The purpose of this study was to establish the relationship between musical self-concept and musical creativity, and to determine the best predictors of musical creativity given the musical self-concept dimensions. Participants (N = 201) were music students, drawn from 21 secondary schools in Kenya. Music Self-perception Inventory-Version 2 (MUSPI) was used to gather data on participants' musical self-concept. Musical creativity was measured using the Consensual Musical Creativity Assessment Scale (CMCAS). Results indicated a positive relationship between musical self-concept and musical creativity (r = .25, p < .01). All the musical self-concept dimensions, except singing and dancing showed positive associations with musical creativity. Further, a significant mean difference in musical creativity for positive and negative musical self-concept was observed. Multiple regression indicated that the best predictors of musical creativity were sense of rhythm and dancing self-concepts and the strongest predictor of musical creativity was sense of rhythm self-concept. A significant gender difference in musical creativity was observed, with males scoring higher than females. However, there was no significant difference in participants' musical creativity based on age. The study recommends interventions and conducive environments for the development of positive musical self-concept.

The purpose of this study was to (a) establish the relationships among achievement goal motivation, cognitive learning strategies, and musical creativity; (b) determine the best predictors of musical creativity among the study variables. Participants ( N = 201) were secondary school music students in Kenya. Two self-report measures, the Achievement Goal Questionnaire-Revised (AGQ-R) and Motivated Strategies for Learning Questionnaire (MSLQ) were used in data collection for the independent variables. Musical creativity was measured by a creative composition task and evaluated according to four dimensions of musical craftsmanship, syntax, originality and aesthetic sensitivity. The results showed that musical creativity was positively correlated with mastery-approach goal and deep processing learning strategy but negatively correlated with surface processing strategy, performance-approach and performance-avoidance goals. The best predictor of musical creativity was deep processing strategy, β = .45, p < .01, which accounted for approximately 26% of the variance in participants’ musical creativity, followed by mastery-approach goal, β = .27, p < .01, R2 =.09. The implication for music education is that music teachers should create conducive environments and adopt teaching strategies that nurture mastery-approach goal orientation and deep processing learning strategies to enhance musical creativity