Content uploaded by Martin Ebner
Author content
All content in this area was uploaded by Martin Ebner on May 23, 2018
Content may be subject to copyright.
A preview of the PDF is not available
Lernen unter der Lupe - Wie „Learning Analytics“ individuelles Lernen unterstützt
Abstract and Figures
Learning Analytics ist die Möglichkeit aus gesammelten Daten durch entsprechende Analyseverfahren neue Erkenntnisse für die pädagogische Praxis zu erhalten. In diesem Beitrag soll eine kurze Einführung gegeben werden, sowie anhand von zwei konkreten Beispielen gezeigt werden wir man es für den Schulalltag nutzen kann.
Figures - uploaded by Martin Ebner
Author content
All figure content in this area was uploaded by Martin Ebner
Content may be subject to copyright.
ResearchGate has not been able to resolve any citations for this publication.
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.
Data-driven learning in combination with emerging academic areas such as Learning Analytics (LA) has the potential to tailor students’ education to their needs [1]. The aim of this article is to present a web-based training platform for primary school pupils who struggle with the acquisition of German orthography. Our objective is the improvement in their writing and spelling competences. The focus of this article is on the development of the platform and the details concerning the requirements and the design of the User Interface (UI). In combination with Learning Analytics, the platform is expected to provide deeper insight into the process of spelling acquisition. Furthermore, aspects of Learning Analytics will help to develop the platform, to improve the exercises and to provide better materials in the long run.
One of the first and basic mathematical knowledge of school children is the multiplication table. At the age of 8 to 10 each child has to learn by training step by step, or more scientifically, by using a behavioristic learning concept. Due to this fact it can be mentioned that we know very well about the pedagogical approach, but on the other side there is rather less knowledge about the increase of step-by-step knowledge of the school children.
In this publication we present some data documenting the fluctuation in the process of acquiring the multiplication tables. We report the development of an algorithm which is able to adapt the given tasks out of a given pool to unknown pupils. For this purpose a web-based application for learning the multiplication table was developed and then tested by children. Afterwards so-called learning curves of each child were drawn and analyzed by the research team as well as teachers carrying out interesting outcomes. Learning itself is maybe not as predictable as we know from pedagogical experiences, it is a very individualized process of the learners themselves.
It can be summarized that the algorithm itself as well as the learning curves are very useful for studying the learning success. Therefore it can be concluded that learning analytics will become an important step for teachers and learners of tomorrow.
With the increase in available educational data, it is expected that Learning Analytics will become a powerful means to inform and support learners, teachers and their institutions in better understanding and predicting personal learning needs and performance. However, the processes and requirements behind the beneficial application of Learning and Knowledge Analytics as well as the consequences for learning and teaching are still far from being understood. In this paper, we explore the key dimensions of Learning Analytics (LA), the critical problem zones, and some potential dangers to the beneficial exploitation of educational data. We propose and discuss a generic design framework that can act as a useful guide for setting up Learning Analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency. Furthermore, the presented article intends to inform about soft barriers and limitations of Learning Analytics. We identify the required skills and competences that make meaningful use of Learning Analytics data possible to overcome gaps in interpretation literacy among educational stakeholders. We also discuss privacy and ethical issues and suggest ways in which these issues can be addressed through policy guidelines and best practice examples.