Keusch, F. & Kreuter, F. (2020). Zukunft der Aus- und Weiterbildung in der Markt- und Sozialforschung [The future of education in market and social research]. In Keller, B., Klein, H.-W., Wachenfeld-Schell, A., & Wirth, Th. (Eds.) Marktforschung für die Smart Data World. Chancen, Herausforderungen und Grenzen. Wiesbaden: Springer Gabler, 3-25. Preprint
Abstract:
Die Nachfrage nach gut ausgebildeten DatenwissenschaftlerInnen, die sowohl die Fähigkeiten besitzen, Daten auf „traditionellem Weg“ zu erheben und auszuwerten und ebenso mit großen semi- oder gar unstrukturierten Datensätzen zu arbeiten, steigt kontinuierlich an. In diesem Beitrag beschreiben wir, welche Kompetenzen Sozial- und MarktforscherInnen heutzutage benötigen, um am Arbeitsmarkt erfolgreich zu sein. Wir diskutieren Herausforderungen und Chancen im Bereich der Lehre dieser neuen Inhalte und deren Potenzial, den steigenden Bedarf an Fachkräften im Bereich Datenerhebung und Datenanalyse in den kommenden Jahren zu decken.
Samoilova, E., Wolbring, T., & Keusch, F. (2020). Datenqualität umfragebasierter Workload-Messungen: Eine Mixed-Methods-Studie auf Grundlage von Learning Analytics und kognitiven Interviews [Data quality problems in survey-based measurement of student workload: A mixed-mode study using learning analytics and cognitive interviewing]. In Großmann, D., Engel, C., Junkermann, J., & Wolbring, T. (Eds.) Studentischer Workload, 205-229. Wiesbaden: Springer. Working Paper
Abstract:
Diese Studie untersucht die Qualität studentischer Selbstangaben zum zeitlichen Aufwand anhand von Learning Analytics und kognitiven Interviews im Rahmen eines berufsbegleitenden online-basierten Fernstudiengangs. Die Selbsteinschätzung des Zeitaufwands wurde mittels einer wöchentlichen webbasierten Umfrage über ein ganzes Semester hinweg erhoben. Die Online-Lernumgebung des Studienprogramms erlaubte eine nichtreaktive Messung des Workloads mittels Videoansicht-Logs in Echtzeit und deren Verknüpfung mit den Umfragedaten. Die Ergebnisse zeigen, dass sich die Schätzungen auf Grundlage von Logs und Umfragedaten deutlich unterscheiden. Im Durchschnitt liegen die Selbstangaben deutlich über den Log-basierten Werten. Die Korrelation zwischen beiden Messungen ist sehr schwach und bivariate Regressionsmodelle deuten darauf hin, dass die Umfrage-basierten Selbstangaben ein statistisch signifikanter, jedoch schwacher Prädiktor des Workloads sind. Die kognitiven Interviews zeigen ergänzend dazu, dass Erinnerungs- und Urteilsfehler die zentrale Ursache für Verzerrungen in den studentischen Angaben darstellen. [IPSDS Working Paper #2]
Kreuter, F., Frößinger, K., Samoilova, E., Keusch, F. (2018): "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.
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.
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.
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 #1]
Kreuter, F. (2018): Master in Data Science. In Bildungsbrief. Informationen für die Personalarbeit 2/2018, 11-12, Deutscher Wirtschaftsdienst. Köln Preprint
2020 Assessment Report: IPSDS in Times of Corona
Abstract:
This report summarizes the results of a survey sent to students of the IPSDS study program in May 2020. The survey focused on the changes related to the coronavirus crisis for the students, especially regarding the work environment of students and the available time for study. While some of the students are now tied up in childcare, others have more free time. Adjustments in deadlines and the like were used by the course instructors/program management to relieve the burden on the students under a lot of pressure. [IPSDS Assessment Report #08].
2020 Program Assessment Report
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 surveys. [IPSDS Assessment Report #07].
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 (1-3)
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]
(June 23, 2020) Frauke Kreuter, "Datenschätze"; TDWI (Transforming Data with Intelligence) Europe Virtual, online
(October 24, 2019) IPSDS-Team, "Big Faculty Meeting"; Big Faculty Meeting, online
(April 04, 2019) Darya Leshenko, "Corporate Presentation"; Camelot Management Consultants: Präsentation Projekt und Studiengang, Mannheim
(January 16, 2019) Frauke Kreuter, "Welcome to Q&A"; Webinar “Question and Answer Session 2019”, online
(July 5, 2018) Frauke Kreuter, "Training – Survey and Data Science"; Australian Bureau of Statistics, Canberra, Australien
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.
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].
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 (1-2)
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]
(August 3, 2017) Frauke Kreuter, "Using Big Data to Improve Official Economic Statistics"; Joint Statistical Meeting, Baltimore
(July 3, 2017) Frauke Kreuter, "Daten das neue Öl. Wie verantwortungsvoll damit umgehen?"; Nibelungia Seminar Series, Darmstadt
(June 26, 2017) Frauke Kreuter, "The Future of the Federal Statistical System"; Bureau of Labor Statistics, Washington DC
(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
(July 6, 2016) Frauke Kreuter, "Survey and Data Science"; 3MC Conference, Chicago
(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
(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