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关键词： computer science   extracurricular activity   fundamentals of programming   games   higher education  

摘要： Higher Education programming courses usually present high levels of student failure and drop-out rates. Given this context, the use of educational video games is proposed as a strategy to increase the students' motivation and engagement, thus helping diminish such rates. However, there is a lack of empirical studies examining such effects, especially when they are proposed outside the formal curriculum (i.e., extracurricular) and when the students are enrolled in different Higher Education bachelors. This paper presents a cross-sectional study following a between-subjects design with 315 students (168 assigned to the experimental condition, 147 to the control condition) enrolled in one of the following bachelors: B.S. in Computer Science (where programming is a core subject), and B.S. in Statistics (where it is not). The study spanned two consecutive academic years. The outcomes were evaluated through a pre-/post-test schema and comparison of final course results to measure the effect on learning (objective assessment) and a survey to get the students' perceptions (subjective assessment). In addition, the level of participation was analyzed and compared between bachelors, considering the optional nature of the activity. Results show statistically significant differences in learning outcomes between the students in the experimental condition and those in the control group, without clear differences between Bachelors (the results are positive for both). In the subjective assessment and participation, the results are also positive, but, in this case, statistically significant differences between bachelors have been observed. These positive outcomes suggest its potential applicability to other Higher Education and Engineering courses.

关键词： Software development management   Software engineering   Internet   Software   Delays   Graphical models   Employment   Computer science   Systematic literature review   Surveys   Collaboration   coordination   global software development  

摘要： In this retrospective, we reflect on the motivation, findings, and impact of our 2003 paper. We see evidence of influence in increasing the visibility of the topic of global software development (GSD), the use of version data for software engineering research, and using a mixed methods empirical approach. These topics have all continued to grow in importance in software engineering, as revealed by citation counts, but radical changes in business and technical environments have substantially changed how the challenges of GSD manifest. We see evidence of this in the paper's "life cycle," as citation counts grew for its first decade, and declined for its second. We consider this a marker of progress, and a very good thing for the field.

关键词： Software   Data collection   Software engineering   Software reliability   Reliability   Software testing   Software algorithms   Software systems   Genetics   Computer science   Automated test data generation   search-based test generation   fitness function   dynamic data flow analysis   alternating variable method   search-based software engineering  

摘要： Generating test data is a critical component of software testing, essential for ensuring the proper functioning of software systems. Automated test data generation, in particular, can greatly improve the efficiency and accuracy of the testing process. In 1990, the paper "Automated Software Test Data Generation," published in IEEE Transactions on Software Engineering (Korel, 1990), introduced a novel approach to automated test data generation, focusing on actual program execution, fitness function minimization methods, and dynamic data flow analysis. This paper discusses the impact of the 1990 IEEE TSE publication (Korel, 1990) on automated test data generation and software engineering.

关键词： Vectors   Transformers   Recommender systems   Feature extraction   Knowledge graphs   Oral communication   Accuracy   Computer science   Training   Timing   Conversational recommender systems   dialog generation   entity focus   latent variables   recommendation  

摘要： Conversational recommendation is one system that can extract the user's preferences and recommend suitable items in a similar way to human-like responses. Existing methods often use the feature extraction combined with the Transformer model to extract user preferences and make recommendations. However, these methods have two limitations. First, they do not consider the order in which entities appear, thus affecting the extraction of user preferences. Second, the generated responses lack diversity that affects the users' experience to the system. To this end, we propose a conversational recommendation model with User Entity focus and Multi-Granularity latent variable enhancement (UEMG). In UEMG, we design a novel neural network that utilizes Bi-GRU to capture the appearing orders of entities in dialogues, and leverages Transformer to capture the global dependencies of entities, and then combines them to extract user preferences. For the second issue, to improve the diversity of dialogue generation, we propose a multi-granularity latent variable mechanism, which can extract more entities from the context information and the knowledge graphs, respectively. We conducted extensive experiments on publicly available dialogue generation datasets. Experimental results demonstrate that compared to current state-of-the-art methods, UEMG achieves 9.7% improvements in recommendation performance and 23% improvements in dialogue generation.

关键词： control process   biodiesel   optimization   sensorless technique  

摘要： This paper demonstrates that biodiesel production processes can be optimized through implementing a controller based on fuzzy logic and neural networks. The system dynamics are identified utilizing convolutional neural networks, enabling tests of the reactor temperature response under different control law proposals. In addition, a sensorless technique using a convolutional neural network to replace the sensor/transmitter signal in case of failure is implemented. Two optimization functions are proposed utilizing a metaheuristic algorithm based on differential evolution, where the aim is to minimize the use of cooling for the control of the reactor temperature. Finally, the control system proposals are compared, and the results show that a neuro-fuzzy controller without optimization restrictions generated unviable ITAE (1.9597x107) and TVU (22.3993) performance metrics, while the restriction proposed in this work managed to minimize these metrics, improving both the ITAE (3.3928x106) and TVU (17.9132). These results show that combining the sensorless technique and our optimization method for the cooling stage enables energy saving in the temperature control processes required for biodiesel production.

关键词： Machine learning   Arts   Computer science  

摘要： Generative artificial-intelligence illustrations can be helpful, but fall short as scientific records.

关键词： Online learning   Learning engagement   Influencing factors   Computer science major  

摘要： Digital technology is reshaping the landscape of higher education, especially in the field of computer science. As digital platforms become central to the learning process, understanding student engagement in these continuously evolving environments is increasingly vital. This study explores the current state of online learning among undergraduate computer science students, investigates factors influencing their engagement, and proposes strategies to enhance online education. The research framework is grounded in the TPACK model, behaviorist learning theory, learning engagement theory, and situated cognition learning theory, encompassing student characteristics, multidimensional online learning engagement, and key influencing factors. Data on participants' basic attributes, levels of engagement, and the major determinants of these engagement levels were collected via a questionnaire survey. Analyses using SPSS 25.0-employing t-tests, ANOVA, and Pearson correlation-revealed significant trends and relationships. Findings show a notable positive correlation between the duration of online learning and overall engagement, whereas gender, home location, and academic major exerted relatively limited influence. Subjective intention, attitude, and motivation emerged as crucial determinants, and interactions with instructors and Peers-reinforced by teaching approaches and feedback-played an essential role in fostering emotional involvement. Building on these insights, the study recommends initiatives to strengthen self-motivation, nurture meaningful online interactions, enhance technical support systems, and reinforce mechanisms for assessing learning outcomes. This work provides empirical evidence for a deeper understanding of online education while indicating directions for ongoing improvement.

关键词： Adversarial machine learning  

摘要： When executed well, project-based learning (PBL) engages students’ intrinsic motivation, encourages students to learn far beyond a course’s limited curriculum, and prepares students to think critically and maturely about the skills and tools at their disposal. However, educators experience mixed results when using PBL in their classrooms: some students thrive with minimal guidance and others flounder. Early evaluation of project proposals could help educators determine which students need more support, yet evaluating project proposals and student aptitude is time-consuming and difficult to scale. In this work, we design, implement, and conduct an initial user study (n = 36) for a software system that collects project proposals and aptitude information to support educators in determining whether a student is ready to engage with PBL. We find that (1) users perceived the system as helpful for writing project proposals and identifying tools and technologies to learn more about, (2) educator ratings indicate that users with less technical experience in the project topic tend to write lower-quality project proposals, and (3) GPT-4o’s ratings show agreement with educator ratings. While the prospect of using LLMs to rate the quality of students’ project proposals is promising, its long-term effectiveness strongly hinges on future efforts at characterizing indicators that reliably predict students’ success and motivation to learn. © 2025, CC BY.