@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}
}
@phdthesis{Betz2005,
  author    = {Betz, Christian},
  title     = {Scalable authoring of diagnostic case based training systems},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-17885},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2005},
  abstract  = {Diagnostic Case Based Training Systems (D-CBT) provide learners with a means to learn and exercise knowledge in a realistic context. In medical education, D-CBT Systems present virtual patients to the learners who are asked to examine, diagnose and state therapies for these patients. Due a number of conflicting and changing requirements, e.g. time for learning, authoring effort, several systems were developed so far. These systems range from simple, easy-to-use presentation systems to highly complex knowledge based systems supporting explorative learning. This thesis presents an approach and tools to create D-CBT systems from existing sources (documents, e.g. dismissal records) using existing tools (word processors): Authors annotate and extend the documents to model the knowledge. A scalable knowledge representation is able to capture the content on multiple levels, from simple to highly structured knowledge. Thus, authoring of D-CBT systems requires less prerequisites and pre-knowledge and is faster than approaches using specialized authoring environments. Also, authors can iteratively add and structure more knowledge to adapt training cases to their learners needs. The theses also discusses the application of the same approach to other domains, especially to knowledge acquisition for the Semantic Web.},
  subject      = {Computerunterst{\"u}tztes Lernen},
  language  = {en}
}