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.

This study seeks to identify the challenges many adult Distance Education students face when learning program-ming. It reviews the literature on strategies intended to address these challenges, focusing on approaches that aim to stimulate the motivation of these students. This systematic mapping study aims to (1) identify the main challenges faced by students when learning programming in Distance Education, specifically those that may have a direct influence on their motivation to continue learning, and (2) investigate the specific characteristics of Distance Education environments that have a positive influence on students' motivation in learning programming, also addressing the pedagogical strategies that guided these investigations. To achieve these objectives, two research questions were used: (RQl) What are the main challenges faced by students when learning programming in Distance Education environments? and (RQ2) What specific characteristics of Distance Education environments can positively influence students' motivation in learning program-ming? This systematic mapping study followed the SEGRESS guidelines. The primary studies were obtained from databases by searching for keywords, focusing on papers published between 2018 and 2023. So, 18 papers were selected that highlighted challenges, such as difficulty in understanding and developing specific skills in the field of Programming, challenges related to the infrastructure available to study, social and emotional aspects, difficulties in defining and following learning strategies, and inefficient teaching support. The strategies adopted in these papers point to technological resources that include adaptive technologies, gamification, personalized feedback, tools that allow the adoption of autonomy and learning management strategies, support for collaboration between peers, social feedback and programming practices using remote laboratories, and flexible environments (text or graphics). This study presents students' challenges and highlights the importance of strategies that pro-mote student motivation and positively impact their performance and retention.

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 presents a systematic literature review on the impact of feedback features on students' subsequent learning strategies. Research indicates that providing quality feedback improves learning performance in higher education, namely in computing and engineering education. Framed by the self-regulated learning model, this enhancement results from the interplay of cognitive, metacognitive, motivational, and behavioral actions driven by feedback toward the learning goal. Such a combination of planned and selected actions is the learning strategy decision to direct the accomplishment of learning tasks. Insights of how feedback interventions lead to increased use of effective learning strategies have predominantly relied on qualitative data from self-reports. However, self-reports mainly reflect students' perceptions but cannot accurately capture the dynamic adjustments of learning strategies in the feedback process. With the growing use of learning management systems that can collect various learning analytics data, recent works have attempted to automatically generate personalized feedback based on mapping students' progress against pre-determined rules. Learning strategy alterations as a result of the various forms of feedback can be detected from the trace data. The findings of these studies provide evidence of how various feedback features are associated with the adjustment of learning strategies. This paper presents a systematic literature review that analyzes papers related to shifting learning strategies upon feedback provision to identify features that can trigger students' adoption of more effective learning strategies. The objective is to collect evidence to highlight feedback as more than information but a process to guide the proper use of learning strategies for better learning achievement. Our analysis shows a need for more studies to observe changes in learner actions due to feedback and discusses limitations in current works. With the rapid development of education data mining and deep learning models, the growing knowledge of feedback features can be potentially used with these computational models to generate learning advice to guide strategy changes for achieving better learning outcomes.

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 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.

Learning programming is a multifaceted process shaped by diverse factors, including effectively utilizing self-regulation of learning (SRL) skills. However, students frequently require a certain level of support to effectively engage in SRL, prompting a question regarding the optimal means of providing such assistance. The current literature is limited in identifying which types of support are most effective, and it's also noted that some students do not benefit from its usage. The underlying causes remain underexplored, and a deeper understanding of this phenomenon can offer insights into improving the design of regulatory support. This research is situated within this scope and was guided by the following research questions: RQ1) How does the use of self-regulation of learning support differ among students with high and low-performance levels? RQ2) How do students use and evaluate the self-regulation of learning support? Fifty-four students enrolled in an online introductory programming course participated in this investigation. The findings indicate that higher-performance students made more extensive use of the provided regulatory support, implying the potential utilization of this resource as a learning aid. Simultaneously, it's noted that some students either avoided or utilized this support to a lesser extent, underscoring that the mere provision of resources is insufficient for their effective usage. A qualitative analysis revealed some factors that might have influenced this behavior.

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.

Self-regulation of learning (SRL) is considered a vital skill in education, empowering students to control their learning process across multiple subjects. However, students often lack SRL abilities, which might impact their academic performance. One strategy to mitigate this problem is supporting students to assist their regulatory processes. Despite positive results in other subjects, how this support influences programming learning still needs further evidence. This study aims to contribute to this field by examining how the provision of SRL support influences students' programming performance and code development. The following research questions were established: RQ1) How does the programming performance of the group that received SRL support compare to that of the control group without support? RQ2) Do students receiving SRL support commit fewer programming errors than the control group without support? RQ3) Do students receiving SRL support maintain a more consistent learning routine than the control group without support? RQ4) What is the relationship between SRL abilities, programming performance, error commitment, and learning routine? To achieve this objective, a single-blind randomized experiment was conducted, and students were grouped into two experimental conditions, one receiving SRL support and a control group without support. Forty-nine students enrolled in an online introductory programming course participated in this study. The findings revealed that students who received SRL support achieved superior course grades, highlighting the benefit of this type of intervention. At the same time, the influence of SRL support and SRL abilities on programming error commitment is still unclear. Possibilities for future research to expand knowledge about the influence of SRL on code develonment are discussed.

This Innovative Practice Full Paper describes the use of a monitoring tool for teachers to assess students' performance and progress, improving their ability to make decisions and interventions in programming classes, in the context of the current COVID-19 pandemic. Considering that learning programming is not an easy task as well as the social, cultural, and educational diversity of the student population, we believe it is crucial that teachers have at their disposal up-to-date information on the learners' progress, skills, and difficulties to properly support them in gaining and maintaining a positive learning momentum. Previously, we suggested the use of this monitoring tool to supplement the information teachers can obtain through direct observation in traditional face-to-face classes. However, in the context of the current pandemic, its use takes on new significance since, in most cases, face-to-face instruction has been suppressed, demanding new strategies to collect assessment data. In this paper, we introduce some features of the system and explain how they can help teachers to support their students. Key findings from a field trial, in which the system was used for about a month and a half to support more than 60 students of an introductory programming course, are also presented. Due to COVID-19 lockdowns and stay-at-home orders, classes took place almost exclusively online via Zoom. During this time, the usage of the system enabled the teacher to monitor student progress regardless of when or where they were working. In post-experiment interviews, the teacher who participated in this study stated that using the system was vital to deal with the challenges that distance learning entails. Similarly, student feedback was also very positive. Several students mentioned that they felt more confident while using the system, knowing that the teacher was able to track their work and give them personalized feedback whenever necessary.

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.

This Research Full Paper presents a collection of evidences about the association between self-regulation variables and programming learning. Researchers have been investigating this thematic and despite the apparent benefits, it is necessary to summarize the published evidence and provide a new collection of them, which this study seeks to contribute. An observational investigation was performed in two countries with fifty-nine students, who had their SRL and programming learning metrics collected and correlated. Moreover, a systematic literature review was also conducted, and the existing evidence summarized. The results support an association between metacognitive and motivational regulatory strategies with programming learning but do not support for cognitive strategies. Our analysis shows a need for more studies to provide a solid body of knowledge on this thematic.

Programming students often show a reduced level of self-regulation of learning (SRL), which is known to impact their academic success. Interventions can be designed to assist in stimulating SRL, but achieving it is a challenging process for educational stakeholders. In this work, we propose a guideline that aims to support the construction of interventions to foster SRL competence in programming education. The guideline was built upon the collected evidence from a systematic literature review that identified features used to aid effective SRL interventions in the context of programming education. Fifteen studies were analyzed, and the results show the feasibility of several features to support programming learning and SRL development.

One of the reasons for failure in introductory programming subjects may be related to the study methods used by students. The teacher awareness of study attitudes and behaviours may help teachers to provide useful recommendations for their students and also to incorporate them into their strategic pedagogical activities in order to meet students’ study preferences. Therefore, in this study the main objective was to know more about the student’s study methods, namely how, when, how much and with which support materials they study. In this study we used two samples in which the results were slightly different. One of the samples seemed to be more self-regulated in the sense that it seeks more diversified methods and strategies when doubts and difficulties occur and studying more in function of their difficulties. Although it was not possible to obtain correlations in relation to the study methods used and the final results obtained in programming, we could recognize some study attitudes and behaviour trends in students that had better and worse results.

Social dimension plays a crucial role in the learning process. The use of technological resources to stimulate and mediate the interaction between students is known as Computer-supported collaborative learning (CSCL). Despite being widely used in programming education, the summarization of the academic literature about this topic is scarce. To create that body of knowledge, a systematic literature review was performed. The findings provide an understanding of how collaboration is explored in introductory programming, resources used to stimulate them and challenges in the process. Opportunities for future research are discussed, especially related to motivation, self-efficacy and engagement in CSCL, and the exploitation of learning analytics.

A new and emergent field in an educational context is the use of Brain-Computer Interaction (BCI) technology to better understand and promote learning processes. In this context, the idea is to obtain information about the user and deduce his/her mental states (e.g. workload, attention, concentration) through user's electroencephalogram signals (EEG). In future researches, we are interested in understanding the performance of users in tasks involving high cognitive processes such as programming problem solving. The goal would be to find metrics and strategies that are adaptable to each user, looking to increase the success in programming learning. However, this work represents a set of initial works that provides an overview of how brain computer interaction can intersect with issues in the field of education, namely in design and in cognitive attention and concentration processes. The main goal of this study is to analyse several cognitive parameters (Attention, Concentration) of crucial importance for learning, while students do a programming problem solving oriented task activity. For a better characterization of the data, an analysis of the ERD/ERS complex was performed, thus analysing the event synchronization or desynchronization, in order to reflect the activation or inhibition of the cerebral activity during the game and consequently the absorption of information and the capacity of learning. Additionally, five EEG features were extracted, namely the powers of Delta, Theta, Alpha, Beta and Gamma bands, as well as, the variability of these bands' energy.

This Innovative Practice Work in Full Paper describes the use of a tool aimed to support the teacher's decisions and interventions within the scope of programming classes. As computers play an increasingly important role in our lives, the demand for programmers is growing. In addition to the intrinsic difficulties of programming, teachers must also deal with the vast diversity of students found in their classes. Close monitoring by teachers has been identified as a critical factor for student's success. However, it may be difficult for teachers to stay up-to-date at any moment about each of their students' overall progress and capabilities. For this purpose, we have developed a tool that collects snapshots of the students' source code whenever they compile it. The snapshots are processed, and a series of visualizations and aggregated data is made immediately available for teachers. Since teachers can't be permanently looking at the system, we have also developed an automatic notification system, based on cognitive theories of selective attention, which will notify teachers about numerous relevant aspects concerning their students' performance. We present the results of a field experiment, which confirms that the tool indeed allows teachers to have a deeper insight into their students' performance and progress, enabling them to make more grounded interventions.

This Research to Practice Full Paper presents a study on student's motivation towards learning to program. Motivation is a key factor in learning. Hence, stimulating student motivation strategies should be present in any pedagogical approach. This is particularly true in courses where a very active student attitude is fundamental. Introductory programming courses in higher education are a good example, which are known to be difficult for many students. To be successful students need to be motivated, as effort and commitment are necessary to overcome the difficulties many of them experience. In our study we analyzed several motivational aspects separately and then we correlated that information with the marks students obtained in introductory programming courses. We used two questionnaires. The Course Interest Survey (CIS) and the Instructional Materials Motivation Survey (IMMS). We could find some interesting correlations that confirm the importance of different motivational aspects to learning. We found other issues that demand more investigation, in order to create the best context to promote student motivation and learning.

Several studies showed that teacher's support is essential to the students learning process. Often it is difficult for teachers to follow all their student's evolution and make timely interventions when needed. Often, in the same class, there are students with substantially different performance levels, and many times a teacher intervention is cructial to help lower performing students. To help the teacher identify these students, we propose the use of CodeInsights, a tool able to capture autonomously and unobtrusively real-time information about the students' performance based on snapshots of their code. The information available can be used by the teachers to support the adoption of the necessary measures to address each student needs or difficulties in a more grounded manner. We present the system and some results of a field test involving students from an introductory course on PHP programming.

The difficulties faced by students and teachers in learning and teaching introductory programming has been a research issue over the years. Demotivation is common in many novice programming students, who are not able to cope with the natural difficulties associated to programming learning. It is up to the teacher to find strategies to help students and keep them motivated during the course. The objective of our research was to know more about the pedagogical and motivational strategies used by teachers in the author's institutions to promote programming student's motivation and learning. Some time ago we interviewed a few Portuguese teachers with diversified experiences in programming teaching. Recently we had the opportunity to do the same kind of research with Professors from Macao (China). The problems identified, the teachers' motivation to teach programming, the educational and motivational strategies and the student-teacher relationship were specifically addressed. This paper describes the research done, stressing the Macanese teacher's views and relating them with the views previously expressed by their Portuguese colleagues.

Unemployment has been a major concern in recent years. This is particular true for young people and in the case of Portugal for youngsters with a Higher Education degree. In this paper we describe the program “Acertar o Rumo”, a two years program to reconvert unemployed graduates in Engineering and Exact and Natural Science in experienced Java programmers. Particularly, we focus on the Programming courses of the program, where the two main programming paradigms, the procedural and the object-oriented, as well as principles and technologies for developing enterprise systems were taught. We describe the main methodologies and approaches followed and report the very positive results obtained with the first edition of the program till now. We finish the paper by proposing some recommendations and improvements for further editions.

The difficulties faced by students and teachers in learning and teaching introductory programming has been a research issue over the years. Programming learning demands effort and motivation. However, demotivation is common in many novice-programming students, who are not able to cope with the natural difficulties associated to programming learning. Since many students lack intrinsic motivation it is up to the teacher to find strategies to help students and keep them motivated during the course. The objective of our research was to know more about the pedagogical and motivational strategies used by teachers in the author's institutions to promote programming student's motivation. So we interviewed teachers with diversified experiences in teaching the first programming course.

Learning computer programming is not simple for many students. They have to develop several complex skills to be able to understand programs and, more important, to create programs that solve problems. This means it is important that students have a high motivation level, so that they engage in that work and do not get frustrated with the natural errors they will make in this process. Digital games are often used in educational contexts to attract and retain students. In literature and on the web, we can find many games related strategies that aim to support learning in introductory computer programming courses. One of these strategies is the using games approach: asking students to play games that include problems that must be solved in order to progress. This paper presents a list of 40 games classified by type and highlights the skills and topics supported by them. We hope this work helps teachers to choose games as part of their teaching strategies, as alternative or complementary exercises to their students.

Initial programming learning is known to be difficult to many students. To improve this situation it is necessary to support students learning effectively. This means that learning activities should be adapted to each student learning pace and specific needs. This is difficult considering that classes often have a large number of students. The definition of individual learning paths adaptable according to the student performance might help to improve the situation. To support the definition of learning paths it is useful to have a large set of exercises, organized according to a taxonomy that includes different dimensions and parameters relevant to the choice of appropriate exercises at any moment. To present this taxonomy is the main objective of this paper.

Building written feedback, pedagogically sound, standardized and flexible enough to accommodate students who may be in different stages and learning curves is a complex and laborious task. In this paper, we describe a multinational case study involving diverse types of pedagogical feedback provided to Portuguese and Brazilian novice programming students. Programming errors, especially logical ones, can be used as a consistent metric for assessing learning. The research done looks for an innovative form to define content of several types of feedback. It also aims to create an efficient method for the discovery and mapping of students' logical programming errors. The results obtained so far using this approach are presented and analyzed.

High failure rates are common in many programming courses worldwide. Many causes for the learning problems have already been identified and different solutions have been proposed. However, the situation remains mostly unchanged. So, new pedagogical approaches are necessary, looking to create learning contexts that motivate students, increase their involvement with course activities, and maximize their learning possibilities. In this paper we present the changes made in the structure of a non-majors introductory programming course, and discuss the results obtained. We also present the results obtained in the first implementation of the new course structure.

Learning difficulties in introductory programming courses are well known to teachers and students. Although several types of causes for those difficulties can be pointed out, in this work we focused on student related issues, namely their study methods and attitudes towards learning to program. We found a strong correlation between students' results and their personal perceptions of competence during the course. This result raises the need for teachers to consider this issue when devising the pedagogical approaches to use in introductory programming courses.

Learning computer programming is known to be difficult for many students. In the context of a wider study, which aims to design a pedagogical strategy for introductory programming, we decided to use some less conventional activities. This strategy was applied in the last three academic years with some success. In this paper we will discuss a component that proved very relevant, the biweekly reflections we asked the students to write during the course. They were expected to reflect on the course, the different activities, their learning, the difficulties felt, and any other aspect they considered important. The analysis of the texts written gave the teacher several hints that lead to some successful individual interventions. From a research point of view this analysis gave also some important clues to the refinement of our pedagogical strategy.

Many students feel difficulties in programming learning and, consequently, the failure rates in introductory programming courses are frequently high. Some students do not have the necessary commitment, while others do not show a natural ability for programming, which means they need more time to master the necessary knowledge and develop appropriate problem solving skills. Often, class pace is very intensive and many students are simply not able to follow all contents and activities. In order to help students with their studies it would be helpful if they could review classes so that they could clear doubts or take additional notes. Students that miss class for some reason could also take advantage of such possibility. In this paper we present a new system that we developed to record and replay lessons. The system is able to record the voice of the teacher, but also the main visual points of a class: the computer in focus in the class, the blackboard and the classroom environment. This system gives students the opportunity to review any part of a particular class, clear doubts that may have remained, review examples or exercises that may contribute to their programming learning.

Novice students often find it difficult to learn programming. Consequently this leads to high failure and high dropout rates. We must ask ourselves if these problems are caused by specific programming issues or if there are other courses that struggle with the same problem. This paper presents a study that involved Computer Science freshmen. The study tried to evaluate the connection of academic results between the first programming course and other first year courses. Subsequently the idea was to encourage teachers to dialogue about teaching and learning strategies concerning courses where the students' results are related, in order to share problems and solutions. Students were also submitted to a learning style test, in order to identify the influence of its dimensions in the obtained results.

SimulCol is an asynchronous collaborative continuous modeling tool for generic systems allowing learners spread in anytime and anywhere to develop models in group. Model building can be abridged if done in group. However, many times, students are not physically together. Still they need to work collectively. That is what SimulCol provides, along with teacher tools to follow studentspsila work.

In this paper we describe the main approach used in our institutions' basic programming courses. The difficulties we experienced in these courses led us to develop tools that, in our view, help students. We created and use animation-based algorithm tools, program simulation tools and problem solving collaboration support tools. The integration of these tools, plus the addition of some other new approaches, gives us a powerful environment that provides a higher level of support to our students.

In 1971 Dijkstra noted that as a teacher of programming he 'feels akin to a teacher of composition at a conservatory. He does not teach his pupils how to compose a particular symphony, he must help his pupils to find their own style and must explain to them what is implied by this' [1]. In similar vein, Don Knuth suggests that 'computer programming is an art, because it applies accumulated knowledge to the world, because it requires skill and ingenuity, and especially because it produces objects of beauty' [2].Traditionally, most Computer Science programs offer an introductory programming methodology course (CS1). In recent years, many institutions have subjected this course to major changes. One common alteration has been a move from a procedural paradigm to an Object Oriented (OO) paradigm. In many cases, this is manifested as a change to programming in Java. Emerging from this transition is the apparent anomaly that many students fail to understand OOP concepts, especially when required to use them in problem solving.Our panel represents researchers from four different countries who have all encountered such problems with a CS1 course. In this light, the panel focuses on CS1 difficulties and aims to address solutions to the 'Java problem'. Although we bring our own insights to the considered issues, we aim to engage the panel audience in discussing the nature of the problem and the propriety of the proposed solutions.