This research-to-practice paper describes a study of motivational factors in introductory programming learning. Learning to program is challenging, as students need to develop multiple skills and competencies. Motivation drives students to confront complex challenges, persevere despite obstacles, and continuously strive for improvement. However, motivation is a complex interplay of internal and external factors. Analyzing the factors that can stimulate student motivation is essential for educators when planning and implementing learning activities and contexts. Therefore, we conducted a study to a) identify factors influencing the motivation of programming students and b) analyze the evolution of students' motivation during the different phases of a programming course. The study involved 137 students enrolled in a Programming I course at a Macao higher education institution. It used the motivation section of the Motivated Strategies for Learning Questionnaire (MSLQ), which comprises 31 statements grouped into six components (Intrinsic Goal Orientation (IGO), Extrinsic Goal Orientation (EGO), Value of Activity (VAT), Control of Learning (COL), Learning Self-Efficacy (LSE), and Test Anxiety (TAX)). These components can be organized into three factors (Value Components, Expectancy Components, and Affective Components). The students were asked to answer the questionnaire in three different moments: the initial phase of the course (3–4 weeks after its start), after knowing the results of the mid-term exam, and at the end of the course. For the analysis, only the answers of the 92 students who completed the questionnaire in the three phases were considered. We applied Principal Component Analysis (PCA) to identify the evolution of the different components and factors during the course. Based on this analysis, it is possible to highlight significant variations between the various phases of the study, especially concerning the factor of Value Components. In Phase 1, participants expressed a more positive perception of the importance of the course contents, as evidenced by the VAT component. In Phase 2, a change in focus was noticed, with the prioritization of obtaining a good grade, as reflected by the EGO component. Finally, in Phase 3, there was again a reorientation of value components, with students demonstrating appreciation for the course topic, as indicated again by the VAT component. Given these results, it is possible to conclude that changes occurred in the different phases of the study, suggesting an evolutionary dynamic in the interests of participants over time.

This Research Full Paper presents a systematic review on knowledge tracing of learning programming based on student performance data in exercises. Programming has become an essential skill to solve realworld problems in modern engineering disciplines. However, when students start to learn how to program, they face a lot of challenges in acquiring various programming knowledge and skills. While it is beneficial to customise learning material to fit individual learning progress, the widely different learning pace of students in an introductory programming course has made it impractical for teachers to track the knowledge acquisition of individual. Hence, many recent works take a data-driven approach to model students' learning progress based on the performance data in programming-related exercises, which include the submitted program codes and answers to closed-ended programming exercises. By analyzing these performance data, a system can evaluate the students' knowledge level of various concepts and skills in programming. This paper performs a systematic review and reports key information about recent works on programming knowledge tracing based on student performance data. An overview of the different choices of knowledge representation, domain knowledge model, performance measure and knowledge tracing algorithms is provided. The nature and granularity of knowledge components and the relationships between them are compared across the reviewed works. The different choice of programming knowledge representation leads to varied methods to assess knowledge levels from empirical performance data in programming-related exercises. Two broad categories of works are identified. The first is to overlay a student model on the domain knowledge model, and the student knowledge levels are updated in distinct time steps. The second trains temporal knowledge tracing models to predict students' future performance based on their performance in previous exercises. In addition, this review discusses the distinct challenges in knowledge tracing in programming education. It also points out limitations in current works and opportunities to improve knowledge tracing in learning programming.