@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{Pieger2017,
  author    = {Pieger, Elisabeth},
  title     = {Metacognition and Disfluency - The Effects of Disfluency on Monitoring and Performance},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-155362},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2017},
  abstract  = {In this thesis, metacognition research is connected with fluency research. Thereby, the focus lies on how disfluency can be used to improve metacognitive monitoring (i.e., students` judgments during the learning process). Improving metacognitive monitoring is important in educational contexts in order to foster performance. Theories about metacognition and self-regulated learning suppose that monitoring affects control and performance. Accurate monitoring is necessary to initiate adequate control and better performance. However, previous research shows that students are often not able to accurately monitor their learning with meaningful text material. Inaccurate monitoring can result in inadequate control and low performance. One reason for inaccurate monitoring is that students use cues for their judgments that are not valid predictors of their performance. Because fluency might be such a cue, the first aim of this thesis is to investigate under which conditions fluency is used as a cue for judgments during the learning process. A fluent text is easy to process and, hence, it should be judged as easy to learn and as easy to remember. Inversely, a disfluent text is difficult to process, for example because of a disfluent font type (e.g., Mistral) or because of deleted letters (e.g., l_tt_rs). Hence, a disfluent text should be judged as difficult to learn and as difficult to remember. This assumption is confirmed when students learn with both fluent and disfluent material. When fluency is manipulated between persons, fluency seems to be less obvious as a cue for judgments. However, there are only a few studies that investigated the effects of fluency on judgments when fluency is manipulated between persons. Results from Experiment 1 (using deleted letters for disfluent text) and from Experiment 4 (using Mistral for disfluent text) in this thesis support the assumption that fluency is used as a cue for judgments in between-person designs. Thereby, however, the interplay with the type of judgment and the learning stage seems to matter. Another condition when fluency affects judgments was investigated in Experiment 2 and 3. The aim of these experiments was to investigate if disfluency leads to analytic monitoring and if analytic monitoring sustains for succeeding fluent material. If disfluency activates analytic monitoring that remains for succeeding fluent material, fluency should no longer be used as a cue for judgments. Results widely support this assumption for deleted letters (Experiment 2) as well as for the font type Mistral (Experiment 3). Thereby, again the interplay between the type of judgment and the learning stage matters. Besides the investigation of conditions when fluency is used as a cue for different types of judgments during the learning process, another aim of this thesis is to investigate if disfluency leads to accurate monitoring. Results from Experiment 3 and 4 support the assumption that Mistral can reduce overconfidence. This is the case when fluency is manipulated between persons or when students first learn with a fluent and then with a disfluent text. Dependent from the type of judgment and the learning stage, disfluency can lead even to underconfidence or to improved relative monitoring accuracy (Experiment 4). Improving monitoring accuracy is only useful when monitoring is implemented into better control and better performance. The effect of monitoring accuracy on control and performance was in the focus of Experiment 4. Results show that accurate monitoring does not result in improved control and performance. Thus, further research is required to develop interventions that do not only improve monitoring accuracy but that also help students to implement accurate monitoring into better control and performance. Summing up, the aim of this thesis is to investigate under which conditions fluency is used as a cue for judgments during the learning process, how disfluency can be used to improve monitoring accuracy, and if improved monitoring accuracy leads to improved performance. By connecting metacognition research and fluency research, further theories about metacognition and theories about fluency are specified. Results show that not only the type of fluency and the design, but also the type of judgment, the type of monitoring accuracy, and the learning stage should be taken into account. Understanding conditions that affect the interplay between metacognitive processes and performance as well as understanding the underlying mechanisms is necessary to enable systematic research and to apply findings into educational settings.},
  subject      = {Metakognition},
  language  = {en}
}
@phdthesis{Sonnenberg2017,
  author    = {Sonnenberg, Christoph},
  title     = {Analyzing Technology-Enhanced Learning Processes: What Can Process Mining Techniques Contribute to the Evaluation of Instructional Support?},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-152354},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2017},
  abstract  = {The current dissertation addresses the analysis of technology-enhanced learning processes by using Process Mining techniques. For this purpose, students' coded think-aloud data served as the measurement of the learning process, in order to assess the potential of this analysis method for evaluating the impact of instructional support. The increasing use of digital media in higher education and further educational sectors enables new potentials. However, it also poses new challenges to students, especially regarding the self-regulation of their learning process. To help students with optimally making progress towards their learning goals, instructional support is provided during learning. Besides the use of questionnaires and tests for the assessment of learning, researchers make use increasingly of process data to evaluate the effects of provided support. The analysis of observed behavioral traces while learning (e.g., log files, eye movements, verbal reports) allows detailed insights into the student's activities as well as the impact of interventions on the learning process. However, new analytical challenges emerge, especially when going beyond the analysis of pure frequencies of observed events. For example, the question how to deal with temporal dynamics and sequences of learning activities arises. Against this background, the current dissertation concentrates on the application of Process Mining techniques for the detailed analysis of learning processes. In particular, the focus is on the additional value of this approach in comparison to a frequency-based analysis, and therefore on the potential of Process Mining for the evaluation of instructional support. An extensive laboratory study with 70 university students, which was conducted to investigate the impact of a support measure, served as the basis for pursuing the research agenda of this dissertation. Metacognitive prompts supported students in the experimental group (n = 35) during a 40-minute hypermedia learning session; whereas the control group (n = 35) received no support. Approximately three weeks later, all students participated in another learning session; however, this time all students learned without any help. The participants were instructed to verbalize their learning activities concurrently while learning. In the three analyses of this dissertation, the coded think aloud data were examined in detail by using frequency-based methods as well as Process Mining techniques. The first analysis addressed the comparison of the learning activities between the experimental and control groups during the first learning session. This study concentrated on the research questions whether metacognitive prompting increases the number of metacognitive learning activities, whether a higher number of these learning activities corresponds with learning outcome (mediation), and which differences regarding the sequential structure of learning activities can be revealed. The second analysis investigated the impact of the individual prompts as well as the conditions of their effectiveness on the micro level. In addition to Process Mining, we used a data mining approach to compare the findings of both analysis methods. More specifically, we classified the prompts by their effectiveness, and we examined the learning activities preceding and following the presentation of instructional support. Finally, the third analysis considered the long-term effects of metacognitive prompting on the learning process during another learning session without support. It was the key objective of this study to examine which fostered learning activities and process patterns remained stable during the second learning session. Overall, all three analyses indicated the additional value of Process Mining in comparison to a frequency-based analysis. Especially when conceptualizing the learning process as a dynamic sequence of multiple activities, Process Mining allows identifying regulatory loops and crucial routing points of the process. These findings might contribute to optimizing intervention strategies. However, before drawing conclusions for the design of instructional support based on the revealed process patterns, additional analyses need to investigate the generalizability of results. Moreover, the application of Process Mining remains challenging because guidelines for analytical decisions and parameter settings in technology-enhanced learning context are currently missing. Therefore, future studies need to examine further the potential of Process Mining as well as related analysis methods to provide researchers with concrete recommendations for use. Nevertheless, the application of Process Mining techniques can already contribute to advance the understanding of the impact of instructional support through the use of fine-grained process data.},
  subject      = {Selbstgesteuertes Lernen},
  language  = {en}
}