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This paper examines the implementation of translanguaging pedagogies within digital learning environments and their effectiveness in developing multilingual competence among Generation Z learners. Through a mixed-methods study involving 186 students and 14 educators across six higher education institutions, this research investigates how strategic integration of translanguaging practices in digital spaces supports linguistic flexibility, metalinguistic awareness, and cross-cultural competence. Findings reveal that digitally-mediated translanguaging approaches resulted in significant improvements in learners' willingness to communicate across languages (p<.01), metalinguistic awareness (p<.001), and cross-cultural communication efficacy (p<.01) compared to monolingual instructional approaches. The research further identifies five key digital translanguaging strategies that effectively leverage Generation Z's technological aptitude and multimodal literacy. This study contributes to the growing field of multilingual digital pedagogy by demonstrating how translanguaging can be systematically integrated into digital learning environments to foster linguistic repertoire expansion that aligns with the communicative needs of increasingly globalized contexts. The pedagogical framework presented offers practical guidance for educators seeking to implement translanguaging approaches in various digital learning modalities while accommodating Generation Z's learning preferences and multilingual development.

This paper examines the implementation of translanguaging pedagogies within digital learning environments and their effectiveness in developing multilingual competence among Generation Z learners. Through a mixed-methods study involving 186 students and 14 educators across six higher education institutions, this research investigates how strategic integration of translanguaging practices in digital spaces supports linguistic flexibility, metalinguistic awareness, and cross-cultural competence. Findings reveal that digitally-mediated translanguaging approaches resulted in significant improvements in learners' willingness to communicate across languages (p<.01), metalinguistic awareness (p<.001), and cross-cultural communication efficacy (p<.01) compared to monolingual instructional approaches. The research further identifies five key digital translanguaging strategies that effectively leverage Generation Z's technological aptitude and multimodal literacy. This study contributes to the growing field of multilingual digital pedagogy by demonstrating how translanguaging can be systematically integrated into digital learning environments to foster linguistic repertoire expansion that aligns with the communicative needs of increasingly globalized contexts. The pedagogical framework presented offers practical guidance for educators seeking to implement translanguaging approaches in various digital learning modalities while accommodating Generation Z's learning preferences and multilingual development.

Attendance management is an essential component in educational institutions, companies, and organizations to monitor the presence and punctuality of participants. Traditional attendance systems, such as manual signatures or identification cards, are prone to various issues including human error, time inefficiency, and identity fraud. To address these challenges, this study aims to develop a smart attendance system using facial recognition technology based on Python and the OpenCV library. The system is designed to automatically detect and recognize faces in real time using a webcam or camera module. It employs computer vision techniques to capture facial images, extract unique features, and match them against a stored database of registered participants. Once the face is verified, the system records the attendance along with a timestamp, ensuring data accuracy and security. The development process involved several stages, including image acquisition, preprocessing, feature extraction, and classification. OpenCV was utilized for image processing tasks, while Python provided the programming framework to integrate all components. To enhance recognition accuracy, the system applied techniques such as histogram equalization for lighting normalization and Haar Cascade classifiers for initial face detection. An experimental evaluation was conducted under various conditions, including different lighting environments and facial orientations. The results demonstrated that the system achieved an accuracy rate of 96% under normal lighting conditions, with only a small decrease in performance under dim or uneven lighting. These findings indicate that the system is reliable for practical applications, especially in controlled environments. Conclusion: The Python-based facial recognition attendance system offers a more efficient, secure, and accurate alternative to conventional attendance methods. Future improvements may include the integration of deep learning models to enhance recognition robustness in diverse real-world scenarios.

Attendance management is an essential component in educational institutions, companies, and organizations to monitor the presence and punctuality of participants. Traditional attendance systems, such as manual signatures or identification cards, are prone to various issues including human error, time inefficiency, and identity fraud. To address these challenges, this study aims to develop a smart attendance system using facial recognition technology based on Python and the OpenCV library. The system is designed to automatically detect and recognize faces in real time using a webcam or camera module. It employs computer vision techniques to capture facial images, extract unique features, and match them against a stored database of registered participants. Once the face is verified, the system records the attendance along with a timestamp, ensuring data accuracy and security. The development process involved several stages, including image acquisition, preprocessing, feature extraction, and classification. OpenCV was utilized for image processing tasks, while Python provided the programming framework to integrate all components. To enhance recognition accuracy, the system applied techniques such as histogram equalization for lighting normalization and Haar Cascade classifiers for initial face detection. An experimental evaluation was conducted under various conditions, including different lighting environments and facial orientations. The results demonstrated that the system achieved an accuracy rate of 96% under normal lighting conditions, with only a small decrease in performance under dim or uneven lighting. These findings indicate that the system is reliable for practical applications, especially in controlled environments. Conclusion: The Python-based facial recognition attendance system offers a more efficient, secure, and accurate alternative to conventional attendance methods. Future improvements may include the integration of deep learning models to enhance recognition robustness in diverse real-world scenarios.