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…

As is rapidly becoming clear, data science increasingly permeates many aspects of life. Educational research recognizes the importance and complexity of learning data science. In line with this imperative, there is a growing need to investigate the factors that influence student performance in data science tasks. In this paper, we aimed to apply…

Emotions of learners are fundamental and significant in e-learning as they encourage learning. Machine learning models are presented in the literature to look at how emotions may affect e-learning results that are improved and optimized. Nevertheless, the models that have been suggested so far are appropriate for offline mode, whereby data for…

Cognitive diagnosis models (CDMs) are the assessment tools that provide valuable formative feedback about skill mastery at both the individual and population level. Recent work has explored the performance of CDMs with small sample sizes but has focused solely on the estimates of individual profiles. The current research focuses on obtaining…

Predicting student performance using educational data is a significant area of machine learning research. However, class imbalance in datasets and the challenge of developing interpretable models can hinder accuracy. This study compares different variations of the Synthetic Minority Oversampling Technique (SMOTE) combined with classification…

This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML outputs. In each of the experiments, participants were given the T and L task with T-shaped targets and…

Automated detection of facial emotions has been an interesting topic for multiple decades in social and behavioral research but is only possible very recently. In this tutorial, we review three popular artificial intelligence based emotion detection programs that are accessible to R programmers: Google Cloud Vision, Amazon Rekognition, and…

University Examination Timetabling Problem is the most important combinational problem to develop a conflict-free timetable to execute all of the exams in and with the limited timeslots and other resources for universities, colleges or schools. It is also an important Nondeterministic Polynomial Time (NP)-hard problem that has no deterministic…

In this study, the accuracy of the artificial neural network (ANN) was assessed considering the uncertainties associated with the randomness of the data and the lack of learning. The Monte-Carlo algorithm was applied to simulate the randomness of the input variables and evaluate the output distribution. It has been shown that under certain…

Recent approaches to the detection of cheaters in tests employ detectors from the field of machine learning. Detectors based on supervised learning algorithms achieve high accuracy but require labeled data sets with identified cheaters for training. Labeled data sets are usually not available at an early stage of the assessment period. In this…

This paper summarises exploratory analysis undertaken to evaluate the effectiveness of using machine learning approaches to calculate projected completion rates for vocational education and training (VET) programs, and compares this with the current approach used at the National Centre for Vocational Education Research (NCVER) -- Markov chains…

Wildfires pose significant environmental threats in Australia, impacting ecosystems, human lives, and property. This review article provides a comprehensive analysis of various empirical and dynamic wildfire simulators alongside machine learning (ML) techniques employed for wildfire prediction in Australia. The study examines the effectiveness of…

To be an effective teacher, one must possess strong learning abilities. Developing lesson planning, pursuing learning objectives, and assessing post-lesson accomplishments all these depend on reflection and ongoing learning. As education is context-specific, the iterative process of preparing, reflecting, and improving is what makes teaching…

In every culture and era, education is considered the most fundamental reality and rule that societies prioritize and deem essential. Throughout the process spanning thousands of years, from the emergence of writing to the present day, education has undergone various forms and formats of change. Education has been a continuous guide for shaping,…

We develop a "latent variable forest" (LV Forest) algorithm for the estimation of latent variable scores with one or more latent variables. LV Forest estimates unbiased latent variable scores based on "confirmatory factor analysis" (CFA) models with ordinal and/or numerical response variables. Through parametric model…