Beyond Rapid Guessing: Evaluating Test Engagement With Successful and Unsuccessful Time Management Through Process Data in TIMSS 2019

Event Abstract Back to Event Cognitive Activation in Mathematics Lessons: Links to Mathematics Interest, Self-Efficacy, and Achievement Shaljan Areepattamannil1*, Myint Swe Khine1 and Michael Melkonian1 1 Emirates College for Advanced Education, United Arab Emirates There are growing concerns among educational policymakers and practitioners over the dwindling numbers of students choosing to study STEM subjects such as science, technology, engineering, and mathematics. As a result, education systems across the world have been ‘frantically’ exploring ways to foster students’ interest in STEM subjects, and to encourage them to pursue a career in STEM-related fields. Of the STEM subjects, mathematics cuts across the other three subjects, and a high-level of content mastery in mathematics is considered indispensable to success in STEM-related careers. The present study, therefore, drawing on data from the fifth cycle of the Program for International Student Assessment (PISA), examined the relations of cognitive activation in mathematics lessons to mathematics interest, self-efficacy, and achievement among a nationally representative sample of over 5000 adolescents hailing from Shanghai-China. The results of the study revealed that mathematics teachers’ use of cognitive activation strategies in their lessons—asking students to apply what they have learned to new contexts, encouraging students to reflect on problems, helping students to learn from mistakes they have made, presenting problems with no immediately obvious method of solution, giving problems with multiple solutions, and asking students to use their own procedures for solving complex problems—was significantly associated with higher mathematics interest, self-efficacy, and achievement. The findings of the study shed light on the key roles that mathematics teachers’ use of cognitive activation strategies play in enhancing students’ mathematics interest, self-efficacy, and achievement. The results of the study also highlight the need to create a mathematics teaching force which is capable of effectively and efficiently employing varied cognitive activation strategies in their lessons. However, further research is warranted to investigate the validity and efficacy of these strategies in other cultures. Keywords: Cognitive activation, mathematics achievement, Mathematics interest, Mathematics self-efficacy, Mathematics lessons Conference: International Conference - Educational Neuroscience, Abu Dhabi, United Arab Emirates, 28 Feb - 29 Feb, 2016. Presentation Type: Poster Presentation Topic: Educational Neuroscience Citation: Areepattamannil S, Swe Khine M and Melkonian M (2016). Cognitive Activation in Mathematics Lessons: Links to Mathematics Interest, Self-Efficacy, and Achievement. Front. Neurosci. Conference Abstract: International Conference - Educational Neuroscience. doi: 10.3389/conf.fnins.2016.92.00022 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 11 Feb 2016; Published Online: 23 Mar 2016. * Correspondence: Dr. Shaljan Areepattamannil, Emirates College for Advanced Education, Abu Dhabi, United Arab Emirates, sareepattamannil@ecae.ac.ae Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Shaljan Areepattamannil Myint Swe Khine Michael Melkonian Google Shaljan Areepattamannil Myint Swe Khine Michael Melkonian Google Scholar Shaljan Areepattamannil Myint Swe Khine Michael Melkonian PubMed Shaljan Areepattamannil Myint Swe Khine Michael Melkonian Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

This study investigates the influence of peer pressure, time management, and self-efficacy on students’ achievement in mathematics in Oyo Metropolis of Oyo state, Nigeria. The study adopts a descriptive survey. A simple random sampling technique was used to select four hundred (400) senior secondary school II students in sixteen (16) secondary schools in the metropolis consisting of four local governments: Afijio, Atiba, Oyo East, and Oyo West Local Government Areas. Three (3) validated instruments were used for the study. Data analysis was done using frequency count, Percentage, Standard Deviation, Pearson Product Moment Correlation, and Multiple Regression Analysis. The result shows that there is a negative, weak non-significant relationship between peer pressure and students’ achievement in mathematics; there is a positive, weak significant relationship between time management and students’ achievement in mathematics. Also, there is a significant positive relationship between self-efficiency and students’ achievement in mathematics. The finding also reveals that the composite contribution of peer pressure, time management, and self-efficacy to the predictor of students’ achievement in mathematics is significant. The relative contribution of peer pressure to students’ achievement in mathematics is not significant but the relative contribution of time management and self-efficacy to students achievement in mathematics is significant. The data shows that the variables that predict students’ achievement in mathematics are time management and self-efficacy. Based on the result, it was recommended that Mathematics teachers should find ways of enhancing Mathematics Self-Efficacy in student’ and emphasize students’ confidence to succeed in the subject.

BackgroundThis empirical study aims to investigate the association between mathematics self-efficacy and mathematics achievement gaps among students in Grades 4, 8, and 12, utilizing data from the 2019 National Assessment of Educational Progress (NAEP). The study also considers student-level (e.g., mathematics self-efficacy, gender, race/ethnicity) and school-level (e.g., school location, proportion of underrepresented students) demographics to provide a comprehensive analysis of the factors contributing to mathematics achievement gaps.MethodsA two-level cross-sectional multilevel modeling approach was employed to analyze the variance in mathematics achievement, partitioning it into within- and between-school components. This approach allowed for an examination of association between mathematics self-efficacy and achievement gaps while considering various student and school characteristics. The rationale behind this methodology lies in its ability to capture the hierarchical nature of educational data and provide a nuanced understanding of the factors associated with mathematics achievement.ResultsThe analysis of the NAEP data revealed substantial variability in mathematics achievement across schools in the United States at all grade levels. Furthermore, mathematics self-efficacy emerged as a robust predictor of students' mathematics achievement, exhibiting significant effect sizes for Grades 4, 8, and 12. Remarkably, when students' mathematics self-efficacy was held constant, the mathematics achievement gaps among different student subgroups by gender, race/ethnicity, ELL, IEP, NSLP status narrowed, highlighting the importance of self-efficacy in addressing these disparities. The study also identified the presence of significant school contextual effects, further emphasizing the role of the educational environment in shaping mathematics achievement.ConclusionsThis study underscores the critical role of mathematics self-efficacy in influencing mathematics achievement gaps among students. By acknowledging the association between self-efficacy beliefs and mathematics achievement, policymakers and educators can develop targeted interventions to enhance students' confidence and motivation in mathematics, ultimately promoting equitable educational outcomes. The findings also emphasize the significance of school-level factors, calling for comprehensive approaches that consider both individual and contextual factors in narrowing achievement gaps. The implications of adopting a self-efficacy perspective to address mathematics achievement gaps extend to educational policy, curriculum development, and instructional practices, ultimately fostering more inclusive and effective mathematics education for all students.

The presence of rapid guessing (RG) presents a challenge to practitioners in obtaining accurate estimates of measurement properties and examinee ability. In response to this concern, researchers have utilized response times as a proxy of RG and have attempted to improve parameter estimation accuracy by filtering RG responses using popular scoring approaches, such as the effort-moderated item response theory (EM-IRT) model. However, such an approach assumes that RG can be correctly identified based on an indirect proxy of examinee behavior. A failure to meet this assumption leads to the inclusion of distortive and psychometrically uninformative information in parameter estimates. To address this issue, a simulation study was conducted to examine how violations to the assumption of correct RG classification influences EM-IRT item and ability parameter estimation accuracy and compares these results with parameter estimates from the three-parameter logistic (3PL) model, which includes RG responses in scoring. Two RG misclassification factors were manipulated: type (underclassification vs. overclassification) and rate (10%, 30%, and 50%). Results indicated that the EM-IRT model provided improved item parameter estimation over the 3PL model regardless of misclassification type and rate. Furthermore, under most conditions, increased rates of RG underclassification were associated with the greatest bias in ability parameter estimates from the EM-IRT model. In spite of this, the EM-IRT model with RG misclassifications demonstrated more accurate ability parameter estimation than the 3PL model when the mean ability of RG subgroups did not differ. This suggests that in certain situations it may be better for practitioners to (a) imperfectly identify RG than to ignore the presence of such invalid responses and (b) select liberal over conservative response time thresholds to mitigate bias from underclassified RG.

The present meta-analysis sought to quantify the average degree of aggregated test score distortion due to rapid guessing (RG). Included studies group-administered a low-stakes cognitive assessment, identified RG via response times, and reported the rate of examinees engaging in RG, the percentage of RG responses observed, and/or the degree of score distortion in aggregated test scores due to RG. The final sample consisted of 25 studies and 39 independent samples comprised of 443,264 unique examinees. Results demonstrated that an average of 28.3% of examinees engaged in RG (21% were deemed to engage in RG on a nonnegligible number of items) and 6.89% of item responses were classified as rapid guesses. Across 100 effect sizes, RG was found to negatively distort aggregated test scores by an average of 0.13 standard deviations; however, this relationship was moderated by both test content area and filtering procedure.

Rapid guessing (RG) behavior can undermine measurement property and score-based inferences. To mitigate this potential bias, practitioners have relied on response time information to identify and filter RG responses. However, response times may be unavailable in many testing contexts, such as paper-and-pencil administrations. When this is the case, self-report measures of effort and person-fit statistics have been used. These methods are limited in that inferences concerning motivation and aberrant responding are made at the examinee level. As test takers can engage in a mixture of solution and RG behavior throughout a test administration, there is a need to limit the influence of potential aberrant responses at the item level. This can be done by employing robust estimation procedures. Since these estimators have received limited attention in the RG literature, the objective of this simulation study was to evaluate ability parameter estimation accuracy in the presence of RG by comparing maximum likelihood estimation (MLE) to two robust variants, the bisquare and Huber estimators. Two RG conditions were manipulated, RG percentage (10%, 20%, and 40%) and pattern (difficulty-based and changing state). Contrasted to the MLE procedure, results demonstrated that both the bisquare and Huber estimators reduced bias in ability parameter estimates by as much as 94%. Given that the Huber estimator showed smaller standard deviations of error and performed equally as well as the bisquare approach under most conditions, it is recommended as a promising approach to mitigating bias from RG when response time information is unavailable.

This research examined the relationship among social media usage, time management, and mathematics achievement among 185 Grade 10 students at Pagsabungan National High School, Cebu Province, during the 2025-2026 school year. Employing a descriptive-correlational design and total enumeration sampling, the study utilized three instruments: a social media usage survey, a time management questionnaire, and a 40-item mathematics achievement test assessing various cognitive competencies. Data analysis involved descriptive statistics (frequency count, weighted mean, standard deviation) and Pearson’s r correlation. Results showed moderate overall social media usage with passive engagement more dominant than active use. Time management was rated high in prioritization but moderate in procrastination and socialization. Mathematics achievement was generally satisfactory to very satisfactory, with 50.81% of students achieving “Very Satisfactory” results and an overall mean score of 26.35. Correlations demonstrated a weak but significant positive relationship between social media usage and time management, an insignificant relationship between social media usage and mathematics achievement, and a small but significant negative relationship between time management and mathematics achievement. The study concluded that moderate, mostly passive social media use does not strongly affect mathematics achievement, while effective time management, particularly prioritization, coexists with varied academic outcomes. A comprehensive action plan recommending enhanced time management workshops, responsible social media practices, peer tutoring, and teacher training was proposed to support improved student performance and holistic development.

Rapid guessing is an aberrant response behavior that commonly occurs in low-stakes assessments with little to no formal consequences for students. Recently, the availability of response time (RT) information in computer-based assessments has motivated researchers to develop various methods to detect rapidly guessed responses systematically. These methods often require researchers to identify an RT threshold subjectively for each item that could distinguish rapid guessing behavior from solution behavior. In this study, we propose a data-driven approach based on random search and genetic algorithm to search for the optimal RT threshold within a predefined search space. We used response data from a low-stakes math assessment administered to over 5000 students in 658 schools across the United States. As we demonstrated how to use our data-driven approach, we also compared its performance with those of the existing threshold-setting methods. The results show that the proposed method could produce viable RT thresholds for detecting rapid guessing in low-stakes assessments. Moreover, compared with the other threshold-setting methods, the proposed method yielded more liberal RT thresholds, flagging a larger number of responses. Implications for practice and directions for future research were discussed.

to general feelings such as liking/disliking of mathematics, nor is it meant to exclude perceptions of the difficulty, usefulness, and appropriateness of mathematics as a school subject. There are several ways affective variables are related to mathematics learning. It is likely that a student who feels very positive about mathematics will achieve at a higher level than a student who has a negative attitude toward mathematics. It is also likely that a high achiever will enjoy mathematics more than a student who

As researchers in the social sciences, we are often interested in studying not directly observable constructs through assessments and questionnaires. But even in a well-designed and well-implemented study, rapid-guessing behavior may occur. Under rapid-guessing behavior, a task is skimmed shortly but not read and engaged with in-depth. Hence, a response given under rapid-guessing behavior does bias constructs and relations of interest. Bias also appears reasonable for latent speed estimates obtained under rapid-guessing behavior, as well as the identified relation between speed and ability. This bias seems especially problematic considering that the relation between speed and ability has been shown to be able to improve precision in ability estimation. For this reason, we investigate if and how responses and response times obtained under rapid-guessing behavior affect the identified speed-ability relation and the precision of ability estimates in a joint model of speed and ability. Therefore, the study presents an empirical application that highlights a specific methodological problem resulting from rapid-guessing behavior. Here, we could show that different (non-)treatments of rapid guessing can lead to different conclusions about the underlying speed-ability relation. Furthermore, different rapid-guessing treatments led to wildly different conclusions about gains in precision through joint modeling. The results show the importance of taking rapid guessing into account when the psychometric use of response times is of interest.

Test-taking behavior is a potential source of construct irrelevant variance for test scores in international large-scale assessments where test-taking effort, motivation, and behaviors in general tend to be confounded with test scores. In an attempt to disentangle this relationship and gain further insight into examinees’ test-taking processes, researchers can now utilize process and timing data to obtain a more comprehensive view of test-taking behaviors, such as test-taking effort. The purpose of this study is to propose and evaluate two novel response-based, standardized indicators of test-taking behaviors that utilize a combination of examinee response and process (timing) data to better understand and describe test-taking effort in ILSAs. These indices were empirically estimated with USA data from two booklets from e-TIMSS 2019 in mathematics for grade 4. In addition, their predictive validity was examined with respect to achievement estimates. Their network of associations with other relevant variables such as motivation, interest in the subject, as well as across subjects were also examined to test their intra-individual stability in e-TIMSS.

ABSTRACTUnmotivated test takers using rapid guessing in item responses can affect validity studies and teacher and institution performance evaluation negatively, making it critical to identify these test takers. The authors propose a new nonparametric method for finding response-time thresholds for flagging item responses that result from rapid-guessing behavior. Using data from a low-stakes assessment of college-level academic skills as an illustration, the authors evaluate and compare model fit and score estimation based on data sets cleaned by both new and existing methods. Flagging rapid-guessing responses is found to generally improve model fit, item parameter estimation, and score estimation, as in the literature. This new method, based on both response time and response accuracy, shows promise in detecting rapid guessing and in improving efficiency of the flagging process when built into data analysis.

Rapid guessing (RG) is a form of non-effortful responding that is characterized by short response latencies. This construct-irrelevant behavior has been shown in previous research to bias inferences concerning measurement properties and scores. To mitigate these deleterious effects, a number of response time threshold scoring procedures have been proposed, which recode RG responses (e.g., treat them as incorrect or missing, or impute probable values) and then estimate parameters for the recoded dataset using a unidimensional or multidimensional IRT model. To date, there have been limited attempts to compare these methods under the possibility that RG may be misclassified in practice. To address this shortcoming, the present simulation study compared item and ability parameter recovery for four scoring procedures by manipulating sample size, the linear relationship between RG propensity and ability, the percentage of RG responses, and the type and rate of RG misclassifications. Results demonstrated two general trends. First, across all conditions, treating RG responses as incorrect produced the largest degree of combined systematic and random error (larger than ignoring RG). Second, the remaining scoring approaches generally provided equal accuracy in parameter recovery when RG was perfectly identified; however, the multidimensional IRT approach was susceptible to increased error as misclassification rates grew. Overall, the findings suggest that recoding RG as missing and employing a unidimensional IRT model is a promising approach.

This study aims to determine the anomalous index (indeks anomali or IA) that considers both response time and responses and compares it with response time effort (RTE) or rapid guessing (tebakan cepat or TC) on various thresholds. Response time and responses from 732 examinees are in natural science subjects consist of 40 multiple choice items with four answer choices. Response time and responses are analyzed to obtain descriptive statistics related to them, calculate the TC and IA index using two methods of the threshold, the first method (M1) is a visualization of identification, and the second method (M2) is based on the amount of time spent responding to each item related to the complexity of items, as proposed by Nitko. The performance of the IA and TC scores is compared related to validity and reliability. The coefficient alpha of IAM1 score 0.84, the coefficient alpha of IAM2 0.82. Both values of the alpha coefficient have fulfilled the reliability requirements of the index determination. The IA proposed in this study has a high correlation with ERP, which is commonly used to determine the solution behavior's magnitude and rapid guessing. The correlation value of IAM1 with TCM1 0.86, the correlation value of IAM2 with TCM2 0.89, and this high correlation value shows that there is a strong relationship between IA and TC. Determination of threshold time uses three categories of multiple choices item that reveal IA and TC distributions that are close to normal distribution so that it reflects natural empirical conditions.

This study examined the relationship between hours in general education and achievement in reading and mathematics for students with disabilities. The study population included more than 1,300 students between the ages of 6 and 9 years old within 180 school districts. Hierarchical linear modeling (HLM) was utilized with the Pre-Elementary Education Longitudinal Study (PEELS) data set (Institute of Education Sciences). The relationship between hours in general education and achievement in reading and mathematics was explored while accounting for student- and district-level factors. Results suggest a strong positive relationship between the number of hours students spent in general education and achievement in mathematics and reading. Implications for policy and practice in special education are presented and discussed.