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DZHW-Promoviertenpanel. Datenbeschreibung, Analysepotential und Zugangswege.

Vietgen, S., de Vogel, S., & Brandt, G. (2020).
DZHW-Promoviertenpanel. Datenbeschreibung, Analysepotential und Zugangswege. Soziale Welt, 71(4), 507-524.

The Debate on student evaluations of teaching: global convergence confronts higher education traditions.

Pineda, P., & Steinhardt, I. (2020).
The Debate on student evaluations of teaching: global convergence confronts higher education traditions. Teaching in Higher Education (online first). https://doi.org/10.1080/13562517.2020.1863351
Abstract

We found that: (1) Attention to SET originated in the US in the 1970s, spreading to German-speaking countries in the mid-1990s and continuing in China and Latin America in the early 2000s. (2) SET is commonly viewed as a control tool deserving methodological improvement, while bias is debated in the US. We also found local trajectories: (3) Whereas in the US and Latin America SET is primarily seen as a management tool, German-speaking and Chinese authors reflect more on improving teaching. Chinese scholars consider SET a valid instrument for state control associated with artificial intelligence. Also, (4) SET is commonly used in medical education in the US and the German-speaking region and in physical education in China.

Entwicklungsdynamik im Feld wissenschaftlicher Weiterbildung – Forminvestition statt Inklusionsdiskurs?

Freitag, W. (2020).
Entwicklungsdynamik im Feld wissenschaftlicher Weiterbildung – Forminvestition statt Inklusionsdiskurs? In DGWF Dt. Gesell. f. Wissensch. Weiterbild. & Fernstudium e.V., C. Iller, B. Lehmann, S. Vergara, & G. Vierzigmann (Hrsg.), Von der Exklusion zur Inklusion. Weiterbildung im Sozialsystem Hochschule (S. 75-92). Bielefeld: wbv.

Die Exzellenzinitiative: Bestandsaufnahme großer Erwartungen.

Möller, T., & Hornbostel, S. (2020).
Die Exzellenzinitiative: Bestandsaufnahme großer Erwartungen. Handbuch Qualität in Studium, Lehre und Forschung (74), 1-22.

The Competent Bibliometrician–A Guided Tour through the Scholarly and Practitioner Literature.

Petersohn, S. (2020).
The Competent Bibliometrician–A Guided Tour through the Scholarly and Practitioner Literature. In Ball, R. (Hrsg.), Handbook Bibliometrics (S. 485-496). Berlin, Boston: De Gruyter Saur.
Abstract

Repeated calls for responsible research metrics and professional codes of conduct in evaluative bibliometrics highlight the need to investigate which qualifications and competencies enable a proficient application of bibliometric methods and indicators. Taking competence research as a point of departure, this chapter delineates salient dimensions of professional competence in bibliometric research evaluation by reviewing a selected subset of the literature. The reviewed literature focuses on handbooks, monographs, and scholarly and practitioner articles that introduce theory and methodology of bibliometrics and showcase applications to scholars and practitioners mainly outside of the scientometric research community.

Publizieren im Lockdown. Erfahrungen von Professorinnen und Professoren.

Rusconi, A., Netz, N., & Solga, H. (2020).
Publizieren im Lockdown. Erfahrungen von Professorinnen und Professoren. WZB-Mitteilungen, 170, 24-26.

Interne LOM und ZLV als Instrumente der Universitätsleitung.

Niggemann, F. (2020).
Interne LOM und ZLV als Instrumente der Universitätsleitung. Qualität in der Wissenschaft. Zeitschrift für Qualitätsentwicklung in Forschung, Studium und Administration, 14(4/2020), 94-98.

Research Ethics, Open Science and CRIS.

Schöpfel, J., Azeroual, O., & Jungbauer-Gans, M. (2020).
Research Ethics, Open Science and CRIS. MDPI Publications, 2020(8), 51. https://doi.org/10.3390/publications8040051

Warum nehmen Männer mit Migrationshintergrund überproportional häufig ein Studium auf, gelangen aber am Ende seltener in die weiterführenden Masterstudiengänge?

Lörz, M. (2020).
Warum nehmen Männer mit Migrationshintergrund überproportional häufig ein Studium auf, gelangen aber am Ende seltener in die weiterführenden Masterstudiengänge? Berliner Journal für Soziologie, 2020 (online first). https://doi.org/10.1007/s11609-020-00421-7

Financial difficulties' relation to students' health.

Schirmer, H. (2020).
Financial difficulties' relation to students' health. EUROSTUDENT Intelligence Brief 2020 (1). Hannover: DZHW.
Abstract

This Intelligence Brief takes a closer look at students’ health. Compared to the overall population of the same age, students less often perceive their health as good or very good. Logistic regression analyses with German student data show strong and negative relations between financial difficulties and students’ health perception. Additionally, a cross-country comparison with EUROSTUDENT data consistently shows comparatively large shares of impaired students among those with financial difficulties. The findings indicate that policy makers should pay special attention to both impaired and financially challenged students in order to ensure their successful higher education participation and prevent negative societal effects of poor health.

Validation of the Astro dataset clustering solutions with external data.

Donner, P. (2020).
Validation of the Astro dataset clustering solutions with external data. Scientometrics, 126, 1619–1645. https://doi.org/10.1007/s11192-020-03780-3
Abstract

We conduct an independent cluster validation study on published clustering solutions of a research testbed corpus, the Astro dataset of publication records from astronomy and astrophysics. We extend the dataset by collecting external validation data serving as proxies for the latent structure of the corpus. Specifically, we collect (1) grant funding information related to the publications, (2) data on topical special issues, (3) on specific journals’ internal topic classifications and (4) usage data from the main online bibliographic database of the discipline. The latter three types of data are newly introduced for the purpose of clustering validation and the rationale for using them for this task is set out.

Gizasks: What Is the Biggest Scientific Fraud of the Past 50 Years?

Hesselmann, F. (23. November 2020).
Gizasks: What Is the Biggest Scientific Fraud of the Past 50 Years [Blogbeitrag]. Abgerufen von https://gizmodo.com/what-is-the-biggest-scientific-fraud-of-the-past-50-yea-1845737669

Current research information systems and institutional repositories: From data ingestion to convergence and merger.

Schöpfel, J., & Azeroual, O. (2020).
Current research information systems and institutional repositories: From data ingestion to convergence and merger. In Baker, D., & Ellis, L. (Hrsg.), Future Directions in Digital Information, 1st Edition: Predictions, Practice, Participation, 11/2020, (S. 19-37). Sawston, Cambridge: Chandos Publishing. https://doi.org/10.1016/B978-0-12-822144-0.00002-1

Studienrelevante Heterogenität in der Studieneingangsphase am Beispiel der Wahrnehmung von Studienanforderungen.

Wallis, M., & Bosse, E. (2020).
Studienrelevante Heterogenität in der Studieneingangsphase am Beispiel der Wahrnehmung von Studienanforderungen. Beiträge zur Hochschulforschung42(3). https://www.bzh.bayern.de/fileadmin/user_upload/Publikationen/Beitraege_zur_Hochschulforschung/2020/3_2020_Gesamt.pdf, (Abgerufen am: 09.02.2021).
Abstract

As entry rates in German higher education have grown considerably, student diversity is of particular concern for educational politics. To shed light on the role of diversity for study success, the paper first focuses on a broad range of social, individual, and organizational diversity factors. Furthermore, study success is investigated in terms of how challenging students perceive the formal and informal requirements of the first year in higher education. Drawing on a student survey conducted after the first year of studies, the study then uses multiple linear regression analysis to examine the relationship of diversity and the perception of requirements.

Treatment of Bad Big Data in Research Data Management (RDM) Systems.

Azeroual, O. (2020).
Treatment of Bad Big Data in Research Data Management (RDM) Systems. Big Data and Cognitive Computing, 4/2020, 29. Basel, Switzerland: MDPI. https://doi.org/10.3390/bdcc4040029

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Anja Gottburgsen
Dr. Anja Gottburgsen +49 511 450670-912