Achievement goal motivation and cognitive strategies as predictors of musical creativity among secondary school music students

Abstract

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

Keywords

achievement goal motivation cognitive learning strategies composition deep processing strategy musical creativity surface processing strategy

Music education is perceived as fundamental in fostering students’ creative potential. Its aesthetic effect gives it a unique role in the school curriculum. A number of psychological variables including intelligence, cognition, knowledge, personality, motivation, emotions and social environment have been identified as important predictors of creativity (Barbot, Besançon & Lubart, 2011; Besançon & Lubart, 2008; Sternberg, 2006). Creativity is usually considered a characteristic relating to a person, process or product (Barbot et al., 2011). Ferrari, Cachia and Punie (2009) define creativity as a product or process that shows a balance of originality and value. In the field of psychology, creativity has been understood as individuals’ ability to produce work that is both novel and appropriate within a particular domain (Sternberg & Lubart 1995). Additionally, creativity is viewed as a psychological attribute that can be nurtured and measured in all students in educational settings (Amabile, 2012; Barbot et al., 2011). Amabile’s (1996, 2012) consensual model of creativity, suggests that a product or idea is creative if expert observers agree it is creative. The author provides a consensual assessment technique (CAT), in which creativity is measured by subjective assessment of creative products by experts in the domain for which the product is created.

Burnard (2012) conceptualizes multiple musical creativities, in which musical creativity is viewed as a situated social activity where different modes of musical creativity including individual, collaborative, and performance creativity can be practiced. In an educational context, musical creativity is understood as “everyday creativity” in which students generate a musical product that is novel to the individual and useful for the situated musical practice (Burnard, 2012; Fautley & Savage, 2007). Musical creativity can be demonstrated through composition, improvisation, performing, listening, writing, and analyzing. However, composition can be considered a prime musical example of the creative act (Burnard, Fautly & Savage, 2010) or a problem-solving activity (Burnard & Younker, 2004) that involves the use of musical skills and understanding (Laycock, 1992) as well as original thinking or imagination (Kokotsaki, 2012) in music creation.

Many empirical studies have adopted Amabile’s CAT as a measure of students’ compositional creativity (Auh, 1997; Hickey, 2001; Priest, 2006). Hickey (2001) found significantly higher inter-rater reliability among music teachers than other judges in the rating of students’ compositional creativity. The author suggests that the music teachers are the appropriate observers because of their expert knowledge and experience in the music classroom (Kokotsaki & Newton, 2015). Currently, an extensive literature exists on musical creativity in educational settings (Auh, 1997; Burnard, 2012; Burnard, et al., 2010; Burnard & Younker, 2004; Hickey, 2001; Kokotsaki, 2012; Kokotsaki & Newton, 2015; Priest, 2006). In addition, several researchers have investigated achievement goal motivation (Diaz, 2010; Miksza, 2009, 2011; Miksza, Tan & Dye, 2016; Nielsen, 2008) and learning strategies (Brand, 2001; Green, 2012; McPherson & McCormick, 1999; Nielsen, 2008, 2010; Varvarigou & Green 2015) in relation to instrumental music practice and performance. However, few studies have specifically examined cognitive learning strategies and achievement goal motivation in relation to compositional creativity among secondary school subjects in Africa. Africa’s cultural, social, and economic aspects of life, including ethnic or religious identities, cultural values, lifestyles, art, political challenges, wars or conflicts, and economic development, are comparatively different from those of developed countries, where the aforementioned studies were primarily conducted.

Cognitive learning strategies are essential for acquisition of musicianship and musical creativity in a music education program. According to Elliot, McGregor and Gable (1999), learning strategies refer to cognitive and motivational processes intentionally engaged by students during learning and preparation for examination. Green (2012) distinguishes between the concepts of “learning styles” and “learning strategies” where “learning style” implies an inherent trait that tends to remain fundamental to the way an individual approaches learning. In contrast, “learning strategy” refers to a set of consciously acquired and applied approaches to learning. According to Craik and Lockhart’s (1972) levels of processing theory, students subject stimuli to different levels of mental processing and retain only the information that has been subjected to the most thorough processing. Surface processing leads to a fragile memory trace or recall, while deep processing results in a more durable memory trace. The authors suggest that it is only elaborative rehearsal that improves long-term memory. Deep processing is therefore posited to have positive impact on creativity while surface processing is detrimental to creativity.

Previous studies in diverse academic domains have demonstrated a link between students’ learning strategies and creativity. These learning strategies include deep processing, persistence, metacognitive knowledge, and team learning (Hirst, Van Knippenberg & Zhou, 2009; Ruscio, Whitney & Amabile, 1998; Van de Kamp, Admiraal & Rijlaarsdam, 2015). In the context of music education, Varvarigou and Green (2015) revealed a range of spontaneous learning styles and learning strategies among instrumental students. Similarly, Brand (2001) examined the styles of learning and cognitive strategies of American and Chinese music students. Findings revealed that the American students showed a greater tendency to rely on extrinsic motivation and greater reliance on a rote learning approach as compared to Chinese music students. McCormick and McPherson (2003) investigated music learning strategies and graded music instrumental performance. The results showed non-significant relations between music instrumental performance and both surface (rehearsal) and deep processing (elaboration and organization) learning strategies.

In a related study, McPherson and McCormick (1999) examined motivational and self-regulated learning components of instrumental musical practice. The findings suggest that students who report higher levels of cognitive strategy use while practicing also report higher levels of intrinsic values for learning their musical instrument. Such musicians not only tend to do more practice but are also more efficient with their learning. Nielsen (2008) investigated achievement goals, learning strategies, and instrumental performance of music students. Similarly, Nielsen (2010) investigated music students’ learning strategies in practicing a musical instrument. However, neither study examined the relation between learning strategies and instrumental performance. In the current study, participants’ learning strategies were categorized into deep processing and surface processing strategies, where deep processing involved dimensions of elaboration, organization of information, and critical thinking, while surface processing essentially involved repetitive rehearsal or rote memorization of facts and information (Pintrich, Smith, García & McKeachie, 1991).

Achievement goal orientation is a motivational orientation that impacts individuals’ behavior in achievement situations (Elliot et al., 1999). Two distinct goal orientations have been commonly identified. They are mastery (learning) and performance (ego) dimensions which can each be divided further into approach and avoidance dimensions (Elliot & McGregor, 2001; Miksza, 2011). A mastery-approach goal is focused on the development of competence and task mastery (Ranellucci et al., 2013). The orientation to this goal may generate greater musical creativity (Schatt, 2011), as the goal is associated with intrinsic interest in the task itself, use of a deep processing strategy, and persistence and effort in problem solving (Elliott et al., 1999; Janssen & Van Yperen, 2004; Ruscio et al., 1998). On the contrary, a mastery-avoidance goal drives the individual to evade the inability to maximize learning. This goal has been associated with disorganized study habits (Elliot & McGregor, 2001).

A performance-approach goal orientation focuses on the demonstration of normative competence and is motivated by extrinsic factors such as competition and receiving incentives (Ranellucci et al., 2013). Previous research has linked performance-approach orientation to surface processing (Elliot & McGregor, 2001; Elliot et al., 1999; Ranellucci et al., 2013), which often results in temporary achievements (Schatt, 2011). In contrast, performance-avoidance goal orientation motivates students to evade demonstrating normative incompetence (Ranellucci et al., 2013). Such students engage in surface learning and disorganization (Elliot et al., 1999). Due to fear of failure, they may avoid tasks such as music auditioning and public performance, which would otherwise enhance their musical creativity (Schatt, 2011).

In a related study, Miksza (2011) investigated the relationship between observed music practice behavior and achievement goal motivation of wind instruments players in the US. The results indicated that the sample endorsed mastery orientation more than performance orientation with the mastery-approach having the highest mean among the achievement goal subscales. In a similar way, Miksza (2009) established that mastery-approach achievement motivation is a significant positive predictor of instrumental performance achievement. Similarly, Schmidt (2005) indicated that instrumental music students’ success is best defined by mastery rather than performance orientation. This concurred with Diaz’s (2010) study, which reported relatively high means for variables associated with mastery goal orientation and relatively low means for variables regarded as performance goal orientation among collegiate instrumentalists in the US. On the contrary, Nielsen (2008) reported non-significant correlations between achievement goal orientation variables and instrumental performance achievement. In yet another study, Miksza et al. (2016) examined achievement motivation for band among American and Singapore instrumental music students. However, the relation between achievement goal motivation and instrumental performance achievement was not examined.

Generally, previous literature indicates that students’ learning strategies and achievement goal motivation influence music practice behavior, performance, and achievement. Therefore, by investigating how students’ learning strategies and achievement goal motivation may predict musical creativity, we hope to add some new and potentially useful insights that could be of interest and benefit to teachers as well as music psychologists and musicians.

Purpose

The primary purpose of this study was to establish the relationships among achievement goal motivation, cognitive learning strategies, and musical creativity. We also sought to determine the best predictors of musical creativity from the study variables. The study was guided by the following research hypotheses:

H_a1:	There is a significant relationship between participants’ achievement goal motivation and musical creativity.
H_a2:	There is a significant relationship between participants’ cognitive learning strategies and musical creativity.
H_a3:	There is a significant predictive model for musical creativity given achievement goal motivation and cognitive learning strategies.

Method

Participants were Form 4 music students (N = 201) mainly drawn from public secondary schools in Nairobi County in Kenya, Africa. The majority of the participants (n = 139, 69%) were female students. The age of the sample ranged from 16 to 21 years (M = 17.24, SD = .78), with the majority (75%) falling in the 16–17 year-old category. Purposive sampling was used in selecting 18 public and 3 private schools that offer the national music curriculum. Then Form 4 was selected for the study because at that level the students had covered sufficient course content necessary for notation of creative music composition. Further, due to the small population of music students in Form 4, a census was used.

The Kenyan national curriculum follows the 8:4:4 system of education, comprising 8 years in primary school (classes 1–8, ages 6–14 years); 4 years in secondary school (Forms 1–4, ages 14–18 years); and 4 years in university. Music education at the primary school level is compulsory, although it is not an examinable subject. Music learning at this level is therefore “singing-oriented” and marginalized. At the secondary school level, music is among optional subjects. The course covers basic skills, melody composition, harmony, aural, history and analysis, Western music, practical, and projects. Music is examined using the Kenya Certificate for Secondary Education (KCSE) examination, which is used for university admission. Few public secondary schools offer music education in Kenya. These schools suffer low enrollment, as approximately 0.3% of the secondary school students choose to study music. This is compounded by an insufficient number of music teachers and shortage of necessary facilities such as pianos, keyboards, and other relevant musical instruments. Consequently, most students have limited skills with musical instruments and major in voice. In addition, the use of computer technology in music teaching and learning is not advanced at secondary level. Apart from the public schools, some private schools and conservatories offer alternative music education.

Data were collected under normal music classroom conditions in two separate sessions within an interval of 1 month. The first session was for the administration of questionnaires while the second session was for the creative composition task. Participation was voluntary and each participant gave his or her informed consent by signing an authorization form to participate.

Instruments

A researcher-adaptation of Elliot and Murayama’s (2008) Achievement Goal Questionnaire-Revised (AGQ-R) was used to assess participants’ motivational orientation towards achievement (see the Appendix). Elliot and Murayama’s original statements were restructured to reflect competence in music-related areas. For example, “I am striving to do well compared to other students in the music class.” The instrument was composed of three subscales: mastery-approach, performance-approach and performance-avoidance goals. Each subscale was assessed by three items, which were rated on a five-point Likert-type scale ranging from 1 = strongly disagree to 5 = strongly agree. Thus, the scores in each subscale ranged from 3 to 15, with higher scores corresponding to relatively stronger orientation to a particular goal. Participants were instructed to indicate the degree to which they agreed or disagreed with each statement in relation to their goals in the music course.

The cognitive learning strategies scale consisted of ten items adapted from the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich et al., 1991; see the Appendix). The instrument comprises two subscales: surface processing strategy (five items, all from rehearsal) and deep processing strategy (five items including organization, elaboration, and critical thinking). The items were scored on a seven-point Likert-type scale, ranging from 1 = not at all true of me to 7 = very true of me. Thus, the scores in each item ranged from 1 to 7 and the overall scores in the items that made up each subscale represented scores in that particular subscale. High scores corresponded to being relatively in favor of the usage of a particular cognitive learning strategy. Participants were required to indicate the degree to which each statement in the questionnaire represented their learning approaches in the music course.

The participants’ musical creativity was based on a creative compositional task and measured by an adaptation of the Consensual Assessment Technique (CAT; Amabile, 1996). For the purpose of this study, four composition dimensions were adapted from Auh (1997): musical craftsmanship (the degree to which the tonal and rhythmic elements in a composition demonstrates technical competence); musical syntax (the degree to which the tonal and rhythmic patterns in a composition are structured in a logical manner, so that the melody makes sense); musical originality (the degree to which the composition is unique, when compared to the existing melodies by other participants); and musical aesthetic sensitivity (the degree to which the melody is musically expressive and appealing).

The participants were asked to employ the musical elements, concepts and principles they had acquired in music class to notate an original melody on music manuscript paper. They were allowed to compose for either voice or an instrument and this lasted approximately 45 minutes. Further, participants’ creative compositions were transcribed by the researchers using Noteworthy Composer 2, a computer program that generated both the audio and scores (see Figure 1). Two expert judges (graduate, secondary school music teachers) were asked to complete the consensual musical creativity assessment based on Amabile’s (1996) procedure. Using both the audio and the score, the judges were required to independently rate the participants’ compositions relative to each other and in accordance with the four composition dimensions of musical craftsmanship, syntax, originality, and aesthetic sensitivity (Auh, 1997). The rating was based on a five-point Likert-type scale, ranging from 1 = Low to 5 = High. A sum of the judges’ ratings on the four composition dimensions generated a composite creativity score (overall) ranging from 4 to 20. Inter-rater reliability was established using Cronbach’s alpha. This yielded satisfactory levels of reliability for craftsmanship (α = .87), syntax (α = .78), originality (α = .72), aesthetic sensitivity (α = .86), and composite creativity (α = .82). Therefore, the composite creativity scores from each judge were averaged to represent the participants’ musical creativity with higher scores corresponding to relatively higher musical creativity.

Figure 1.

Participants’ compositions showing average musical creativity scores.

A pilot study was conducted prior to the main study among 20 (15 females) music students. The participants’ creative compositions generated during the pilot phase were used to standardize the scoring procedures for the consensual musical creativity assessment among the two expert judges and enhance reliability of the instrument. A significance level of p < .05 was chosen for this study.

Results

Table 1 shows descriptive statistics and reliability coefficients for the independent and dependent variables. Results of a confirmatory factor analysis provided evidence in support of the construct validity of each independent variable. Cronbach’s alpha reliability coefficients for the achievement goal motivation and cognitive learning strategy subscales were moderately high to very high (between α = .70 and .92). Inter-rater reliability for musical creativity dimensions ranged from α = .72 to .87; overall α = .82. The skewness and kurtosis values were below ± 1, which indicated a relatively normal distribution for all variables. Among the achievement goal motivation subscales, the highest mean (M = 12.19, SD = 2.24) was found for mastery-approach (maximum possible = 15). Contrary, relatively lower means (M = 11.76, SD = 2.92) and (M = 11.10, SD = 3.74) respectively, were observed for performance-approach and performance-avoidance. Surface processing strategy had a relatively higher mean (M = 27.86, SD = 5.38) than deep processing strategy (M = 24.51, SD = 5.82, maximum possible = 35). In general participants had moderately high musical creativity (M = 12.75, SD = 2.68, maximum possible 20). Figure 1 shows a range of participants’ creative compositions and the average musical creativity ratings by the two judges.

Table 1.

Descriptive statistics and reliability coefficients for the independent and dependent variables.

VR	M	SD	Sk	Kr	α
MAP	12.19	2.24	−0.71	−0.08	0.82
PAP	11.76	2.92	−1.06	0.77	0.85
PAV	11.10	3.74	−0.80	−0.55	0.92
DP	24.51	5.38	−0.26	−0.67	0.70
SP	27.86	5.82	−0.90	0.42	0.76
C	3.55	0.62	0.53	−0.47	0.87
S	3.51	0.66	0.01	−0.20	0.78
O	2.72	0.82	0.43	0.21	0.72
A	2.96	0.87	0.03	0.72	0.86
MC	12.75	2.68	0.37	−0.36	0.82

Note: VR = variable; M = mean; SD = standard deviation; Sk = skewness; Kr = kurtosis; α = reliability; MAP = mastery-approach; PAP = performance-approach; PAV = performance-avoidance; DP = deep processing; SP = surface processing; C = musical craftsmanship; S = musical syntax; O = musical originality; A = musical aesthetic sensitivity; MC = musical creativity.

Table 2 presents correlations among the independent and dependent variables. To determine the inter-correlations among the study variables, a bivariate Pearson’s product–moment correlation coefficient was computed. The relations among the independent variables were generally low r(199) = ‒.08, p > .05 to r(199) = .28, p < .01, with the exception of the correlation between performance-approach and performance-avoidance r(199) = .54, p < .01. In contrast, the relations among the musical creativity dimensions were relatively high r(199) = .68, p < .01 to r(199) = .83, p < .01.

Table 2.

Correlational matrix for the independent and dependent variables.

	Independent variables					Dependent variables
	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.
VR	MAP	PAP	PAV	DP	SP	C	S	O	A	MC
1.	1	.10	−.25**	.17*	−.08	.37**	.36**	.32**	.37**	.39**
2.		1	.54**	−.01	.28**	−.16*	−.21**	−.15*	−.16*	−.19**
3.			1	−.08	.28**	−.26**	−.23**	−.26**	−.27**	−.28**
4.				1	−.08	.48**	.40**	.51**	.46**	.52**
5.					1	−.27**	−.20**	−.24**	−.18**	−.24**
6.						1	.77**	.72**	.68**	.86**
7.							1	.75**	.74**	.89**
8.								1	.83**	.93**
9.									1	.92**
10.										1

Note: ** = p < .01; * = p < .05; VR = variable; MAP = mastery-approach; PAP = performance-approach; PAV = performance-avoidance; DP = deep processing; SP = surface processing; MC = musical creativity; C = musical craftsmanship; S = musical syntax; O = musical originality; A = musical aesthetic sensitivity.

To address the first research hypothesis, a bivariate Pearson’s product–moment correlation coefficient was conducted to establish the relations among achievement goal motivation subscales and musical creativity. The findings revealed that musical creativity was positively correlated r(199) = .39, p < .01 with mastery-approach goal and negatively correlated r(199) = −.19, p < .01 and r(199) = −.28, p < .01 with performance-approach and performance-avoidance goals respectively. These results successfully allowed for rejection of the first null hypothesis, which stated that there is no significant relationship between achievement goal motivation and musical creativity.

Furthermore, to examine the second research hypothesis, a bivariate Pearson’s product–moment correlation coefficient was conducted. The findings showed that musical creativity was positively correlated r(199) = .52, p < .01 with deep processing strategy and negatively correlated r(199) = −.24, p < .01 with surface processing strategy. The second null hypothesis, which stated that there is no significant relationship between participants’ cognitive learning strategies and musical creativity was therefore rejected.

To address the third research hypothesis, stepwise multiple regression analysis was computed to establish the prediction of musical creativity from achievement goal orientations and cognitive learning strategies subscales (see Table 3). A significant regression model was found F(4,196) = 34.29, p < .01, R² = .40. The best combination of predictors of musical creativity was deep processing strategy, mastery-approach goal, surface processing strategy and performance-avoidance goal, which accounted for approximately 40% of the variance in participants’ musical creativity. The strongest positive predictor of musical creativity was having a deep processing strategy, β = .45, p < .01, which accounted for 26% of the variance in participants’ musical creativity. This was followed by mastery-approach goal, β = .27, p < .01. On the contrary, surface processing strategy and performance-avoidance goal had negative predictive values on musical creativity, β = -.15, p < .05 and β = -.14, p < .05, respectively. Deep processing strategy and mastery-approach goal were the most common positive predictors of the musical creativity dimensions, while surface processing strategy and performance-avoidance were the most common negative predictors of the musical creativity dimensions except musical syntax. These results successfully allowed for rejection of the third null hypothesis, which stated that there was no significant prediction model of musical creativity from achievement goal motivation and cognitive learning strategies subscales.

Table 3.

Stepwise multiple regression analysis for the best predictors of musical creativity.

Dimension	Predictor	Standardized	Correlation	Multiple		Increase in
		Beta	r	R	Adj. R²	Adj. R²
Musical craftsmanship
	Deep processing	.41**	.48**	.48	.23
	Mastery-approach	.28**	.37**	.56	.31	.08
	Surface processing	−.22**	−.27**	.60	.35	.04
Musical syntax
	Deep processing	.35**	.40**	.40	.16
	Mastery-approach	.28**	.36**	.50	.24	.08
	Performance-approach	−.18*	−.21**	.53	.27	.03
Musical originality
	Deep processing	.46**	.51**	.51	.26
	Mastery-approach	.19**	.32**	.56	.31	.05
	Surface-processing	−.15*	−.24**	59	.34	.03
	Performance-avoidance	−.14*	−.26**	61	.36	.02
Musical sensitivity
	Deep processing	.40**	.46**	.46	.21
	Mastery-approach	.26**	.37**	.55	.29	.08
	Performance-avoidance	−.17*	−.27**	.57	.32	.03
Musical creativity (overall)
	Deep processing	.45**	.52**	.52	.26
	Mastery-approach	.27**	.39**	.60	.35	.09
	Surface processing	−.15*	−.24**	.63	.39	.04
	Performance-avoidance	−.14*	−.28**	.64	.40	.01

Note: ** = p < 0.01; * = p < 0.05.

Discussions and conclusion

The main objective of this study was to establish the relations among achievement motivation, cognitive learning strategies, and musical creativity. First, the correlations revealed that mastery-approach goals were positively and significantly related with musical creativity. In contrast, both performance-approach and performance-avoidance achievement goals were negatively and significantly correlated with musical creativity. These findings were consistent with the principle held in previous educational psychology studies that mastery-approach goals are more beneficial than performance goals in academic achievement circumstances (Elliot and McGregor, 2001; Elliott et al., 1999). Similarly, the findings corroborated social psychology research by Janssen and Van Yperen, (2004) and Ruscio et al. (1998) which report positive relations between mastery-approach goal orientation and creativity, and non-significant relations between performance goal orientations and creativity among workers. Furthermore, the results concurred with previous psychology of music research, which generally reports significant positive relationship between mastery-approach orientation and instrumental performance achievement, and non-significant correlations between performance goal orientations and instrumental performance achievement (Diaz, 2010; Miksza, 2009, 2011; Schmidt, 2005). The results implied that students who favor mastery-approach achievement goals are likely to attain high musical creativity, while those who favor performance-approach and performance-avoidance goals may attain low musical creativity. Therefore, school and home environments should become more cooperative and task oriented as opposed to competitive and ego-oriented. Additionally, parents and music teachers should aim at devising ways of nurturing the students’ intrinsic motivation towards accomplishment of music learning tasks and practice.

Second, the correlations showed that musical creativity related positively and significantly with a deep processing learning strategy but negatively with surface processing learning strategy. These findings were aligned to Craik and Lockhart’s (1972) levels of processing theory. The findings corroborated social psychology studies by Hirst et al. (2009) and Ruscio et al. (1998), which report positive relations between deep processing strategies and creativity. The implication is that deep processing strategies are likely to especially enhance musical creativity in creative composition, while surface strategies may hinder musical creativity in this area. Hence, music teachers and other stakeholders in music education should aim to help students to adopt deep processing learning strategies. For instance, music teachers should embrace student-centred and discovery learning approaches that allow music students to engage in knowledge organization, elaboration, and critical thinking in order to realize excellence in musical creativity.

Correlations among the independent variables showed that deep processing strategies related positively and significantly to mastery-approach goals but negatively and non-significantly with both performance-approach and performance-avoidance goals. On the contrary, surface processing strategies correlated positively and significantly with both performance-approach and performance-avoidance goals but negatively and non-significantly with mastery-approach goals. The findings suggest that there is a relationship between mastery-approach goal orientation and use of deep processing strategy. Additionally, orientation to both performance-approach and performance-avoidance goals is associated to use of surface processing strategy. This findings corroborated previous research by Elliott et al. (1999) and Ruscio et al. (1998).

A last objective of the present study sought to determine the best predictors of musical creativity from the study variables. Deep processing learning strategies had the highest positive predictive value on participants’ musical creativity. Additionally, deep processing strategies and mastery-approach goal orientation had positive predictive value on participants’ musical creativity, jointly accounting for approximately 35% of the variance in participants’ musical creativity. On the contrary, surface processing strategies and performance-avoidance goals had negative predictive values on participants’ musical creativity. The implication is that the interplay between mastery- approach goals and use of deep processing strategies is important in promoting musical creativity, while performance-avoidance goal orientation and use of surface processing strategies are likely to hinder musical creativity. Based on these results, both deep processing learning strategies and mastery-approach goal orientation are important predictors of students’ musical creativity. Schools should therefore pay more attention to the development of deep processing learning strategies and mastery-approach goal orientation and consider them as key variables in determining students’ musical creativity in the music education program.

In conclusion, this study used self-report questionnaires to measure participants’ achievement goal orientations and cognitive learning strategies. However, self-report measures have an inherent subjective response bias. The participants may have over-rated themselves with socially and/or academically desirable responses. Future investigation of these variables with interviews and focused group discussions are recommended. This would allow for cross-checking the consistency of the participants’ responses. Second, the findings of this study were based on secondary school music students. Therefore, caution should be exercised in over-generalizing these results. To enhance generalizability, future investigations in this area should extend sampling to include primary, college and university level music students. The research instruments used in this study could be adjusted and standardized to suit students at the different levels of schooling.

Footnotes

Appendix

Items used to assess participants’ cognitive learning strategies and achievement goal motivation. Adapted from Achievement Goal Questionnaire-Revised (AGQ-R; Elliot & Murayama, 2008) and Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich et al., 1991).

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Lucy L. Mawang

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