Sudoku is a puzzle that has a unique solution. No matter how many methods are used, the result will always be the same. The player thought that the number of givens or clues, the initial value on the Sudoku puzzles, would significantly determine the difficulty level, which is not necessarily correct. This research uses two search algorithms,…

With the emergence of Artificial Intelligence (AI), smart education has become an attractive topic. In a smart education system, automated classrooms and examination rooms could help reduce the economic cost of teaching, and thus improve teaching efficiency. However, existing AI algorithms suffer from low surveillance accuracies and high…

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…

Educators evaluate student knowledge using knowledge component (KC) models that map assessment questions to KCs. Still, designing KC models for large question banks remains an insurmountable challenge for instructors who need to analyze each question by hand. The growing use of Generative AI in education is expected only to aggravate this chronic…

Cheating detection in large-scale assessment received considerable attention in the extant literature. However, none of the previous studies in this line of research investigated the stacking ensemble machine learning algorithm for cheating detection. Furthermore, no study addressed the issue of class imbalance using resampling. This study…

Generative artificial intelligence (AI) has the potential to revolutionize social science research. However, researchers face the difficult challenge of choosing a specific AI model, often without social science-specific guidance. To demonstrate the importance of this choice, we present an evaluation of the effect of alignment, or human-driven…

Knowledge tracing (KT) models predict how students will perform on future interactions, given a sequence of prior responses. Modern approaches to KT leverage "deep learning" techniques to produce more accurate predictions, potentially making personalized learning paths more efficacious for learners. Many papers on the topic of KT focus…

As science and technology advance rapidly, video semantic understanding (VSU) technology has made significant strides. This technology has garnered widespread recognition within the music industry and piqued the interest of film and television music creators. In the realm of film music creation, VSU technology serves as a powerful tool,…

This exploratory study investigates the convergence of marketing communications and AI-powered technology in higher education, adopting a perspective on student interactions with generative AI tools. Through a comprehensive content analysis of learners' responses, we employed a blend of manual scrutiny, Python-generated Word Cloud, and Latent…

Although self-regulated learning (SRL) plays an important role in supporting online learning performance, the lack of student self-regulation skills poses a persistent problem to many educators. Recommender systems have the potential to promote SRL by delivering personalized feedback and tailoring learning strategies to meet individual learners'…

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…

Recycling waste is essential to mitigate environmental damage caused by human activity. Environmentally responsible behaviors, shaped during early ages, are closely linked to environmental attitudes, as demonstrated by prior research. This study aims to predict middle school students' recycling behaviors using machine learning algorithms. A…

This article introduces a novel method that integrates collaborative filtering into the naive Bayes model to enhance predicting student academic performance. The combined approach leverages collaborative user behavior analysis and probabilistic modeling, showing promising results in improved prediction precision. Collaborative Filtering explores…

The innovation capability largely determines the initiative for future development of a region. Higher school is the main position for training innovative talents. Accurate and comprehensive assessment of innovation cultivation capability is an important basis of higher schools for continuous improvement. Thus, this paper focuses on assessing…