Educational Data Mining (EDM) supports early detection of learning difficulties by predicting student performance. However, machine learning models often operate as black boxes. Explainable Artificial Intelligence (XAI) helps to explain why black-box models produce specific predictions. This paper systematically reviews the past five years of research on XAI applications for interpreting student performance prediction in Computer Science and STEM education. We found that behavioral and academic performance data were the most commonly used features, with the main prediction goals focused on course failure risk or grades. This study also examined the application areas of XAI, revealing that the most common uses were global feature importance analysis, individual prediction explanations, and supporting interventions and decision-making. Moreover, we found that SHapley Additive exPlanations (SHAP) were the most frequently utilized XAI technique, predominantly applied at the global level, with limited use at the individual level. Furthermore, a research gap was identified in utilizing XAI to support course improvements, customize visualizations, and generate personalized recommendations. Addressing this gap could enable educators to provide personalized, data-driven guidance to better support individual students.

In the digital age, programming education has become increasingly important, even in primary schools. However, introducing programming at such an early stage presents unique challenges, given the need for students to grasp mathematical concepts, abstract thinking, and the intricacies of programming syntax. Educational Data Mining (EDM) offers a potential contribution by predicting learning performance, facilitating the optimization of the learning processes, and providing real-time guidance. A notable gap in the current literature about EDM in programming education is its predominant emphasis on the university level. Our research objectives were to identify features influencing primary school students' programming capabilities. A more comprehensive dataset was introduced, incorporating psychometric data and highlighting features such as learning motivation and attitude, computational thinking data, and other potentially influential variables, which set our study apart from previous studies. We found that the strongest predictor was academic performance in Information Technology, followed by psychometric data on students' learning attitudes and motivation. Computational thinking also emerged as a significant feature in predicting programming performance. It's worth highlighting that involvement in extra-curricular activities, like Olympic Mathematics training, showed a significant association, underscoring the importance of mathematical logic and reasoning in programming. This is further bolstered by the evident correlation with academic performance in Mathematics, confirming its pivotal role in shaping programming abilities. Interestingly, the correlation of academic performance in Chinese is also significant, indicating that the language medium of instruction can notably influence success.

This Research Full paper explores teachers' perceptions of how VEX robotics programming courses influence secondary education students' programming learning motivation. Educational robotics is increasingly recognized as an effective teaching tool for enhancing students' interest in and proficiency in STEM (Science, Technology, Engineering, and Math). VEX Robotics is one of the most well-known, practical learning tools that allow students to showcase their programming abilities and creativity. However, little research has been done on how VEX robotics impact teaching practices and students' learning motivation from teachers' perspectives. While VEX robotics is widely utilized in educational environments for teaching robotics and programming concepts, more detailed research is needed to understand how its implementation influences student learning motivation, and overall teaching effectiveness. This study was conducted by interviewing teachers, who were experienced in using VEX in this pedagogical context at an international secondary school in Macao. In particular, the study focused on teachers' opinions about the impact of VEX robotics on students' learning motivation. Qualitative data was obtained through semi-structured interviews with robotic teachers, providing deeper insights into students' motivation and participation in the robotics environment. The study employed the ARCS model as a theoretical framework. The teachers' interview outputs were examined considering the model's four dimensions: attention, relevance, confidence, and satisfaction. The findings highlighted that teachers valued the use of VEX robotics and believed that it increased students' motivation in all the dimensions of the ARCS model.

This Research-to-Practice full paper presents an exploratory study investigating the impact of using a Bee-Bot educational robot simulator to enhance learning sequencing concepts and student motivation among Macao primary school students. Sequencing in computational thinking (CT) is understanding and applying the logical order of steps in problem-solving processes. We introduced a Bee-Bot computer simulator for children to learn sequencing. Our study adopted a pretest-posttest method involving 35 grade two students. The Computational Thinking Test for Beginners (BCTt) was used to assess CT abilities, and the Instructional Materials Motivation Survey (IMMS) was utilized to measure learning motivation. We found a significant improvement in sequencing ability and more advanced CT concepts (loops and conditions) and a significant correlation between those concepts. Departing from the existing literature, we delved deeper into how Bee-Bot's influence on sequencing extended to more advanced CT concepts. Moreover, considering the ARCS motivation model, this study examined how Bee-Bot affects learning motivation at the primary education level. After the intervention, the findings revealed that the students showed significantly higher learning motivation, meaning that the different learning activities using the Bee-Bot simulator positively influenced various sub-dimensions of the ARCS model: attention, relevance, confidence, and satisfaction. The correlation between the IMMS scores and the BCTt outcomes further suggested that enhanced motivation positively correlated with better CT abilities.

This Research Full Paper focuses on predicting learning performance using machine learning algorithms and interpreting the results using Explainable Machine Learning (EML) techniques. The study compared a comprehensive set of machine learning algorithms, including Logistic Regression, Decision Trees, AdaBoost, XGBoost, SVM, and KNN. The performance of these algorithms in predicting students' final grades in a course was accessed using various evaluation metrics. Our study used feature selection to identify the most relevant predictors to enhance predictive accuracy, implemented the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance, and performed hyperparameter optimization to find the most effective model settings. This comprehensive approach improved the predictive accuracy of our models over previous studies. Additionally, the importance of early prediction in identifying at-risk students was explored, with models demonstrating promising accuracy at the first checkpoint of the course. Departing from traditional machine learning research that often focused on model performance, our study integrated the EML technique of Shapley Additive exPlanations (SHAP), which is grounded on the theoretical framework of Game Theory, to facilitate the interpretation of the predictive outcomes. This approach offered an explanatory perspective on the key factors influencing model decisions. By contributing to the predictability and interpretability of student performance, this research enriched the field of Educational Data Mining (EDM) and enhanced the understanding of student learning trajectories.

This Research-to-Practice full paper presents an explorative study investigating the effects of flipped learning on the learning motivation of primary school students enrolled in a block-based programming course. Technology development has increased the importance of information technology-related competencies for the new generation. Programming skills are essential in various industries nowadays. Thus, programming education has become necessary to cultivate students' relevant abilities to meet the rapid pace of development. Programming learning encourages students to think logically and systematically, solve problems effectively, and develop computational thinking skills. However, learning programming is challenging for many students for different reasons, such as its inherent complexity, inadequate study methods, and pedagogical approaches unsuited to promote programming learning. Traditional teaching methods are often impersonalized and only suitable for some learning styles present in class. Moreover, the challenges encountered by the students as novice programmers are attributed to their low learning motivation. To learn to program, students must comprehend different syntactic conventions, complex instructions, and logical operators and actively engage in practical learning activities, often facing difficulties. This may reduce their learning motivation leading to failure and dropout. To accommodate different learning styles and increase students' motivation, this study employed the ARCS motivation model to design various innovative activities in a flipped classroom setting to increase students' motivation and improve teachers' teaching effectiveness. The study utilized a pretest-posttest method with two groups of grade five students to compare the effectiveness of teaching and learning between the flipped and traditional classroom students. The study's findings revealed that the flipped classroom experimental group showed significantly higher learning motivation than the students in the control group. Moreover, it was found that different flipped classroom activities, including the use of gamification, flipped videos, self-study, self-questioning, self-assessment, split programming tasks, group cooperation, and demonstration activities, had a positive influence on various sub-dimensions of the ARCS model, such as attention, relevance, confidence, and satisfaction.

Programming education has become an essential skill for the digital generation. However, it presents a unique set of challenges that can be difficult for beginners. Educational data mining (EDM) has been increasingly utilized in programming education to enhance learning outcomes and understand students' learning behavior. By collecting and analyzing data from various sources, such as students' learning activities, interactions with learning resources, and assessment results, EDM can provide valuable insights into students' learning performance and potential areas for improvement. This paper presents a systematic literature review of recent literature (last five years) and reports on state of the art and trends in using EDM for student performance prediction in programming courses. It provides a comprehensive analysis of the input data used in previous work, exploring the different types of datasets used and the features that affect student performance. In addition, it addresses the predictive objectives and target variables for performance prediction in programming courses. On the other hand, it explores the most common prediction approaches, data pre-processing procedures, cross-validation methods, and evaluation metrics used to describe the performance of prediction algorithms. In addition, we discuss the limitations and challenges of various prediction approaches and provide valuable insights and directions for future research.