Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning techniques can help in this direction by providing models able to detect at-risk students earlier. Therefore, lecturers, tutors or staff can pedagogically try to mitigate this problem. To early predict at-risk students in introductory programming courses, we present a comparative study aiming to find the best combination of datasets (set of variables) and classification algorithms. The data collected from Moodle was used to generate 13 distinct datasets based on different aspects of student interactions (cognitive presence, social presence and teaching presence) inside the virtual environment. Results show there are no statistically significant difference among models generated from the different datasets and that the counts of interactions together with derived attributes are sufficient for the task. The performances of the models varied for each semester, with the best of them able to detect students at-risk in the first week of the course with AUC ROC from 0.7 to 0.9. Moreover, the use of SMOTE to balance the datasets did not improve the performance of the models.

This paper describes the application of Data Science and Educational Data Mining techniques to data from 4529 students, seeking to identify behavior patterns and generate early predictive models at the Universidad de la República del Uruguay. The paper describes the use of data from different sources (a Virtual Learning Environment, survey, and academic system) to generate predictive models and discover the most impactful variables linked to student success. The combination of different data sources demonstrated a high predictive power, achieving prediction rates with outstanding discrimination at the fourth week of a course. The analysis showed that students with more interactions inside the Virtual Learning Environment tended to have more success in their disciplines. The results also revealed some relevant attributes that influenced the students’ success, such as the number of subjects the student was enrolled in, the students’ mother’s education, and the students’ neighborhood. From the results emerged some institutional policies, such as the allocation of computational resources for the Virtual Learning Environment infrastructure and its widespread use, the development of tools for following the trajectory of students, and the detection of students at-risk of failure. The construction of an interdisciplinary exchange bridge between sociology, education, and data science is also a significant contribution to the academic community that may help in constructing university educational policies.

Communicating in social and public environments are considered professional skills that can strongly influence career development. Therefore, it is important to proper train and evaluate students in this kind of abilities so that they can better interact in their professional relationships, during the resolution of problems, negotiations and conflict management. This is a complex problem as it involves corporal analysis and the assessment of aspects that until recently were almost impossible to quantitatively measure. Nowadays, a number of new technologies and sensors have being developed for the capture of different kinds of contextual and personal information, but these technologies were not yet fully integrated inside learning settings. In this context, this paper presents a framework to facilitate the analysis and detection of patterns of students in oral presentations. Four steps are proposed for the given framework: Data collection, Statistical Analysis, Clustering, and Sequential Pattern Mining. Data Collection step is responsible for the collection of students interactions during presentations and the arrangement of data for further analysis. Statistical Analysis provides a general understanding of the data collected by showing the differences and similarities of the presentations along the semester. The Clustering stage segments students into groups according to well-defined attributes helping to observe different corporal patterns of the students. Finally, Sequential Pattern Mining step complements the previous stages allowing the identification of sequential patterns of postures in the different groups. The framework was tested in a case study with data collected from 222 freshman students of Computer Engineering (CE) course at three different times during two different years. The analysis made it possible to segment the presenters into three distinct groups according to their corporal postures. The statistical analysis helped to assess how the postures of the students evolved throughout each year. The sequential pattern mining provided a complementary perspective for data evaluation and helped to observe the most frequent postural sequences of the students. Results show the framework could be used as a guidance to provide students automated feedback throughout their presentations and can serve as background information for future comparisons of students presentations from different undergraduate courses.

The significant number of fails and dropouts in computing undergraduate courses, especially in mathematics and programming, remains a challenge. Although the motivation can be directly related to the success of the student, it has only been addressed by a few studies. The purpose in this paper is to assess the impact of factors prior to university on the performance and motivation of undergraduate freshmen students in Software Engineering. Questionnaires were applied to students, and their grades on introduction to programming (CS101) and discrete mathematics (MAT101) were analyzed. Using statistical analysis of variance and correlation, we identified that motivation was impacted only by "knowledge and previous experience in programming". Performance was impacted by "previous scholar knowledge", "way to access university", "age" and "taste/knowledge of the area". We identified as well, unlike other studies, that the initial motivation had no impact on students' performance.

One of the requirements of the Industry 4.0 is the concern about the stability of its product development. Applying emerging technologies to improve product quality control is a significant step in achieving balance in the production process. The acoustic emission is a highly efficient technique used in industry to test products to detect structural failures. This type of method is well known as non-destructive testing. We assert that quality control of the products can be significantly improved by combining the acoustic emission with machine learning techniques. To achieve our claim, we built a dataset from ceramic tiles acoustic emission, and we model two different machine learning techniques, such as Support Vector Machine, with five distinct kernel functions, and k-Nearest Neighbors, to classify the quality of ceramic tiles. In this work, we use several ceramic tiles as the objects to be tested. To extract the features of the ceramic tiles sound, we have built a prototype to hit the ceramic tile to record the sounds. As the results shown, we obtained a high precision classification of the control quality of the ceramic tile, ranged from 95% to 99.9%, depending on the parameters set and the algorithm chosen.

This paper presents a systematic mapping study on factors that affect motivation in computing undergraduate students. Although there are several initiatives to improve teaching and learning computing, the panorama is still negative. Motivation and engagement are usually singled out as reasons for students' retention or success, but we found a lack of standards in studies on motivation of computing students, and we have not found literature reviews about this subject. We identified twenty-nine (29) motivation factors presented in 32 selected studies. We divided the factors into five categories: student, professor/teaching, program/course/content, environment/university, and social aspects. The most mentioned factor was related to the teaching-learning strategies (37.5%), professor-student interaction (31.3%) and prior knowledge (31.3%). We also have identified that there is no convergence on literature about the identification and measurement of motivation and engagement. Another interesting aspect was that, despite the literature saying that intrinsic motivation has a greater impact on the student's results, many studies are being carried out considering extrinsic aspects. This shows the concern of the computing community regarding these aspects. However, there are only a few studies with controlled studies and evaluations on the impact of the factors in the motivation of students.