Veranstaltungsprogramm

Deaf individuals frequently rely on sign language to communicate, a sophisticated form of communication that represents a unique challenge for those unfamiliar with it. For people with hearing and speech impairments, sign language provides a crucial medium for expressing thoughts and emotions, yet this becomes problematic when those without sign language proficiency need to interact with them. The challenge lies in bridging this communication gap. This study introduces a system that converts spoken words or sentences into Indonesian Sign Language (SIBI) video sequences through a voice recognition interface. The developed system holds immense potential for various applications, including education and personal learning, allowing users to convert auditory inputs into visual sign language sequences. The system features a database of 3,368 basic Indonesian sign language words and letters, enabling smooth communication for everyday scenarios. The development process utilized the prototype methodology, while the system was evaluated using Blackbox-Testing, achieving an 84% success rate across 30 test scenarios.

The accelerating pace of digital transformation has placed growing pressure on vocational education and training (VET) systems to equip learners with the digital competencies essential for the 21st-century workforce. Despite increasing attention, the literature remains fragmented in identifying the specific digital skills required, the role of learning analytics (LA) in supporting these competencies, and the global trends, challenges, and innovations surrounding their implementation in vocational education. Addressing this gap, the present study aims to explore five key research questions: (RQ1) What are the key digital skills required in vocational education to meet current and future workforce demands? (RQ2) How is learning analytics being utilized in vocational education to support the development and assessment of digital skills? (RQ3) What are the prevailing global trends in the integration and advancement of digital skills within vocational education systems? (RQ4) What are the main challenges faced by vocational education institutions and learners in acquiring and implementing digital skills? (RQ5) What future directions and innovative strategies can enhance the development and application of digital skills in vocational education?

This study employs a systematic literature review method guided by PRISMA, analyzing 241 peer-reviewed articles published between 2019 and 2024 from the Scopus databases. The findings indicate a significant increase in research addressing digital skills and LA in VET, underscoring global urgency for digital readiness in the workforce. Ten core digital competencies were identified as critical for vocational learners: (1) basic digital literacy, (2) specific technology skills, (3) data management skills, (4) emerging technology skills (including AI, AR, VR, MR, and IoT), (5) digital communication, (6) cybersecurity literacy, (7) collaboration in digital environments, (8) creativity and problem-solving, (9) ethical information use and evaluation, and (10) digital leadership.

Learning analytics emerges as a promising data-driven approach for enhancing instructional design, monitoring skill acquisition, and supporting personalized learning pathways in VET. However, its integration faces challenges such as limited digital infrastructure, inconsistent teacher competencies, and inadequate collaboration between education and industry sectors. As a response, the study proposes future strategies including adaptive curricula embedded with advanced technologies, targeted ICT-based teacher development programs, and strengthened multi-sector partnerships. This review contributes to the global discourse on the digital transformation of vocational education by offering empirical insights and practical recommendations to build responsive, inclusive, and future-ready VET systems.

Kiken Yochi Training (KYT) is a critical method in occupational safety training, yet its implementation in vocational education faces challenges, particularly in risk visualization and learner engagement. Mobile Augmented Reality (MAR) offers potential to enhance KYT through interactive and immersive learning experiences. This study presents a systematic literature review using bibliometric analysis supported by Publish or Perish and VOSviewer to explore trends, challenges, and opportunities for integrating MAR into KYT in vocational education. Findings indicate that MAR improves situational awareness, engagement, and knowledge retention, although implementation is limited by infrastructure, instructor readiness, and the need for locally relevant content. These insights support the development of adaptive and contextual MAR applications for vocational settings.

This study aims to develop an electrical circuit training design with the help of Trainer Kit and Video Module for students and college students who are relevant to the field of electrical engineering. The method used is Research and Development (R&D) with the ADDIE research model. The model goes through a 5-stage development procedure, namely (1) Analyze; (2) Design; (3) Development; (4) Implementation and (5) Evaluation. The data collection method uses interview instruments, documentation, literature studies and questionnaires with a Likert scale as the measuring instrument. The assessment of the level of feasibility uses 9 media and material evaluators. The results of the analysis in the study showed that the electrical circuit Trainer kit media obtained an average of 61.3 with the category "Very Feasible", the training material obtained an average of 56.6 with the category "Very Feasible", the video module obtained an average of 42.3 with the category "Very Feasible". Meanwhile, for the feasibility test of 16 student respondents, an average of 53.1 was obtained and entered the "Very Feasible" category. Thus, based on the results of research and development, the design of the training based on the Trainer Kit and video modules is very feasible to use.