This study introduces an optimized ensemble deep neural network (Optimized Ensemble Deep-NN) to enhance the accuracy of predicting student grades. This model solves the problem of different and complicated student performance data by using deep neural networks, ensemble learning, and a number of optimization algorithms, such as Adam, SGD, and RMS…

Across two studies, children ages 6-9 (N = 160, 82 boys, 78 girls; 75% White, 91% non-Hispanic) rated an inaccurate expert's knowledge and provided explanations for the expert's inaccurate statements. In Study 1, children's knowledge ratings decreased as he provided more inaccurate information. Ratings were predicted by age (i.e., older children…

Predicting student performance has been a critical focus of educational research. With an effective predictive model, schools can identify potentially at-risk students and implement timely interventions to support student success. Recent developments in educational data mining (EDM) have introduced several machine learning techniques that can…

Child protection social workers in England are required to make many decisions in their day-to-day work, including whether to accept a referral, undertake a child protection investigation, pursue care proceedings, or close the case. Many of these decisions involve implicit or explicit predictions about the likelihood of future actions, events, and…

This study addressed a critical gap in the literature by investigating the relationship between organizational culture and mission drift within Christian higher education. While mission drift has been widely discussed in popular press, limited academic research has explored the factors influencing it, particularly organizational culture. Utilizing…

The research of multi-category learning behaviors is a hot issue in interactive learning environment, and there are many challenges in data statistics and relationship modeling. We select the massive learning behaviors data of multiple periods and courses and study the decision application of regression analysis. First, based on the definition of…

Metacognition is important for successful goal-directed behavior. It consists of two main elements: metacognitive knowledge and online awareness. Online awareness consists of monitoring and self-regulation. Metacognitive sensitivity is the extent to which someone can accurately distinguish their own correct from incorrect responses and is an…

Algorithms may be better at prediction than humans in a variety of contexts, but they are not perfect. A deeper understanding of the ways in which educators use and question algorithmic advice within their professional domain is needed. Educators are a particularly unique professional group, in comparison with the other groups studied in the…

This dissertation examined classification outcome differences among four popular individual supervised machine learning (ISML) models (logistic regression, decision tree, support vector machine, and multilayer perceptron) when predicting minor class membership within imbalanced datasets. The study context and the theoretical population sampled…

Across the UK, child protection social workers are routinely called upon to assess the likelihood of future significant harm to children. Yet making consistently accurate judgements about what may or may not happen in future can be a difficult task. In a previous study, we tested social workers' abilities (n = 283) to forecast the likelihood of…

Past research on people's moral judgments about moral dilemmas has revealed a connection between utilitarian judgment and reflective cognitive style. This has traditionally been interpreted as reflection is conducive to utilitarianism. However, recent research shows that the connection between reflective cognitive style and utilitarian judgments…

This quantitative study investigates the predictive power of machine learning (ML) models on degree completion among adult learners in higher education, emphasizing the enhancement of data-driven decision-making (DDDM). By analyzing three ML models - Random Forest, Gradient-Boosting machine (GBM), and CART Decision Tree - within a not-for-profit,…

When human adults make decisions (e.g., wearing a seat belt), we often consider the negative consequences that would ensue if our actions were to fail, even if we have never experienced such a failure. Do the same considerations guide our understanding of other people's decisions? In this paper, we investigated whether adults, who have many years…

Due to severe time constraints, goalkeepers regularly face the challenging task to make decisions within just a few hundred milliseconds. A key finding of anticipation research is that experts outperform novices by using advanced cues which can be derived from either kinematic or contextual information. Yet, how context modulates decision-making…

The artificial intelligence methods might be applied to see through the education problems, and make effective prediction and decision. The transformation from data to decision are inseparable from the learning analytics. In order to solve the dynamic multi-objective decision problems, a decision learning algorithm is designed to analyze the…