Search Results (597)

Virtual reality offers unique opportunities to personalize learning by adapting instructions to individual learning styles. This study explores the relationships between learning styles, cognitive load, and learning outcomes in a virtual reality environment designed for engineering education. Drawing on Kolb’s experiential learning theory, the research investigates how immersion and flow, in relation to learning styles, influence learning outcomes within the Submarine Simulator, an educational tool for underwater engineering. To enhance instructional design in virtual reality, this study proposes to aggregate existing and validated models, such as Kolb’s framework, to develop new models tailored specifically for learning environments in virtual reality. This research aims to highlight the interplay of these variables in a learning process focused on acquiring knowledge in the Science, Technology, Engineering, and Mathematics fields, specifically hydrodynamics, through designing and operating a simulated submarine model in virtual reality. A cohort of 26 students from MINES Paris—PSL participated in a three-phase testing process to evaluate the effectiveness of original virtual reality software designed to support learning in underwater engineering. The findings enhance our understanding of how learning styles influence learner engagement and performance and how virtual reality environments can be optimized through adaptive instructional design guided by these novel models tailored specifically for such immersive settings. Full article

In the age of digital transformation, where students increasingly rely on technology for learning and communication, concerns arise regarding its potential association with academic outcomes. This study quantifies the relationship between Digital Behaviour (DB) and Academic Behaviour (AB) among engineering undergraduates at Misurata, Al-Asmarya Islamic, and Al-Marqab universities in Libya. DB is conceptualised as a spectrum ranging from excessive, compulsive device use (addictive behaviour) to purposeful academic technology use (digital immersion). Using a descriptive-analytical design, a convenience sample of 300 undergraduate engineering students completed a validated 20-item questionnaire (Cronbach’s α = 0.711–0.899 for subscales). Data were analysed using descriptive statistics, Pearson correlation, simple regression, and analysis of variance (ANOVA) in the Statistical Package for the Social Sciences (SPSS) v23. The analysis identified a weak but statistically significant positive correlation between students’ DB and their AB (r = +0.19, p = 0.002). Notably, AB scores increased in the senior study years, while digital engagement remained consistently high across all years, suggesting an evolving capacity among students to regulate their digital habits. ANOVA results revealed significant differences in AB by year of study, while gender showed no significant overall association. These findings contradict the conventional assumption that heavy digital use uniformly diminishes academic outcomes; instead, in digitally immersed learning environments, strategic DB may coexist with or support academic performance. The study concludes that DB is not inherently detrimental to AB and may provide benefits when managed effectively, especially among more advanced engineering students. It recommends early educational interventions that promote digital self-regulation and the strategic use of technology for academic purposes. Full article

This study addresses the shortcomings of traditional exercise volume assessment methods in dynamic modeling and individual adaptation by proposing a multi-modal data fusion framework based on a spatio-temporal attention-enhanced LSTM neural network for personalized exercise volume optimization in college basketball courses. By integrating physiological signals (heart rate), kinematic parameters (triaxial acceleration, step count), and environmental data collected from smart wearable devices, we constructed a dynamic weighted fusion mechanism and a personalized correction engine, establishing an evaluation model incorporating BMI correction factors and fitness-level compensation. Experimental data from 100 collegiate basketball trainees (60 males, 40 females; BMI 17.5–28.7) wearing Polar H10 and Xsens MVN devices were analyzed through an 8-week longitudinal study design. The framework integrates physiological monitoring (HR, HRV), kinematic analysis (3D acceleration at 100 Hz), and environmental sensing (SHT35 sensor). Experimental results demonstrate the following: (1) the LSTM-attention model achieves 85.3% accuracy in exercise intensity classification, outperforming traditional methods by 13.2%, with its spatio-temporal attention mechanism effectively capturing high-dynamic movement features such as basketball sudden stops and directional changes; (2) multi-modal data fusion reduces assessment errors by 15.2%, confirming the complementary value of heart rate and acceleration data; (3) the personalized correction mechanism significantly improves evaluation precision for overweight students (error reduction of 13.6%) and beginners (recognition rate increase of 18.5%). System implementation enhances exercise goal completion rates by 10.3% and increases moderate-to-vigorous training duration by 14.7%, providing a closed-loop “assessment-correction-feedback” solution for intelligent sports education. The research contributes methodological innovations in personalized modeling for exercise science and multi-modal time-series data processing. Full article

Narrative methods are increasingly recognized in science teaching for their potential to deepen conceptual understanding and foster meaningful connections to scientific content. This review explores their educational significance by examining three main formats—historical narratives, realistic fiction, and science fiction or fantasy—highlighting how each can render complex scientific principles more accessible and memorable. Special attention is given to chemistry education, a field where abstract, multilevel concepts often pose significant challenges for students. Furthermore, the review explores the integration of narratives into gamified environments, examining how storytelling functions as both a motivational engine and a cognitive scaffold to support deeper learning in science. Finally, the review proposes directions for future research, underscoring the need for empirically grounded narrative resources that balance imaginative appeal with scientific accuracy across diverse educational settings. Full article

This study used the Q methodology to examine how Korean science, technology, engineering, and mathematics (STEM) students perceive the experience of reading classical texts and how such experiences relate to their overall well-being. We developed 31 statements for the Q-sorting process and collected data from 39 undergraduate students majoring in science, technology, engineering, and mathematics (STEM). The analysis identified three distinct perception types: type 1—exploratory type, which broadens thinking through diverse perspectives, type 2—experience type, which shares achievement and enjoyment through reading together, and type 3—insight type, which seeks universal values and truth. These findings suggest that, for science and engineering students, reading classics offers a multidimensional experience—encompassing intellectual expansion, relational engagement, and philosophical reflection—beyond conventional academic activities. In particular, the therapeutic dimension of reading, as discussed in bibliotherapy, has emerged as a mechanism that supports self-reflection and emotional resilience. Although each type approached classical reading differently, the participants demonstrated varied perceptions that reflect dimensions of well-being, such as emotional awareness, relational connection, and self-reflection, as expressed through the Q-sorting of pre-defined statements. Based on these results, this study concludes that classical reading can function as a significant mechanism for promoting well-being, offering new directions and practical implications for classical reading education. Full article

Recently, the usage of mobile phone-based games has increased due to the growing accessibility and convenience they provide. Using a descriptive-quantitative design, a disaster-based mobile application was developed in this study to enhance disaster literacy among the private senior high schools in science, technology, engineering, and mathematics (STEM) education in Davao City, the Philippines. The developed application was provided together with survey questionnaires to 364 students randomly selected from different schools in Davao City usingF a simple random sampling method. The technology acceptance (TAM) model was used to explain how users accepted the new technology. The mobile application was designed with features in four disaster scenarios—fire, flood, volcano, and earthquake. The results revealed a high acceptance, with an average score of the perceived usefulness (PE) of 4.52, perceived ease of use (PEOU) of 4.44, and a behavioral intention (BI) of 4.12. The students accepted the application to enhance disaster risk reduction and management. Aligned with SDG 4 and SDG 11, the application can be used to equip users with the knowledge to respond to disasters and ensure community resilience. Full article

Physics concepts are considered an essential component of STEM (science, technology, engineering, and mathematics) education and fundamental for economic and technological development in the world. However, there can be student academic underperformance, such as the school environment, learning media and infrastructure, student interest and emotions, as well as social and economic development factors in communities. These problems are even more acute in rural areas of developing countries, where poverty is high and teachers often lack the necessary technological skills. The aim of this study was to evaluate the impact of a low-cost STEM tool focused on motivation in learning, in terms of five variables of interest in physics in rural areas, as well as the durability of the tools used to learn 12 physics concepts. A quasi-experimental study was conducted with the participation of 78 high school students, with an average age of 15.82 years, in a rural area of Guerrero, Mexico. The results showed that using the STEM tool significantly increased students’ interest in learning methodology, active participation, and attitude towards physics, facilitating the teacher’s work. In addition, the 3D construction kit used in the experimentation, besides being low-cost, proved to be affordable and durable, making it ideal for use in rural areas. Full article

This study presents the outcomes of the Erasmus+ European project Higher Education Classroom of the Future (HECOF), with a particular focus on chemical engineering education. In the digital era, the integration and advancement of artificial intelligence (AI) in higher education, especially in engineering, are increasingly important. The main goal of the HECOF project is to establish a system of new higher education teaching practices and national reforms in education. This system has been developed and tested through an innovative personalized and adaptive method of teaching that exploited digital data from students’ learning activity in immersive environments, with the aid of computational analysis techniques from data science. The unit operations—extraction process course—a fundamental component of the chemical engineering curriculum, was selected as the case study for the development of the HECOF learning system. A group of undergraduate students evaluated the system’s usability and educational efficiency. The findings showed that the HECOF system contributed positively to students’ learning—although the extent of improvement varied among individuals—and was associated with a high level of satisfaction, suggesting that HECOF was effective in delivering a positive and engaging learning experience. Full article

Improving curriculum structure is critical for enhancing student success and on-time graduation, yet few methods exist to evaluate how prerequisite paths shape student progression and graduation outcomes. This study proposes a data-driven, graph-based framework that integrates course prerequisite networks with student performance data to systematically analyze curricular structure and student outcomes. We identify high-risk courses by jointly modeling their structural importance and pass rates, and quantify the time and survivability of different prerequisite paths using probabilistic models. Additionally, we introduced grade transition patterns to capture more nuanced transitions in student performance and pinpoint bottlenecks along prerequisite paths. Applying the model on four science and engineering majors from a public institution, the results not only identify high-risk courses often missed in conventional analyses, but also reveal path-level disparities and structural bottlenecks that affect student progression and time to graduation. For example, in the Computer Science major, we identified that the architecture and operating systems pathway is more challenging than the software engineering pathway. A closer examination of the course pairs along this trajectory revealed that the difficulty stems from a significant drop in student performance between a prerequisite–successor course pairs.This type of analysis fills a gap in conventional curriculum studies, which often overlook path-level dynamics, and offers actionable insights for educators a to identify high risk curricular components. Full article

Effective studying is important to learn better and increase academic achievement in postsecondary education, which also holds true for the challenging content of science, technology, engineering, and mathematics (STEM). Informed by previous research, this study mainly aimed to investigate first-year STEM college students’ study habits and perceptions of the effectiveness of different study strategies, and the frequency of use of these strategies. To this end, this study employed a cross-sectional survey using the Prolific platform. The results revealed that participants use various study strategies, including more and less effective ones, generally do not study in a planned way nor believe that learning takes hard work, and also prioritize approaching deadlines. The results also showed that the participants (a) frequently use the study strategies that they think are effective, suggesting that perceived effectiveness can have an important role in students’ strategy choice, and (b) mostly use study strategies for studying only or for both studying and while learning for fun. However, the frequency of the use of strategies partially aligned with the perceived effectiveness of the strategies. Overall, these results suggest the need to further investigate the conditions under which college students find study strategies effective, which can affect their choices. Full article

Gender harassment is the most common variant of sexual harassment and is often seen in male-dominated workplaces. Importantly, persistent gender harassment produces pronounced impairment in psychological and occupational domains. The current study aimed to examine the degree to which male dominated academic degree programs are associated with higher rates of sexual harassment compared to female dominated programs. Four academic fields were chosen for their gender disparity—business, engineering, health sciences, and education. Consistent with hypotheses, cisgender women students in the college of engineering were most likely to experience gender harassment by peers and faculty. Implications and future directions are discussed. Full article

This research is focused on exploring the importance of STEM (Science, Technology, Engineering, and Mathematics) education in the development of critical competencies among secondary school students in the 21st century. This was aimed to assess the impact of STEM-based activities on students’ creative and computational thinking skills. A quasi-experimental design that included 77 secondary school students from public and private schools in Bandar Lampung, Indonesia, who participated in STEM interventions for over 5 weeks, was adopted. Data were collected through creative thinking tests and questionnaires on CT and STEM attitudes. The results showed that students who participated in STEM activities exhibited significantly higher creative thinking scores compared to peers taught with the traditional curriculum. Specifically, the experimental group showed a progressive increase in weekly test scores, suggesting that STEM methods improved students’ performance over time. Structural equation modeling (SEM) disclosed strong positive associations between attitudes towards STEM, CT, and creativity. The implications of these results outlined the need to integrate STEM education into curricula to foster essential skills for future challenges. This research contributes to the understanding of effective educational strategies and also advocates for a shift towards more interactive and integrative methods in secondary education to meet the demands of the contemporary workforce. Full article

This article presents a comparison of two qualitative case studies. The first case study is a partnership group involving two urban secondary school teachers working with one engineer and one education faculty member where they implemented several science, technology, engineering, and mathematics (STEM) lessons over the course of an academic year. The second case study is a partnership group involving undergraduate college students working together to build a data collection device attached to a high-altitude balloon to answer a scientific question or solve an engineering problem and translate the project into engaging lessons for a K-12/secondary student audience. The studies employed a socio-cultural theoretical framework as the lens to examine the individuals’ perspectives, experiences, and engineering meaning-making processes, and to consider what these meant to the partnership itself. The methods included interviews, focus groups, field notes, and artifacts. The analysis involved multi-level coding. The findings indicated that the strength of the partnership (pre, little p, or big P) among participants influenced the strength of the secondary engineering lessons. The partnership growth implications in terms of K-12/secondary and collegiate engineering education included the engineering lesson strength, partnership, and engineering project sustainability The participant partnership meanings revolved around lesson creation, incorporating engineering ideas into the classroom, increasing communication, and increasing secondary students’ learning, while tensions arose from navigating (not quite negotiating) roles as a team. A call for attention to school–university partnerships and the voices heard in engineering partnership building are included since professional skills are becoming even more important due to advances in artificial intelligence (AI) and other technologies. Full article

The aim of this study is to identify the key factors contributing to student dropout and to develop a predictive model that estimates the dropout risk of students based on their entry characteristics and enrolment registration data. Our analysis is based on the registration and academic data of 971 full-time and part-time bachelor’s students in five faculties, who were enrolled in the academic year 2021–2022 at the Latvia University of Life Sciences and Technologies (LBTU). The dropout analysis was done during the 3.5 years of study, when the students started their last semester in engineering and information technology, agriculture and food technology, economics and social sciences, and forest and environmental studies and when veterinary medicine students had completed more than half of their program of study. Survival analysis methods were used during the study. Students’ dropout risk in relation to gender, faculty, priority to study in the program, and secondary school performance (SM) was estimated using the Proportional hazard model (Cox model). The highest student dropout was observed during the first year of study. Secondary school performance was a significant predictor of students’ dropout risk; students with higher SM had a lower dropout risk (HR = 0.66, p < 0.05). As well, student dropout can be explained by faculty or study programme. Students in economics and social sciences were at lower dropout risk than the students from the other faculties. Results show the model’s concordance index was 0.59, and this indicates that additional or stronger predictors may be needed to improve model performance. Full article