DiSEA: Analysing Success and Dropout in Online-Degrees

April 2021

Although several research works show that students at risk of dropping out of a course or a study program can be predicted with relatively high accuracy, this information has so far often not been accessible to course directors, teachers, or students. The DiSEA project aims to research this issue and close this gap in the context of online-degrees. Building on previous research, machine learning methods will be used to identify risk and success factors. The overall aim is to develop an integrated model to predict success in digital study programs and derive recommendations and interventions for course design, student counseling, and student selfreflection. A user-centered design involving all stakeholders will be followed.

In Companion Proceedings of the 11th Learning Analytics and Knowledge Conference (LAK’21), p. 261-269. Workshop on Addressing Dropout Rates in Higher Education, Online – Everywhere, 2021.

Student-centered Development of an Online Software Tool to Provide Learning Support Feedback: A Design-study Approach

October 2022

Students in online degree programs have a higher risk of dropping out (Diaz, 2002; Beard and Harper, 2002; Baker et al., 2015). The use of learning support tools such as learner dashboards (LD) can promote self-regulated learning, which can have a positive impact on student learning (Jivet et al., 2018; Konert et al., 2016). This paper presents a three-stage design study and shows how the layout of the proposed LD was implemented from the initial digital design to a low-fidelity prototype. First, the developed wireframes were checked for consistency with respect to the Gestalt laws (Wertheimer, 1922). From the resulting wirefame design, a clickable low-fidelity prototype was developed. In the second step, this interactive prototype was reviewed by students (n=24) respect to the seven interaction principles (DIN EN ISO 9241-110, 2020). In the third step, the revised prototype was subjected to an eye-tracking procedure using the Thinking Aloud technique (n=10). The results so far show that the LD should be presented at a reduced information level during initial access, but that this level can be supplemented by additional elements if necessary. The navigation hierarchy should be kept flat and the information should be easy to understand.

Drzyzga, G. and Harder, T. (2022). Student-centered Development of an Online Software Tool to Provide Learning Support Feedback: A Design-study Approach. In Proceedings of the 6th International Conference on Computer-Human Interaction Research and Applications, ISBN 978-989-758-609-5, ISSN 2184-3244, pages 244-248. DOI: 10.5220/0011589100003323