Search Results for
"learning"

There is sufficient evidence that work culture is an important factor in improving learning outcomes. Habituation of Industrial Culture Highly Intensive, called HAICHI training is an alternative solution that needs to be tested to improve the learning outcomes of prospective machining instructors. This study aims to:1) Explore the application of HAICHI for prospective machining instructors in improving learning outcomes; 2) Test the differences in learning outcomes between classes that implement HAICHI and classes that do not implement HAICHI; 3) Determine the significance of HAICHI in influencing the achievement of basic machining practice learning outcomes. This study uses a descriptive quantitative approach to reveal the implementation and quasi-experimental with a posttest only control group design to test the differences in two different learning outcomes. The study was conducted at the Machining Workshop, Department of Mechanical Engineering Education, Yogyakarta State University, and was carried out from February 12 to May 31, 2024. The subjects of this study were 2nd semester Mechanical Engineering Education (S1) students. The test technique with a written exam question instrument was used to obtain data on the knowledge aspect. Observation with vernier caliper measuring instrument and assessment sheet of practical result were used to obtain data of skill aspect. Observation with instrument in the form of observation sheet was used to obtain data of attitude and behavior aspect. The result of the research showed that: 1) In HAICHI model, habituation is done by giving stimulus continuously by lecturer, so that students are accustomed to applying 5S, HSE (Health and Safety Environment), and Kaizen behavior during practical activity in workshop which has impact on practical result. In addition, habituation of 5S, HSE and Kaizen is done with Gemba principle where habituation is integrated and simultaneous with basic machining practice activity in workshop; 2) There is difference of learning result of basic machining practice between class that applied HAICHI and class that did not apply HAICHI; 3) HAICHI has positive and significant influence on achievement of learning result of basic machining practice. © 2025 Author(s).

This study aims to analyze significant differences in chemical literacy and learning independence simultaneously and respectively between students who learn with the ethnochemical-based Flipped Problem-based learning (FPBL) model and students who learn with a scientific approach, determine the effective contribution of the ethnochemical-based FPBL learning model compared to the scientific approach to chemical literacy and learning independence simultaneously and respectively and determine the profile of learning independence and chemical literacy of high school students who take part in ethnochemical-based FPBL learning on buffer solution material. The research method was conducted with a quasi-experiment using a pre-test post-test control group design. The population of this study were students of grade XI MIPA in the northern part of Sleman Regency. The data obtained were then analyzed with the MANOVA test using a significance level of 5%. The results of the MANOVA test showed: (1) there are significant differences in chemical literacy skills and learning independence between students who learn with the ethnochemical-based FPBL model and the scientific approach to buffer solution material both simultaneously and individually; (2) the effective contribution of the ethnochemical-based FPBL model compared to the scientific approach to the chemical literacy skills and learning independence of students simultaneously is in the high category, while individually for learning independence is in the medium category and for chemical literacy is in the high category; (3) the profile of chemical literacy and learning independence of students in the experimental class is dominated in the Good (B) category. Overall, the ethnochemical-based FPBL model has an effect on improving students' chemical literacy and learning independence in buffer solution material. © 2025 Author(s).

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In the context of the flourishing development of artificial intelligence in the education field, the improvement of normal students’ educational practice ability through AI - based means has become an urgent issue. This study developed an AI - supported”three-step” training model for normal students. It uses the K - means clustering algorithm (k = 5) for learning style classification and personalized learning path creation. BiLSTM - attention networks (3 - layer, dropout = 0.3) predict teaching behavior (F1 - score = 0.87), and a BERT - based NLP model (fine - tuned with 12,000 examples) provides 91.3% accurate real - time feedback. A virtual lab (89.2% task completion rate) is integrated into the training. In the six-month controlled experiment (N=350), compared with the traditional method, the”three-step” model achieved a significant improvement in the teaching ability score, with an overall improvement of 28.4% (P < 0.001, η = 0.42), especially in the aspects of classroom management and teaching design, which increased by 37.1% and 29.6% respectively. In addition, in another group of experiments (n=200), the evaluation score of TPACK is verified by A/B test, which shows that the model can improve the teaching efficiency by 23.7%(p<0.05, Cohen’s d=1.2). These results show that the AI-driven training model, which combines machine learning and natural language processing technology, has a significant effect on improving the comprehensive ability of teacher candidates. © 2025 Copyright held by the owner/author(s).

In the face of the high requirements of vocational education for practical skills training, as well as the poor results and high costs of traditional training, this study aims to build an efficient and intelligent virtual training and simulation operation platform by introducing generative artificial intelligence technology to improve teaching quality and learning efficiency. This paper uses the adversarial generation ability of GAN and combines it with the knowledge graph in the field of vocational education to generate a high-fidelity and scalable virtual training environment. The variational autoencoder (VAE) is used to dynamically adjust the scene parameters to adapt to the needs of different majors and skill levels. Based on the Transformer multimodal dialogue model, a real-time operation guidance system integrating virtual and real is constructed. The physical engine is combined to simulate the feedback of equipment operation, and the long short-term memory network (LSTM) is used to capture the characteristics of the trainee’s operation sequence, dynamically predict potential errors and trigger correction prompts. This paper also uses the deep reinforcement learning (DRL) framework to build a personalized evaluation agent, using the trainee’s operation data as the state space to generate targeted training suggestions. The average operation accuracy of the experimental group students reaches 89.715%, the highest skill assessment pass rate reaches 100%, and the overall distribution is more concentrated, reducing the cost of practical training. In addition, teachers and students highly evaluated the teaching adaptability, operation guidance and error correction functions of the platform. The teaching platform proposed in this study provides a new path and strategy for the modernization of vocational education. © 2025 Copyright held by the owner/author(s).

The complexity of microcontroller learning that must combine theoretical concepts and real practices makes it difficult for many students to master the competencies of microcontroller control systems. In addition, it is difficult to achieve practical skills that students must master without a training kit that can interpret the application of microcontroller control systems. Thus, the purpose of this study is to examine the effectiveness of mobile robotic training kits to improve student learning achievement, practical skills, and problem-solving skills. This study used a mixed method with a sequential explanatory design, combining true-experimental research with semi-structured interviews. It involved 76 participants, divided into 38 experimental groups and 38 control groups selected at random. The results of this study show that the mobile robotic training kit is significantly effective in improving students’ learning achievement, practical skills, and problem-solving skills in the field of microcontroller control systems. This study provides empirical evidence of the importance of implementing a mobile robotics training kit in the learning process to improve students’ competencies and prepare them with competencies relevant to the needs of the world of work. © 2025 by the authors of this article.

Purpose – Engineering education plays a crucial role in fostering innovation and technological advancements across industries. This study examines the impact of academic achievement on engineering placements, identifying key factors influencing students’ organizational success. The research aims to enhance students’ skills and competencies to improve their employability in preferred companies. Design/methodology/approach – The study employs a data-driven approach, analyzing historical student placement records and applying predictive analytics to forecast future placement trends. A comprehensive evaluation of demographic, academic and skill-based factors is conducted to determine their influence on student employability outcomes. Findings – The results highlight the complex relationship between academic performance, demographic factors and employability competencies. The study underscores the importance of a holistic education model that integrates technical knowledge with essential skills development. Insights from this research provide valuable guidance for educational institutions, policymakers and industry stakeholders in optimizing engineering curricula to enhance student placement success. Originality/value – The novelty of this study lies in its multidimensional approach to analyzing student placement outcomes. By exploring the interplay between academic performance, demographic influences and employability skills, this research contributes to a more inclusive and equitable engineering education system. The findings serve as a foundation for future discussions, collaborations and strategic initiatives aimed at improving engineering students’ transition into the workforce. © 2025 Emerald Publishing Limited

To increase participation in STEM related programs and careers, education stakeholders have explored the reconfiguration of high schools into STEM theme-based career academies However, it is critical to acknowledge the academic and psychological challenges impacting high school academy students during the 2020 coronavirus pandemic, particularly for ethnically and racially diverse learners. In this study, we examined the perspectives of high school academy stakeholders as well as students in terms of the academic and psychological challenges they encountered. Further, we discuss an educational intervention to provide virtual internships for students in the STEM academies. We followed a qualitative case study design to examine the perspectives of school stakeholders and students who participated in a high school STEM academy. We conducted 25 semi-structured interviews with school stakeholders and 23 Black male academy of engineering students. Four themes emerged: Social and Student Engagement Lacking; Growing Pains: School Stakeholders and Students as Adaptive Learners; Work-based learning Implementation Challenges; and The NAF Future Ready Lab as an Intervention. We discuss implications of our findings for educational scholars and practitioners. © 2025 Information Age Publishing Inc.

This study deals with the use of Augmented Reality (AR) for mathematics learning and its influence on student self-efficacy. This study aims to assess the use of AR technology using the integration approach in the mathematics learning process affecting students' self-efficacy variable towards understanding and solving a mathematical problem. The collection of data was conducted through self-efficacy questionnaires applied before and after the AR application, with questions designed for quantitative research purposes, as well as on-site interviews with students. The results of the study showed that in terms of self-efficacy, this application also increased significantly for students when learning mathematics using AR. In addition to that, the implementation of AR Applications has fostered engaged learning in class as per students. AR applications such as GeoGebra AR also provide clear and interactive visualization, which makes it easier for students to understand complex mathematical concepts more clearly with the help of graphics. These findings indicate that AR technology can serve as a powerful tool in supporting mathematics learning and increasing students' self-efficacy, as well as providing recommendations for the integration of AR in mathematics learning to optimize students' academic potential. © 2025 Author(s).

This study proposes an intelligent and adaptive evaluation framework for assessing e-commerce talents in higher vocational colleges under the 'New Business' paradigm. Drawing on the Analytic Hierarchy Process (AHP), the framework integrates expert-driven judgments with competency-based evaluation indicators across six dimensions: professional knowledge, technical application, innovation, market analysis, communication, and ethics. Based on 192 valid survey responses and 10 expert consultations, an empirical AHP-based model was constructed, with indicator weights validated through consistency testing.To address the inherent subjectivity and static nature of traditional AHP, this research introduces a forward-looking design that incorporates AI-driven concepts. A three-layer intelligent evaluation architecture is proposed, consisting of data input, evaluation logic, and feedback layers. While actual AI modeling is reserved for future work, the conceptual integration outlines how machine learning, fuzzy logic, and behavioral data from LMS and internship platforms can enable dynamic weight adjustments, personalized feedback, and real-time competency profiling. This hybrid approach not only enhances the flexibility and scalability of the evaluation process but also aligns with the digital transformation of vocational education. The study provides a theoretical and methodological foundation for the development of smart evaluation platforms and future AI-powered educational decision-support systems tailored to evolving vocational demands in the digital economy. © 2025 The Authors.