Joint project for Individualization of Studies through Digital, Data-Driven Assistants
As part of the “Innovation Potentials of Digital Higher Education” funding line, the Federal Ministry of Education and Research is funding the joint project for “Individualization of Studies through Digital, Data-Driven Assistants” (SIDDATA) since November 1, 2018.
The interdisciplinary project “SIDDATA” examines how students can be supported in achieving individual study goals. To this end, previously unlinked data and information are combined in a digital study assistant and prepared for self-responsible use. Students can use the assistant flexibly and determine individually which factors and data sources should be considered. The data that can be used includes data from learning management systems, offers and resources of other universities and institutions, and data on individual learning and working behavior.
With the use of the assistant, students should be encouraged to define their own study goals and to follow them consistently. In the future, the data-driven environment will be able to give hints, reminders and recommendations appropriate to the situation, as well as regarding local and remote courses and Open Educational Resources (OER). These tips and recommendations should help students to make informed decisions for their own individual study path.
The collaborative project uses methods from Higher Education Research, Cognitive Science, Information Systems as well as Software Development. This interdisciplinary approach makes it possible to test the digital study assistant in a model experiment with several universities and to evaluate it with the aim of identifying transferable success conditions, i.e. critical success factors and barriers.
Higher Education Research
- Measuring the effectiveness of the study assistant in terms of the central project objectives
- Multimethod approach using quantitative and qualitative methods
- Collection of student requirements for personal digital study assistants through quantitative online surveys
- Guided interviews with student users
- Evaluation to correct ongoing measures
- Analysis and processing of semi-structured data from Learning Management Systems
- Analysis and processing of semi-structured data from Campus Management Systems
- Application of artificial intelligence techniques, in particular machine learning
- Recording of attention processes with regard to self-regulation and self-monitoring processes
- Empirical survey of Success and Failure Factors during the introduction and use of the assistant
- Quantitative determination of Perceived Usefulness and Perceived Ease of Use
- Development of target processes for collaboration using the methods and techniques of Business Process Management
- Development of data extraction models and interfaces to Learning Management and Campus Management Systems
- Development of fast-to-use prototypes
- Application of hybrid IT Project Management models
- Management of Software Quality
Didactic and pedagogical-psychological conditions of success
- Q1.1: Can the self-study competence be strengthened by using the assistant?
- Q1.2: How can a high actual usage be achieved?
- Q1.3: How can users be sensitized successfully for the informed handling of their own data?
Organizational conditions for success
- Q2.1: How can intra-organizational data be made available and accessible?
- Q2.2: How can teaching and inter-organizational data be exchanged?
Technical conditions for success
- Q3.1: Which technical conditions are necessary for the integration of data from different systems?
- Q3.2: How can attention processes be analyzed?
- Q3.3: How can heterogeneous data be related to individual study goals?