Research & Publications

Research

As the IPSDS is committed to enabling personalized, engaged, and lifelong learning, the project resorts to research and support of new modes of learning. The research is focused on the efficacy, effectiveness, as well as sustainability of the technological and didactic concepts used. A core feature of the first development phase constitutes a series of studies, in which different online learning modules will be tested empirically. 

 

Publications

Kreuter, F., Frößinger, K., Samoilova, E., Keusch, F. (2018, in Druck): "Survey and Data Science" - Ein neuer, berufsbegleitender Studiengang (Fallbeispiel eines "flipped classroom" Online-Programms). In Handbuch der Aus- und Weiterbildung. Aktualisierungslieferung Nr. 304, Oktober 2018 (2434, pp. 43-68). München: Beck

Abstract:

Das „International Program in Survey and Data Science“ (IPSDS) nutzt den Trend des digitalen Lernens zur berufsbegleitenden Weiterbildung, um den steigenden Bedarf an Fachkräften im Bereich Datenerhebung und Datenanalyse zu decken. Kern der Darreichungsform des Programms sind moderne Methoden des asynchronen und synchronen Lernens. Die damit gewonnene Flexibilität erlaubt in Beschäftigung befindlichen Personen und Personen mit Familienverpflichtungen den Erwerb von Zusatzqualifikationen in diesem zunehmenden wichtigen Bereich. Der Aufbau des Studienganges und die zum Aufbau notwendige Forschung wurde vom Bundesministerium für Bildung und Forschung im Rahmen der Ausschreibung „Aufstieg durch Bildung: offene Hochschulen“ unterstützt. Dabei wurde die Wirksamkeit und Effektivität technischer und didaktischer Konzepte zur Durchführung von Online-Studiengängen untersucht. Mittlerweile sind drei Teilnehmer-Kohorten gestartet und verschiedenste internationale Partner haben Interesse gezeigt, sich an diesem Studiengang zu beteiligen. Die dadurch entstehenden Netzwerke sind neben den Lerninhalten für die Teilnehmer des Programms von großem Nutzen.

Kreuter, F. (2018): Data Sciences as Essential Job Skill! A Social Science, Economics, and Public Policy Training Perspective. In  Paderborn Symposium on Data Science Education at School Level 2017: Biehler, R., Budde, L., Frischemeier, D. et al. (Eds.) (2018), The Collected Extended Abstracts (pp. 21-26). Paderborn: Universitätsbibliothek Paderborn. http://doi.org/10.17619/UNIPB/1-374

Abstract:

Understanding data, understanding what information can be derived from data, understanding which decisions can be made based on such information, and knowing whom to give data to and when, will be essential for everyone joining the workforce. Even if current students are not seeking degrees in STEM/MINT fields, they will be confronted with data and will be asked to make evidenced-based decisions. This paper describes two continuing education programs that showcase which skill gaps need to be filled and why. The broad outline of these programs can serve as a baseline for discussions on the establishment of data science curricula in high schools.

Kreuter, F., Keusch, F., Samoilova, E., & Frößinger, K. (2018): International Program in Survey and Data Science. In C. König, J. Schröder, E. Wiegand (Eds.), Big Data – Chancen, Risiken, Entwicklungstendenzen (pp. 27-41). Wiesbaden: Springer VS.

Abstract:

Das International Program in Survey and Data Science (IPSDS) zielt darauf ab, den steigenden Bedarf an Fachkräften im Bereich Datenerhebung und Datenanalyse durch einen berufsbegleitenden Studiengang zu decken. Anders als viele Weiterbildungsangebote greift das Programm stark auf moderne Formen des asynchronen und synchronen Lernens zurück, um in Beschäftigung befindlichen Personen und Personen mit Familienverpflichtungen entgegen zu kommen. Der Studiengang wurde mit finanzieller Hilfe des Bundesministeriums für Bildung und Forschung im Rahmen der Ausschreibung „Aufstieg durch Bildung: offene Hochschulen“ unterstützt. Ausgangspunkt für das Programm ist das seit 1993 an der University of Maryland bestehende zweijährige Joint Program in Survey Methodology (JPSM), das dort gemeinsam mit der University of Michigan und dem Datenerhebungsinstitut Westat angeboten wird. Ausgehend von den dort gesammelten Erfahrungen und Lehrinhalten wurde IPSDS konzipiert und in Deutschland an der Universität Mannheim verankert. Als Teil des BMBF-geförderten Forschungsprojektes wurde die Wirksamkeit und Effektivität technischer und didaktischer Konzepte zur Durchführung von Online-Studiengängen untersucht. Die erste Testkohorte von IPSDS startete im Februar 2016 [Full text]

Samoilova, E., Keusch, F., & Kreuter, F. (2018): Integrating Survey and Learning Analytics Data for a Better Understanding of Engagement in MOOCs. In Jiao, H., Lissitz, R. & Wie A.V. (Eds.): Data Analytics and Psychometrics: Informing Assessment Practices. MARCES Book Series.

Abstract:

The purpose of this paper is to examine the relationship between participation in a MOOC pre- and post-course evaluation survey and student engagement in the course. Given that topic salience is one of the most important predictors of survey participation, we could hypothesize that respondents to the pre- or post-course survey are more interested in the course topic than nonrespondents. Although we do not have auxiliary data on students’ interest, we can use activity logs as a proxy, as we know that interest and engagement are closely related. Drawing on data from the first session of the Coursera MOOC Questionnaire Design for Social Surveys with 16 846 registered participants in summer 2014, we first compare survey response rate of students who engaged in at least one course activity and non-active students, that is, students who do not show any activity on the course platform after having registered for the course. Second, we compare survey response rates across three identified clusters of active students with different engagement patterns.  [IPSDS Working Paper]

Kreuter, F. (2018): Master in Data Science. In Bildungsbrief. Informationen für die Personalarbeit 2/2018, 11-12, Deutscher Wirtschaftsdienst. Köln

Samoilova, E., Keusch, F., & Wolbring, T. (2017): Learning Analytics and Survey Data Integration in Workload Research. Zeitschrift für Hochschulentwicklung. Special Edition: Learning Analytics: Implications for Higher Education, 12(1), 65-78.

Abstract:

While Learning Analytics (LA) has a lot of potential, educators sometimes doubt whether it is worth to invest in the analysis of LA and whether its use yields additional insights. Drawing on data from a pilot study, we illustrate an application of LA for the evaluation of student workload in online or blended learning courses. Although measuring student workload is essential for optimizing learning, workload research is still under development. The study compares results provided by two data sources: viewing activity logs and a weekly evaluation survey. The results indicate that self-reported data provide higher estimates of workload than LA. Moreover, the two measures are only weakly correlated. The results should be replicated with a larger sample size, different sub-populations, and in different contexts [Full text].

 

Assessment Reports

2018 Program Assessment Report: Reviewing one year milestone survey results

Abstract:

The funding of the project allowed for accepting three test-cohorts (in total 48 students) who were allowed to take IPSDS courses at no costs in exchange for participation in the evaluation. The goal of this report is to summarize key results of the one year follow-up survey conducted three times since 2017 (for the first cohort (n=16) in 2017 and 2018 and for the second cohort (n=15) in 2018). [IPSDS Assessment Report #06].

2018 Program Assessment Report: Target group and accepted test-cohorts

Abstract:

The funding of the project allowed for accepting three test-cohorts (in total 48 students) who were allowed to take IPSDS courses at no costs in exchange for participation in the evaluation. The goal of this report is to compare the planned target group of the project and the accepted test-cohorts (students’ characteristics are evaluated at the moment of them being accepted, i.e. before the courses start) [IPSDS Assessment Report #05].

2018 Program Assessment Report: Post-course survey evaluation

Abstract:

The goal of this report is to present results of the post-course survey evaluations conducted within the period of March 2016-February 2018 covering 7 terms: spring 2016, summer 2016, fall 2016, winter 2016-2017, spring 2017, fall 2017, and winter 2017-2018. Special attention is given to such topics as student learning (including various forms of assignments), workload, interaction with the instructor(s) and other students, and technical challenges [IPSDS Assessment Report #04].

2018 Program Assessment Report: Assessing Student Success and Retention

Abstract:

The goal of this report is to summarize and assess success and retention of the two cohorts of IPSDS participants (n=31) by looking at completed courses and dropout rates (within single courses as well as for the entire program). The time period (March 2016-February 2018) covers 7 terms: spring 2016, summer 2016, fall 2016, winter 2016-2017, spring 2017, fall 2017, and winter 2017-2018. [IPSDS Assessment Report #03]

2017 Program Assessment Report: Flipping classroom in online courses for working professionals: challenges and opportunities for student engagement

Abstract:

One of the most popular contemporary approaches to education is the flipped classroom design that involves a particular blend of online and face-to-face components. Although there is a growing literature on the use of this approach, it often focuses on general elements (e.g. contrasting this approach to a traditional classroom), without elaborating on the details of the specific blends. Drawing on data from a project International Program in Survey and Data Science (IPSDS) - funded by the German Federal Ministry of Education, we discuss implementation of the flipped classroom design in online courses (using online video conferencing) with an eye towards engagement of non-traditional students. Considering student engagement in designing learning environments is crucial, as engagement is malleable and could be targeted to address low levels of achievement as well as to increase retention. While the flipped classroom approach is expected to be more engaging than a traditional class due to mastery orientation and higher flexibility, there are numerous ways to implement this design that might have different implications for engagement given different context, learning objectives as well as learner characteristics. In addition to describing the IPSDS flipped classroom primary design features (including substitution of the face-to-face component with synchronous video conference sessions), we also report results of three pilot studies in which we tested three changes to the following dimensions of the course design: presentation of video material, synchrony of main online communication, and pacing. We conclude with suggestions for practice and further examination. [IPSDS Assessment Report #02]

2017 Program Assessment Report: Target group and accepted test-cohorts

Abstract:

The funding of the project allowed for accepting 2 test-cohorts (in total 31 students) who were allowed to take IPSDS courses at no costs in exchange for participation in the evaluation. The goal of this report is to compare the planned target group of the project and the accepted test-cohorts (prior to their program participation). [IPSDS Assessment Report #01]

Presentations

(November 13, 2017) Frauke Kreuter, "Data Science as Essential Job Skill. A Social Science Economics, and Public Policy Training Perspective"; Data Science Education Symposium, Paderborn

(September 19, 2017) Evgenia Samoilova, ” International Program in Survey and Data Science”; Statistische Woche 2017, Rostock

(August 3, 2017) Frauke Kreuter, "Using Big Data to Improve Official Economic Statistics"; Joint Statistical Meeting, Baltimore

(July 18, 2017) Evgenia Samoilova, ”Integrating Learning Analytics, Survey Self-Reports, and Qualitative Data: Insights from Two Pilot Studies”; European Survey Research Association 2017, Lisbon

(July 3, 2017) Frauke Kreuter, "Daten das neue Öl. Wie verantwortungsvoll damit umgehen?"; Nibelungia Seminar Series, Darmstadt

(June 29, 2017) Frauke Kreuter, "International Program in Survey and Data Science"; Conference on Big Data. Chancen, Risiken und Entwicklungstendenzen. Arbeitsgemeinschaft Sozialwissenschaftlicher Institute und Arbeitsgemeinschaft Deutscher Marktforschungsinstitute, Wiesbaden

(June 28, 2017) Frauke Kreuter, "International Program in Survey and data Science"; Arbeitsgemeinschaft Sozialwissenschaftlicher Institute, Wiesbaden

(June 26, 2017) Frauke Kreuter, "The Future of the Federal Statistical System"; Bureau of Labor Statistics, Washington DC

(March 30, 2017) Evgenia Samoilova, ”Workload-Untersuchung: Vergleich von Learning Analytics und Selbsteinschätzung”; German Centre for Higher Education Research and Science Studies 2017, Hannover 

(March 29, 2017) Evgenia Samoilova, “Wie kann man Engagement in der berufsbegleitenden Online-Lehre fördern? Erkenntnisse von drei Pilotstudien”; German Centre for Higher Education Research and Science Studies 2017, Hannover 

(March 7, 2017) Frauke Kreuter, "Collaborations between national statistical institutes and the academy"; Catholic University Santiago de Chile, Institute for Social Science, Santiago de Chile

(March 6, 2017) Frauke Kreuter, "Collaborations between national statistical institutes and the academy"; UN CEPAL Conference, Santiago de Chile

(September 10, 2016) Frauke Kreuter, "Survey and Data Science"; Universität Mannheim Science Marathon, Mannheim

(July 6, 2016) Frauke Kreuter, "Survey and Data Science"; 3MC Conference, Chicago

(June 23, 2016) Evgenia Samoilova, “Learning Analytics and Survey Data Integration in Workload Research. Studentischer Workload: Definition, Messung und Einflüsse"; Quantel 2016, Leipzig

(June 23, 2016) Frauke Kreuter, "Survey and Data Science"; Falk Forum 8.0: Digitale Transformation, Heidelberg

(April 13, 2016) Frauke Kreuter, "WSS presidents lecture – Tailored Training for Employees of Federal Statistical Agencies: Looking Back and Looking Forward"; Washington Statistical Society, Washington DC

(March 3, 2016) Evgenia Samoilova, “Engagement patterns of nontraditional students in the Questionnaire Design for Social Surveys Coursera MOOC”; General Online Research (GOR) Conference 2016, Dresden 

(November 26, 2015) Frauke Kreuter, "Data Integration – Applications in Survey Research and Official Statistics"; Research Seminar Economics, St. Gallen

(November 25, 2015) Frauke Kreuter, "Big Data"; Institut für Arbeitsmarkt- und Berufsforschung, Nürnberg

(November 23, 2015) Frauke Kreuter, "Big Data in Survey Research and Official Statistics"; GESIS, Mannheim

(2015) Frauke Kreuter, "Training in Survey Methods Current Practice and Looking ahead", Chile