This paper examines the distribution of eigenvalues for a $2\times 2$ random confusion matrix used in machine learning evaluation. We also analyze the distributions of the matrix’s trace and the difference between the traces of random confusion matrices. Furthermore, we demonstrate how these distributions can be applied to calculate the superiority probability of machine learning models. By way of example, we use the superiority probability to compare the accuracy of four disease outcomes machine learning prediction tasks.

In this paper, we derive the distribution of eigenvalues for a 2×2 random confusion matrix stemming from a machine learning (ML) evaluation problem. Additionally, we present the distributions of both the matrix’s trace and the difference between the two traces of random confusion matrices. We also illustrate the application of these derived distributions in calculating the superiority probability of four baseline ML models.

Purpose. The aim was to develop and validate (SLCWAS)so as to provide support for its application among primary school students. It was postulated the following: (1) the (SLCWAS) was developed for application to primary school students in a Saudi Arabian cultural context; and (2) the (SLCWAS) has appropriate psychometric properties. Methods and Procedure. 190 primary school students were included in this study. CFA was conducted for testing item-factor structure of the scale. The fit indices were found to be Chi square = 32.16 (p < .001), degree of freedom = 190 (χ2 = 32.16; df = 190, χ2/df = 3.24), root mean square error of approximation (RMSEA) = .07 (p < .05) standardized root mean- square residual (SRMR) = .05, comparative fit index (CFI) = .93, non-normed fit index (NNFI) = .96, goodness of fit index (GFI) = .96, and adjusted goodness of fit index (AGFI) = .95. These outcomes prove that SLCWAS is a valid and reliable measure. Results. Findings indicate that the empirical ICC curves fit well with the expected ICC curves for all the items. the levels of SLCWAS in primary school students are widely distributed. Higher Logits scores indicated that students have higher levels of SLCWAS. Males and females had the same location in answering SLCWAS items and that there was no DIF for the SLCWAS items with respect to gender. Conclusions: SLCWAS was validated in the Rasch analysis with a sample of primary school students. The preliminary reliability investigation revealed a very high internal consistency for the scale.

This paper explores the utilization of ensemble models and learning analytics techniques to predict student academic performance. With the advent of educational big data, institutions are increasingly leveraging advanced analytics to gain insights into student learning patterns and optimize educational outcomes. Ensemble models, which combine the predictive power of multiple algorithms, offer a robust approach to enhance prediction accuracy. The performance of the ensemble models was analyzed and compared using the Open University Learning Analytics Dataset, which consists of sources such as demographic information, historical performance data, and engagement metrics for 23,344 students. The evaluation of various ensemble models across different classification scenarios revealed that the proposed stacking model consistently emerges as the best-performing model, excelling in both multi-class and binary classification tasks.

Executive functions (EFs) are the fundamental cognitive processes that enable humans to process diverse information and generate complex functions and behaviors. They involve planning, controlling,...