Teaching practicum plays a critical role in teacher education for student-teachers in enabling th... more Teaching practicum plays a critical role in teacher education for student-teachers in enabling them to adapt educational theories to real practices in authentic contexts. Pre-service and in-service teachers also acknowledge practicum as an opportunity to interact with students in classrooms with supervision from mentors and university supervisors, different from other taught courses in teacher training programmes. This study investigates two groups of student-teachers in Hong Kong and Macao, grounded in a pilot study, to identify the critical features of practicum practices during the COVID-19 pandemic. At this time, normal school operations were affected in the two special administrative regions in the Greater Bay Area, and this study examines eight student-teachers' practicum experiences during this period. The practical implementation of the practicum, including lesson planning and supervision from universities and schools, was studied. Post-practicum interviews with the student-teachers were also conducted to explore their perspectives on the distance learning aspect of the practicum. Other corresponding forms of data, consisting of teaching plans and video-recorded lessons, were collected to triangulate the analysis. The major findings of the study include the identification of various pedagogical foci in the practicum, the different assessment tasks being developed, consequences of parental involvement, evaluations of the exercise, as well as challenges faced by student-teachers during the practicum. Implications include innovation of practicum as well as reconsideration of some of the critical elements of the teaching practicum offered by current teacher training programmes.
2024 IEEE Frontiers in Education Conference (FIE), 2024
This Research-to-Practice full paper presents an exploratory study investigating the impact of us... more 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.
2024 IEEE Frontiers in Education Conference (FIE), 2024
This Research Full Paper focuses on predicting learning performance using machine learning algori... more 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.
2024 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), 2024
This study compared several machine learning algorithms to predict student programming learning p... more This study compared several machine learning algorithms to predict student programming learning performance in an online learning environment. It used Explainable Machine Learning (EML) techniques to enhance interpretability. A range of algorithms, including Random Forest, Extra Trees, CatBoost, XGBoost, Naive Bayes, and K-Nearest Neighbors (KNN), were compared, with Extra Trees delivering the best results. Distinct from other EDM research mainly focused on predictive efficiency, we contributed by using the EML technique of SHapley Additive Explanations (SHAP), rooted in the Game Theory framework, to enhance model interpretability at both global and individual levels. At the global level, summary plots showed overall feature impacts, bar plots quantified the average effect of each feature, and dependence plots highlighted specific relationships. At the individual level, force plots identified critical features for individual predictions, decision plots traced the cumulative impact of features from the base value to the final output, and waterfall plots provided a breakdown of predictions. This study contributes to EDM by offering accurate predictive models and detailed interpretability, helping educational stakeholders make data-informed decisions to improve student outcomes.
2024 IEEE Global Engineering Education Conference (EDUCON), 2024
In the digital age, programming education has become increasingly important, even in primary scho... more 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.
2023 IEEE Frontiers in Education Conference (FIE), 2023
This Research-to-Practice full paper presents an explorative study investigating the effects of f... more 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 subdimensions of the ARCS model, such as attention, relevance, confidence, and satisfaction.
Programming education has become an essential skill for the digital generation. However, it prese... more 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.
2023 11th International Conference on Information and Education Technology (ICIET), 2023
With the rapid development of science and technology over the world, programming education has sh... more With the rapid development of science and technology over the world, programming education has shown as a trend in decades. Programming courses have been offered in primary education to promote students' computational thinking skills. However, teaching and learning programming in primary schools involve different knowledge including complex mathematical concepts and programming syntax. Also, traditional teaching methods may not effectively cater for students' individual needs and learning trajectories. Thus, learning difficulties are common among students. Teachers keep examining innovation on programming education to improve their teaching. This study investigated used a pretest-posttest method with two groups of grade five students in Macao, to compare the influence of two pedagogical strategies, flipped classroom and traditional teaching, in students' learning achievements. The major findings of the study showed that the experimental group in flipped classroom had significantly higher learning achievements than the control group in the programming course. Moreover, the students in the flipped classroom showed a significant effective mastery of more complex programming concepts, such as conditions and loops.
Teaching practicum plays a critical role in teacher education for student-teachers in enabling th... more Teaching practicum plays a critical role in teacher education for student-teachers in enabling them to adapt educational theories to real practices in authentic contexts. Pre-service and in-service teachers also acknowledge practicum as an opportunity to interact with students in classrooms with supervision from mentors and university supervisors, different from other taught courses in teacher training programmes. This study investigates two groups of student-teachers in Hong Kong and Macao, grounded in a pilot study, to identify the critical features of practicum practices during the COVID-19 pandemic. At this time, normal school operations were affected in the two special administrative regions in the Greater Bay Area, and this study examines eight student-teachers' practicum experiences during this period. The practical implementation of the practicum, including lesson planning and supervision from universities and schools, was studied. Post-practicum interviews with the student-teachers were also conducted to explore their perspectives on the distance learning aspect of the practicum. Other corresponding forms of data, consisting of teaching plans and video-recorded lessons, were collected to triangulate the analysis. The major findings of the study include the identification of various pedagogical foci in the practicum, the different assessment tasks being developed, consequences of parental involvement, evaluations of the exercise, as well as challenges faced by student-teachers during the practicum. Implications include innovation of practicum as well as reconsideration of some of the critical elements of the teaching practicum offered by current teacher training programmes.
2024 IEEE Frontiers in Education Conference (FIE), 2024
This Research-to-Practice full paper presents an exploratory study investigating the impact of us... more 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.
2024 IEEE Frontiers in Education Conference (FIE), 2024
This Research Full Paper focuses on predicting learning performance using machine learning algori... more 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.
2024 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE), 2024
This study compared several machine learning algorithms to predict student programming learning p... more This study compared several machine learning algorithms to predict student programming learning performance in an online learning environment. It used Explainable Machine Learning (EML) techniques to enhance interpretability. A range of algorithms, including Random Forest, Extra Trees, CatBoost, XGBoost, Naive Bayes, and K-Nearest Neighbors (KNN), were compared, with Extra Trees delivering the best results. Distinct from other EDM research mainly focused on predictive efficiency, we contributed by using the EML technique of SHapley Additive Explanations (SHAP), rooted in the Game Theory framework, to enhance model interpretability at both global and individual levels. At the global level, summary plots showed overall feature impacts, bar plots quantified the average effect of each feature, and dependence plots highlighted specific relationships. At the individual level, force plots identified critical features for individual predictions, decision plots traced the cumulative impact of features from the base value to the final output, and waterfall plots provided a breakdown of predictions. This study contributes to EDM by offering accurate predictive models and detailed interpretability, helping educational stakeholders make data-informed decisions to improve student outcomes.
2024 IEEE Global Engineering Education Conference (EDUCON), 2024
In the digital age, programming education has become increasingly important, even in primary scho... more 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.
2023 IEEE Frontiers in Education Conference (FIE), 2023
This Research-to-Practice full paper presents an explorative study investigating the effects of f... more 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 subdimensions of the ARCS model, such as attention, relevance, confidence, and satisfaction.
Programming education has become an essential skill for the digital generation. However, it prese... more 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.
2023 11th International Conference on Information and Education Technology (ICIET), 2023
With the rapid development of science and technology over the world, programming education has sh... more With the rapid development of science and technology over the world, programming education has shown as a trend in decades. Programming courses have been offered in primary education to promote students' computational thinking skills. However, teaching and learning programming in primary schools involve different knowledge including complex mathematical concepts and programming syntax. Also, traditional teaching methods may not effectively cater for students' individual needs and learning trajectories. Thus, learning difficulties are common among students. Teachers keep examining innovation on programming education to improve their teaching. This study investigated used a pretest-posttest method with two groups of grade five students in Macao, to compare the influence of two pedagogical strategies, flipped classroom and traditional teaching, in students' learning achievements. The major findings of the study showed that the experimental group in flipped classroom had significantly higher learning achievements than the control group in the programming course. Moreover, the students in the flipped classroom showed a significant effective mastery of more complex programming concepts, such as conditions and loops.
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Papers by Wan Chong Choi