@inproceedings{VladovaRuedian2020,
  author    = {Vladova, Gergana and R{\"u}dian, Sylvio Leo},
  title     = {From learners to educators},
  series = {The future of education},
  volume    = {10},
  booktitle = {The future of education},
  publisher = {Pixel},
  address   = {Florenz},
  isbn      = {978-88-85813-87-8},
  issn      = {2420-9732},
  doi       = {10.26352/E618_2384-9509},
  pages     = {5},
  year      = {2020},
  abstract  = {The rapid growth of technology and its evolving potential to support the transformation of teaching and learning in post-secondary institutions is a major challenge to the basic understanding of both the university and the communities it serves. In higher education, the standard forms of learning and teaching are increasingly being challenged and a more comprehensive process of differentiation is taking place. Student-centered teaching methods are becoming increasingly important in course design and the role of the lecturer is changing from the knowledge mediator to moderator and learning companion. However, this is accelerating the need for strategically planned faculty support and a reassessment of the role of teaching and learning. Even though the benefits of experience-based learning approaches for the development of life skills are well known, most knowledge transfer is still realized through lectures in higher education. Teachers have the goal to design the curriculum, new assignments, and share insights into evolving pedagogy. Student engagement could be the most important factor in the learning success of university students, regardless of the university program or teaching format. Against this background, this article presents the development, application, and initial findings of an innovative learning concept. In this concept, students are allowed to deal with a scientific topic, but instead of a presentation and a written elaboration, their examination consists of developing an online course in terms of content, didactics, and concept to implement it in a learning environment, which is state of the art. The online courses include both self-created teaching material and interactive tasks. The courses are created to be available to other students as learning material after a review process and are thus incorporated into the curriculum.},
  language  = {en}
}
@incollection{VladovaGronauRuedian2019,
  author    = {Vladova, Gergana and Gronau, Norbert and R{\"u}dian, Sylvio Leo},
  title     = {Wissenstransfer in Bildung und Weiterbildung},
  series = {Digitale Transformation - gutes Arbeiten und Qualifizierung aktiv gestalten},
  booktitle = {Digitale Transformation - gutes Arbeiten und Qualifizierung aktiv gestalten},
  publisher = {Gito},
  address   = {Berlin},
  isbn      = {978-3-95545-309-1},
  pages     = {89 -- 106},
  year      = {2019},
  language  = {de}
}
@article{RuedianVladova2021,
  author    = {R{\"u}dian, Sylvio Leo and Vladova, Gergana},
  title     = {Kostenfreie Onlinekurse nachhaltig mit personalisiertem Marketing finanzieren},
  series = {HMD Praxis der Wirtschaftsinformatik},
  volume    = {58},
  journal   = {HMD Praxis der Wirtschaftsinformatik},
  number    = {3},
  publisher = {Springer Vieweg},
  address   = {Wiesbaden},
  issn      = {1436-3011},
  doi       = {10.1365/s40702-021-00720-4},
  pages     = {507 -- 520},
  year      = {2021},
  abstract  = {Selbstbestimmtes Lernen mit Onlinekursen findet zunehmend mehr Akzeptanz in unserer Gesellschaft. Lernende k{\"o}nnen mithilfe von Onlinekursen selbst festlegen, was sie wann lernen und Kurse k{\"o}nnen durch vielf{\"a}ltige Adaptionen an den Lernfortschritt der Nutzer angepasst und individualisiert werden. Auf der einen Seite ist eine große Zielgruppe f{\"u}r diese Lernangebote vorhanden. Auf der anderen Seite sind die Erstellung von Onlinekursen, ihre Bereitstellung, Wartung und Betreuung kostenintensiv, wodurch hochwertige Angebote h{\"a}ufig kostenpflichtig angeboten werden m{\"u}ssen, um als Anbieter zumindest kostenneutral agieren zu k{\"o}nnen. In diesem Beitrag er{\"o}rtern und diskutieren wir ein offenes, nachhaltiges datengetriebenes zweiseitiges Gesch{\"a}ftsmodell zur Verwertung gepr{\"u}fter Onlinekurse und deren kostenfreie Bereitstellung f{\"u}r jeden Lernenden. Kern des Gesch{\"a}ftsmodells ist die Nutzung der dabei entstehenden Verhaltensdaten, die daraus m{\"o}gliche Ableitung von Pers{\"o}nlichkeitsmerkmalen und Interessen und deren Nutzung im kommerziellen Kontext. Dies ist eine bei der Websuche bereits weitl{\"a}ufig akzeptierte Methode, welche nun auf den Lernkontext {\"u}bertragen wird. Welche M{\"o}glichkeiten, Herausforderungen, aber auch Barrieren {\"u}berwunden werden m{\"u}ssen, damit das Gesch{\"a}ftsmodell nachhaltig und ethisch vertretbar funktioniert, werden zwei unabh{\"a}ngige, jedoch synergetisch verbundene Gesch{\"a}ftsmodelle vorgestellt und diskutiert. Zus{\"a}tzlich wurde die Akzeptanz und Erwartung der Zielgruppe f{\"u}r das vorgestellte Gesch{\"a}ftsmodell untersucht, um notwendige Kernressourcen f{\"u}r die Praxis abzuleiten. Die Ergebnisse der Untersuchung zeigen, dass das Gesch{\"a}ftsmodell von den Nutzer*innen grundlegend akzeptiert wird. 10 \% der Befragten w{\"u}rden es bevorzugen, mit virtuellen Assistenten - anstelle mit Tutor*innen zu lernen. Zudem ist der Großteil der Nutzer*innen sich nicht dar{\"u}ber bewusst, dass Pers{\"o}nlichkeitsmerkmale anhand des Nutzerverhaltens abgeleitet werden k{\"o}nnen.},
  language  = {de}
}
@inproceedings{RuedianHaasePinkwart2021,
  author    = {R{\"u}dian, Sylvio Leo and Haase, Jennifer and Pinkwart, Niels},
  title     = {The relation of convergent thinking and trace data in an online course},
  series = {Die 19. Fachtagung Bildungstechnologien (DELFI) / Lecture Notes in Informatics (LNI)},
  booktitle = {Die 19. Fachtagung Bildungstechnologien (DELFI) / Lecture Notes in Informatics (LNI)},
  publisher = {Gesellschaft f{\"u}r Informatik},
  address   = {Bonn},
  pages     = {181 -- 186},
  year      = {2021},
  abstract  = {Many prediction tasks can be done based on users' trace data. In this paper, we explored convergent thinking as a personality-related attribute and its relation to features gathered in interactive and non-interactive tasks of an online course. This is an under-utilized attribute that could be used for adapting online courses according to the creativity level to enhance the motivation of learners. Therefore, we used the logfile data of a 60 minutes Moodle course with N=128 learners, combined with the Remote Associates Test (RAT). We explored the trace data and found a weak correlation between interactive tasks and the RAT score, which was the highest considering the overall dataset. We trained a Random Forest Regressor to predict convergent thinking based on the trace data and analyzed the feature importance. The result has shown that the interactive tasks have the highest importance in prediction, but the accuracy is very low. We discuss the potential for personalizing online courses and address further steps to improve the applicability.},
  language  = {en}
}
@inproceedings{RudianHaasePinkwart2022,
  author    = {Rudian, Sylvio Leo and Haase, Jennifer and Pinkwart, Niels},
  title     = {Predicting creativity in online courses},
  series = {2022 International Conference on Advanced Learning Technologies (ICALT)},
  booktitle = {2022 International Conference on Advanced Learning Technologies (ICALT)},
  publisher = {IEEE},
  address   = {Piscataway, NJ},
  isbn      = {978-1-6654-9519-6},
  doi       = {10.1109/ICALT55010.2022.00056},
  pages     = {164 -- 168},
  year      = {2022},
  abstract  = {Many prediction tasks can be done based on users' trace data. This paper explores divergent and convergent thinking as person-related attributes and predicts them based on features gathered in an online course. We use the logfile data of a short Moodle course, combined with an image test (IMT), the Alternate Uses Task (AUT), the Remote Associates Test (RAT), and creative self-efficacy (CSE). Our results show that originality and elaboration metrics can be predicted with an accuracy of ~.7 in cross-validation, whereby predicting fluency and RAT scores perform worst. CSE items can be predicted with an accuracy of ~.45. The best performing model is a Random Forest Tree, where the features were reduced using a Linear Discriminant Analysis in advance. The promising results can help to adjust online courses to the learners' needs based on their creative performances.},
  language  = {en}
}