@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}
}
@article{ChevalereLazaridesYunetal.2023,
  author    = {Cheval{\`e}re, Johann and Lazarides, Rebecca and Yun, Hae Seon and Henke, Anja and Lazarides, Claudia and Pinkwart, Niels and Hafner, Verena V.},
  title     = {Do instructional strategies considering activity emotions reduce students' boredom in a computerized open-ended learning environment?},
  series = {Computers and education},
  volume    = {196},
  journal   = {Computers and education},
  publisher = {Elsevier},
  issn      = {1873-782X},
  doi       = {10.1016/j.compedu.2023.104741},
  year      = {2023},
  abstract  = {Providing students with efficient instruction tailored to their individual characteristics in the cognitive and affective domains is an important goal in research on computer-based learning. This is especially important when seeking to enhance students' learning experience, such as by counteracting boredom, a detrimental emotion for learning. However, studies comparing instructional strategies triggered by either cognitive or emotional characteristics are surprisingly scarce. In addition, little research has examined the impact of these types of instructional strategies on performance and boredom trajectories within a lesson. In the present study, we compared the effectiveness of an intelligent tutoring system that adapted variable levels of hint details to a combination of students' dynamic, self-reported emotions and task performance (i.e., the experimental condition) to a traditional hint delivery approach consisting of a progressive, incremental supply of details following students' failures (i.e., the control condition). Linear mixed models of time-related changes in task performance and the intensity of boredom over two 1-h sessions showed that students (N = 104) in the two conditions exhibited equivalent progression in task performance and similar trajectories in boredom intensity. However, a consideration of students' achievement levels in the analyses (i.e., their final performance on the task) revealed that higher achievers in the experimental condition showed a reduction in boredom during the first session, suggesting possible benefits of using emotional information to increase the contingency of the hint delivery strategy and improve students' learning experience.},
  language  = {en}
}