Our Research Projects

“Don’t value what we measures -> measure what we value.”Bob Mislevey

The realization of the DELTA project (Towards Digital Education with modern Learning Technologies and Assesment approaches) takes place in 5 stages:
  1. Writing personas: creation of so-called “personas” which reflect key users in the future of digital education at the Goethe University. Personas are developed on the basis of different empirical data – to this end, survey and structured interviews will be used.
  2. Expert group concept mapping study: An expert GCM study will be run to elicit success criteria for the development of a digital education infrastructure at the Goethe University.
  3. Innovation workshops: organisation of innovation workshops involving key actors at the Goethe University, external research institutes, the state ministry of education in Hesse and international experts, to formulate a plan for digital education at the Goethe University.
  4. Fruits & challenge workshops: organisation of workshops for the identification of so-called “low hanging fruits“, that is quickly accessible opportunities and long-term challenges to digital education at the Goethe University.
  5. DELTA report and conference: organisation of a wrap-up conference which summarises project outcomes and necessary activities for achieving DELTA plan objectives by 2025. The DELTA report will be handed to the President of the Goethe University at the conference.

Joint project Digital Education Architecture Open learning resources in distributed learning Infrastructures - EduArc

State of the art In order to realize the potential of digitization for higher education, a cross-university digital ecosystem is needed that provides digital educational resources for distributed use (see M Kerres & Heinen, 2015). The BMBF feasibility study by Blees et al. (2016) on the infrastructure for open educational resources has shown for the higher education sector: There is an increasing amount of digital content on learning platforms and there are technologies available to provide this via repositories. What is needed is a solution that is based on a networked, federated infrastructure of decentralized repositories (see Heinen al., 2016) and that makes targeted use of open educational resources. Digital educational architectures must thus relate to the digital research infrastructure, in particular to the infrastructures for literary and research data and research data management, and the associated developments in the scientific information and librarianship (under the keyword "open science"). take into account (see Siegfried, 2017). For example, the Information Infrastructure Council (RfII) has issued recommendations to the GWK on the development of a national research data infrastructure. Since 2016, the DFG has been funding the Generic Research Data Infrastructure (GeRDI) project, which is developing a model for a distributed infrastructure. A federal infrastructure for research data in education is funded by the BMBF project "Verbund Forschungsdaten Bildung (VFDB)". At European level, the High Level Expert Group on the European Science Cloud (EOSC) deals with similar issues. Furthermore, the GOFAIR initiative aims to treat research data fairly, which was initiated by the BMBF and the Ministry of Science of the Netherlands. Similar to BW, Berlin and other federal states, the Digital University NRW has commissioned the planning of a subsidized infrastructure, through which open educational resources of the universities are made available and distributed. Project overview The project is developing a proven design concept for distributed learning infrastructures that will federate digital educational resources and other study-related information. It explores the technical, didactic and organizational conditions of an educational architecture that results from the networking of the digital infrastructure of universities and the interaction of state, public and private actors. It brings together distributed systems through open standards and interfaces, and is open to integrate future content providers and users. The announced architecture is to be interpreted for open as well as not openly licensed (references to) resources, because in teaching also perspectively different license variants will be relevant and expedient. The project focuses on the challenges posed by the dissemination of openly licensed educational resources (OER / CC licenses) in an "informationally open ecosystem". Depending on the license, these can be used, commented on and edited by teachers and students free of charge , mixed and made available again on the net, which opens up special didactic opportunities for higher education (Heinen et al., 2016). At the same time there are technical-conceptual challenges in the provision of OER, but also in access to distributed repositories, especially in dealing with edits and versions, the return of user-generated data to the author of the OER, and the resulting quality mechanisms for reputation acquisition ( for authors) and quality assurance (for resources). Methodical approach and interdisciplinary cooperation The project pursues a design-oriented research approach in which design concepts are developed with prototypes and field trials. The type and intensity of use is recorded on the basis of (anonymised) objective data (behavioral traces), in addition to online questionnaires and guideline-based interviews with which the various groups of users are asked about their experiences and assessments. First of all, the universities represented by the actors function as pilot universities. In addition to the spatial and organizational proximity to the applicants, predecessor projects of the applicants to the universities provide infrastructures in various stages of development, which can be used as the basis for a distributed learning infrastructure. On the basis of preparatory work, a dataset is defined that defines success parameters at various levels (technical, didactic, organizational) in order to be able to record the impact of the project and to verify the achievement of the objectives. Mind map - Essential functions s Literature
  1. Blees, Ingo, Hirschmann, Doris, Kühnlenz, Axel, Rittberger, Marc, Schulte, Jolika, Cohen, Nadia, … Khenkitisack, Phoutsada. (2016). Machbarkeitsstudie zum Aufbau und Betrieb von OER-Infrastrukturen in der Bildung. Frankfurt: Deutsches Institut für Internationale Pädagogische Forschung.
  2. Drachsler, H., Verbert, K., Santos, O. C., & Manouselis, N. (2015). Panorama of Recommender Systems to Support Learning. In F. Ricci, L. Rokach, & B. Shapira (Hrsg.), Recommender Systems Handbook (S. 421–451). Springer, Boston, MA.
  3. Euler, D., Hasanbegovic, J., Kerres, M., & Seufert, S. (2006). Handbuch der Kompetenzentwicklung für eLearning Innovationen: Eine Handlungsorientierung für innovative Bildungsarbeit in der Hochschule. Bern: Huber.
  4. Kerres, M. (2015). E-Learning vs. Digitalisierung der Bildung: Neues Label oder neues Paradigma? In A. Hohenstein & K. Wilbers (Hrsg.), Handbuch E-Learning. Köln: Deutscher Wirtschaftsdienst.
  5. Kerres, M., Getto, B., & Kunzendorf, M. (2010). RuhrCampusOnline: Strategische Hochschulkooperation in der Universitätsallianz Metropole Ruhr. Zeitschrift für Hochschulentwicklung, 5.
  6. Kerres, M., & Heinen, R. (2015). Open informational ecosystems: The missing link for sharing resources for education. Interna- tional Review of Research in Open and Distributed Learning, 16.
The TIILA project wants to give agency to the users by allowing them to decide on the Learning Analytics that will be done with their data. With this outspoken approach, we intend to raise the user's data literacy, teach them privacy awareness and enlighten them about dangers and opportunities of learning analytics. The German sociologist Niklas Luhmann defined trust as a way to cope with risk, complexity, and a lack of system understanding. Following this, we believe that users will at some point feel the urge to investigate the Learning Analytics system in place. We want to allow for this by providing automated trust-building features. In our opinion, such features encourage a gain of trust in the system leading to a raise in commitment and engagement with Learning Analytics. The hypothesis of this project is that this will ultimately improve the overall impact of the Learning Analytics.  
 
The infrastructure consists of three core engines and possibly a variety of research project engines docking in.
Literature
  • Ciordas-Hertel, Schneider, Ternier, Drachsler, Towards a Trusted Big Data Learning Analytics Infrastructure, In Review, J.UCS Journal of Universal Computer Science
Serene is a tool to support self-regulated learning, both in research and in everyday practice.

Planning
Serene supports the planning process by offering a template that is especially suited for the creation of learning goals. Learning goals have to be associated with a point in time and learners are asked to also create sub-plans on how they aim to achieve the goals.

We are currently working on features to analyze the written goals via NLP and give direct feedback during the creation (e.g. keeping them SMART).

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Monitoring
Monitoring of the learning in serene is done via an interface that
  • Asks the learner for the progress on their tasks
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  • Asks them for the reasons that affected their learning
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Learners can therefore already connect their goal achievement performance with the reasons, providing them with better insight why they perform particularly good or bad.

Reflection
The reflection tab has several visualizations that are there to help the learner reflect on his progress.

The screenshot shows an example of two possible visualizations, others can be easily activated.

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We are currently working on functionality that will give individualized recommendations to learners based on their goal achievement habbits.

Isn't searching on a search engine a kind of communication between man and machine? Would it make sense to turn this process around and also ask questions about the person searching?

In TALK we would like to do exactly that and ask questions about the circumstances, goals and interests of information seekers. This approach could enable individual knowledge about learners and thus make communication between learners and learning environments more natural and intuitive.

Related topics:
  • Learner Modeling
  • Domain Modeling
  • Dialogue-based Learning
  • Personalization
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If you are interested in the products we have developed in the research projects navigate to the product page.