Technology has contributed so much to the development and innovation of humankind. Artificial Intelligence (AI) is an off-shoot of such. This article explored the influence of AI anxiety on AI self-efficacy among college students. The investigators used a cross-sectional research design for 695 purposively chosen college students in one higher…

Psychologists are emphasizing the importance of predictive conclusions. Machine learning methods, such as supervised neural networks, have been used in psychological studies as they naturally fit prediction tasks. However, we are concerned about whether neural networks fitted with random datasets (i.e., datasets where there is no relationship…

Data are an essential factor in the fourth industrial revolution, demanding engineers and scientists to leverage and analyze their potential for significantly improving the efficiency of industrial processes and their control systems. In classical industrial process control systems, the models are constructed using linear data-driven approaches,…

Random forests are a nonparametric machine learning method, which is currently gaining popularity in the behavioral sciences. Despite random forests' potential advantages over more conventional statistical methods, a remaining question is how reliably informative predictor variables can be identified by means of random forests. The present study…

Learner behaviours often provide critical clues about learners' cognitive processes. However, the capacity of human intelligence to comprehend and intervene in learners' cognitive processes is often constrained by the subjective nature of human evaluation and the challenges of maintaining consistency and scalability. The recent widespread AI…

To address three issues identified in previous research this study proposes a clustering-based MOOC dropout identification method and an early prediction model based on deep learning. The MOOC learning behavior of self-paced students was analyzed, and two well-known MOOC datasets were used for analysis and validation. The findings are as follows:…

In this paper, we examine whether early warning signals from accounting courses (such as early engagement and early formative performance) are predictive of first-year progression outcomes, and whether this data is more predictive than personal data (such as gender and prior achievement). Using a machine learning approach, results from a sample of…

In learning analytics and in education at large, AI explanations are always computed from aggregate data of all the students to offer the "average" picture. Whereas the average may work for most students, it does not reflect or capture the individual differences or the variability among students. Therefore, instance-level…

Early detection of autism spectrum condition is crucial for children to maximally benefit from early intervention. The study examined a machine learning model predicting the increased likelihood for autism from wellness records from 0 to 24 months. The study included 591,989 non-autistic and 12,846 autistic children. A gradient boosting model with…

Artificial intelligence in higher education is becoming more prevalent as it promises improvements and acceleration of administrative processes concerning student support, aiming for increasing student success and graduation rates. For instance, Academic Performance Prediction (APP) provides individual feedback and serves as the foundation for…

Background: Life satisfaction is a key component of students' subjective well-being due to its impact on academic achievement and lifelong health. Although previous studies have investigated life satisfaction through different lenses, few of them employed machine learning (ML) approaches. Objective: Using ML algorithms, the current study predicts…

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,…

In recent years, there has been a growing emphasis on integrating Artificial Intelligence (AI) applications in educational settings. As a result, it is essential to assess teachers' competencies in Technological, Pedagogical, and Content Knowledge (TPACK) as it pertains to AI and examine the factors that influence these competencies. This study…

The integration of AI-chatbots into higher education offers the potential to enhance learning practices. This research aims to explore the factors influencing AI-chatbots adoption within higher education, with a focus on the moderating roles of technological proficiency and academic discipline. Utilizing a survey-based approach and advanced…

The successful completion of remedial mathematics is widely recognized as a crucial factor for college success. However, there is considerable concern and ongoing debate surrounding the low completion rates observed in remedial mathematics courses across various parts of the world. This study applies explainable artificial intelligence (XAI) tools…