@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{GoebelgebAichele2016,
  author    = {G{\"o}bel [geb. Aichele], Thorsten Philipp},
  title     = {Marginalien als Explikation der lokalen Makrostruktur beim Lernen mit Hypertext},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-148136},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2016},
  abstract  = {Im Rahmen der vorliegenden Arbeit wurden vier Experimente zur Eignung von Marginalien als Lernhilfen im Hypertext durchgef{\"u}hrt. Die grundlegende Annahme lautet dabei, dass Marginalien als Kommentar zum Text aufgefasst werden und somit im Vergleich zu intratextuellen Lernhilfen wie {\"U}berschriften oder absatzeinleitenden Makropropositionen zu einer interaktiven und tieferen Verarbeitung der Lerninhalte f{\"u}hren. Als Lernmedium wurden eine hierarchische Hypertextumgebung zum Thema Fragebogenkonstruktion und eine netzf{\"o}rmige Hypertextumgebung zur Bedeutung des Buchdrucks in der Medientheorie eingesetzt. Experiment 1 (N= 41) verglich mittels between-Design die Lernleistung bei Marginalien mit einer Pr{\"a}sentation derselben Makropropositionen als absatzeinleitende Topic-S{\"a}tze und einer Platzierung der Makropropositionen am Absatzende. Die Ergebnisse zeigen, dass absatzweise Marginalien im Vergleich zu absatzeinleitenden Makropropositionen und der Kontrollgruppe zu einem besseren Abschneiden bei geschlossenen Inferenzfragen f{\"u}hren. Hinsichtlich geschlossener Fragen zur Textbasis konnten jedoch die absatzeinleitenden Makropropositionen im Vergleich mit den beiden anderen Bedingungen die besten Ergebnisse erzielen. Experiment 2 (N= 105) verglich den Einfluss von Marginalien mit {\"U}berschriften und einer Kontrollgruppe ohne absatzweise Explikation der Makrostruktur auf das Schreiben einer Zusammenfassung des Lerntextes. Zus{\"a}tzlich wurden erneut geschlossene Inferenzfragen pr{\"a}sentiert. Erg{\"a}nzend wurde das Rezeptionsverhalten mittels Blickbewegungsmessung ermittelt. Dabei zeigten sich signifikante Unterschiede zwischen {\"U}berschriften und Marginalien. Marginalien wurden in der hierarchischen Hypertextumgebung allgemein seltener gelesen als {\"U}berschriften und zeigten auch hinsichtlich der Anzahl der strategischen Rezeptionen und der absatzeinleitenden Rezeption geringere Werte. Einzig nach der Rezeption des zugeh{\"o}rigen Absatzes wurden Marginalien h{\"a}ufiger konsultiert als {\"U}berschriften. Diese Unterschiede gingen einher mit signifikanten Einbußen der Lernleistung der Marginalienbedingung im Vergleich zur {\"U}berschriftenbedingung. So erinnerten Lerner mit Marginalien weniger explizite Makropropositionen des Lerntextes, weniger Fakteninformationen, sowie weniger Inhalte verschiedener Hypertextknoten und bildeten außerdem weniger eigene Makropropositionen. Hinsichtlich der letzten beiden Variablen war die Marginalienbedingung sogar der Kontrollbedingung unterlegen. Experiment 3 (N = 54) verwendete im Gegensatz zu den Experimenten 1 und 2 einen netzf{\"o}rmig organisierten Hypertext mit embedded Links anstelle eines Navigationsmen{\"u}s. Die untersuchten Versuchsbedingungen sowie die Messung der Lernleistung waren jedoch analog zu Experiment 1. Auch hier konnte ein Effekt von Marginalien auf die Inferenzleistung nachgewiesen werden. Allerdings schnitten Marginalien nur besser als die absatzeinleitenden Makropropositionen ab, wohin-gegen kein Unterschied zur Kontrollbedingung festgestellt werden konnten. Hinsichtlich der Leistung bei geschlossenen Faktenfragen konnte die {\"U}berlegenheit absatzeinleitender Makropropositionen gegen{\"u}ber den anderen beiden Pr{\"a}sentationsformen der Makrostruktur erneut best{\"a}tigt werden. Experiment 4 (N= 75) verglich analog zu Experiment 2 unter Verwendung der netzf{\"o}rmigen Lernumgebung aus Experiment 3 erneut den Einfluss von Marginalien, {\"U}berschriften und einer Kontrollbedingung ohne explizite absatzweise Makropropositionen auf das Schreiben einer Zusammenfassung sowie die Beantwortung geschlossener Inferenzfragen. Auch die Blickbewegungsmessung kam wieder zum Einsatz. Die Ergebnisse von Experiment 2 konnten jedoch nicht best{\"a}tigt werden. Es fanden sich keine signifikanten Unterschiede hinsichtlich der Lernleistung zwischen den drei Versuchsbedingungen und auch hinsichtlich des Rezeptionsverhaltens konnte eine Angleichung von Marginalien und {\"U}berschriften festgestellt werden. Hinsichtlich der Lernleistung wird angenommen, dass die embedded Links in Kombination mit der Instruktion, eine Zusammenfassung zu schreiben mit den {\"U}berschriften und den Marginalien, die jedoch im Vergleich zu Experiment 2 fast vollst{\"a}ndig wie {\"U}berschriften genutzt wurden, interferiert haben und somit eine Hemmung dieser Lernhilfen stattgefunden hat. Anhand der vier durchgef{\"u}hrten Experimente wird gefolgert, dass Marginalien als Explikation der lokalen Makrostruktur sowohl bei hierarchisch strukturiertem Hypertext als auch bei netzf{\"o}rmig organisiertem Hypertext unter der Instruktion eines verstehenden Lernens eine Verbesserung der Inferenzleistung bewirken k{\"o}nnen. Lautet die Instruktion jedoch, eine Zusammenfassung der In-halte zu schreiben, sind Marginalien speziell bei hierarchisch strukturiertem Hypertext wenig geeignet, die Lernleistung zu f{\"o}rdern.},
  subject      = {Hypertext},
  language  = {de}
}
@phdthesis{Muenchow2016,
  author    = {M{\"u}nchow, Hannes},
  title     = {I feel, therefore I learn - Effectiveness of affect induction interventions and possible covariates on learning outcomes},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-148432},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2016},
  abstract  = {Affective states in the context of learning and achievement can influence the learning process essentially. The impact of affective states can be both directly on the learning performance and indirectly mediated via, for example, motivational processes. Positive activating affect is often associated with increased memory skills as well as advantages in creative problem solving. Negative activating affect on the other hand is regarded to impair learning outcomes because of promoting task-irrelevant thinking. While these relationships were found to be relatively stable in correlation studies, causal relationships have been examined rarely so far. This dissertation aims to investigate the effects of positive and negative affective states in multimedia learning settings and to identify potential moderating factors. Therefore, three experimental empirical studies on university students were conducted. In Experiment 1, N = 57 university students were randomly allocated to either a positive or negative affect induction group. Affects were elicited using short film clips. After a 20-minute learning phase in a hypertext-based multimedia learning environment on "functional neuroanatomy" the learners' knowledge as well as transfer performance were measured. It was assumed that inducing positive activating affect should enhance learning performance. Eliciting negative activating affect on the other hand should impair learning performance. However, it was found that the induction of negative activating affect prior to the learning phase resulted in slight deteriorations in knowledge. Contrary to the assumptions, inducing positive activating affect before the learning phase did not improve learning performance. Experiment 2 induced positive activating affect directly during learning. To induce affective states during the entire duration of the learning phase, Experiment 2 used an emotional design paradigm. Therefore, N = 111 university students were randomly assigned to learn either in an affect inducing multimedia learning environment (use of warm colours and round shapes) or an affectively neutral counterpart (using shades of grey and angular shapes) on the same topic as in Experiment 1. Again, knowledge as well as transfer performance were measured after learning for 20 minutes. In addition, positive and negative affective states were measured before and after learning. Complex interaction patterns between the treatment and initial affective states were found. Specifically, learners with high levels of positive affect before learning showed better transfer performance when they learned in the affect inducing learning environment. Regarding knowledge, those participants who reported high levels of negative activating affect prior to the learning period performed worse. However, the effect on knowledge did not occur for those students learning in the affect inducing learning environment. For knowledge, the treatment therefore protected against poorer performance due to high levels of negative affective states. Results of Experiment 2 showed that the induction of positive activating affect influenced learning performance positively when taking into account affective states prior to the learning phase. In order to confirm these interaction effects, a conceptual replication of the previous experiment was conducted in Experiment 3. Experiment 3 largely retained the former study design, but changed the learning materials and tests used. Analogous to Experiment 2, N = 145 university students learning for 20 minutes in either an affect inducing or an affectively neutral multimedia learning environment on "eukaryotic cell". To strengthen the treatment, Experiment 3 also used anthropomorphic design elements to induce affective states next to warm colours and round shapes. Moreover, in order to assess the change in affective states more exactly, an additional measurement of positive and negative affective states after half of the learning time was inserted. Knowledge and transfer were assessed again to measure learning performance. The learners' memory skills were used as an additional learning outcome. To control the influence of potential confounding variables, the participants' general and current achievement motivation as well as interest, and emotion regulation skills were measured. Contrary to the assumptions, Experiment 3 could not confirm the interaction effects of Experiment 2. Instead, there was a significant impact of positive activating affect prior to the learning phase on transfer, irrespective of the learners' group affiliation. This effect was further independent of the control variables that were measured. Nevertheless, the results of Experiment 3 fit into the picture of findings regarding "emotional design" in hypermedia learning settings. To date, the few publications that have used this approach propose heterogeneous results, even when using identical materials and procedures.},
  subject      = {Affekt},
  language  = {en}
}
@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{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}
}