Hsi-Min Chen

Using version control tools is an indispensable skill for engineers in the software industry. This study introduces a gamification approach together with a serious game called the Git Education Game (GEG) to teach Git concepts and usage, intending to improve students’ motivation and learning performance compared to traditional lectures. An experiment was designed with two classes of the same course to compare the effect of GEG. A post-test was designed to verify whether the game could help students achieve better learning outcomes and higher motivation. The results show that our approach had a positive effect on students’ motivation, so the experimental group had a higher pass rate than the control group for most items in the post-test. Based on this study’s results, we emphasize the impact of interactive learning environments in software engineering education.

In Computer Science education, the ever-changing nature of professional environments demands that students be actively prepared for future challenges. Project-based learning (PBL) has become increasingly significant, enhancing students' abilities to manage and execute projects effectively, and has attracted considerable attention in academic research. Importantly, there is a growing advocacy from the industry for PBL's incorporation into the academic syllabus. Agile management practices have become essential with the software industry particularly susceptible to rapid environmental and demand changes. Integrating agile methodologies into software design education has encouraged students to embrace complex problem-solving approaches. Educators are increasingly blending agile methodologies with PBL in software development courses to foster a level of flexibility that aligns with the shifting patterns of the workforce. Comprehensive software development education now requires students to immerse themselves in agile philosophies, necessitating introductory training and consistent, structured mentorship. Platforms like GitHub have become instrumental for task organization, while automated tools support unbiased grading practices. This study leverages data from GitHub to evaluate individual contributions, covering task organization and management skills. Such an approach seeks to improve the caliber of Computer Science education and better equip students for the dynamic nature of future work environments.

Autonomous vehicle systems rely heavily upon depth estimation, which facilitates the improvement of precision and stability in automated decision-making systems. Noteworthily, the technique of monocular depth estimation is critical for one of these feasible implementations. In the area of segmentation of medical images, UNet is a well-known encoder–decoder structure. Moreover, several studies have proven its further potential for monocular depth estimation. Similarly, based on UNet, we aim to propose a novel model of monocular depth estimation, which is constructed from the benefits of classical UNet and residual learning mechanisms and named URNet. Particularly, we employ the KITTI dataset in conjunction with the Eigen split strategy to determine the efficacy of our model. Compared with other studies, our URNet is significantly better, on the basis of higher the precision and lower error rate. Hence, it can deal properly with the depth estimation issue for autonomous driving systems.

Project-based learning is among the most common learning approaches aimed at conveying professional standards and best practices to students in programming courses. However, team projects commonly impose problems related to responsibility sharing, such as low effort or inequality in contributions. This paper presents a collaborative programming assessment system featuring a code quality assessment function with specific metrics to measure individual contributions. Student engagement data is used to detect nonconformities in collaboration using a learning analytical approach. Latent profile analysis was used to detect four theoretical team profiles differentiated by team effort (2 levels) and within-team collaboration (2 levels). We demonstrated the efficacy of assessing code to evaluate team dynamics and student behaviour, wherein efforts to resolve coding style failures can be used as a proxy by which to estimate the taskwork awareness of team members. Submission data from 146 students in 41 web-programming projects revealed four behavioural patterns that could potentially hinder the effective functioning of programming teams: free-riding, social loafing, the bystander effect, and lone wolves. We also demonstrated the applicability of automated programming assessment systems to the monitoring of learning progress, thereby facilitating timely interventions to correct difficulties at the team level.

Automated programming assessment systems are useful tools to track the learning progress of students automatically and thereby reduce the workload of educators. They can also be used to gain insights into how students learn, making it easier to formulate strategies aimed at enhancing learning performance. Rather than functional code which is always inspected, code quality remains an essential aspect to which not many educators consider when designing an automated programming assessment system. In this study, we applied data mining techniques to analyze the results of an automated assessment system to reveal unexpressed patterns in code quality improvement that are predictive of final achievements in the course. Cluster analysis is first utilized to categorize students according to their learning behavior and outcomes. Cluster profile analysis is then leveraged to highlight actionable factors that could affect their final grades. Finally, the same factors are employed to construct a classification model by which to make early predictions of the students’ final results. Our empirical results demonstrate the efficacy of the proposed scheme in providing valuable insights into the learning behaviors of students in novice programming courses, especially in code quality assurance, which could be used to enhance programming performance at the university level.

In this paper, we propose an intelligent item searching framework for smart handheld devices through mobile cloud computing based on the Internet of Things (IoT) architecture. The proposed framework enables mobile users to drag a website/software icon over interested/unknown items for learning details, comparing prices, purchasing goods, etc. Our framework explores the IoT architecture, mobile cloud computing, and gesture-based interacting interfaces to alleviate the complexity and difficulty of device manipulation encountered by mobile users. In addition, we integrate our framework with an item caching mechanism that can reduce the total searching time as much as possible for popular items. Furthermore, we implement an Android-based item searching system to verify the feasibility and superiority of the proposed framework. Experimental results show that our implemented system outperforms existing methods and can significantly reduce the total consumption time of mobile item searching.