Bayesian network is a probabilistic graphical model that has wide applications in various domains due to its peculiarity of knowledge representation and reasoning under uncertainty. This research aims at Bayesian network structure learning and how the learned model can be used for reasoning. Learning the structure of Bayesian network from data is…

Properly predicting students' academic performance is crucial for elevating educational outcomes in various disciplines. Through precise performance prediction, schools can quickly pinpoint students facing challenges and provide customized educational materials suited to their specific learning needs. The reliance on teachers' experience to…

Computer Science (CS) Unplugged activities are designed to engage students with CS concepts. It is an active learning approach combining physical interaction with visual representation. This research article investigates the impact of CS Unplugged on students' understanding of the bubble sort algorithm. Algorithm visualization, traditionally…

In the current higher education landscape, universities are facing expanding requirements beyond teaching and research. Evaluation methods must evolve accordingly to prevent universities from facing development dilemmas. Current mainstream evaluation methods primarily emphasize the research domain, often failing to holistically capture a…

Choosing an undergraduate major is an important decision that impacts academic and career outcomes. In this work, we investigate augmenting personalized human advising for major selection using a large language model (LLM), GPT-4. Through a three-phase survey, we compare GPT suggestions and responses for undeclared first- and second-year students…

Artificial intelligence (AI) literacy is critical to shaping students' academic experiences and future opportunities inhigher education. This study examines AI literacy among university students, examining variables such as gender, frequency of use of AI applications, completion of AI-related courses, and field of study. The research involved 664…

Digitalization needs to be storied for me to become critical of and creative with its functionings. In today's algorithmic condition, knowledge production and learning are complex posthuman entanglements: the human as materially affective has become fabricated hybrids of organism and machine. Storying is seen as simultaneous processes of…

Purpose: This study aims to investigate factors that could predict the continued usage of e-learning systems, such as the learning management systems (LMS) at a Technical University in Ghana using machine learning algorithms. Design/methodology/approach: The proposed model for this study adopted a unified theory of acceptance and use of technology…

Interactive learning environments can generate massive learning behavior data and the support of learning behavior big data can ensure the completeness of data analysis and robustness of relationship verification. In this study, learning behaviors are divided into training set and testing set, BP neural network and recurrent Elman network are…

There is a broad impetus across policy and institutional domains to expand public engagement and involvement with emerging technology research and innovation. Yet innovative theory, methods, and practices to critically explore algorithmic system controversies and democratic possibilities are still in nascent form. In this paper, we bring together…

Current learning platforms generally have problems such as fragmented knowledge, redundant information, and chaotic learning routes, which cannot meet learners' autonomous learning requirements. This paper designs a learning path recommendation system based on knowledge graphs by using the characteristics of knowledge graphs to structurally…

The learning processes have been significantly impacted by technology. Numerous learners have adopted technology-based learning systems as the preferred form of learning. It is then necessary to identify the learning styles of learners to deliver appropriate resources, engage them, increase their motivation, and enhance their satisfaction and…

Within scientific debate on post-digital and education, we present a position paper to describe a research project aimed at the design of a predictive model for students' low achievements in mathematics in Italy. The model is based on the INVALSI data set, an Italian large-scale assessment test, and we use decision trees as the classification…

The various empirical models built for enrollment management, operations, and program evaluation purposes may have lost their predictive power as a result of the recent collective impact of COVID restrictions, widespread social upheaval, and the shift in educational preferences. This statistical artifact is known as model drifting, data-shift,…

The objective of this research is based on human-centered AI in education to develop a personalized hybrid course recommendation system (PHCRS) to assist students with course selection decisions from different departments. The system integrates three recommendation methods, item-based, user-based and content-based filtering, and then optimizes the…