This study aimed to investigate the role of systemic intelligence factors in explaining cognitive flexibility and cognitive holding power among university students using measures of the aforementioned phenomena. A random sample of (519) students participated in this research, and it was found that factors relating to systemic intelligence could…

Although previous research has found trait emotional intelligence (TEI) to be a moderate predictor of bullying behaviors in adolescents, this work has limited generalizability. The current study is the first to use a multidimensional approach to both TEI and bullying behaviors when looking at their relationship in high school students. The study…

Given the complex nature of teachers' professional development (PD) processes, it is crucial to examine how various factors surrounding teachers are associated with the evaluation of their PD experience. By applying a machine-learning technique, least absolute shrinkage and selection operator (LASSO), we were able to include numerous factors in an…

This study explored factors influencing ChatGPT adoption among higher education students in five Nordic countries (Sweden, Finland, Denmark, Norway, and Iceland) and the USA. The unified theory of acceptance and use of technology 2 (UTAUT2) framework was employed and extended to incorporate personal innovativeness. Data was collected from 586…

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,…

The problem was that Historically Black Colleges/Universities (HBCU) stakeholders must observe ways that student support professionals increases organizational effectiveness, for there is a need to understand the correlation of emotional intelligence (EI) and job satisfaction. The purpose of this quantitative, causal-comparative study was to…

Much has been made of the apparent capacity for creativity of generative AI. However, as research expands the knowledge base regarding the capabilities and performance of this technology, the prevailing view is shifting away from "AI is creative" and towards a more balanced model of Human-AI co-creativity. Nevertheless, even this…

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…

This study explores the role of Artificial Intelligence Generated Content (AIGC) in undergraduates' learning and research, and its increasing significance in higher education. Against this backdrop, understanding college students' attitudes, behaviors, and intentions towards AIGC is beneficial for better guiding their learning under the support of…