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Predicting college students' mathematics anxiety by motivational beliefs and self-regulated learning strategies.(Report).

Predicting college students' mathematics anxiety by motivational beliefs and self-regulated learning strategies.(Report).


College Student Journal 43.2 (June 2009): p.631(12). (4889 words) 

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Sahin Kesici and Ahmet Erdogan. 

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The purpose of this study is to determine whether motivational beliefs and self-regulated learning strategies are significant predictors of college students' mathematics anxiety. The subscales for the motivation scale are intrinsic goal orientation, extrinsic goal orientation, task value, control of learning beliefs, self-efficacy for learning and performance, and test anxiety; while the subscales for the learning strategies scale are rehearsal, elaboration, organization, critical thinking, metacognitive self-regulation, time and study environment management, effort regulation, peer learning, and help-seeking. The study group was comprised of 183 college students. It was determined that college students' test anxiety and self-efficacy for learning and performance are significant predictors of college students' mathematics anxiety. In addition, college students' rehearsal and elaboration of cognitive learning strategies were found to be significant predictors for their mathematics anxiety.

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Gaining mathematics ability is key for most career opportunities (Rekdal, 1984). Moreover, it is possible to make use of a math ability in certain areas of science, like economics, politics, social studies, genetics and medicine (Roman, 2004). Mathematics ability, used in many fields, is composed of computation skills and problem-solving skills (Schunk, 2000). There are some questions (why, how, when, where, and with whom they will leam these skills) the students should ask themselves in order to gain these skills (Zimmerman, 1994). The answers for these questions focus on the concepts of motivational beliefs (Eccles & Wigfield, 2002; Linnenbrink & Pintrich, 2002; Metallidou & Vlachou, 2007; Pintrich, 2004; Wolters & Yu, 1996) and self-regulation (Paris & Newman, 1990; Pintrich & De Groot, 1990; Schunk & Zimmerman, 1997; Zimmerman, 2002; Zimmerman & Kitsantas, 1997; Zimmerman & Martinez-Pons, 1986).

The literature suggests that when students use motivational beliefs and learning strategies for self-regulation, their successes increase (Camahalan, 2006; Dresel, & Haugwitz, 2005; Eshel & Kohavi, 2003; Malmivuori, 2006; Metallidou & Vlachou, 2007; Pape, 2002; Whipp & Chiarelli, 2004; Wolters & Yu, 1996; Yukselturk & Bulut, 2007; Zimmerman, 1990). On the other hand, if they do not use these beliefs and strategies effectively, their failure and anxiety may increase (Fulk & Brigham, 1998; Kurman, 2006; Pekrun et al., 2002). Students must organize their motivational beliefs and self-regulated learning strategies to decrease their mathematics anxiety and become successful in mathematics. In other words, examining motivational beliefs and self-regulated learning strategies can serve as a clue for reasons of success (mathematics success) or failure (mathematics anxiety). Hence, the aim of this study is to determine whether motivational beliefs and self-regulated learning strategies are predictors of college students' mathematics anxiety.

Bandura (1997) emphasizes the importance of individuals' motivational processes and he further states that individuals should shape their beliefs about their abilities, set negative and positive outcomes, and anticipate different pursuits and goals for themselves. He points out that self-efficacy beliefs have a significant role in regulation of motivation in addition to these. On the other hand, Linnenbrink and Pintrich (2002) dwell upon a different dimension of motivation and define it as an academic enabler. They state that self-efficacy, attributions, intrinsic motivation, and goals are significant for students' motivation. Pintrich (2004) emphasizes the importance of motivational beliefs in the learning process and underlines the fact that motivational beliefs--goal orientation, self-efficacy, perceptions of task difficulty, task value beliefs, and personal interest in the task--should be regulated by the students to be effective in this process.

For students to become successful, they should have both motivational beliefs and self regulated learning strategies. Students' effective learning is positively related to their motivational beliefs, such as more adaptive attributional patterns, higher levels of self-efficacy and perceived competence, goal orientation, intrinsic interest, and task value beliefs. However, it is negatively related to test anxiety (Wolters & Yu, 1996; Young, 1997). Students' success increases when these motivational beliefs are supported with cognitive, metacognitive, and self-regulatory strategies (Pintrich & De Groot, 1990). Eccles and Wigfield (2002) state that expectancies (self-efficacy and locus of control), task value (intrinsic motivation, self-determination, flow, interest, and goals), expectancies and values (attribution, the expectancy-value and self-worth), and motivation and cognition (social cognitive theories of self-regulation and motivation) are important for success. As Pintrich (2004) states, this importance is based on general assumptions of the self-regulated learning model: active, constructive assumption, potential for control assumption, goal, criterion, or standard assumption, and mediators' assumption (self-regulatory activities are mediators between personal and contextual characteristics and actual achievement or performance). As Wolters (2003) points out, in models of self-regulated learning, students become more effective when they take a purposeful task (students' desire to reach various goals associated with completing academic tasks). Furthermore, students' motivational beliefs, attitudes, cognitive, and metacognitive strategies contribute most to the students to carry out this task.

Lapan, Kardash, and Turner (2002) state they applied a variety of strategies, like self-instructional strategies, encoding and retrieval strategies, and attention focusing tactics, to screen out distracting events and concentrate on the task to be accomplished, reconfigure the tasks, and observe and track different aspects of their performance in order to improve self-regulated learners' performance and learning. Cleary and Zimmerman (2004) make it clear that highly self-regulated learners approach learning tasks in a mindful, confident manner, proactively set goals, and develop a plan to realize their own learning and reach their learning goals.

Whether self-regulated learners are aware of the ability they have or not is a predictor of their success or failure. Self-regulated learners search information themselves and they do the necessities to improve the information they have reached (Zimmerman, 1990). According to Pintrich and Garcia (1994), cognitive learning strategies (elaboration, rehearsal, organization), metacognitive control strategies (plan, monitor, and evaluate learning outcomes) and resource management (time management and management of the learning environment) should be used effectively in self-regulated learning strategies, especially in self-regulation. Zimmerman and Campillio (2003) state learners should have the characteristics of self-generated thoughts, feelings, and actions cyclically planned to reach their personal targets. At the same time, as Malpass, O'Neil, and Hocevar (1999) indicate, these characteristics should be backed up with cognitive strategies and effort, and metacognitive strategies and effort. This support, as Pintrich (2000) states, is achieved with the self-determined and active process of planning, executing, monitoring, and controlling of strategic learning.

Self-regulated learning materializes with both students' motivational beliefs, and cognitive and metacognitive learning strategies. Self-regulated learning requires a process. According to Schunk and Zimmerman (1997), the self-regulation process has three major levels: self-observation, self-judgment, and self-reaction. In this process, planning, managing time, attending to and concentrating on instruction, using cognitive learning strategies, building a productive study environment, and making use of social sources are crucial. In addition to these, strategies for evaluating motivational processes like setting performance goals and outcomes, holding a positive attitude about one's capabilities, and evaluating learning, its outcomes, and positive experiences that can affect learning have a considerable role. As Boekaerts and Corno (2005) point out, students can gain skills in the areas of decision-making, problem-solving, and resource management in education, assessing teaching (assessment instruments), and completing the intervention required, depending on the assessment results, based on a certain process and program.

Zimmerman (2002) states that just having knowledge is not sufficient for gaining a skill in self-regulated learning. Some skills, like self-awareness, self-motivation, and behavioral skills, are also needed to apply these skills, and students should be personally adapted to each learning task.

The components of these skills are:

(a) setting specific proximal goals for oneself

(b) adopting powerful strategies for attaining the goals

(c) monitoring one's performance selectively for signs of progress

(d) restructuring one's physical and social context to make it compatible with one's goals

(e) managing one's time use efficiently

(f) self-evaluating one's methods

(g) attributing causation to results

(h) adapting future methods.

In short, if students study taking their motivational beliefs and self-regulated learning strategies into consideration, the possibility of their becoming successful increases. Their success will decrease, if they do not consider these two variables as important in their learning process. Then, anxiety and stress may occur. The level of anxiety may increase in courses, especially classes similar to mathematics. Mathematics anxiety is a stress and anxiety situation related to students' negative experiences with mathematical concepts--numbers, formulas, and evaluation of problem-solving procedures. Many variables like age, sex, self-efficacy, mathematics attitudes, test anxiety, and general anxiety are related to the existence of mathematics anxiety (Cates & Rhymer, 2003).

Oberlin (1982) indicates that common teaching techniques, like using the same teaching method for all students and teaching just one method for solving the problems lead to mathematics anxiety. Jackson and Leffingwell (1999) note that mathematics anxiety can occur if students have negative experiences at elementary and secondary schools, and they state that having a hostile or intensive attitude towards the students, treating students prejudicially because of their gender, demonstrating an uncaring attitude, expressing anger, having unrealistic expectations, embarrassing students in front of peers, communication and language barriers, quality of instruction, evaluation methods, and difficulty of material are among the behaviors and attitudes of mathematics teachers that can cause mathematics anxiety. Likewise, Furner and Duffy (2002) indicate that the school system, gender, socioeconomic status, and parental background may affect mathematics anxiety, too. On the other hand, Harper and Daane (1998) claim that mathematics anxiety occurs and students, who are afraid of mathematics up to the secondary levels because of negative experiences in mathematics during the formal mathematics teaching have lower confidence, depending on these negative experiences.

This study aims to investigate whether the motivational beliefs and self-regulated learning strategies are significant predictors of mathematics anxiety incurred by college students. In this respect, the following questions are answered in this study:

1. Are motivational beliefs (intrinsic goal orientation, extrinsic goal orientation, task value, control of learning beliefs, self-efficacy for learning and performance, and test anxiety) significant predictors of college students' mathematics anxiety?

2. Are self-regulated learning strategies (rehearsal, elaboration, organization, critical thinking, metacognitive self-regulation, time and study environment management, effort regulation, peer learning and help-seeking) significant predictors of college students' mathematics anxiety?

Methodology

Research Method

In this study, a quantitative method was used. The quantitative data helped determine whether significant associations exist between independent variables and dependent variables. Motivational beliefs and self-regulation were independent variables for the study, while mathematics anxiety was the dependent variable. In this study, to what extent motivational beliefs and self-regulated learning strategies served as predictors of mathematics anxiety was analyzed.

Participants

Participants of this study were students taking a general mathematics course in the Faculty of Education at Selcuk University in Konya, Turkey. This course was taught to freshman students of physics, chemistry, computer and teaching technologies, and science education. The study group was formed from these students (42 students from the department of physics education, 44 students from the department of chemistry education, 48 students from the department of computer and teaching technologies education, and 49 students from the department of science education). The group consisted of a total of 183 students--77 male (42%) and 106 female (58%).

Research Instruments

Motivated Strategies for Learning Questionnaire: The Motivated Strategies for Learning Questionnaire (MSLQ) is used to collect data related to motivational beliefs and self-regulated learning components. MSLQ consists of two scales: Motivation Scale and Learning Strategies Scale. The subscales for the motivation scale are intrinsic goal orientation, extrinsic goal orientation, task value, control of learning beliefs, self-efficacy for learning and performance, and test anxiety; while the subscales for the learning strategies scale are rehearsal, elaboration, organization, critical thinking, metacognitive self-regulation, time and study environment management, effort regulation, peer learning, and help-seeking (Pintrich et al., 1991). The survey was translated into Turkish, and the pilot study was administrated to students enrolled in the Department of Foreign Languages Education at METU, Turkey (Hendricks, Ekici, & Bulut, 2000). Also, the survey was used to investigate mathematics achievement and self-regulated learning with ninth-grade students in Denizli, Turkey. In this study, some items of the scale were slightly adjusted to ensure applicability to all students (Ozturk, 2003).

Mathematics Anxiety Rating Scale: The mathematics anxiety rating scale was developed by Richardson and Suinn (1972) to measure the levels of math anxiety. It consists of 98 items. The participants are required to rate each item according to "the degree of anxiety it causes when the participant carries it out nowadays." A total scale point is obtained by adding all the points given to the items. Higher points are signs of higher mathematics anxiety. The range for the total scale points is between 98 and 490. The reliability of the test, analyzed by Baloglu (2005) in terms of Turkish language validity and pre-psychometric analysis, is examined by using the consistency of the items method. He determined the internal consistency coefficient of the scale is 0.97.

Data Analysis

In this study, multiple linear regression analysis was used. In multiple linear regression analysis, the relationship between the predictor variables, students' motivational beliefs (intrinsic goal orientation, extrinsic goal orientation, task value, control of learning beliefs, self-efficacy for learning and performance, and test anxiety), and learning strategies (rehearsal, elaboration, organization, critical thinking, metacognitive self-regulation, time and study environment management, effort regulation, peer learning, and help-seeking), and the dependent variable, math anxiety, were tested. Data were analyzed using SPSS 13.0 (Statistical Package for Social Sciences) software.

Findings

A stepwise regression analysis method was used to determine whether motivational beliefs are predictors of college students' mathematics anxiety. Findings from stepwise regression analysis are summarized in Table 1. According to the findings of the study, test anxiety and self-efficacy for learning and performance are significant predictors of college students' mathematics anxiety (p<.01). About 18% of the variance in mathematics anxiety was explained by test anxiety, while about 22% of its variance was explained by test anxiety and self-efficacy for learning and performance.

 

The stepwise regression analysis method was used to determine whether self-regulated learning strategies are predictors of college students' mathematics anxiety. Findings from stepwise regression analysis are summarized in Table 2. According to these results, rehearsal and elaboration cognitive learning strategies were found to be significant predictors of college students' mathematics anxiety (p<.05). About 3% (2.6%) of the variance in mathematics anxiety was explained by rehearsal cognitive learning strategy, while about 7% (6.8%) of its variance was explained by rehearsal and elaboration cognitive learning strategies.

Discussion and Conclusions

Based on the findings of this study, test anxiety is one of the most significant predictors for mathematics anxiety. A similar finding is provided by Zettle and Raines (2000). In their study, they determine a significant correlation between mathematics anxiety and test anxiety. This finding also shows parallelism with the study by Fulk and Brigham (1998), who find that students with emotional or behavioral disorders have significantly higher test anxiety compared to those students with learning disabilities or average achievement.

Both test anxiety and self-efficacy for learning and performance, the subscales of motivational beliefs, are significant predictors of mathematics anxiety (p<.01). The literature supports this result that self-efficacy is a significant predictor of math anxiety (Bong & Skaalvik, 2003). Although there seems to be a high correlation between anxiety, self-efficacy, and performance, anxiety is generally negatively correlated to performance (Rouxel, 2000), which upholds the findings of this study.

Bandura (1993) indicates that individuals with high self-efficacy heighten and sustain their efforts in order to cope with failure when they fail. Another study that supports the finding of this study is self-efficacy belief is positively related to mathematics achievement and negatively related to mathematics anxiety (Malpass et al., 1999). The literature also supports the finding that anxiety influences achievement (Ross, Hogaboam-Gray, & Rolheiser, 2002). In addition, the finding of the study shows parallelism with research results of Cooper and Robinson (1991). They determined that mathematics self-efficacy is positively correlated with mathematics ability and negatively correlated with mathematics anxiety. Furthermore, an increase in the levels of students' self-efficacy leads to a decrease in their levels of test anxiety (Dykeman, 1994). Briefly, motivational beliefs (test anxiety, and self-efficacy for learning and performance) are significant predictors of college students' mathematics anxiety. The strong negative correlations between mathematics anxiety, motivation, and self-confidence in mathematics provide considerable support for the findings of this study (Ashcraft, 2002).

 

Rehearsal and elaboration of cognitive learning strategies, the subscales of self-regulated learning strategies, are also significant predictors of mathematics anxiety. If the failure of students stems from an inappropriate learning strategy, the teacher may need to work with the students to help them develop the strategies and skills necessary to succeed in the future (Linnenbrink & Pintrich, 2002). Schunk (2000) states that mathematics ability consists of computation skills and problem-solving skills. Pape and Smith (2002) state that students use a mathematical algorithm or procedure to solve a problem in mathematics. The findings of this study are supported by Corte (2004). He determined that about 60% of the solution attempts and cognitive self-regulatory activities, so typical of expert problem solving, are totally lacking. That is, as Pintrich (2004) states, individuals should not use a surface approach (rehearsal strategies) to learn and gain mathematics abilities. Pintrich (2002) provides support for the findings of this study that rehearsal strategy, in general, is not the most effective strategy for learning more complex cognitive processes. Elaboration strategy consists of paraphrasing, summarizing, and selecting main ideas from texts (Patrick, Ryan, & Pintrich, 1999). This statement shows a low possibility of elaboration strategy's being sufficient for learning subjects, such as mathematics, which consist of computational skills and problem-solving skills. Using metacognitive strategies besides cognitive strategies leads to better performance in students (Metallidou & Vlachou, 2007) and this supports the finding of this study.

Students' motivational beliefs should be increased and they should gain self-regulated learning strategies for being successful in mathematics or coping with mathematics anxiety. In a study in which the correlations between the goal orientations of the students, motivational beliefs, and self-regulated learning, adopting a learning goal orientation, and a relative ability are examined, goal orientation results in a generally positive pattern of motivational beliefs, including adaptive levels of task value, self-efficacy, and test anxiety, as well as cognition, including higher levels of cognitive strategy use, self-regulation, and academic performance (Wolters & Yu, 1996). To becomne successful, students should determine their individual beliefs about how well they will do in upcoming tasks, specific, proximal, and divergent goals, and values, motivational beliefs and learning strategies, such as cognitive and metacognitive strategies, and they should behave in a planned way, according to these beliefs and strategies (Eccles & Wigfield, 2002).

Test anxiety has a negative influence on mathematics achievement. In fact, anxiety is significantly and negatively correlated to mathematics achievement. So, teachers should avoid attitudes and behaviors that may cause test anxiety in students. Besides, teachers should increase students' self-efficacy levels to help them become successful in mathematics and cope with mathematics anxiety. They should increase the students' self-efficacy, if they are low, with in-classroom activities. Especially, they should pay special attention to students' self-efficacy beliefs, one of the subscales of motivational beliefs and significant predictors of mathematics anxiety. The importance of increasing the students' self-efficacy beliefs is supported by the study by Jinks and Morgan (1999), who determined that low self-efficacy probably leads to less effort, which, in turn, could lead to lower success. Students show higher efforts, persistence, and resilience when they have higher self-efficacy beliefs (Pajares, 2002). Self-efficacy beliefs also influence the amount of stress and anxiety students experience as they engage in a task. This finding provides important support for the fact that self-efficacy belief is one of the predictors of mathematics anxiety.

Besides having high motivational beliefs, students should be aware of their self-regulated learning strategies and they should use these strategies effectively in mathematics to cope with mathematics anxiety. Most important, students should take responsibility for their learning. Related with the awareness of self-regulated learning, Zimmerman (1990) states that self-regulated learners are aware of regulating the learning process, learning responses, and learning outcomes, and they use these strategies to reach their academic goals. In addition to strategies of self-regulated learning, other strategies like self-evaluation, organization, transformation, goal setting and planning, information seeking, record keeping, self-monitoring, environmental structuring, giving self-consequence, rehearsing and memorizing, seeking social assistance, and reviewing should be used (Zimmerman, 1990). For effectiveness of self-regulated learning, individuals should engage in self-monitoring in order to ignore the obstacles in using cognitive learning strategies and the conflicts that occur while gaining a learning objective (Zimmerman, 1995).

Self-regulated learners are proactive learners and they have the capacity to incorporate various self-regulation processes (e.g., goal setting, self-observation, self-evaluation) with task strategies (e.g., study, time-management, and organizational strategies) and self-motivational beliefs (e.g., self-efficacy, intrinsic interest) (Cleary & Zimmerman, 2004). This is important in terms of students' learning responsibilities. Especially, teachers should teach students to use cognitive and metacognitive learning strategies. The students should make an effort to use these strategies effectively.

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Author Contact information:

sahinkesici@selcuk.edu.tr

ahmeterdogan@selcuk.edu.tr

SAHIN KESICI PH.D.

Department of Educational Science

AHMET ERDOGAN PH.D.

Department of Secondary Science and Mathematics Education

Ahmet Kelesoglu Education Faculty, Selcuk University

Meram, Konya, Turkey

 

Table: 1

Model Summary for Stepwise Regression Analysis of Motivational Beliefs

 

                       R        Adj. R   Std.    R Square   F

Model (a)      R

                       Square   Square   Err.     Change    Change

 

1           .423 (b)    .179     .174    43.37     .179     39.35

2           .472 (c)    .222     .214    42.32     .044     10.14

 

                                      Sig. F

Model (a)   [df.sub.1]   [df.sub.2]

                                      Change

 

1               1           181        .000

2               1           180        .000

 

(a:) Dependent variable: Mathematics anxiety.

 

(b:) Predictors: (Constant), Test anxiety.

 

(c:) Predictors: (Constant), Test anxiety, Self-efficacy for learning

and performance.

 

Table: 2

Model Summary for Stepwise Regression Analysis of Learning Strategies

 

                      R        Adj. R   Std.    R Square   F

Model (a)  R

                      Square   Square   Err.    Change     Change

 

1          .161 (b)    .026     .021    47.23     .026      4.81

2          .261 (c)    .068     .058    46.32     .042      8.20

 

                                    Sig. F

Model (a)  [df.sub.1]  [df.sub.2]

                                    Change

 

1               1          181       .030

2               1          180       .005

 

(a:) Dependent variable: Mathematics anxiety.

 

(b:) Predictors: (Constant), Rehearsal cognitive learning strategy.

 

(c:) Predictors: (Constant), Rehearsal and elaboration cognitive

learning strategies.

Source Citation

Kesici, Sahin, and Ahmet Erdogan. "Predicting college students' mathematics anxiety by motivational beliefs and self-regulated learning strategies." College Student Journal 43.2 (2009): 631+. InfoTrac Humanities & Education Collection. Web. 31 Jan. 2010. 

 


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