial. Due to the lack of a systematic analysis of the approaches to support these two user groups in this context, a scoping review was conducted with the goal of appraising related work and deriving the foundations for the research questions of this thesis. It was found that the proposed combined approaches typically have three drawbacks that limit their applicability. 1) ALM can often not be used for different LMS. 2) ALM only partially take existing content and interaction data into account. 3) The authoring capabilities provided for ALM are often limited and tend to be oriented towards users with a high level of technical expertise. This leads to the goal of this thesis, which is to investigate how instructors and researchers can be intelligently supported in system-independent modeling of ALM that can be used with existing data in different LMS.
First results towards reaching this goal as well as the further research direction are presented.