NOVA:ea

The optimization of examination conditions is a central topic for universities. In this context, electronic assessments are increasingly being used instead of traditional face-to-face examinations, a trend that has been additionally driven by the Corona crisis. As part of the project, the project partners are currently conducting over 75,000 e-exams. However, the upward trend in e-assessment formats is affecting all higher education institutions in Germany. E-assessments pose a variety of didactic, diagnostic and technical challenges to universities in general and to the project partners in particular.

In the project NOVA:ea, the implementation of student-friendly e-assessments is therefore being implemented, scientifically accompanied and evaluated.

NOVA:ea aims to promote academic education in all biographical phases with student-friendly e-assessments. It places student diversity at the center of the didactic, technical and diagnostic design of e-assessments. The guiding principle is to link student-centered innovation with technological advancement, which is used as open source for e-assessment systems at more than 230 universities.

Within the framework of the project, the FernUniversität in Hagen is pursuing three central goals:

Classification, development, and criteria-driven selection of task formats, as well as the development of project-wide guidelines for assessment diagnostics and error analysis.
Empirical consideration of the role of student diversity for the didactic, technical, and psychometric design and optimization of competency-oriented and diversity-appropriate e-assessments based on the Dynexite e-assessment system.
Identification and consideration of the determinants of e-assessment acceptance among teachers and students as well as the design and implementation of measures to increase acceptance.

CATALPA – Center of Advanced Technology for Assisted Learning and Predictive Analytics

As a central scientific institution of the FernUniversität in Hagen, CATALPA integrates theoretical knowledge with practical applications. This results in scalable findings and AI-supported prototypes for innovations in higher education, including feedback, self-regulation, assessments, teaching and learning concepts, and group processes.

More than 60 scientists collaborate on an interdisciplinary basis at CATALPA. They examine practical issues from the perspectives of psychology, computational linguistics, educational science, computer science, and organizational sociology. All CATALPA researchers share the vision of offering the best possible individual support to all students.

The FernUniversität in Hagen’s 70,000 students are more diverse in terms of age, origin, and educational background than those of almost any other German university. Where better to research how digital systems can improve teaching and learning than here?

CATALPA operates as a living lab, generating findings from practice that feed directly back into it — be it through interventions to reduce stereotyping or technological developments in digital learning environments. In close cooperation with the Center for Learning and Innovation (ZLI) and the Center for Digitalization and IT (ZDI) at the FernUniversität, CATALPA investigates the impact and potential of educational technologies in a protected environment and tests prototypes in practice.

FID

The specialised information service “Educational Science and Educational Research” (Fachinformationsdienst Erziehungswissenschaft und Bildungsforschung) renders a contribution to a supply of scientific information, across locations in Germany. It aims at delivering the scientific resources required by education scientists for their particular research needs quickly and directly – preferably in digital format. The Information Centre for Education and the Research Library for the History of Education at DIPF collaborate closely with three other co-operation partners and the pertinent community in developing services with a priority on the following disciplines:

Education science
Educational research
Subject didactics
Higher education research
Research on textbooks and educational media
History of education

FID services have largely been integrated into the German Education Portal, which the project partners are supporting in order to create a comprehensive search and documentation area and subject-related service portal.

The specialized information service FID is monitored by an advisory board that brings together subject-specific and librarian expertise, and usage is continually being evaluated.

KonsortSWD

Which research data are needed to drive research of societal contexts and phenomena? How can these data be documented and made available, how can they be indexed and networked, to facilitate excellent research? Together with other partners, DIPF engages in setting up KonsortSWD, the national research data infrastructure for social, behavioural, educational and economic sciences.

KonsortSWD targets a further development of the existing research data infrastructure as a joint venture, stopping existing gaps and extending the infrastructure in a sustainable way.

IWWB-Plus

In this age of rapid and continual change in life and work, continuing education is more important than ever. Individuals are often faced with the challenge of finding the continuing education course that suits their needs. InfoWebWeiterbildung (IWWB) is a web-based meta search engine that has for many years supported individuals in finding suitable courses. IWWB-PLUS implements new web and information technology to concentrate on an improvement and functional expansion of the existing platform.

The following main objectives are pursued:

Chatbots: Implementation of dialogue technologies to support users in finding appropriate continuing education offers
Light Assessments: Development and integration of appropriate procedures to assess the current state of knowledge in people with an interest in continuing education, to find matching continuing education offers.
Interoperability with external information providers (Europass): linkage to the EUROPASS system to personalise users’ lists of search results

IWWB-PLUS furthermore aims at re-engineering the UX design to clearly improve user experiences by an optimal human-machine interface design.

SoQuZ

Quantitative and qualitative social research have become the preferred means of introspection in industrial societies. The social data generated in the research process are therefore an indispensable source for research in contemporary history. In recent years, therefore, there has been a turn towards data-based research within contemporary historical research. Historians use data from quantitative and qualitative social research to answer their research questions. In doing so, the data are extracted from their original contexts of origin, contextualized and (re)evaluated. Typical for the historical approach is that very different materials are combined (e.g. data, publications about the data, producer interviews, etc.).

The use of social data, however, poses greater challenges for contemporary historians:

Potentially relevant data sets are fragmented in different repositories or data centers, or have not yet been secured for research at all.
Legal questions concerning the use of the data are still unresolved. This concerns on the one hand questions of legal ownership and on the other hand questions of data protection.
The analysis of social science data requires special skills (e.g., knowledge of statistics) that are rarely included in university history curricula.
Currently, there is no offer of established data infrastructures that systematically support historians in indexing and securing rediscovered data.

This project aimed to develop a framework for a data infrastructure for contemporary history research. Before resources are spent on building a sustainable infrastructure, several fundamental questions must be answered:

How great is the potential use of social data in contemporary historical research? Can relevant data sets already be identified that are suitable for contemporary historical research?
What are the legal boundaries to the use of social data by historians?
What kind of support do historians need for the development and use of social data? Which services of existing social science or humanities research infrastructures already cover these needs?
How can a data infrastructure for research in contemporary history be implemented? How can sustainability be ensured?

The project aimed to answer these questions and subsequently identify perspectives for the further development of contemporary history data infrastructures within the dynamic developments of the National Research Data Infrastructure and the European Open Science Cloud.

National Report on Education

The national report on education periodically presents a major empirical review which covers the entire German education system.

Published every two years, each national report provides indicator-based information about the general conditions, features, results and output of education processes. The reports analyse the entire structure of the education system from early childhood education and school education to vocational training, higher education and adult education.

As a data-based, problem-centred analysis the reports do not include assessments and recommendations. What is special about these reports is that they are mainly based on selected indicators, i. e. statistical parameters, each representing a central feature of education processes or a central aspect of education quality. These indicators are derived from official data and representative socio-scientific assessments and wherever possible, they are compared as regards developments over the past years and decades, broken down by the federal states (Länder) and compared internationally. However, this claim to quality and explanatory power of the data also renders evident the limits of the educational report. It can only take into account current problems in the development of education to the extent that reliable data have been ascertained. The core set of indicators remains the same in each report, hence a comparison of developments is guaranteed while the accentuation differs. Educational reporting receives its specific informative power from this consistency. Moreover, each volume includes further indicators for additional subject areas.

DDP-Education

The joint project Domain Data Protocols for Empirical Educational Research in Germany (DDP-Education) is concerned with the development of standardised data protocols to ensure data quality and the subsequent use of research data. The aim is to create publicly accessible and referenceable model protocols for research data management in empirical educational research. Domain data protocols describe all relevant aspects of research data management with regard to data quality, preparation and documentation as well as the handling of legal requirements – in concrete terms and in relation to the specific data type or the specific data collection method. The sample protocols support researchers in generating quality-assured and reusable research data that fulfils current requirements in terms of reproducibility, FAIRness and open science. In addition, the model protocols help to make the process of applying for funding and the associated review and monitoring processes more efficient.

The effects of technology diffusion and narratives on firms and workers

This research project explores the diffusion and perception of digital technologies in the German labor market. We measure diffusion via information conveyed in online job vacancies and perception via media reports. Concentrating on emerging digital technologies, we stress both, their potentials, such as wage and employment growth, and pitfalls, such as inequality and biased narratives. Our analyses rely on novel text data, which we partially construct ourselves, and modern Machine Learning and NLP methods to analyze textual data. By combining our text data with survey and administrative information, we are able to shed light on the impact of digitalization on Germany’s labor market outcomes and educational decisions from different perspectives.

ALICE

The Coronavirus Crisis has spurred an urgent need to support students’ learning via digital technologies. Digital technologies can also adapt to learners’ knowledge and skills and can adaptively provide them with learning materials and instruction that are tailored to their level of competence. However, this requires that the digital learning environment is able to model learners’ understanding and performance during the learning process and make predictions about each individual learner’s potential progress during the learning activity.

This project aimed at establishing theoretical and methodological foundations for providing learners with adaptive support during mathematics and science education. To this end, the project combined four research strands, that are

1) developing digital learning materials that is based on learning-progressions in mathematics, biology, chemistry, or physics,

2) collecting authentic data from students who are engaging with this learning material in order to develop predictor models of how learners’ competence develops over time,

3) reconstructing learners’ learning trajectories, and finally 4) investigating the effectiveness of different learning trajectories and developed instructional support that helps learners achieve their learning goals.