Assessing Mathematics Achievement Goals in a Sample of Italian Students from Sixth Grade
Author(s):
Donatella Poliandri (submitting) Stefania Sette (presenting)
Emanuela Vinci (presenting)
Sara Romiti
Conference:
ECER 2015
Format:
Paper

Session Information

09 SES 07 D, Developing and Validating Instruments for Tests and Assessments

Paper Session

Time:
2015-09-09
17:15-18:45
Room:
395. [Main]
Chair:
Petr Soukup

Contribution

Academic success represents one of the best indicators of psychological adjustment in adolescence (Verboom, Sijtsema, Verhulst, Penninx, & Ormel, 2014; Zuffianò et al., 2013). With the aim of understanding predictors of academic achievement, previous researchers analyzed the impact of students' individual characteristics in explaining their academic success at school. High levels of academic performance are, indeed, positively related to adolescents’ self esteem (Marsh, Trautwein, Ludtke, Koller, & Baumert, 2006), self efficacy beliefs (Pajares & Urdan, 2005), and socially appropriate behaviors (Caprara et al., 2014).

Noteworthy, students’ achievement goals, defined as the reason for which students orient themselves to achieving academic success, may also contribute to explain adolescents’ academic performance (Vassiou, Mouratidis, Andreou, & Kafetsios, 2014). In this regard, Elliot and Church (1997) suggested a trichotomous achievement goal model. A Mastery-approach goal corresponds to achievement goals focused on improvement and learning, and in general to students’ desire to understand  and to develop new skills. A Performance-approach goal consists in the desire to perform better than peers and to look competent. Finally, a Performance-avoidance goal reflects an approach aimed at avoiding to perform worse than classmates, and, thus, to demonstrate incompetence. With regard to the associations between academic goals and academic performance, researchers demonstrated a positive relation between the mastery-approach goal and the students’ academic performance and a negative association between students’ performance avoidance goal and their academic performance (Patrick & Ryan, 2008). Consistent findings were not found in the relation between performance-approach goals and academic performance (Vassiou et al., 2014).

Therefore, we need to provide valid and reliable measures to assess students’ achievement goals because the students’ academic performance is interrelated to academic goals,. The Patterns of Adaptive Learning scale (PALS; Midgley et al., 2000) represents a valid questionnaire widely used in the international context to assess students’ academic achievement. The PALS is composed of three related scales: mastery-approach goals, performance-approach goals and performance-avoid goals. As the PALS is one of the most used instruments to assess students’ academic achievement goals, it is important to analyze the validity of the scale across countries. The present study aims to evaluate the factor validity of the PALS of Italian sixth grade  students and has three main goals. First, we are interested in examining the factor structure of the PALS, giving particular attention to mathematics discipline. We considered mathematics discipline for its focus on the development of the skills (Middleton, Kaplan, & Midgley, 2004). Secondly, we tested the measurement invariance of the scale across gender to examine wether the PALS measures the same constructs in both females and males. Finally, we analyzed the relations between PALS with students’ mathematics performance. We expected to find a positive association between mastery-approach goals and students' scores in mathematics and a negative relation between students' scores in Math and performance-avoidance goals. Therefore, the use of the PALS may be particularly important in the Italian educational context, especially at the beginning of middle school where students interact with the same teacher and classmates for three years. The administration of a reliable questionnaire on students’ academic goals in Maths domain may help teachers to promote students’ academic performance in Maths over years.

Method

Participants were 7773 students (50% females and 50% males) enrolled in 235 public middle Italian schools (37% North Italy, 21% Middle Italy, and 42% South Italy). The majority of students were Italian (89%) between the age of 11 and 15 years old (M = 11.97, SD = 0.5). All participants were at the end of sixth grade. Most of parents had a high school degree (30.4%) or finished middle school (30.6%). The parents have signed a Parental consent form. Measures of students' achievement goal orientations and academic scores in Maths were collected at the end of sixth grade (2013-2014 school year). We used the PALS (Midgley et al., 2000) to assess achievement goals in Maths classroom. The PALS is composed of three subscales: mastery goals, performance-approach goals and performance-avoidance goals. Responses were given on a 5-point Likert scale (1 = I strongly disagree, 5 = I strongly agree). This scale was translated in the Italian context through a back-translation procedure. The mastery goal subscale is composed of items that describe students' achievement goals in the classroom focused on improvement and learning. The performance-approach subscale consists of items that assess students focus on demonstrating competence in Maths. Finally, the performance-avoidance subscale is composed of items that describe students desire to avoid demonstrating incompetence in Maths. To assess students' performance in mathematics discipline, each student completed a multiple choice test and an open ended test. The test was implemented by the National Institute for the Educational Evaluation of Instruction and Training (INVALSI). We conducted an Exploratory Factor Analysis (EFA) and a Confirmatory Factor Analysis (CFA) to measure the psychometric properties of the PALS in the Italian educational context. After that multigroup CFAs were used to test the measurement invariance of the scale across females and males. Finally, using a Structural Equation Modeling (SEM) we analyzed the concurrent validity of the PALS with scores of students in Maths. Each model was assessed using the comparative fit index (CFI), root mean square error of approximation (RMSEA) with the 90% confidence interval (90% CI), and standardized root-mean-square residual (SRMR; Brown, 2006). For all models, we used maximum likelihood estimation with robust standard errors. In addition, to handle the hierarchical structure of data, we used the type = complex option (Geiser, Eid, Nussbeck, Courvoisier, & Cole, 2010) in Mplus 5.1.

Expected Outcomes

The EFA extracted three correlated factors as reported in the original version of the PALS. The factor loadings of the items ranged from .42 to .80, from .34 to.83, and from .59 to .87 for mastery goals (34% of the total variance), performance-avoidance goals (14%), and performance-approach goals (4%) factors, respectively. The CFA showed a model with a adequate fit to the data, S-B χ2 (74) = 1301.536, c = 1.377, p < .0001, CFI = .96, RMSEA = .05 (90% CI = .04-.05), SRMR = .05. The internal consistency was .81, .84, and .79 for mastery goals, performance-approach goals, and performance-avoidance goals subscales. Moreover, as suggested by the differences in CFI (Cheung & Rensvold, 2002), we found a configural invariance, [S-B χ2 (148) = 1390.714, c = 1.356, p < .0001, CFI = .957, RMSEA = .05 (90% CI = .04-.05), SRMR = .05], a metric invariance [S-B χ2 (159) = 1424.914, c = 1.338, p < .0001, CFI = .957, RMSEA = .05 (90% CI = .04-.05), SRMR = .06], and a scalar invariance, [S-B χ2 (173) = 1828.614, c = 1.313, p < .0001, CFI = .943, RMSEA = .05 (90% CI = .04-.05), SRMR = .07], of the PALS across genders. Finally, we found a positive relation between mastery goals and students' scores in mathematics (β = .25, p < .001) and a negative association between performance-avoid goals and scores in mathematics (β = -.24, p < .001). In conclusion, the PALS demonstrated good psychometric properties in the Italian context. Therefore, the scale may be used to better understand the role of achievement goals on students' performance in mathematics during the sixth grade, when students are in the middle school and remain in this same context for three years.

References

Brown, T. A. (2006). Confirmatory factor analysis for applied research. New York, NY: Guilford Press. Caprara, G.V., Luengo Kanacri, B.P., Gerbino, M., Zuffianò, A., Alessandri, G., Vecchio, G., Pastorelli, C., & Bridgall, B. (2014). Positive effects of promoting prosocial behavior in early adolescents: Evidence from a school-based intervention. Advance online publication. International Journal of Behavioral Development. doi: 10.1177/0165025414531464 Cheung G.W., & Rensvold R.B. (2002). Evaluating goodness of fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233-255. Elliot, A. J., & Church, M. A. (1997). A hierarchical model of approach and avoidance achievement motivation. Journal of Personality and Social Psychology, 72(1), 218–232. Geiser, C., Eid, M., Nussbeck, F.W., Courvoisier, D.S.,& Cole, D.A. (2010). Analyzing True Change in Longitudinal Multitrait-Multimethod Studies: Application of a Multimethod Change Model to Depression and Anxiety in Children. Developmental Psychology, 1,29-45. Marsh, H. W., Trautwein, U., Ludtke, O., Koller, O., & Baumert, J. (2006). Integration of multidimensional self-concept and core personality constructs: Construct validation and relations to well-being and achievement. Journal of Personality, 74, 403–456. Middleton, J., Kaplan, A., & Midgley, C. (2004). The change in middle school students’ achievement goals in mathematics over time. Social Psychology of Education, 7, 289-311 Midgley, C., Maehr, M. L., Hruda, L. Z., Anderman, E., ANderman, L., Freeman, K. E., et al. (2000). Manual for the patterns of adaptive learning scales. Ann Arbor: University of Michigan. Pajares, F., & Urdan, T. (2005). Academic motivation of adolescents. Charlotte, NC: Information Age Publishing. Patrick, H., & Ryan, A. M. (2008). What do students think about when evaluating their classroom’s mastery goal structure? An examination of young adolescents’ explanations. The Journal of Experimental Education, 77, 99–124. Vassiou, A., Mouratidis, A., Andreou, E., & Kafetsios, K. (2014). Students’ achievement goals, emotion perception ability and affect and performance in the classroom: a multilevel examination. Educational Psychology: An International Journal of Experimental Educational Psychology. Advance online publication. doi: 10.1080/01443410.2014.950192 Verboom, C. E., Sijtsema, J. J., Verhulst, F. C., Penninx, B. W. J. H., & Ormel, J. (2014). Longitudinal associations between depressive problems, academic performance, and social functioning in adolescent boys and girls. Developmental Psychology, 50, 247-257. Zuffianò, A., Alessandri, G., Gerbino, M., Luengo Kanacri, B. P., Di Giunta, L., Milioni, M., & Caprara G.V. (2013). Academic achievement: The unique contribution of self-efficacy beliefs in self-regulated learning beyond intelligence, personality traits, and self-esteem. Learning and Individual Differences, 23, 158–162.

Author Information

Donatella Poliandri (submitting)
INVALSI - National Institute for the Educational Evaluation of Instruction and Training
School Evaluation
Roma
Stefania Sette (presenting)
INVALSI
Rome
Emanuela Vinci (presenting)
INVALSI - National Institute for the Educational Evaluation of Instruction and Training, Italy
INVALSI - National Institute for the Educational Evaluation of Instruction and Training, Italy

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