Search Results for
"learning"

The COVID-19 pandemic has posed unprecedented challenges, significantly affecting economic development, the hospitality industry, and the educational sector. In Taiwan, internships are a mandatory component of Hospitality Management studies, offering students critical exposure to real-world environments. This study employs a qualitative case study approach to explore the experiences of hospitality students during their internships, particularly under alert level 3 of the pandemic. By integrating coping strategies into the analysis, the research provides insights into how students and organizations navigated through the crisis. Data were collected through online meetings and surveys with 67 undergraduate students, with 62 valid responses from the subsequent questionnaire survey. Content analysis and triangulation techniques were used to enhance research quality. The findings highlight the importance of prioritizing student safety and fostering resilience through internship programs. The study offers practical implications for educators, advocating for robust contingency planning and the integration of resilience-building strategies in hospitality education. © 2025 ICHRIE.

Introduction. The use of digital technologies is now an important part of the educational process. This process is constantly changing due to various factors and the emergence of new digital tools. Such changes are significant in the context of the quality of education and require constant monitoring. The purpose of this study is to investigate the characteristics of the use of digital educational tools and the tasks that teachers solve using digital tools. Materials and Methods. A total of 594 teachers between the ages of 20 and 76 participated in the study. 93 percent of respondents were women. The questionnaire “Digital Technologies in the Educational Process” was used for data collection. Results. Electronic gradebooks or diaries are a popular digital tool, while digital educational resources and communication systems with students and parents are used less frequently. E-textbooks proved to be an unpopular digital tool. Educators more frequently use digital technologies for communication with colleagues and students, and less often for planning extracurricular activities and implementing project-based learning. Comparative analysis shows that young specialists frequently use lesson planning systems, while teachers with over 30 years of experience more often use digital technologies for attendance tracking. Primary school teachers utilize digital technologies more often than others. Educators in larger population centers use digital technologies for attendance tracking, extracurricular activity planning, and communication. Discussion and Conclusion. The results of the study indicate a high level of integration of digital technologies into the educational process and a transition from routine to innovative use. The results of the study can be used both to clarify or expand the functions of existing digital educational tools and to provide more targeted professional development for teachers. © Руднова Н. А., Корниенко Д. С., Смирнова Я. К., 2025.

Gamification and Artificial Intelligence (AI) have transformed the educational environment, offering innovative approaches to enhance student engagement and learning outcomes. By using the power of gamification and AI, educators can create immersive, personalized, and interactive learning experiences that increase motivation, participation, and academic achievement. This study investigates the relationships between gamification, artificial intelligence, student engagement, and achievement in educational contexts. A quantitative approach was employed, combining online surveys and structural equation modeling (SEM) analysis with 290 students from Malaysia. SEM analysis was performed using SmartPLS 4 software. The findings reveal significant positive relationships between gamification and student achievement, gamification and student engagement, gamification and artificial intelligence, and AI with both student engagement and achievement. Furthermore, student engagement was found to influence student achievement positively. The study’s implications suggest that educators and instructional designers should adopt these technologies to create intelligent, interactive, and personalized learning environments. Future research directions include exploring long-term effects and contextual factors influencing student outcomes to promote student success in the digital age. However, the study is limited to a single country context (Malaysia), and relies on self-reported survey data, which may influence generalizability and introduce potential biases. © 2013 IEEE.

Online instructional videos have been presented as an efficient instructional method in workplace learning and vocational education and training (VET). Increasingly, public video-sharing sites like YouTube are used by firms, educators and learners to teach and learn about work practices, new work roles and skills. However, more knowledge is needed about what instructional videos exist and how they facilitate vocational learning. This article draws from online video research to explore vocational learning on YouTube for interactive service work. Focusing on connected service encounters in which transactions and customer service are intertwined through interaction with digital technologies, cashier work was used as the empirical case. With concepts from narrative and multimodal analysis, the theory of practice architectures was the framework for analysing 160 YouTube videos for cashier work. The article proposes a taxonomy of online instructional video types for VET and workplace learning comprising nine video types divided into two main categories: professional instructional videos and peer instructional videos (vlogs). It is found that transactions and customer service are instructed separately. Consequently, YouTube videos do not yet prepare learners for connected service encounters. Overall, the taxonomy can contribute to a more purposeful use of video in VET and workplace learning. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

In the wake of the fourth industrial revolution, artificial intelligence is gaining momentum and is widely applied in various aspects of life, particularly education. This study investigates the factors influencing students' use of artificial intelligence (AI) in learning, focusing on students at Ho Chi Minh City University of Industry. The research uses a combination of the technology acceptance model and the theory of planned behavior to examine the relationships between subjective norms, image, job relevance, output quality, result demonstrability, self-efficacy, anxiety, perceived playfulness, perceived enjoyment, perceived ease of use, perceived usefulness, and behavioral intention. Combining these technological models brings new insights into the context of AI that can support or hinder user behavior through bias. The results were then analyzed based on the least squares linear structural model, with 390 students participating in the survey using the stratified sampling approach. The study found that perceived ease of use and usefulness are the most significant factors influencing students' intention to use AI in learning. Subjective norms also play an essential role in shaping students' image and intention to use AI. The research also highlights the importance of self-efficacy, perceived enjoyment, playfulness, output quality, result demonstrability, and job relevance in influencing students' perceptions and use of AI. The findings of this study underscore the need for educational institutions to create a supportive environment that encourages students to use AI in learning. In contrast, AI technology creators need to focus on simplifying the user experience to make AI tools more accessible and easy to use. These practical recommendations of the research can guide policy and design decisions in the field of educational technology. Finally, in place of a conclusion, the study also aims to open up further approaches for AI platforms in academia. © 2025 Malque Publishing. All rights reserved.

The persistent skills mismatch between higher education outcomes and labour market needs presents a significant challenge for graduate employability, particularly in fast-evolving sectors such as artificial intelligence (AI) and data science. This paper introduces a data-driven internship framework designed to bridge this gap by optimising student placements, providing real-time feedback, and ensuring academic curricula remain aligned with labour market demands. The framework integrates three systems: the Student Industry Placement System (SIPS), which uses machine learning to match students with relevant internships; the Intern learning Management System (ILMS), which tracks skill development through continuous feedback loops; and the Industry Profiling System (IPS), which leverages predictive analytics to provide real-time labour market insights. The framework was evaluated through a mixed-methods approach, incorporating pilot program data from five universities and 15 industry partners and simulated data based on historical internship trends. The SIPS algorithm achieved a placement accuracy of 89%, significantly improving over traditional methods (60-65%). 78% of students received continuous feedback through the ILMS, resulting in measurable improvements in technical skills (30% increase in coding proficiency) and problem-solving abilities (25% improvement). The IPS enabled universities to update 15% of their curricula in response to real-time labour market data, particularly in high-growth sectors such as AI and digital transformation. 68% of the students secured full-time employment within six months of graduation, with 65% in roles directly aligned with their internships. These findings demonstrate the framework's scalability and potential for reducing skills mismatch, making it a viable model for other emerging economies. © 2025 IEEE.

Integration (joined up learning of different subjects) is an important part of modern undergraduate medical education. We argue that integration in a UK medical school is experienced principally as multiple subject learning via enquiry-based learning and requires facilitators. By analysis of student interactions and the views expressed by students and staff, we demonstrate three facets of integration: student experience, with the principal means of integration being discussions of multiple subjects simultaneously, encountering related subject matter in different sessions, and making explicit links to other parts of the course; presence of facilitators of integration such as staff liaison, awareness amongst staff of other parts of the course, integrated teaching, tutor support, group bonding, early clinical experience, and integrated assessments; existence of barriers to integration such as compartmentalisation of subjects, poor student bonding, poor attitude to certain subject matter, and boundaries between course elements. © The Author(s) 2025.

In the UK context of an ageing population, degree apprenticeships represent a new opportunity to study for a degree while working. Apprentices are full-time employees granted time to study for a degree with a significant workplace learning component. The aim of this study was to focus on whether degree apprenticeships are working for adult apprentices (aged 26 and up in this context). New apprentices (n = 162) in six universities in Scotland, UK were surveyed to gain a better understanding of background, prior work and study experience, motivations and expectations. Results show that adult apprentices join apprenticeship programmes with significant work experience and workplace metaskills, together with a consolidated sense of self as a professional. Adult apprentices aimed to gain a degree while remaining in work, thus increasing skills in situ. The main barrier identified was the challenge of achieving work-study-life balance. The findings can be used to more closely align apprenticeship provision with adult apprentices’ skills needs while reducing barriers to accessing and succeeding in apprenticeships. We make recommendations for more flexibility in terms of advanced entry and Masters-level apprenticeships, with better recognition of prior experience, motivations and anticipated challenges. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

While an increasing number of studies postulate that vocational education and training (VET) activities have a positive impact on the innovative capacity of training companies, empirical evidence on the topic remains contradictory. This study exploits establishment data from a representative survey of German companies to estimate the relationship between firms’ participation in initial VET and their innovation outcomes. Our results show that the direct effects of initial VET on technological innovation in small and medium-sized enterprises (SMEs) are on average quite weak. If at all, a training firm’s initial VET activities are associated with product innovation activities and not with process innovation. Larger effects can only be observed in case of microenterprises with fewer than ten employees. In these firms, initial VET is associated with a higher probability of (local) new-to-market product innovation if it is accompanied by changes in organisational processes that support individual learning and knowledge creation. We conclude from this finding that the knowledge diffusion function of the VET system primarily holds relevance for the smallest of the training companies and that initial VET is only positively related to technological innovation when it goes along with organisational learning in the training company. © 2023 The Vocational Aspect of Education Ltd.

Classroom engagement is a critical factor for evaluating students' learning outcomes and teachers' instructional strategies. Traditional methods for detecting classroom engagement, such as coding and questionnaires, are often limited by delays, subjectivity, and external interference. While some neural network models have been proposed to detect engagement using video data, they generally rely on fixed feature combinations, which fail to capture the logical connections and temporal dynamics of engagement.To address these challenges, this article introduces the MSC-Trans Engagement Detecting Network, a temporal multimodal data fusion framework that integrates a convolutional neural network (CNN) and a multilayer encoder–decoder structure. The proposed network includes two key components: first, a multilabel classifier based on ResNet and Transformer, which embeds labels into image features extracted by the CNN for high-precision classification through background inference, second, a temporal feature fusion module, which leverages an encoder–decoder structure to integrate multimodal features over time, enabling stable tracking of classroom engagement. Meanwhile, this open framework allows users to freely select feature combinations for temporal fusion based on specific scenarios and needs.The MSC-Trans Engagement Detecting Network was validated on the DAiSEE dataset, augmented with real classroom data. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in continuous engagement tracking metrics, with flexible and scalable feature selection. This work offers a robust and effective approach for advancing engagement detection in educational settings. © 2008-2011 IEEE.