As an excellent means of improving students' effective learning, knowledge tracking can assess the level of knowledge mastery and discover latent learning patterns based on students' historical learning evaluation of related questions. The advantage of knowledge tracking is that it can better organize and adjust students' learning plans, provide…

Sociologists increasingly face choices among competing algorithms that represent reasonable approaches to the same task, with little guidance in choosing among them. We develop a strategy that uses simulated data to identify the conditions under which different methods perform well and applies what is learned from the simulations to predict which…

Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque…

In this article, I present a new multivariate regression model for analyzing outcomes with network dependence. The model is capable to account for two types of outcome dependence including the mean dependence that allows the outcome to depend on selected features of a known dependence network and the error dependence that allows the outcome to be…

Binary outcomes are often analyzed in cluster randomized trials (CRTs) using logistic regression and cluster robust standard errors (CRSEs) are routinely used to account for the dependent nature of nested data in such models. However, CRSEs can be problematic when the number of clusters is low (e.g., < 50) and, with CRTs, a low number of…

Background/Context: Predictive analytics has emerged as an indispensable tool in the education sector, offering insights that can improve student outcomes and inform more equitable policies (Friedler et al., 2019; Kleinberg et al., 2018). However, the widespread adoption of predictive models is hindered by several challenges, including the lack of…

Clinical prediction models are widely used in modern clinical practice. Such models are often developed using individual patient data (IPD) from a single study, but often there are IPD available from multiple studies. This allows using meta-analytical methods for developing prediction models, increasing power and precision. Different studies,…

One of the main goals of an individual participant data meta-analysis (IPD-MA) of intervention studies is to investigate whether treatment effect differences are present, and how they are associated with patient characteristics. Examining treatment heterogeneity due to a continuous covariable (e.g., BMI or age) may be challenging, since there is…

The nature of education has been transformed by technological advances and online learning platforms, providing educational institutions with more options than ever to thrive in a complex and competitive environment. However, they still face challenges such as academic underachievement, graduation delays, and student dropouts. Fortunately, by…

Location tracking devices are becoming increasingly popular in practice to study movement of customers or track inventory. However, using location tracking devices in education contexts is quite novel. In this paper, we present a robust Bayesian nonparametric mixture model that clusters location data. We successfully apply this model on location…

Logistic regression models have traditionally been used to identify the factors contributing to students' conceptual understanding. With the advancement of the machine learning-based research approach, there are reports that some machine learning algorithms outperform logistic regression models in terms of prediction. In this study, we collected…

Intelligence has been declared as a necessary but not sufficient condition for creativity, which was subsequently (erroneously) translated into the so-called threshold hypothesis. This hypothesis predicts a change in the correlation between creativity and intelligence at around 1.33 standard deviations above the population mean. A closer…

This research uses a three-phase method to evaluate and forecast the academic efficiency of engineering programs. In the first phase, university profiles are created through cluster analysis. In the second phase, the academic efficiency of these profiles is evaluated through Data Envelopment Analysis. Finally, a machine learning model is trained…

For a productive life, education plays a critical role to fill individual life with value and excellence. Education is compulsory to provide things that individuals partake in to compete in the modern world. Predicting the academic performance of the student is the most successive research in this era. A different set of approaches and methods are…

The main challenge in higher education is student retention. While many methods have been proposed to overcome this challenge, early and continuous feedback can be very effective. In this article, we propose a method for predicting student final grades in a course using only their performance data in the current semester. It assists students in…