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.

As programming skills are increasingly required world-wide and across disciplines, many students use online platforms that provide automatic feedback through a Programming Online Judge (POJ) mechanism. POJs are very popular e-learning tools, boasting large collections of programming problems. Despite their many benefits, students often struggle when solving problems not compatible with their prior knowledge. One important cause of this is that usually statements of problems are not classified according to programming topics (paradigms, data structures, etc.) and, hence, students waste time and effort in trying to solve exercises that are not tailored to their level and needs. Thus, to support students, we propose a new, "front-heavy" pipeline method to predict topics of POJ problems, using Bidirectional Encoder Representations from Transformers (BERT) for contextual text augmentation over the problem statements and further allowing for (lighter-weight) classical machine learning for classification. Our model outperformed all current state-of-the art, with an F1-score of 86% using stratified 10 fold cross-validation in a classically challenging multi-classification problem with seven categories. As a proof of concept, we conducted an experiment to show how our predictive model can be used as a human-AI hybrid complement for POJ, where learners would use AI-based recommendations to find the most appropriate problems.

This paper presents the friendship and selfishness forwarding (FSF) algorithm for Delay Tolerant Networks. This novel solution is based on two social characteristics of nodes: friendship and selfishness. When a contact opportunity arises, FSF analyzes two aspects to make message forwarding decisions: first, FSF assesses the friendship strength among the pair of nodes, then it determines the individual selfishness of the relay node. Unlike other algorithms proposed in the DTN literature, we use a machine learning algorithm to quantify the friendship strength among pairs of nodes in the network. The individual selfishness of the relay node is determined by using a model based on the current level of its resources. The primary goal is to take into account the case where, despite a strong friendship with the message destination, the relay node does not accept processing the message to save its resources. By using trace-driven simulations we show that the FSF algorithm achieves better results in terms of delivery rate, average cost and efficiency.

People spend a lot of time navigating on the web. When moving from one computer device to another, it would be useful to have access to the navigation data produced in the previous web session. In this article, a synchronization service of navigation data, called Browserver, is presented. It is responsible for keeping user navigation information (tabs, history, forms, cookies, etc.) so that it can be recovered from any device connected to the Internet. Finally, Browserver performance is compared to a similar service, according to hardware and network consumption metrics.

Owing to packet forwarding throughout different paths and asynchronous behavior of TCP/IP networks, voice packets of a voice flow may arrive at their destination with varying delay. This impairment, known as jitter, is smoothed at the receiver side by applying dejitter buffer algorithms, which store some packets for an amount of time before playout. This paper presents a manager algorithm of dejitter buffers, which monitors network loss, delay and jitter conditions and change the dejitter buffer algorithm used by the receiver's softphone according to the parameters conjuncture in order to improve speech quality.

Header compression techniques can improve the speech quality of VoIP applications. However, its deployment over wireless mesh environments may not offer much improvement, since the wireless and multi-hop characteristics of such a network impose a high packet loss rate. In this work, we propose a new approach for header compression, using packet aggregation to cooperate with the header compression scheme. Simulation results show that the new cooperative approach can decrease significantly the packet loss, impacting positively on the speech quality.

In this paper we propose a VoIP study case to evaluate the impact of different radio propagation models on performance parameters of application layer in simulation studies of Wireless Mesh Backbones. For each presented model, we carried out experiments taking into account the credibility aspects suggested by simulation theory of stochastic processes. Results showed that, for the same scenario, using different propagation models, voice speech quality can switch from acceptable to unacceptable.

Voice over IP is a major trend in applications for wireless networks, but even so it is not immune to the risks usually related with IP networks. Proposed solutions for VoIP security are already in the market, but these solutions must take into account the real-time constraint of voice service and their mechanisms should address possible attacks and overhead associated with it. One of these solutions is to use IETF IPSec to guarantee confidentiality in order to address security design holes of wireless VoIP networks. This article performs an experimental comparison of the impact of encryption mechanisms on voice speech quality in widely deployed wireless technologies: 802.11 and Bluetooth. Evaluates the upper bound on number of simultaneous VoIP calls which can be placed in a single cell of both networks when security is applied and uses the computational model E-Model to assess quantitatively the quality of service

This article presents a voice quality measurement tool based on the ITU-T E-model. Firstly, the ITU-T and ETSI specifications of E-model are briefly reviewed and some errors found in these documents are pointed. After, a measurement tool based on the corrections is described. VoIP calls through the Brazilian National Education and research network (RNP) backbone were used to verify the tool operation.