Moving Up! Or Down? Mathematics Anxiety in the Transition From Elementary School to Junior High

Changes in Mathematics Anxiety Over Time

There is a paucity of research examining levels of mathematics anxiety over time. Some longitudinal work has examined how changes in mathematics anxiety are related to changes in motivational variables. For example, with a sample of seventh grade students and using a cross-lagged model with three time points, Ahmed, Minnaert, Kuyper, and van der Werf (2012) found a reciprocal relationship with lower mathematics self-concept leading to higher mathematics anxiety, and in turn, higher mathematics anxiety leading to subsequent lower mathematics self-concept. In work with two time points and a sample of first-year university students, changes in academic control from before midterms to before finals were related to concurrent changes in mathematics anxiety (Niculescu et al., 2016).

Two studies have examined mathematics anxiety in the Longitudinal Study of American Youth dataset, which includes data following students from Grades 7 through 12. Growth-curve analyses indicated that students’ mathematics anxiety increases across middle and high school grades at the same time that their attitudes toward and utility of mathematics is declining (Ma & Cartwright, 2003). Gender differences in that analysis showed higher mathematics anxiety for females at the initial time point in Grade 7 and their anxiety increased at a faster rate than that of males. A cross-lagged model examining Grades 7 through 12 showed that prior low mathematics achievement leads to higher mathematics anxiety in subsequent grades (Ma & Xu, 2004). In contrast, results indicated that prior high mathematics anxiety had less impact on later mathematics achievement. Interestingly, this model also suggested that mathematics achievement is stable whereas mathematics anxiety was less consistent. Finally, their analysis included an examination of gender differences that found males’ prior achievement impacts subsequent mathematics anxiety each year; however, for females, their prior achievement only impacts subsequent mathematics anxiety during key transition points from elementary to junior high and from junior high to high school.

One study has specifically examined mathematics anxiety during the transition to junior high school. Madjar et al. (2018) followed 226 Israeli students as they transitioned and 187 students who did not transition. Data collection included two time points in sixth grade, the year prior to transition, and two time points in seventh grade, the year after transition. Hierarchical linear modeling indicated that gender, school transition, and grade point average (GPA) were all significant predictors of change in mathematics anxiety. Further analysis suggested that those who transition show a spike in their anxiety immediately before the transition which then declines in the following school year, whereas their nontransition counterparts had relatively stable anxiety. Similarly, females and those with high GPAs also had these quadratic slopes, with increases before the transition followed by declines during the school year. In contrast, males and those with low GPAs had relatively consistent levels of anxiety. These results point to the transition between elementary and junior high school as a key time point to study for potential changes in mathematics anxiety.

Transition to Junior High School as Critical Time

The transition from elementary to junior high or middle school is a time of change as students shift from a general classroom to subject-specific ones and many aspects of their motivation shift during this time (Eccles et al., 1993). For example, the transition to junior high school is marked by an initial drop in self-esteem and self-concept in mathematics (Wigfield, Eccles, MacIver, Reuman, & Midgley, 1991). The drop is seen at the beginning of the school year following transition but has been shown to rebound for self-esteem, social self-concept, sports self-concept, liking of English, and liking of social activities. Mathematics self-concept and liking of mathematics did not rebound after the initial drop. Similarly, importance of and interest in mathematics declined over the summer before junior high school and then continued to decline over the school year, suggesting lasting effects of the transition (Wigfield & Eccles, 1994). These and other studies suggest that environmental changes during the transition to junior high greatly impact students’ motivation (Eccles et al., 1993). It stands to reason, then, that the transition to junior high may cause changes in mathematics anxiety similar to those seen in other motivational constructs, especially given that the environment has been identified as a significant predictor of anxiety (e.g., Frenzel et al., 2007b; Goetz et al., 2013).

Expectancy-Value Theory, Control-Value Theory, and Mathematics Anxiety

Much of the literature examining the transition period between elementary and junior high or middle school is grounded in expectancy-value theory (Wigfield & Eccles, 1994). Previous work has shown that mathematics anxiety is related to expectations (i.e., beliefs about potential performance) and value (i.e., the reasons for attempting to perform), and is predictive of later choices and performance (Meece et al., 1990). Expectancy-value theory places affective reactions like mathematics anxiety in between students’ background—including beliefs, behaviors, and prior experiences—and their expectations and values (Wigfield & Eccles, 2000). Building on the work of expectancy-value theory, control-value theory also associates affective reactions with expectations and values; however, the order of association depends on the emotion being studied (Pekrun, 2006). In the case of anxiety, appraisals of control and value precede the emotional response because anxiety is a prospective emotion (Pekrun, 2006).

Purpose of This Study

There is a growing body of work on mathematics anxiety. Many studies feature the connection of mathematics anxiety with motivational variables including expectations, values, and the influence of the teacher. Although much of the work has been with only one time point for mathematics anxiety, existing longitudinal studies suggest that there is variation in mathematics anxiety across time. A key transition time to examine mathematics anxiety in adolescents is the move from elementary to junior high school. One longitudinal study has examined mathematics anxiety during this period, finding that mathematics anxiety was stable for students who did not transition between schools and mathematics anxiety increased during the transition for those students who changed schools (Madjar et al., 2018). In their study, Madjar and colleagues (2018) relied on a growth curve model to examine change which does not allow for multiple distinctive patterns to emerge from the data. Rather, the analysis assumes that there is one developmental pattern in the population and then predictors model how individuals deviate from this pattern. However, there is evidence to suggest intraindividual variation in mathematics anxiety; using an experience sampling method over a 10-day period, Goetz and colleagues (2013) found significant within-person variance in mathematics anxiety. Therefore, an exploratory, person-centered approach could reveal a variety of patterns in students’ mathematics anxiety. The purpose of the current study is to examine patterns of mathematics anxiety within adolescents as they transition to junior high school. Should distinctive patterns emerge, it seems likely that student expectations and value for mathematics, as well as their perceptions of the classroom environment, will be related to the patterns in mathematics anxiety.

Data Source

The Michigan Study of Adolescent Life Transitions (MSALT) data collected by Eccles and colleagues (Eccles, 2010; Eccles et al., 1993) were drawn from 12 public school districts in southeastern Michigan, including primarily working and middle-income families. Mathematics anxiety in the MSALT data was measured at four time points: Time 1, fall of sixth grade; Time 2, spring of sixth grade; Time 3, fall of seventh grade; and Time 4, spring of seventh grade. These time points capture the transition to junior high between sixth and seventh grade. Mathematics anxiety scores were available for 2,378 students (53.3% female; 89.4% White) in all four time points and 3,741 (52.7% female; 86.1% White) for at least one time point (see Table 1 for descriptive statistics of anxiety at each time point). Follow-up data were collected 7 years later with data available for 1,393 students at this time point.

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Table 1.

Descriptive Statistics for Anxiety at Each Time Point.

	n	X ¯	SD
Time 1: Fall sixth grade	3,144	3.40	1.56
Time 2: Spring sixth grade	3,172	3.43	1.51
Time 3: Fall seventh grade	3,096	3.21	1.51
Time 4: Spring seventh grade	3,016	3.13	1.48

Measures

Data Analysis

Group-based trajectory modeling assumes that there are multiple groups of developmental trajectories that differ from each other. In short, it is a “person-centered” approach that focuses on the distinctive characteristics of individuals (Nagin, 2009; Nagin & Odgers, 2010). This is in contrast to hierarchical modeling and latent curve analysis, which both assume a single population distribution and attempt to model how individuals vary around the average population growth curve (Nagin, 2009). Group-based trajectory modeling is a way to model multiple distinctive patterns of growth for phenomena that are unlikely to contain a common growth process (Nagin, 2009; Nagin & Odgers, 2010). It lends itself to questions like the one in this study that are focused on the shape of the distribution for different groups. In other words, it reveals patterns that exist for a given phenomenon over time (Nagin, 2009). Both group-based trajectory modeling and more general growth mixture modeling consider that interindividual differences lead to heterogeneity (i.e., different possible groups) within a larger population and allow the modeling of these different growth trajectories. These latent groups are created from the modeling of continuous variables; in the case of the present study, it is the patterns in mathematics anxiety measures over time that create the latent group trajectories.

Group-based trajectory models is a special case of growth mixture modeling that assumes that all individual growth trajectories within a group are homogeneous. In both approaches, individuals are placed into latent classes based on similarities in patterns of data; however, these two approaches differ in what values are allowed to vary within and between latent classes. In group-based trajectories, the variance of the latent group’s intercept and slope is fixed within the group and only allowed to vary between the groups. Thus, in a group-based trajectory model, the assumption is that all individuals within the group are identical. In contrast, a more general mixture model allows the latent intercept and slope to vary both within and between the groups, allowing individual differences in trajectory within a group. Both types of growth-based trajectory models were tested in the current study. We use the group-based trajectory approach to help determine how many trajectories to use and then move to growth mixture models, allowing variation with the created latent groups (Jung & Wickrama, 2008).

It is logical to assume that mathematics anxiety is such a phenomenon that contains multiple patterns over time. Students display varying levels of mathematics anxiety and there are certainly students who trend upward in levels and even those who decrease in anxiety under certain circumstances. In an effort to explore the accuracy of these assumptions and determine the appropriateness of a group-based trajectory analysis, five spaghetti plots were run with roughly 25 randomly selected individuals in each. All five plots looked very similar and showed that there were multiple distinct patterns. For example, some students showed decreases during school years and an increase during summer. Still others had one direction of growth from Time 1 all the way through Time 4. These variations suggested multiple patterns of growth rather than one common pattern with individual variation around the average. Group-based trajectory modeling was completed in SAS 9 with PROC TRAJ (Jones, Nagin, & Roeder, 2001) and the models were confirmed using Mplus 8 (Muthén & Muthén, 1998-2017). Mplus analyses were conducted using robust full information maximum likelihood estimation. This approach adjusts standard errors accounting for any nonnormal distribution of data and makes use of all available data points (Little, Jorgensen, Lang, & Moore, 2014).

To examine how these longitudinal groups might differ from one another, we conducted some supplemental analyses. We first attempted to predict trajectory membership using baseline (Time 1) variables with a multinomial logistic regression in order to explain what types of characteristics were associated with trajectory membership. Next, we conducted analyses with a subsample of data collected 2 years after high school graduation (7 years after the initial anxiety measures) using two approaches to explore how the categorical variable created from trajectory membership related to motivational and demographic variables. We used one-way ANOVA and chi-square analyses to examine trajectory membership differences in expectations and choices 2 years after high school graduation.

Results

The first component of group-based trajectory modeling is deciding on the number of latent trajectories. Following the procedures laid out by Nagin (2009) and Nagin and Odgers (2010), we tested a range of three to nine groups. One indicator of group selection can be based on the Bayesian information criterion (BIC), a fit indicator that should plateau at the optimal number of trajectories. However, when we tested the recommended range of three to nine groups, the BIC continued to increase. Researcher analysis was used to determine the optimal number of groups following the guidelines laid out by Nagin (2009), emphasizing the relative size of groups and the uniqueness of the various trajectories. Based on these criteria, we tested many models ranging from three to nine trajectories and determined that five group-based trajectories is the best approximation of the data with the most parsimonious solution that still captures as much variation as possible. If more than five groups are included, the additional trajectories mimic the shapes of already included trajectories but at different levels. In other words, additional groups beyond five do not reveal any useful or important distinctions in trajectories. Furthermore, the size of additional groups becomes quite small. Next, we allowed the intercepts and slopes of the latent groups to vary, testing a true mixture model. Because the number of group trajectories identified can vary between group-based and growth mixture models, we again tested a range of models. Allowing random slope and intercepts resulted in convergence issues and Heywood cases with all models tested. Attempts to address these issues included increasing iterations for estimation, changing start values, and constraining variances to positive values (Jung & Wickrama, 2008); however, we were not able to identify a model with adequate fit when intercept and slope constraints were relaxed and thus we present the group-based trajectories with homogeneous groups.

Mathematics Anxiety Trajectories

The five trajectory solution can be found in Figure 1 and Table 2. Important considerations when viewing trajectories include the percentage of group membership—groups should not be too small and large groups indicate prevalent trajectories; the statistical significance of cubic growth—four time points allow for consideration of cubic, quadratic, or linear growth; and the patterns in the figure itself—increases and decreases are shown visually. We also examined the model entropy which is marginal at .63. Entropy measures the accuracy of classification with values closer to 1.0 being most desired. Furthermore, acceptable posterior probabilities ranged from .66 to .84. Two trajectories are flat: Trajectory 1 includes the consistently low anxiety students and Trajectory 5 includes the students who are consistently high in anxiety. Three trajectories display statistically significant cubic patterns. Trajectory 2 has slightly increasing but almost stagnant change during the school years accompanied by a decrease over the summer. In Trajectory 3, there is no change in anxiety during the school year but increases in anxiety during the summer. Finally, Trajectory 4 is marked by an overall decrease in anxiety. The largest drop occurs over summer, but decreases are still clear over the two school years.

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Figure 1.

Graph of mathematics anxiety trajectories for four time points with percentage of sample in each trajectory group.

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Table 2.

Trajectory Parameter Estimates for Intercept and Shape.

Trajectories	Parameter estimates (SE)
	Intercept	Linear	Quadratic	Cubic
Trajectory 1: Low anxiety (29.5%)	1.98 (0.05)**	0.03 (0.15)	−0.03 (0.13)	0.01 (0.03)
Trajectory 2: Summer decline (29.3%)	3.27 (0.11)**	0.97 (0.19)**	−1.06 (0.19)**	0.23 (0.04)**
Trajectory 3: Summer increase (22.3%)	3.75 (0.13)**	−0.27 (0.22)	0.59 (0.19)**	−0.15 (0.04)**
Trajectory 4: Constant decline (10.5%)	5.34 (0.13)**	0.16 (0.37)	−0.92 (0.39)*	0.21 (0.09)*
Trajectory 5: High anxiety (8.3%)	5.72 (0.09)**	0.09 (0.29)	−0.05 (0.26)	−0.00 (0.06)

*

p < .05. **p < .01.

We then examined patterns in the relationship between trajectory membership and gender, χ²(4) = 93.01, p < .01. Trajectory 1, the low anxiety group, included a much higher proportion of boys (56.6%) than the original sample. This is in contrast to Trajectory 5, the high anxiety group, that had a very high proportion of females (72.3%). Trajectory 2, the summer decrease group, had a similar quantity of females (52.9%) to the original sample. Conversely, Trajectory 3, the summer increase group, included more females (55.4%). Interestingly, the group with decreasing anxiety, Trajectory 4, had the highest proportion of females (59.4%).

Predictors of Trajectory Membership

Next, we examined gender as well as motivational variables (i.e., self-concept and importance) and a perception of teacher beliefs from the first wave of data as predictors of trajectory group membership, controlling for first semester grades. Use of these variables reduces total sample size from 3,471 to 2,062, as not all students had achievement data; however, relative trajectory membership group sizes remained consistent.

The multinomial logistic regression method focused first on the low anxiety (Trajectory 1) as the referent group because this group is considered desirable by most and thus we compared less desirable patterns with this. However, we also examined all other possible comparisons (Table 3). All four trajectory patterns differed from the low anxiety group on self-concept. Students with higher levels of self-concept were about half as likely to be in the summer decreasing anxiety (Trajectory 2; odds ratio [OR] = 0.56) and the summer increasing anxiety (Trajectory 3; OR = 0.54) groups as compared with the low anxiety trajectory group. Comparatively, the ORs for the decreasing anxiety group (Trajectory 4) and the high anxiety group (Trajectory 5) were 0.46 and 0.42, respectively, indicating that as self-concept increases the likelihood of being in either of these groups is lower. Similar results regarding self-concept were found in the other comparisons. Self-concept distinguishes among the groups with the exception of comparisons between the summer change groups and comparisons between the high anxiety and constant decrease groups.

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Table 3.

Odds Ratios From Logistic Regressions Predicting Trajectory Membership.

Referent trajectory group	Predictor	Trajectories
		1Low anxiety (28.9%)	2Summer decline (14.1%)	3Summer increase (24.7%)	4Constant decline (16.8%)	5High anxiety (7.1%)
Low anxiety	Self-concept		0.56**	0.54**	0.46**	0.42**
	Importance		1.07	1.05	1.25*	1.50**
	Teacher belief		1.02	1.15**	1.18*	1.32**
	Performance		0.92**	0.83**	0.72**	0.70**
	Female		1.54**	1.79**	2.96**	4.18**
Summer decline	Self-concept	1.78**		0.96	0.81*	0.74**
	Importance	0.93		0.98	1.16	1.39**
	Teacher belief	0.98		1.13*	1.15*	1.29**
	Performance	1.09**		0.90**	0.78**	0.76**
	Female	0.65**		1.16	1.92**	2.70**
Summer increase	Self-concept	1.85**	1.04		0.84	0.77**
	Importance	0.95	1.02		1.19	1.43**
	Teacher belief	0.87**	0.89*		1.02	1.15
	Performance	1.21**	1.11**		0.87**	0.85**
	Female	0.56**	0.86		1.65**	2.33**
Constant decline	Self-concept	2.19**	1.23*	1.19		0.91
	Importance	0.80*	0.86	0.84		1.2
	Teacher belief	0.85*	0.87*	0.98		1.13
	Performance	1.39**	1.28**	1.15**		0.98
	Female	0.34**	0.52**	0.61**		1.41
High anxiety	Self-concept	2.41**	1.35**	1.30**	1.1
	Importance	0.67**	0.72**	0.70**	0.84
	Teacher belief	0.76**	0.77**	0.87	0.89
	Performance	1.42**	1.31**	1.18**	1.02
	Female	0.24**	0.37**	0.43**	0.71

*

p < .05. **p < .01.

Importance significantly predicted membership in the high anxiety group (Trajectory 5; OR = 1.50) and the decreasing anxiety group (Trajectory 4; OR = 1.25). As importance increases, students were more than 1.5 times as likely to be in the high anxiety group and 1.25 times as likely to be in the overall decrease group. In the other comparisons, importance was a significant predictor of the summer groups and the low anxiety group when compared with the high anxiety group. It was also significant when comparing constant decrease group with the low anxiety group.

Gender was a significant predictor for all four trajectories with girls being more likely to be in any of the anxiety trajectories as compared with the consistently low anxiety group. Particularly striking is the OR for girls in the consistently high anxiety group (Trajectory 5, OR = 4.18) indicating they are 4 times more likely to be in the consistently high group as compared with the consistently low group. Moreover, gender was a significant predictor in almost all of the comparisons run. The only comparisons in which gender was not a significant predictor were between the high anxiety and constant decrease groups and between the summer change groups.

Students’ perceptions of their teachers’ beliefs that some students cannot do well in math class also differentiated trajectory membership. For students who endorsed this item, there was a greater likelihood of being in the summer increasing anxiety group (Trajectory 3; OR = 1.16), the overall decrease group (Trajectory 4; OR = 1.18), or the consistently high anxiety group (Trajectory 5; OR = 1.32) as compared with the consistently low anxiety group. When examining the other comparison results, it becomes clear that perceptions of teacher beliefs are key to understanding differences between the summer increase group, the constant decrease group, and the high anxiety group.

Long-Term Expectations and Career Choices

Finally, we examined follow-up items for a portion of the sample used to determine the trajectory patterns. Data were collected again 2 years after these students graduated from high school from 1,393 students (37.23% of the original sample used to formulate the trajectories), which is 7 years after the elementary to junior high transition data from Grades 6 and 7. Remaining in the sample were 94 students (31.76% of original group) in the high anxiety group (Trajectory 5), 102 students (33.12% of original group) in the decreasing anxiety group (Trajectory 4), 296 students (33.91% of original group) in summer increasing group (Trajectory 3), 446 students (39.23% of original group) in the summer decreasing group (Trajectory 2), and 455 students (40.37% of original group) in the consistently low anxiety group (Trajectory 1).

This follow-up sample is much smaller than the original sample used to create the trajectories as only slightly more than one third of the original sample responded to the follow-up survey. We examined several key demographic and motivational variables to determine how this sample compares with the original sample. Chi-square tests indicated that there were more girls in the follow-up sample (62%) than the original, χ²(1) = 49.34, p < .01, and there were more White students (92%) than in the original, χ²(1) = 66.46, p < .01. T tests indicated that those in the follow-up sample had significantly higher baseline self-concept ( X ¯ = 5.48, SD = 1.02) than those without follow-up data ( X ¯ = 5.31, SD = 1.31), t(2,791) = −4.52, p < .01, as well as higher baseline importance ( X ¯ = 6.08, SD = 0.91) than those without data ( X ¯ = 5.97, SD = 1.04), t(2,834) = −3.24, p < .01, and lower perceptions of teacher beliefs ( X ¯ = 2.20, SD = 1.23) than those without data ( X ¯ = 2.30, SD = 1.27), t(2,604) = 2.42, p = .02.

A one-way ANOVA indicated significant differences in future expectations for mathematics between trajectory groups, F(4, 1388) = 29.40, p < .01, χ² = 0.08. Group differences were examined using Games-Howell post hoc tests. In particular, the low anxiety group (Trajectory 1; X ¯ = 4.59) is significantly higher in confidence for advanced math than all four other groups. The high anxiety group (Trajectory 5; X ¯ = 2.85) has a significantly lower level of confidence than both the summer decrease group (Trajectory 2; X ¯ = 3.76) and the summer increase group (Trajectory 3; X ¯ = 3.51). The constant decline group (Trajectory 4; X ¯ = 3.26) was only significantly different from the low anxiety group (Figure 2).

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Figure 2.

Group differences in response to “Compared to others, how good are you at each of the following: Doing advanced math?” (1 = a lot worse; 7 = a lot better).

Similarly, students were also asked whether there were any careers that they had ruled out because they do not like math, physics, or chemistry classes that they would have to take. More students from the high anxiety group (Trajectory 5) said yes, whereas more students in the low anxiety group (Trajectory 1) said “no.” Remaining students in Trajectories 2, 3, and 4 were approximately evenly divided between “no” and “yes” (Figure 3). It should be noted that only 738 students answered this question, which is 19.73% of the original sample; however, the chi-square analysis was significant, χ²(4) = 13.02, p = .01.

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Figure 3.

Percentage of responses by group to “Are there any careers that you have ruled out because you do not like the math, physics, or chemistry classes that you would have to take?”.

Implications

Understanding more about how students’ motivation and perceptions of the learning environment impact mathematics anxiety is important, especially when considering the potential for long-term effects on beliefs about mathematics ability and career intentions many years after adolescence. The current study demonstrates that there are multiple patterns in how mathematics anxiety changes over time and future research could validate these trajectories, especially if future data fit with growth mixture modeling. Our findings align with the theoretical predictions that students with low self-concept and low importance will display higher anxiety; however, more research is needed into what specific predictors are tied to each trajectory. For example, future research should examine the declining anxiety group with the incorporation of additional measures to verify what is occurring with these students. Similarly, more research with data collection during the summer could shed light on whether the changes seen during the summer transition occur because of anticipation or because of a reaction when the school year starts. Finally, researchers need to examine more closely the classroom dynamic between student and teacher, collecting additional measures of perceptions of teacher beliefs, especially during critical transition periods in adolescence.

The findings of this research have important classroom implications. Gender differences in mathematics anxiety exist at this key developmental time for adolescents. Girls should be encouraged in mathematics and teachers should endorse the value and importance of mathematics for both genders. Perhaps even more important, however, are the motivational variables and classroom perceptions variable that are related to these trajectories. Teachers should focus on bolstering students’ self-concept and helping them see the importance of mathematics. Other research supports the importance of focusing on the payoff of effort and not attributing failure to ability (see Dweck & Leggett, 1988, for a review of such research); this communication is perhaps even more important in mathematics where many students struggle. Reaching students early, especially during transition, will hopefully keep them from being in the high anxiety group that was less likely to see themselves as good at mathematics and more likely to rule out careers requiring mathematics. Furthermore, teachers need to be aware that how they communicate their own beliefs about learning mathematics impacts the emotions of their students (Beilock et al., 2010; Beilock & Maloney, 2015). Teachers who communicate that math is difficult and that not every student can be successful can lead to elevated levels of students’ mathematics anxiety, which undermines students’ classroom behaviors and achievement outcomes.

Conclusion

This study contributes to the literature on mathematics anxiety by uncovering the multiple trajectory patterns in mathematics anxiety for adolescents during a critical transition period. The trends found in mathematics anxiety are not the same for all students. Some students remain consistently high or low, but even more students experience some sort of change during the transition. Awareness of these varying patterns and more research about what influences these patterns will eventually lead to more effective interventions for mathematics anxiety. The current study finds that there are malleable factors including students’ valuing of mathematics and students’ expectations that can influence how students’ mathematics anxiety changes over time. Furthermore, this research demonstrates that teachers convey their own beliefs about who can learn mathematics to their students and the perceptions students have of their teacher are influential to students’ levels of mathematics anxiety. Hence, mathematics anxiety is not only a characteristic that a student brings into a classroom but can also be learned or diminished by interactions with teachers. Knowing under what circumstances and at what times anxiety changes will allow researchers to develop targeted interventions at critical points (e.g., the transition to junior high school) and focus on key influences (e.g., expectations and values).