International Comparative Study of Statistics Learning Trajectories Based on PISA Data on Cognitive Diagnostic Models.
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ABSTRACT:
SUBMITTER: Jia B
PROVIDER: S-EPMC8329356 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:As one of the most influential international large-scale educational assessments, the Program for International Student Assessment (PISA) provides a valuable platform for the horizontal comparisons and references of international education. The cognitive diagnostic model, a newly generated evaluation theory, can integrate measurement goals into the cognitive process model through cognitive analysis, which provides a better understanding of the mastery of students of fine-grained knowledge points. On the basis of the mathematical measurement framework of PISA 2012, 11 attributes have been formed from three dimensions in this study. Twelve test items with item responses from 24,512 students from 10 countries participated in answering were selected, and the analyses were divided into several steps. First, the relationships between the 11 attributes and the 12 test items were classified to form a Q matrix. Second, the cognitive model of the PISA mathematics test was established. The liner logistic model (LLM) with better model fit was selected as the parameter evaluation model through model comparisons. By analyzing the knowledge states of these countries and the prerequisite relations among the attributes, this study explored the different learning trajectories of students in the content field. The result showed that students from Australia, Canada, the United Kingdom, and Russia shared similar main learning trajectories, while Finland and Japan were consistent with their main learning trajectories. The primary learning trajectories of the United States and China were the same. Furthermore, the learning trajectory for Singapore was the most complicated, as it showed a diverse learning process, whereas the trajectory in the United States and Saudi Arabia was relatively simple. This study concluded the differences of the mastery of students of the 11 cognitive attributes from the three dimensions of content, process, and context across the 10 countries, which provided a reference for further understanding of the PISA test results in other countries and shed some evidence for a deeper understanding of the strengths and weaknesses of mathematics education in various countries.
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Project description:Large-scale educational testing data often contain vast amounts of variables associated with information pertaining to test takers, schools, or access to educational resources-information that can help explain relationships between test taker performance and their learning environment. This study examines approaches to incorporate latent and observed explanatory variables as predictors for cognitive diagnostic models (CDMs). Methods to specify and simultaneously estimate observed and latent variables (estimated using item response theory) as predictors affecting attribute mastery were examined. Real-world data analyses were conducted to demonstrate the application using large-scale international testing data. Simulation studies were conducted to examine the recovery and classification for simultaneously estimating multiple latent (using dichotomous and polytomous items as indicators for the latent construct) and observed predictors for varying sample sizes and number of attributes. Results showed stable parameter recovery and consistency in attribute classification. Implications for latent predictors and attribute specifications are discussed.
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Project description:BackgroundAlthough amnestic mild cognitive impairment (aMCI) is generally considered to be a prodromal stage of Alzheimer's disease, patients with aMCI show heterogeneous patterns of progression. Moreover, there are few studies investigating data-driven cognitive trajectory in aMCI. We therefore classified patients with aMCI based on their cognitive trajectory, measured by clinical dementia rating sum of boxes (CDR-SOB). Then, we compared the clinical and neuroimaging features among groups classified by cognitive trajectory.MethodsWe retrospectively recruited 278 patients with aMCI who underwent three or more timepoints of neuropsychological testing. They also had magnetic resonance imaging (MRI) including structured three-dimensional volume images. Cortical thickness was measured using surface-based methods. We performed trajectory analyses to classify our aMCI patients according to their progression and investigate their cognitive trajectory using CDR-SOB.ResultsTrajectory analyses showed that patients with aMCI were divided into three groups: stable (61.8%), slow decliner (31.7%), and fast decliner (6.5%). Changes throughout a mean follow-up duration of 3.7 years in the CDR-SOB for the subgroups of stable/slow/fast decliners were 1.3-, 6.4-, and 12-point increases, respectively. Decliners were older and carried apolipoprotein E4 (APOE4) genotypes more frequently than stable patients. Compared with the stable group, decliners showed a higher frequency of aMCI patients with both visual and verbal memory dysfunction, late stage aMCI, and multiple domain dysfunction. In addition, compared with the stable group, the slow decliners showed cortical thinning predominantly in bilateral parietotemporal areas, while the fast decliners showed cortical thinning predominantly in bilateral frontotemporal areas. Both decliner groups showed worse cognitive function in attention, language, visuospatial, memory, and frontal/executive domains than the stable group.ConclusionsOur data-driven trajectory analysis provides new insights into heterogeneous cognitive trajectories of aMCI and further suggests that baseline clinical and neuroimaging profiles might predict aMCI patients with poor prognosis.
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Project description:IntroductionEarlier research has suggested that that the international large-scale assessment, PISA (Programme for International Student Assessment), may be looked upon as a form of school test that is mostly explained by participating students' socioeconomic status, non-cognitive factors, and various school factors, whereas another strand of research focuses on the similarities between PISA and cognitive ability assessments such as IQ tests. The latter position does also highlight the strong relationships between PISA scores and IQ test scores, typically aggregated to the country level. The current article adds to this scholarly debate by examining the latest PISA survey from 2018.MethodsCorrelation, regression, moderator, and mediation analyses were run for aggregated country-level data (N = 77) from PISA 2018.ResultsThe results indicate that PISA scores indeed are strongly associated with both cognitive ability test scores and socioeconomic status indicators such as average annual national income.DiscussionA more nuanced position suggests that PISA should be labelled a test that measures cognitive school skills rather than a proxy of national IQ, as the link between country-level abilities and school age abilities is neither causal, nor theoretically and empirically palpable, yet partly robust.
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