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Programming online judges (POJs) have been increasingly used in CS1 classes, as they allow students to practice and get quick feedback. For instructors, it is a useful tool for creating assignments and exams. However, selecting problems in POJs is time consuming. First, problems are generally not organized based on topics covered in the CS1 syllabus. Second, assessing whether problems require similar effort to be completed and map onto the same topic is a subjective and expert-dependent task. The difficulty increases if the instructor must create variations of these assessments, e.g., to avoid plagiarism. Thus, here, we research how to support CS1 instructors in the task of selecting problems, to compose one-size-fits-all or personalized assignments/exams. Our solution is to propose a novel intelligent recommender system, based on a fine-grained data-driven analysis of the students' effort on solving problems in the integrated development environment of a POJ system, and automatic detection of topics for CS1 problems, based on problem descriptions. Data collected from 2714 students are processed to support, via our artificial intelligence (AI) method recommendations, the instructors' decision-making process. We evaluated our method against the state of the art in a simple blind experiment with CS1 instructors (N = 35). Results show that our recommendations are 88% accurate, surpassing our baseline (p = 0.05). Finally, our work paves the way for novel POJ smart learning environments, wherein instructors define learning tasks (assignments/exams) supported by AI.

The proliferation of mobile devices and mobile applications has changed the way people communicate, work, and study, namely in higher education contexts. However, users have very different application usage habits from each other, and the data collecting instruments that typically support the studies usually rely on the perception that users report on their use. Hence, the reported user perception may not match the actual usage. Based on an exploratory approach, this article aims to analyze the use of mobile applications by students at the University of Aveiro. The study has a mixed approach that contemplates non-participant observation, the application of a survey, and log analysis. The triangulation between log records, the obtained data from the non-participant observation, and the surveys allows for a more objective assessment of the user profiles and will help to identify potential discrepancies between self-perception and actual use of mobile devices. The main conclusions show that the usage is quite significant and there is a huge tendency for the use of social media during classes. The most used applications are social networks which had a more intensive use than reported in the surveys. The methodology is also an important output of this study.

Students face several difficulties in introductory programming courses (CS1), often leading to high dropout rates, student demotivation, and lack of interest. The literature has indicated that the adequate use of gamification might improve learning in several domains, including CS1. However, the understanding of which (and how) factors influence gamification's success, especially for CS1 education, is lacking. Thus, there is a clear need to shed light on pre-determinants of gamification's impact. To tackle this gap, we investigate how user and contextual factors influence gamification's effect on CS1 students through a quasi-experimental retrospective study (N=399), based on a between-subject design (conditions: gamified or non-gamified) in terms of final grade (academic achievement) and the number of programming assignments completed in an educational system (i.e., how much they practiced). Then, we evaluate whether and how user and contextual characteristics (e.g., age, gender, major, programming experience, working situation, internet access, and computer access/sharing) moderate that effect. Our findings indicate that gamification amplified to some extent the impact of practicing. Overall, students practicing in the gamified version presented higher academic achievement than those practicing the same amount in the non-gamified version. Intriguingly, those in the gamified version that practiced much more extensively than the average showed lower academic achievements than those who practiced comparable amounts in the non-gamified version. Furthermore, our results reveal gender as the only statistically significant moderator of gamification's effect: in our data, it was positive for females but non-significant for males. These findings suggest which (and how) personal and contextual factors moderate gamification's effects, indicate the need to further understand and examine context's role, and show that gamification must be cautiously designed to prevent students from playing instead of learning.

This paper was developed within the scope of a PhD thesis that intends to characterize the use of mobile applications by the students of the University of Aveiro during class time. The main purpose of this paper is to present the results of an initial pilot study that aimed to fine-tune data collection methods in order to gather data that reflected the practices of the use of mobile applications by students in a higher education institution during classes. In this paper we present the context of the pilot, its technological settings, the analysed cases and the discussion and conclusions carried out to gather mobile applications usage data logs from students of an undergraduate degree on Communication Technologies. Our study gathered data from 77 participants, taking theoretical classes in the Department of Communication and Arts at the University of Aveiro. The research was based on the Grounded Theory method approach aiming to analyse the logs from the access points of the University. With the collected data, a profile of the use of mobile devices during classes was drawn. The preliminary findings suggest that the use of apps during the theoretical classes of the Department of Communication and Art is quite high and that the most used apps are Social networks like Facebook and Instagram. During this pilot the accesses during theoretical classes corresponded to approximately 11,177 accesses per student. We also concluded that the students agree that accessing applications can distract them during these classes and that they have a misperception about their use of online applications during classes, as the usage time is, in fact, more intensive than what participants reported.

Metabolic syndrome (MS) increases the risk of cardiovascular disease and affects children and adolescents. Butyrylcholinesterase (BChE) is an enzyme associated with obesity. The aim of this study was to investigate the effects of different physical training protocols on MS indicators and their relationship with BChE activity. This randomized clinical trial included 80 adolescents randomly assigned to 4 groups (CG: Control Group; ATG: Aerobic Training Group; STG: Strength Training Group; and CTG: Concurrent Training Group). The EFC, lipid profile, glycemia, waist circumference, and blood pressure were analyzed. With the exception of the CG, all the groups underwent training protocols for 12 consecutive weeks, 4 times a week, as follows: (ATG: 75% of heart rate on an electric treadmill; STG: 85% of 1 maximum repetition; CTG: 20 min of aerobic training at the same intensity as the ATG, and 20 min of resistance training in the same way as the STG). The training reduced MS-related biomarkers, such as the lipid profile, glycemia, waist circumference, and blood pressure. STG reduced BChE activity. The training methods led to improvements in the majority of the MS indicators. In addition, aerobic training significantly reduced BChE activity after a 12-week training protocol. The results suggest that different types of exercise can benefit MS. [ABSTRACT FROM AUTHOR]

Tools for automatic grading programming assignments, also known as Online Judges, have been widely used to support computer science (CS) courses. Nevertheless, few studies have used these tools to acquire and analyse interaction data to better understand the students' performance and behaviours, often due to data availability or inadequate granularity. To address this problem, we propose an Online Judge called CodeBench, which allows for fine-grained data collection of student interactions, at the level of, eg, keystrokes, number of submissions, and grades. We deployed CodeBench for 3 years (2016-18) and collected data from 2058 students from 16 introductory computer science (CS1) courses, on which we have carried out fine-grained learning analytics, towards early detection of effective/ineffective behaviours regarding learning CS concepts. Results extract clear behavioural classes of CS1 students, significantly differentiated both semantically and statistically, enabling us to better explain how student behaviours during programming have influenced learning outcomes. Finally, we also identify behaviours that can guide novice students to improve their learning performance, which can be used for interventions. We believe this work is a step forward towards enhancing Online Judges and helping teachers and students improve their CS1 teaching/learning practices.