Learning Analytics & Academic Analytics
Blended Learning Analytics II
Text as Knowledge Storage
The current project in the context of blended learning analyzes deals with the extraction of information from unstructured texts in the context of hybrid learning scenarios in order to approach questions about the effectiveness of digitally supported teaching. The research project defines unstructured text (e.g. in the form of natural language) as a (generalizable) store of knowledge. In particular, the following questions are considered important: (1) How can (unstructured) text be extracted in LMS and used to evaluate the effectiveness of digital education? and (2) To what extent can non-reactive qualitative methods in particular be implemented in or coupled with existing LMS as an exploration and analysis instrument? In addition to answering the research questions, the project aims to model processes for the (semi-)automatic extraction of meaning in unstructured texts. The overarching goal is to use the digital teaching and learning space as a research space and to make a lasting contribution to evaluating the effectiveness of digital higher education through the implementation of interdisciplinary research approaches.
Period:
05.2019 bis 12.2020
Promoter and Funding Program
Arbeitskreis E-Learning der LRK Sachsen
Sächsisches Staatsministerium für Wissenschaft und Kunst (SMWK)
Project Management
Dr. Cathleen M. Stützer
E-Mail:
Scientific Staff
Marcel Jablonka
Johannes Winter, M.A.
Student Assistants
Jonas Wifek
Publications
Stützer, C. M., Winter, J., & Jablonka, M. (2020). Blended Learning Analytics (II) - Text als Wissensspeicher. 18. Workshop on E-Learning - Tagungsband. (134), 126–132. Retrieved from https://zfe.hszg.de/fileadmin/NEU/Redaktion-Zfe/Dateien/wel/wel20/Tagungsband_WeL20.pdf
Stützer, C. M. (2020). Innovative Forschungsmethoden in der Evaluation – Text Mining und Data Analytics zur Erfolgsmessung und Wirksamkeitsanalyse. In B. Keller, H.-W. Klein, A. Wachenfeld-Schell, & T. Wirth (Eds.), Marktforschung für die Smart Data World (pp. 157–175). Wiesbaden: Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-28664-4_12
Stützer, C. M., Frohwieser, D., & Lenz, K. (2020). Was digitale Lehre zur „guten“ Lehre macht. Potentiale Und Herausforderungen Digitaler Hochschulbildung. (1), 3-10. Retrieved from https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-720372
Stützer, Cathleen M., Stephanie Gaaw (2020, March). Zur Zukunft der Netzwerkforschung in den Sozialwissenschaften. Schader Stiftung. Warum Netzwerkforschung?, Darmstadt. Retrieved from Conference Proceedings Interaktive Visualisierung
Stuetzer, C. M., & Jablonka, M. & Gaaw, S. (2019, March). Impact evaluation by using text mining and sentiment analysis. Deutsche Gesellschaft für Online-Forschung e.V. (DGOF). 21th General Online Research Conference (GOR). Proceedings, Köln. Retrieved from Conference Proceedings