Mastearbeit Donau-Universität Krems
While a large number of scientific publications explain the development of prototypes or the implementation of case studies in detail, descriptions of the challenges and proper solutions when implementing learning analytics initiatives are rare. In this chapter, we provide a practical tool that can be used to identify risks and challenges that arise when implementing learning analytics (LA) initiatives and discuss how to approach these to find acceptable solutions. In this way, implementers are given the opportunity to handle challenges early on and avoid being surprised at a critical moment in the project, which will save time, resources, and effort.
This papers focuses on the use of learning dashboards in higher education to foster self-regulated learning and open education. Students in higher education have to evolve to independent and lifelong learners. Actionable feedback during learning that evokes critical self-reflection, helps to set learning goals, and strengthens self-regulation will be supportive in the process. Therefore, this paper presents three case studies of learning analytics in higher education and the experiences in transferring them from one higher education institute than the other. The learning dashboard from the three case studies is based on two common underlying principles. First, they focus on the inherent scalability and transferability of the dashboard: both considering the underlying data and the technology involved. Second, the dashboard use as underlying theoretical principles Actionable Feedback and the Social Comparison Theory.
Talk 1: Transferring learning dashboards to new contexts: experiences from three case studies Talk 2: It’s in your pocket: A MOOC about programming for kids and the role of OER in teaching and learning contexts
Learning Analytics is a promising research field, which is advancing quickly. Therefore, it finally impacts research, practice, policy, and decision making in the field of education. Nonetheless, there are still influencing obstacles when establishing Learning Analytics initiatives on higher education level. Besides the much discussed ethical and moral concerns, there is also the matter of data privacy. In 2015, the European collaboration project STELA started with the main goal to enhance the Successful Transition from secondary to higher Education by means of Learning Analytics. Together, the partner universities develop, test, and assess Learning Analytics approaches that focus on providing feedback to students. Some promising approaches are then shared between the partner universities.
This article introduces the goal and activities of the LAK 2018 half-day workshop on the involvement of stakeholders for achieving learning analytics at scale. The goal of the half-day workshop is to gather different stakeholders to discuss at-scale learning analytics interventions. In particular the workshop focuses on learning analytics applications and learning dashboards that go beyond the implementation in a single course or context, but that have at least the potential for scaling across different courses, programs, and institutes. The main theme of the workshop is to explore how the involvement of different stakeholders can strengthen or hinder learning analytics at scale.
Durch die Erfindung des Computers hat sich das Leben in verschiedenen Bereichen stark verändert: Mobilität und Vernetzung halten Einzug in das tägliche Leben. Um am Puls der Zeit zu bleiben, ist es notwendig auch das bis jetzt nahezu unveränderte Bildungssystem in die Zukunft zu tragen. Ob der zahlreichen politischen Diskussionen konnte man sich bis heute nicht flächendeckend darauf einigen, Informatik als eines der grundlegenden Fächer im Schulbetrieb zu verankern. Ziel der Bildungsinformatik ist es ein Bewusstsein für die notwendigen Kompetenzen der informatischen und medialen Belange der Gesellschaft der Zukunft zu schaffen.
Dieser Beitrag beschreibt die Ziele, Methoden und Interessengruppen von Learning Analytics. Darüberhinaus werden die unterschiedlichen Struktu- ren der Daten von Big Data und Learning Analytics betrachtet. Dargestellt werden ferner die Planung des Einsatzes von Learning Analytics und die Phasen der technischen Implementierung.