I detta arbete undersöks den musikaliska medvetenheten med hjälp av inspelningsutrustning, genom att identifiera aspekter i ens spel som inte märktes förrän genomlyssning i efterhand. Den moderna musikerns förutsättningar skiljer sig markant från tidigare, där det nu är enklare än någonsin att göra en inspelning med mobilmikrofonen. Genom att spela in stycken, lyssna på dem och föra anteckningar, identifierades flertal konkreta aspekter i pianospelet som inte märktes vid själva genomspelningen. Dessa tilldelades kategorierna: ”precision”, ”balans”, ”kroppsspråk”, ”agogik och tolkning”, ”frasering" och ”tempo och rytm". Vad gäller precision och balans erhölls inga större insikter utöver att feltoners betydelse kunde underskattas vid enstaka fall samt att jämnhet i anslaget var viktigt. I kroppsspråket fanns det en generell tendens att positiva rörelser gjordes i underdrift och de rytmiska aspekterna berörde ofta instabiliteter av olika slag, exempelvis ojämna rubato eller plötsliga tempoändringar. Slutligen handlade det agogiska och interpretativa ofta om att tydliggöra musikaliska strukturer, vilket gav en signifikant förbättring i upplevelse. Genom att aktivt arbeta med denna analysmetod märktes på sikt en förbättring i ens musikaliska självinsikt, där medvetenheten blev allt bättre med tiden. / In this project, my musical self-awareness is examined with the help of various recording devices, by identifying different aspects in the music that weren’t noticed until the recording was listened to. The possibilities given to the modern musician differ significantly from earlier times, where it is now easier than ever to make a recording with your mobile device. By recording pieces, listening to them and taking notes, several concrete aspects in my piano playing that weren’t noticed when playing through were identified. These were assigned the categories: ”precision”, ”balance", ”body language”, ”agogic and interpretation”, ”phrasing” and ”tempo and rhythm”. When it comes to precision and balance, no interesting insights were made apart from the significance of certain wrong notes as well as the importance of evenness in touch. The body language had the general tendency of not being exaggerated enough and the rhythmical aspects often concerned different instabilities, such as uneven rubato or sudden tempo changes. Lastly, the key to improving agogic and interpretation often lied in making musical structures more clear. By actively working with this method of analysis, one’s musical self-awareness was gradually improved through time. / <p>Den klingande delen är arkiverad.</p>

This dissertation explores the use of natural language processing (NLP) and large language models (LLMs) to analyze student self-regulated learning (SRL) strategies in response to exam wrappers. Exam wrappers are structured reflection activities that prompt students to practice SRL after they get their graded exams back. The dissertation consists of three manuscripts that compare traditional qualitative analysis with NLP-assisted approaches using transformer-based models including GPT-3.5, a state-of-the-art LLM. The data set comprises 3,800 student responses from an engineering physics course. The first manuscript develops two NLP-assisted codebooks for identifying learning strategies related to SRL in exam wrapper responses and evaluates the agreement between them and traditional qualitative analysis. The second manuscript applies a novel NLP technique called zero-shot learning (ZSL) to classify student responses into the codes developed in the first manuscript and assesses the accuracy of this method by evaluating a subset of the full dataset. The third manuscript identifies the distribution and differences of learning strategies and SRL constructs among students of different exam performance profiles using the results from the second manuscript. The dissertation demonstrates the potential of NLP and LLMs to enhance qualitative research by providing scalable, robust, and efficient methods for analyzing large corpora of textual data. The dissertation also contributes to the understanding of SRL in engineering education by revealing the common learning strategies, impediments, and SRL constructs that students report they use while preparing for exams in a first-year engineering physics course. The dissertation suggests implications, limitations, and directions for future research on NLP, LLMs, and SRL. / Doctor of Philosophy / This dissertation is about using artificial intelligence (AI) to help researchers and teachers understand how students learn from their exams. Exams are not only a way to measure what students know, but also a chance for students to reflect on how they studied and what they can do better next time. One way that students can reflect is by using exam wrappers, which are short questions that students answer after they get their graded exams back. A type of AI called natural language processing (NLP) is used in this dissertation, which can analyze text and find patterns and meanings in it. This study also uses a powerful AI tool called GPT-3.5, which can generate text and answer questions. The dissertation has three manuscripts that compare the traditional way of analyzing exam wrappers, which is done by hand, with the new way of using NLP and GPT-3.5, evaluate a specific promising NLP method, and use this method to try and gain a deeper understanding in students self-regulated learning (SRL) while preparing for exams. The data comes from 3,800 exam wrappers from a physics course for engineering students. The first manuscript develops a way of using NLP and GPT-3.5 to find out what learning strategies and goals students talk about in their exam wrappers and compares it to more traditional methods of analysis. The second manuscript tests how accurate a specific NLP technique is in finding these strategies and goals. The third manuscript looks at how different students use different strategies and goals depending on how well they did on the exams using the NLP technique in the second manuscript. I found that NLP and GPT-3.5 can aid in analyzing exam wrappers faster and provide nuanced insights when compared with manual approaches. The dissertation also shows what learning strategies and goals are most discussed for engineering students as they prepare for exams. The dissertation gives some suggestions, challenges, and ideas for future research on AI and learning from exams.

Expert sport performance is developed by engaging in large volumes of high-quality training, particularly among endurance athletes, which must be supported by recovery. Despite the importance of recovery for sustaining and enhancing training, the concept has been largely absent from sport expertise research due in part to a lack of identified athlete-led recovery skills. Moreover, research on recovery has focused on a limited range of modalities, informed by practitioners' perspectives, at the expense of more complex, athlete-centered perspectives of what recovery may involve. The overarching purpose of this dissertation was to explore and describe the psychology of recovery in relation to sport expertise, and in doing so answer, "What might it mean for an endurance athlete to be skilled at recovery?". This purpose was addressed in four articles, organized in an exploratory, sequential, mixed-methods design. Expert sport performance is developed by engaging in large volumes of high-quality training, particularly among endurance athletes, which must be supported by recovery. Despite the importance of recovery for sustaining and enhancing training, the concept has been largely absent from sport expertise research due in part to a lack of identified athlete-led recovery skills. Moreover, research on recovery has focused on a limited range of modalities, informed by practitioners' perspectives, at the expense of more complex, athlete-centered perspectives of what recovery may involve. The overarching purpose of this dissertation was to explore and describe the psychology of recovery in relation to sport expertise, and in doing so answer, "What might it mean for an endurance athlete to be skilled at recovery?". This purpose was addressed in four articles, organized in an exploratory, sequential, mixed-methods design. Article 1 explored what recovery means to a sample of 13 elite endurance athletes with experience at multiple World Championships/Olympics. Each athlete participated in two semi- structured interviews, separated by an intervening week of keeping an activity journal of their recovery-related thoughts/actions. Using inductive reflexive thematic analysis, the findings portrayed recovery as encompassing a wide range of potential approaches that spanned multiple dimensions of feelings, levels of focus, and personal solutions. Further, the athletes assigned meaning to recovery in a particular time and context based on processes of 'defining short and long-term purposes', 'breaking and engaging', and 'negotiating and prioritizing'. These findings suggested that recovery is highly complex and individual, and that athletes define recovery according to personal and contextual conditions. Article 2 described the process of implementing recovery from the perspective of elite endurance athletes, using data from the same interviews as Article 1. Through inductive reflexive thematic analysis, I found that these athletes felt recovery was athlete-led: it involved processes of self-knowledge and planning (captured in the theme of 'Knowing my body'), self-awareness and interpretation ('Listening to my body'), and self-control and adjustment ('Respecting my body'), all connected in on-going development ('Learning my body'). During reflexive analysis, I further found that recovery self-regulation was integrated with people and places in the athletes' environments in ways that supplemented, facilitated, and provided for aspects of recovery. I integrated the athlete-led themes and environment-influenced themes in the Athlete Recovery Regulation Model, a heuristic model outlining how athletes shape their recovery using a set of athlete-led skills of recovery self-regulation. Article 3 aimed to describe how 22 elite cyclists and triathletes implemented certain recovery self-regulation skills between two key workouts, placed 2-3 days apart in their planned training. Using experience sampling methods, participants reported their momentary use of certain self-regulatory processes, as well as states of recovery and stress, up to eight times per day, leading up to and between the workouts. These processes were strongly correlated but differed in frequency, intensity, consistency, and predictors of use, which suggested that the processes represent synergistic yet unique competencies. Greater use of recovery self-regulation processes was associated with higher perceived stress and, to a lesser extent, lower perceived recovery, but there was no association with the time remaining to or elapsed after the key workouts. These findings indicated that elite endurance athletes self-regulate their recovery frequently and dynamically, largely in response to multidimensional feelings of stress. Article 4 refined the methods of Article 3 into a more controlled, representative task to assess and describe recovery between two key workouts. Using that task, planned analyses aimed to (a) describe the patterns of recovery self-regulation employed by 16 non-elite endurance athletes, and (b) assess those patterns in relation to the recovery of performance between successive workouts. Sixteen recreationally competitive cyclists participated remotely in two prescribed workouts, 48 hrs apart, on the Zwift virtual cycling platform. Between workouts, they participated in the same experience sampling design as Article 3. Findings showed that the non- elite cyclists also self-regulated their recovery frequently and dynamically. In contrast to the elite athletes in Article 3, this sample made greater use of self-regulatory processes specifically when experiencing physical stress, following the first workout, and use declined with time between the workouts. The recovery of performance in workout 2 relative to workout 1 was associated with more frequent use of certain self-regulatory processes, although overall, various characteristics of recovery self-regulation were not associated with performance recovery. These findings indicated that non-elite athletes engage in recovery self-regulation, albeit in potentially simpler and more reactive patterns compared to elite athletes. This dissertation makes several contributions. It proposes that an athlete's role in recovery may be conceptualized through athlete-led skills, as described in the Athlete Recovery Regulation Model. Further, it suggests that recovery may be understood in relation to various processes and perspectives of self-regulated learning. Methodologically, this dissertation advances a proof of concept that recovery can be examined in a traditional expertise paradigm, using experience sampling methods employed around a representative task in the context of inter-workout recovery. Finally, this dissertation advances an athlete-centered and skills-based understanding of recovery, which provides an alternative avenue for applied practitioners and sport organizations to address recovery with endurance athletes. Overall, this dissertation centers recovery on the athletes who engage in it by describing skills they can own and hone to shape their recovery.

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The objectives of this study include examining whether distress tolerance (DT) mediates the relation between students' perceived social support and their engagement in self-regulated learning activities associated with engaging help of others. Further, this research examines whether gender or race moderate the relations between social support and self-regulation or social support to DT. Finally, the research examines whether the mediation of the association between social support and self-regulated learning is moderated by either gender or race. DT fully mediates the relationship between social support and self-regulated learning. This study revealed that there are certain subskills of self-regulated learning (i.e., effort regulation, peer learning, help seeking) on which social support does not have a direct effect but is mediated by students' levels of distress tolerance, with students with higher levels of distress tolerance appearing to be able to engage with support. However, without direct examination of the direction of the relations given the cross-sectional nature of the research, it may be as well that students who perceive greater support from their social environment have greater ability to tolerate a certain amount of distress. This was related to higher inclination to reaching out to others for peer learning or help seeking as well as utilizing effort regulation strategies for learning to achieve academic goals.