@phdthesis{Hoermann2020,
  author    = {H{\"o}rmann, Markus},
  title     = {Analyzing and fostering students' self-regulated learning through the use of peripheral data in online learning environments},
  doi       = {10.25972/OPUS-18009},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180097},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2020},
  abstract  = {Learning with digital media has become a substantial part of formal and informal educational processes and is gaining more and more importance. Technological progress has brought overwhelming opportunities for learners, but challenges them at the same time. Learners have to regulate their learning process to a much greater extent than in traditional learning situations in which teachers support them through external regulation. This means that learners must plan their learning process themselves, apply appropriate learning strategies, monitor, control and evaluate it. These requirements are taken into account in various models of self-regulated learning (SRL). Although the roots of research on SRL go back to the 1980s, the measurement and adequate support of SRL in technology-enhanced learning environments is still not solved in a satisfactory way. An important obstacle are the data sources used to operationalize SRL processes. In order to support SRL in adaptive learning systems and to validate theoretical models, instruments are needed which meet the classical quality criteria and also fulfil additional requirements. Suitable data channels must be measurable "online", i.e., they must be available in real time during learning for analyses or the individual adaptation of interventions. Researchers no longer only have an interest in the final results of questionnaires or tasks, but also need to examine process data from interactions between learners and learning environments in order to advance the development of theories and interventions. In addition, data sources should not be obtrusive so that the learning process is not interrupted or disturbed. Measurements of physiological data, for example, require learners to wear measuring devices. Moreover, measurements should not be reactive. This means that other variables such as learning outcomes should not be influenced by the measurement. Different data sources that are already used to study and support SRL processes, such as protocols on thinking aloud, screen recording, eye tracking, log files, video observations or physiological sensors, meet these criteria to varying degrees. One data channel that has received little attention in research on educational psychology, but is non-obtrusive, non-reactive, objective and available online, is the detailed, timely high-resolution data on observable interactions of learners in online learning environments. This data channel is introduced in this thesis as "peripheral data". It records both the content of learning environments as context, and related actions of learners triggered by mouse and keyboard, as well as the reactions of learning environments, such as structural or content changes. Although the above criteria for the use of the data are met, it is unclear whether this data can be interpreted reliably and validly with regard to relevant variables and behavior. Therefore, the aim of this dissertation is to examine this data channel from the perspective of SRL and thus further close the existing research gap. One development project and four research projects were carried out and documented in this thesis.},
  subject      = {Selbstgesteuertes Lernen},
  language  = {en}
}
@article{SonnenbergBannert2015,
  author    = {Sonnenberg, Christoph and Bannert, Maria},
  title     = {Discovering the Effects of Metacognitive Prompts on the Sequential Structure of SRL-Processes Using Process Mining Techniques},
  series = {Journal of Learning Analystics},
  volume    = {2},
  journal   = {Journal of Learning Analystics},
  number    = {1},
  issn      = {1929-7750},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-152362},
  pages     = {72-100},
  year      = {2015},
  abstract  = {According to research examining self-regulated learning (SRL), we regard individual regulation as a specific sequence of regulatory activities. Ideally, students perform various learning activities, such as analyzing, monitoring, and evaluating cognitive and motivational aspects during learning. Metacognitive prompts can foster SRL by inducing regulatory activities, which, in turn, improve the learning outcome. However, the specific effects of metacognitive support on the dynamic characteristics of SRL are not understood. Therefore, the aim of our study was to analyze the effects of metacognitive prompts on learning processes and outcomes during a computer-based learning task. Participants of the experimental group (EG, n=35) were supported by metacognitive prompts, whereas participants of the control group (CG, n=35) received no support. Data regarding learning processes were obtained by concurrent think-aloud protocols. The EG exhibited significantly more metacognitive learning events than did the CG. Furthermore, these regulatory activities correspond positively with learning outcomes. Process mining techniques were used to analyze sequential patterns. Our findings indicate differences in the process models of the EG and CG and demonstrate the added value of taking the order of learning activities into account by discovering regulatory patterns.},
  language  = {en}
}