Test emotions, value, and self-efficacy: A longitudinal model predicting examinee effort and performance on low-stakes test
Faculty Advisor Name
Dr. Sara Finney
Department
Department of Graduate Psychology
Description
Scores from low-stakes tests are often used for institutional accountability, program improvement, and national or international comparison purposes (Liu, 2017). However, researchers have demonstrated that the validity of scores may be compromised by various sources of construct-irrelevant variance – one of which is test-taking motivation (Cole, Bergin, & Whitaker, 2008). In low-stakes testing contexts, there are usually no penalties or consequences for poor performance. As such, not all students are motivated to complete low-stakes tests. Researchers have tried increasing the stakes of the tests (i.e., offering rewards for good performance) to increase students’ motivation, however such approach may be costly and resource intensive. Researchers have also developed interventions to change students’ beliefs about the importance of the tests, which is based on the Expectancy-Value (EV) Theory of examinee effort (Flake, Barron, Hulleman, McCoach, & Welch, 2015; Wigfield & Eccles, 2000). According EV theory, students’ motivation is largely dependent on their beliefs about the test. More specifically, it is hypothesized and empirically showed that there is a relationship between perceived value of a test and students’ motivation (Finney, Myers, & Mathers, 2018; Penk & Richter, 2017). Interventions developed on the basis of EV theory has shown to be effective at times and ineffective at other times (Hawtthorne, Bol, & Pribesh, 2015; Liu, Rios, & Borden, 2015).
Control-Value (CV) theory of Achievement Emotions (Pekrun et al., 2006) offers another framework for understanding students’ motivation in low-stakes tests. According to CV theory, the effect of perceived value of the test on students’ motivation is important, however the effect is hypothesized to be mediated by students’ emotions. Thus, in the current study, CV theory was compared to EV theory in hopes to determine which theory can help understand antecedents of students’ motivation in low-stakes tests. A series of longitudinal autoregressive panel models were estimated to evaluate EV and CV theory. Moreover, by employing rigorous research design, the effects of perceived value and test emotions on students’ motivation and test performance were evaluated. 955 Students completed measures on perceived value, test emotions (i.e., pride, anger, enjoyment, worry, & boredom), and motivation three times during a math and scientific reasoning test. Data were collected after the first third of the test, after the second third of the test, and after the last third of the test. The scores on math and scientific reasoning test were used for test performance measure.
Model based on CV theory fit the data better than model based on EV theory. In other words, results suggest that the pattern of relationships between constructs in the study are explained in support of CV theory. However, more complex CV theory models (e.g., estimating reciprocal effects between constructs) were championed to fit the data the best. In sum, results suggest that emotions are important to consider and model when predicting students’ motivation. Future research should replicate findings, and evaluate student emotions in different contexts.
Test emotions, value, and self-efficacy: A longitudinal model predicting examinee effort and performance on low-stakes test
Scores from low-stakes tests are often used for institutional accountability, program improvement, and national or international comparison purposes (Liu, 2017). However, researchers have demonstrated that the validity of scores may be compromised by various sources of construct-irrelevant variance – one of which is test-taking motivation (Cole, Bergin, & Whitaker, 2008). In low-stakes testing contexts, there are usually no penalties or consequences for poor performance. As such, not all students are motivated to complete low-stakes tests. Researchers have tried increasing the stakes of the tests (i.e., offering rewards for good performance) to increase students’ motivation, however such approach may be costly and resource intensive. Researchers have also developed interventions to change students’ beliefs about the importance of the tests, which is based on the Expectancy-Value (EV) Theory of examinee effort (Flake, Barron, Hulleman, McCoach, & Welch, 2015; Wigfield & Eccles, 2000). According EV theory, students’ motivation is largely dependent on their beliefs about the test. More specifically, it is hypothesized and empirically showed that there is a relationship between perceived value of a test and students’ motivation (Finney, Myers, & Mathers, 2018; Penk & Richter, 2017). Interventions developed on the basis of EV theory has shown to be effective at times and ineffective at other times (Hawtthorne, Bol, & Pribesh, 2015; Liu, Rios, & Borden, 2015).
Control-Value (CV) theory of Achievement Emotions (Pekrun et al., 2006) offers another framework for understanding students’ motivation in low-stakes tests. According to CV theory, the effect of perceived value of the test on students’ motivation is important, however the effect is hypothesized to be mediated by students’ emotions. Thus, in the current study, CV theory was compared to EV theory in hopes to determine which theory can help understand antecedents of students’ motivation in low-stakes tests. A series of longitudinal autoregressive panel models were estimated to evaluate EV and CV theory. Moreover, by employing rigorous research design, the effects of perceived value and test emotions on students’ motivation and test performance were evaluated. 955 Students completed measures on perceived value, test emotions (i.e., pride, anger, enjoyment, worry, & boredom), and motivation three times during a math and scientific reasoning test. Data were collected after the first third of the test, after the second third of the test, and after the last third of the test. The scores on math and scientific reasoning test were used for test performance measure.
Model based on CV theory fit the data better than model based on EV theory. In other words, results suggest that the pattern of relationships between constructs in the study are explained in support of CV theory. However, more complex CV theory models (e.g., estimating reciprocal effects between constructs) were championed to fit the data the best. In sum, results suggest that emotions are important to consider and model when predicting students’ motivation. Future research should replicate findings, and evaluate student emotions in different contexts.
