Web surveys in higher education are particularly important for assessing the quality of academic teaching and learning. Traditionally, mainly quantitative data is used for quality assessment. Increasingly, questions are being raised about the impact of attitudes of the individuals involved. Therefore, especially the analysis of open-ended text responses in web surveys offers the potential for impact evaluation. Despite the fact that qualitative text mining and sentiment analysis are being introduced in other research areas, these instruments are still slowly gaining access to evaluation research. On the one hand, there is a lack of methodological expertise to deal with large numbers of text responses (e.g. via semantic analysis, linguistically supported coding, etc.). On the other hand, deficiencies in interdisciplinary expertise are identified in order to be able to contextualize the results. The following contribution aims to address these issues.
The presentation will contribute to the field of impact evaluation and reveals methodological implications for the development of text mining and sentiment analysis in evaluation processes.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:72055 |
| Date | 03 September 2020 |
| Creators | Stuetzer, Cathleen M., Jablonka, Marcel, Gaaw, Stephanie |
| Contributors | Technische Universität Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:conferenceObject, info:eu-repo/semantics/conferenceObject, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | info:eu-repo/grantAgreement/Bundesministerium für Bildung und Forschung/Innovationspotenziale digitaler Hochschulbildung/16DHB2103//Personalisierte Kompetenzentwicklung durch skalierbare Mentoringprozesse/tech4comp |
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