Abstracts:This study investigates the impact of images and visual schemes on students’ performance during mathematical problem solving (MPS). Forty primary education preservice teachers participated in this study and were divided into two groups. They were presented with a questionnaire consisting of 18 different mathematical problems, half of them accompanied by images or visual schemes, and the rest without any kind of visual aid. The group determined whether the participant would receive problems with visual aids before or after the ones without. Statistical tests were conducted to analyze the data, revealing that while overall performance did not significantly differ between the two groups, the presence of visual aids significantly improved performance in certain problem categories, such as percentages, reversal error problems, and fractions. Eye-tracking data were collected during problem solving, and the gaze patterns of 13 participants were analyzed, which shed light on students’ problem-solving strategies, including counting, multiplication, and the detection of the reversal error phenomenon. Additionally, this eye-tracking data were used to develop predictive models based on neural networks (NNs) to infer success or failure in MPS tasks.

Abstracts:Contribution: This study explores how physical computing (PhyC) activities supported by mobile devices can enhance learning, motivation, and computational thinking (CT) in engineering students. By adopting a learning-by-doing approach, tablets and smartphones were transformed into active learning devices for algorithm creation and experimentation through an educational app developed for block-based programming and hardware handling.Background: Typically, learning through mobile learning (m-learning) devices have been adopted in e-learning settings, especially for content delivery. Conversely, this study utilizes mobile devices for active learning, enabling students to engage in programming and PhyC activities within an introductory engineering course.Intended Outcomes: The methodology sought to enhance four areas for the students: academic performance, motivation, collaboration, and CT through mobile devices and PhyC activities. The 76 undergraduate engineering students participated in the methodology from 2022 to 2024.Application Design: The methodology comprised active learning tasks developed by the students and aligned with the educational outcomes expected in the course. These tasks integrated handling of the app mentioned with hardware devices, i.e., sensors and basic robotics, along with the curriculum of an introductory electronics course. Data from 76 students were collected through academic grades, a questionnaire on a Likert scale, and semi-structured interviews. Data were analyzed utilizing a mixed research approach.Findings: The educational outcomes suggest that the students improved their understanding of PhyC, programming, and electronics concepts in the course, with a large Wilcoxon effect size ( $r \geq 0.5$ ) for most of the courses. At the qualitative level, five crucial components were identified in the m-learning intervention, namely, learning development, affective engagement, teacher presence, technology affordances, and PhyC interactivity and debugging, which influenced the students’ performance and engagement.

Abstracts:The escalating prevalence of cybersecurity threats, coupled with the growing significance of blockchain technology, necessitates advanced practical training in undergraduate computer science and engineering curricula. However, traditional educational approaches often fall short in providing realistic, scalable, and collaborative learning environments. Addressing this gap, this study proposes an innovative educational framework integrating hands-on cybersecurity and blockchain experiences using large-scale cyber infrastructure (CI), specifically leveraging the FABRIC testbed. Key elements of the proposed methodology include open-ended problem-solving, evidence-based learning, flexible and collaborative laboratory experiments, and structured reflective practices. Evaluation results from comprehensive student surveys indicate that this approach significantly enhances students’ practical skills, theoretical understanding, and adaptability. The study demonstrates that incorporating real-world CI resources within STEM education not only prepares students to navigate complex professional challenges but also fosters deeper engagement and effective learning in rapidly evolving fields.

Abstracts:Contribution: This study demonstrates that a modular constructivist approach to Internet of Things (IoT) education significantly enhances student engagement, knowledge retention, and practical application of concepts. The distinctive aspect of this approach lies in its adaptability to various teaching modalities, including traditional classrooms and massive open online courses (MOOCs), while effectively covering the comprehensive IoT ecosystem.Background: The rapid proliferation of IoT technologies across various sectors has created a pressing demand for a skilled workforce adept in IoT principles. However, existing educational models often provide a limited perspective on IoT, underscoring the necessity for a holistic educational framework that can be applied across diverse educational programs.Intended Outcomes: The primary outcomes of this approach include improved student knowledge and understanding of IoT concepts, increased engagement in the learning process, enhanced retention rates, and the ability to apply learned concepts in practical scenarios.Application Design: The course employs a modular constructivist instructional approach, which allows for the integration of modern learning theories and constructivist principles. This design facilitates adaptability to various teaching modalities and encourages active learning through hands-on experiences in each module, covering critical aspects of the IoT ecosystem.Findings: The findings show significant improvements in student knowledge, with self-assessment data showing increases between 41.5% and 89.6% across all topic areas. Performance metrics and qualitative feedback consistently indicate that the course effectively enhances understanding of IoT concepts, demonstrating its versatility and effectiveness in different learning environments.

Abstracts:Problem-solving in programming requires not only cognitive but also affective engagement from students. Despite many novice programmers struggling with foundational programming concepts and problem-solving using these concepts, few studies explore how students’ emotional states impact their learning in introductory programming. This article investigates the association among affective states, programming concepts, and performance in novice programmers from a rural Brazilian school. Specifically, we explore how different affective states relate to success rates across core programming concepts. Unlike prior research often relying on IDE logs or broad academic indicators, this study employs a sensor-free, self-reported approach to assess students’ emotions in real time during programming tasks. Utilizing causal inference methods, we analyze both the direct and indirect effects of affective states on programming performance. Through two exploratory studies, our findings reveal that positive emotions such as enjoyment, motivation, and engagement are significantly associated with higher success rates, while negative states, like boredom, anxiety, and frustration, correlate with lower outcomes. These results enhance the understanding of the relationship among affective states, performance, and concepts in introductory programming tasks. Furthermore, this study offers valuable insights for improving educational practices and tools in programming education by emphasizing the critical role of addressing emotional dimensions in teaching and learning.

Abstracts:Contribution: This article describes and interprets the quantitative results from a survey meant to evaluate the mentorship capabilities of engineering teaassistants. Background: teaching assistants (TAs) are a common facet at several higher learning institutions. In this position, they perform multiple duties to improve their students’ learning experiences, refining their own teaching abilities in the process. Given both their prevalence and impact, various researchers have been interested in studying these assistants’ capabilities and factors affecting their efficacy. Some of the most prominent lenses utilized for these investigations include self-efficacy, pedagogical behavior, and classroom structure. Research Questions: Building upon that previous literature, this article aims to answer the following questions: 1) How prepared is the current group of engineering TAs in the United States to facilitate improved student comprehension in class? 2) What potential avenues of growth can colleges in the United States follow to increase the overall efficacy of engineering TAs? Methodology: A survey with thirty-five 5-point Likert-based items was distributed to engineering program chairs at several universities across the United States. There was a total of 400 respondents for this survey once incomplete or insincere entries were excluded. Afterwards, exploratory factor analysis (EFA) was utilized to determine both the factorability of this data and its reliability. Regression analysis was also used to study the impact of certain factors across the survey’s scales. Findings: Three of the four initial questionnaire scales—self-efficacy, pedagogical practice, and content knowledge—emerged from EFA with high internal consistency reliability scores. Regression analysis found significance in student interaction, training, gender, and native language.

Abstracts:The recent advances in large-language models (LLMs) have started shifting the way computing-related students write codes. LLM-based AI assistants, such as ChatGPT and Copilot, are now increasingly adopted to produce functional code by computing-related students. Although studies have shown that these AI assistants can improve coding efficiency, they also raise security challenges, especially when users lack a security mindset. Given the fact that AI assistants are increasingly integrated into computing education, this article performed an empirical study to explore the impact of a security mindset on the use of AI assistants for computing-related students in their coding and development. Our three-stage study showed that a significant portion of computing-related students currently lack security awareness toward the use of AI assistants. In addition, their usage of AI assistants has a high chance of producing insecure programs in programming tasks that frequently appear in computing curricula. Meanwhile, the results of our study indicate that a security mindset can greatly contribute to students’ usage of AI assistants in terms of code security. Our study further discussed and evaluated strategies to improve students’ secure usage of AI assistants in computing education by integrating a security mindset.