@article{ŞahinEgloffsteinBotheetal.2021, author = {Şahin, Muhittin and Egloffstein, Marc and Bothe, Max and Rohloff, Tobias and Schenk, Nathanael and Schwerer, Florian and Ifenthaler, Dirk}, title = {Behavioral Patterns in Enterprise MOOCs at openSAP}, series = {EMOOCs 2021}, volume = {2021}, journal = {EMOOCs 2021}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, isbn = {978-3-86956-512-5}, doi = {10.25932/publishup-51735}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-517350}, pages = {281 -- 288}, year = {2021}, language = {en} } @inproceedings{JacqminOezdemirFellKurbanetal.2021, author = {Jacqmin, Julien and {\"O}zdemir, Paker Doğu and Fell Kurban, Caroline and Tun{\c{c}} Pekkan, Zelha and Koskinen, Johanna and Suonp{\"a}{\"a}, Maija and Seng, Cheyvuth and Carlon, May Kristine Jonson and Gayed, John Maurice and Cross, Jeffrey S. and Langseth, Inger and Jacobsen, Dan Yngve and Haugsbakken, Halvdan and Bethge, Joseph and Serth, Sebastian and Staubitz, Thomas and Wuttke, Tobias and Nordemann, Oliver and Das, Partha-Pratim and Meinel, Christoph and Ponce, Eva and Srinath, Sindhu and Allegue, Laura and Perach, Shai and Alexandron, Giora and Corti, Paola and Baudo, Valeria and Turr{\´o}, Carlos and Moura Santos, Ana and Nilsson, Charlotta and Maldonado-Mahauad, Jorge and Valdiviezo, Javier and Carvallo, Juan Pablo and Samaniego-Erazo, Nicolay and Poce, Antonella and Re, Maria Rosaria and Valente, Mara and Karp Gershon, Sa'ar and Ruip{\´e}rez-Valiente, Jos{\´e} A. and Despujol, Ignacio and Busquets, Jaime and Kerr, John and Lorenz, Anja and Sch{\"o}n, Sandra and Ebner, Martin and Wittke, Andreas and Beirne, Elaine and Nic Giolla Mhich{\´i}l, Mair{\´e}ad and Brown, Mark and Mac Lochlainn, Conch{\´u}r and Topali, Paraskevi and Chounta, Irene-Angelica and Ortega-Arranz, Alejandro and Villagr{\´a}-Sobrino, Sara L. and Mart{\´i}nez-Mon{\´e}s, Alejandra and Blackwell, Virginia Katherine and Wiltrout, Mary Ellen and Rami Gaddem, Mohamed and Hern{\´a}ndez Reyes, C{\´e}sar Augusto and Nagahama, Toru and Buchem, Ilona and Okatan, Ebru and Khalil, Mohammad and Casiraghi, Daniela and Sancassani, Susanna and Brambilla, Federica and Mihaescu, Vlad and Andone, Diana and Vasiu, Radu and Şahin, Muhittin and Egloffstein, Marc and Bothe, Max and Rohloff, Tobias and Schenk, Nathanael and Schwerer, Florian and Ifenthaler, Dirk and Hense, Julia and Bernd, Mike}, title = {EMOOCs 2021}, editor = {Meinel, Christoph and Staubitz, Thomas and Schweiger, Stefanie and Friedl, Christian and Kiers, Janine and Ebner, Martin and Lorenz, Anja and Ubachs, George and Mongenet, Catherine and Ruip{\´e}rez-Valiente, Jos{\´e} A. and Cortes Mendez, Manoel}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, isbn = {978-3-86956-512-5}, doi = {10.25932/publishup-51030}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-510300}, pages = {vii, 295}, year = {2021}, abstract = {From June 22 to June 24, 2021, Hasso Plattner Institute, Potsdam, hosted the seventh European MOOC Stakeholder Summit (EMOOCs 2021) together with the eighth ACM Learning@Scale Conference. Due to the COVID-19 situation, the conference was held fully online. The boost in digital education worldwide as a result of the pandemic was also one of the main topics of this year's EMOOCs. All institutions of learning have been forced to transform and redesign their educational methods, moving from traditional models to hybrid or completely online models at scale. The learnings, derived from practical experience and research, have been explored in EMOOCs 2021 in six tracks and additional workshops, covering various aspects of this field. In this publication, we present papers from the conference's Experience Track, the Policy Track, the Business Track, the International Track, and the Workshops.}, language = {en} } @article{AckfeldRohloffRzepka2021, author = {Ackfeld, Viola and Rohloff, Tobias and Rzepka, Sylvi}, title = {Increasing personal data contributions for the greater public good}, series = {Behavioural public policy}, journal = {Behavioural public policy}, publisher = {Cambridge University Press}, address = {Cambridge}, issn = {2398-063X}, doi = {10.1017/bpp.2021.39}, pages = {1 -- 27}, year = {2021}, abstract = {Personal data increasingly serve as inputs to public goods. Like other types of contributions to public goods, personal data are likely to be underprovided. We investigate whether classical remedies to underprovision are also applicable to personal data and whether the privacy-sensitive nature of personal data must be additionally accounted for. In a randomized field experiment on a public online education platform, we prompt users to complete their profiles with personal information. Compared to a control message, we find that making public benefits salient increases the number of personal data contributions significantly. This effect is even stronger when additionally emphasizing privacy protection, especially for sensitive information. Our results further suggest that emphasis on both public benefits and privacy protection attracts personal data from a more diverse set of contributors.}, language = {en} } @phdthesis{Rohloff2021, author = {Rohloff, Tobias}, title = {Learning analytics at scale}, doi = {10.25932/publishup-52623}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-526235}, school = {Universit{\"a}t Potsdam}, pages = {xvii, 138, lxvii}, year = {2021}, abstract = {Digital technologies are paving the way for innovative educational approaches. The learning format of Massive Open Online Courses (MOOCs) provides a highly accessible path to lifelong learning while being more affordable and flexible than face-to-face courses. Thereby, thousands of learners can enroll in courses mostly without admission restrictions, but this also raises challenges. Individual supervision by teachers is barely feasible, and learning persistence and success depend on students' self-regulatory skills. Here, technology provides the means for support. The use of data for decision-making is already transforming many fields, whereas in education, it is still a young research discipline. Learning Analytics (LA) is defined as the measurement, collection, analysis, and reporting of data about learners and their learning contexts with the purpose of understanding and improving learning and learning environments. The vast amount of data that MOOCs produce on the learning behavior and success of thousands of students provides the opportunity to study human learning and develop approaches addressing the demands of learners and teachers. The overall purpose of this dissertation is to investigate the implementation of LA at the scale of MOOCs and to explore how data-driven technology can support learning and teaching in this context. To this end, several research prototypes have been iteratively developed for the HPI MOOC Platform. Hence, they were tested and evaluated in an authentic real-world learning environment. Most of the results can be applied on a conceptual level to other MOOC platforms as well. The research contribution of this thesis thus provides practical insights beyond what is theoretically possible. In total, four system components were developed and extended: (1) The Learning Analytics Architecture: A technical infrastructure to collect, process, and analyze event-driven learning data based on schema-agnostic pipelining in a service-oriented MOOC platform. (2) The Learning Analytics Dashboard for Learners: A tool for data-driven support of self-regulated learning, in particular to enable learners to evaluate and plan their learning activities, progress, and success by themselves. (3) Personalized Learning Objectives: A set of features to better connect learners' success to their personal intentions based on selected learning objectives to offer guidance and align the provided data-driven insights about their learning progress. (4) The Learning Analytics Dashboard for Teachers: A tool supporting teachers with data-driven insights to enable the monitoring of their courses with thousands of learners, identify potential issues, and take informed action. For all aspects examined in this dissertation, related research is presented, development processes and implementation concepts are explained, and evaluations are conducted in case studies. Among other findings, the usage of the learner dashboard in combination with personalized learning objectives demonstrated improved certification rates of 11.62\% to 12.63\%. Furthermore, it was observed that the teacher dashboard is a key tool and an integral part for teaching in MOOCs. In addition to the results and contributions, general limitations of the work are discussed—which altogether provide a solid foundation for practical implications and future research.}, language = {en} }