Classroom versus individual working memory assessment: predicting academic achievement and the role of attention and response inhibition

Mind Matters: Exploring the Intersection of Psychological Factors and Cognitive Abilities of University Students by Using ANN Model

Purpose

While previous studies have suggested close association of psychological variables of students withtheir higher-order cognitive abilities, such studies have largely been lacking for third world countries like India, with their unique socio-economic-cultural set of challenges. We aimed to investigate the relationship between psychological variables (depression, anxiety and stress) and cognitive functions among Indian students, and to predict cognitive performance as a function of these variables.

Patients and Methods

Four hundred and thirteen university students were systematically selected using purposive sampling. Widely used and validated offline questionnaires were used to assess their psychological and cognitive statuses. Correlational analyses were conducted to examine the associations between these variables. An Artificial Neural Network (ANN) model was applied to predict cognitive levels based on the scores of psychological variables.

Results

Correlational analyses revealed negative correlations between emotional distress and cognitive functioning. Principal Component Analysis (PCA) reduced the dimensionality of the input data, effectively capturing the variance with fewer features. The feature weight analysis indicated a balanced contribution of each mental health symptom, with particular emphasis on one of the symptoms. The ANN model demonstrated moderate predictive performance, explaining a portion of the variance in cognitive levels based on the psychological variables.

Conclusion

The study confirms significant associations between emotional statuses of university students with their cognitive abilities. Specifically, we provide evidence for the first time that in Indian students, self-reported higher levels of stress, anxiety, and depression are linked to lower performance in cognitive tests. The application of PCA and feature weight analysis provided deeper insights into the structure of the predictive model. Notably, use of the ANN model provided insights into predicting these cognitive domains as a function of the emotional attributes. Our results emphasize the importance of addressing mental health concerns and implementing interventions for the enhancement of cognitive functions in university students.

Cognitive Predictors of the Overlap of Reading and Math in Middle School

Math and reading skills are known to be related, and predictors of each are well researched. What is less understood is the extent to which these predictors, uniquely and collectively, overlap with one another, are differentially important for different academic skills, and account for the overlap of math and reading. We examined 20 potential predictors from four domains (working memory, processing speed, attention, and language) using latent variables and both timed and untimed achievement skill, in a sample (N=212) of at-risk middle schoolers, half of whom were English learners. The predictors accounted for about half of the overlap among achievement skills, suggesting that other factors (e.g., domain specific skills) might also be relevant for the overlap. We also found some differential prediction (language for reading, working memory for math). The present results extend and refine our understanding of the contribution of these cognitive predictors for reading and math.

Individual characteristics moderate listening effort in noisy classrooms

Comprehending the teacher's message when other students are chatting is challenging. Even though the sound environment is the same for a whole class, differences in individual performance can be observed, which might depend on a variety of personal factors and their specific interaction with the listening condition. This study was designed to explore the role of individual characteristics (reading comprehension, inhibitory control, noise sensitivity) when primary school children perform a listening comprehension task in the presence of a two-talker masker. The results indicated that this type of noise impairs children's accuracy, effort, and motivation during the task. Its specific impact depended on the level and was modulated by the child's characteristics. In particular, reading comprehension was found to support task accuracy, whereas inhibitory control moderated the effect of listening condition on the two measures of listening effort included in the study (response time and self-ratings), even though with a different pattern of association. A moderation effect of noise sensitivity on perceived listening effort was also observed. Understanding the relationship between individual characteristics and classroom sound environment has practical implications for the acoustic design of spaces promoting students' well-being, and supporting their learning performance.

Boys' visuospatial abilities compensate for their relatively poor in-class attentive behavior in learning mathematics

The mathematics and reading achievement of 322 adolescents (159 boys) was assessed in seventh and eighth grades, as were their intelligence, working memory, and spatial abilities. Their seventh- and eighth-grade mathematics and English language arts teachers reported on their in-class attentive behavior. The latter emerged as an important predictor of achievement, but more so for mathematics than for reading. Boy were less attentive in classroom settings than girls (d = -.34) and performed better than expected in mathematics given their level of engagement in the classroom. Boys' better-than-expected mathematics achievement was related to advantages on visuospatial measures (ds = .28-.56), which fully mediated a sex difference in mathematics (ds = .27-.28) but not in reading achievement, with control of in-class attentive behavior. The results suggest that boys' advantages in visuospatial skills compensate for lower levels of classroom engagement in the learning of mathematics but not in reading competencies.

Task Monitoring and Working Memory as Executive Components Predictive of General and Specific Academic Achievements in 6-9-Year-Old Children

Academic achievement has been linked to executive functions. However, it is necessary to clarify the different predictive role that executive functions have on general and specific academic achievement and to determine the most predictive executive factor of this academic achievement. The relationship and predictive role between executive functions and their components (initiative, working memory, task monitoring, organization of materials, flexibility, emotional control, inhibition, self-monitoring) with academic achievement are analyzed in this study, both globally and specifically in the areas of Language Arts and Mathematics, in 133 students from 6 to 9 years of age. The relationship obtained in Pearson’s correlation analysis does not differ substantially between overall achievement (r = 0.392) and specific achievement (r = 0.361, r = 0.361), but task monitoring (r = 0.531, r = 0.455, r = 0.446) and working memory (r = 0.512, r = 0.475, r = 0.505) had a greater relationship with general and specific achievement. Finally, regression analyses based on correlation results indicate that executive functions predict general academic performance (14.7%) and specific performance (12.3%, 12.2%) for Language Arts and Mathematics, respectively. Furthermore, working memory and task supervision represent 32.5% of general academic performance, 25.5% of performance in Language Arts, and 27.1% of performance in Mathematics. In conclusion, this study yielded exploratory data on the possible executive functions (task supervision and working memory) responsible for good general academic achievements and specific academic achievements in Mathematics and Language Arts.