Matching Items (2)
Description
People are motivated to participate in musical activities for many reasons. Whereas musicians may be driven by an intrinsic desire for musical growth, self-determination theory suggests that this drive must also be sustained and supported by the social environment. Social network analysis is an interdisciplinary theoretical framework and collection of analytical methods that allows us to describe the social context of a musical ensemble. These frameworks are utilized to investigate the relationship of participatory motivation and social networks in a large Division I collegiate marching band. This study concludes that marching band members are predominantly self-determined to participate in marching band and are particularly motivated for social reasons, regardless of their experience over the course of the band season. The members who are highly motived are also more integrated into the band's friendship and advice networks. These highly integrated members also tend to be motivated by the value and importance others display for the marching band activity suggesting these members have begun to internalized those values and seek out others with similar viewpoints. These findings highlight the central nature of the social experience of marching band and have possible implications for other musical leisure ensembles. After a brief review of social music making and the theoretical frameworks, I will provide illustrations of the relationship between motivation and social networks in a musical ensemble, consider the implications of these findings for promoting self-determined motivation and the wellbeing of musical ensembles, and identify directions for future research.
ContributorsWeren, Serena (Author) / Hill, Gary W. (Thesis advisor) / Granger, Douglas (Committee member) / Bailey, Wayne (Committee member) / Norton, Kay (Committee member) / Reber, William (Committee member) / Arizona State University (Publisher)
Created2015
Description
Built upon Control Value Theory, this dissertation consists of two studies that examine university students’ future-oriented motivation, socio-emotional regulation, and diurnal cortisol patterns in understanding students’ well-being in the academic-context. Study 1 examined the roles that Learning-related Hopelessness and Future Time Perspective Connectedness play in predicting students’ diurnal cortisol patterns, diurnal cortisol slope (DS) and cortisol awakening response (CAR). Self-reported surveys were collected (N = 60), and diurnal cortisol samples were provided over two waves, the week before a mid-term examination (n = 46), and the week during students’ mid-term (n = 40). Using multi-nomial logistic regression, results showed that Learning-related Hopelessness was not predictive of diurnal cortisol pattern change after adjusting for key covariates; and that Future Time Perspective Connectedness predicted higher likelihood for students to have low CAR across both waves of data collection. Study 2 examined students’ future-oriented motivation (Future Time Perspective Value) and socio-emotional regulation (Effortful Control and Social Support) in predicting diurnal cortisol patterns over the course of a semester. Self-reported surveys were collected (N = 67), and diurnal cortisol samples were provided over three waves of data collection, at the beginning of the semester (n = 63), during a stressful academic period (n = 47), and during a relaxation phase near the end of the semester (n = 43). Results from RM ANCOVA showed that Non-academic Social Support was negatively associated with CAR at the beginning of the semester. Multi-nomial logistics regression results indicated that Future Time Perspective Value and Academic Social Support jointly predicted CAR pattern change. Specifically, the interaction term marginally predicted a higher likelihood of students switching from having high CAR at the beginning or stressful times in the semester to having low CAR at the end the semester, compared to those who had low CAR over all three waves. The two studies have major limits in sample size, which restricted the full inclusion of all hypothesized covariates in statistical models, and compromised interpretability of the data. However, the methodology and theoretical implications are unique, providing contributions to educational research, specifically with regard to post-secondary students’ academic experience and well-being.
ContributorsCheng, Katherine C (Author) / Husman, Jenefer (Thesis advisor) / Lemery-Chalfant, Kathryn (Committee member) / Granger, Douglas (Committee member) / Eggum-Wilkens, Natalie D (Committee member) / Pekrun, Reinhard (Committee member) / Arizona State University (Publisher)
Created2017