A Systematic Literature Review on the Impact of Reading Literacy on Students’ Critical Thinking Skills in Vocational Education Muhammad Nurtanto, Septiari Nawanksari, Okianna, Valiant Lukad Perdana Sutrisno, Nur Wachid Abdul Majid, et al. Journal of Teaching and Learning, 2026 This study is a systematic literature review (SLR) aimed at identifying and analyzing the relationship between reading literacy and critical thinking skills among vocational school pupils. Following the PRISMA guidelines, a total of 20 selected articles published between 2015 and 2025 were thoroughly reviewed using databases such as Scopus, ERIC, Web of Science, and Google Scholar. The findings indicate that reading literacy serves not only as a tool for text comprehension but also as a fundamental component in fostering pupils' analytical, reflective, and evaluative thinking abilities. Instructional strategies such as reciprocal teaching, problem-based learning, and critical questioning have been proven effective in simultaneously enhancing both reading literacy and critical thinking skills. Moreover, digital literacy plays a crucial role in fostering critical thinking in the age of information. Nevertheless, the study identifies several limitations, including inconsistencies in measurement methods and the underrepresentation of studies from developing countries. The results highlight the importance of integrating reading literacy and critical thinking into the vocational education curriculum, as well as the need for strengthened policy support, teacher training, and the development of valid and context-sensitive assessment instruments.
Image processing for 3D flat surfaces from a capture camera with the Kalman Filter Triangle Algorithm Ihsan Auditia Akhinov, B. S. Rahayu Purwanti, Hasvienda Muhammad Ridwlan, Muhammad Nurtanto, Anggi Mardiono, et al. Journal of Physics Conference Series, 2026 This study focuses on reducing noise in 2D-to-3D image conversion using a combination of Masking Algorithms, Triangulation, and Kalman Filter Formulas (KF F ). Although these methods are widely applied, masking techniques for minimizing height errors due to noise are rarely discussed. The objective is to reduce noise through a hybrid masking method, followed by KF F for accuracy enhancement. Experiments were conducted on planar objects with white and black surfaces, captured along a laser line to generate 2D images with dominant red hues. The 3D dimension was reconstructed using HSV-HPF, RGB, and a combined RGB-HSV (HPF) masking approach in OpenCV. Results show that the combined masking (COMB) method achieves the lowest noise levels. Integration with KFF further improves accuracy, achieving a Root Mean Square Error (RMSE) of 0.087375, compared to 0.117 without KF F . Accuracy testing used RMSE with Q = 0.3 and R = 0.8. The findings indicate that KF F reduces noise by approximately 27% and significantly improves precision in the 2D-to-3D image conversion process.
Unraveling Factors Affecting Engineering Students’ Acceptance of Artificial Intelligence in the Context of a Blended Learning Environment Muh. Hamkah, Heri Retnawati, Muthmainah Muthmainah, Muhammad Hakiki, Mustofa Abi Hamid, et al. Online Learning Journal, 2025 The rapid advancement of artificial intelligence (AI) has significantly transformed various educational domains, including engineering education. Despite AI’s growing prevalence, limited research has explored the determinants influencing engineering students' acceptance of AI. This study investigates the factors shaping AI acceptance among engineering students in Indonesia. Using Structural Equation Modeling (SEM) with the Partial Least Squares (PLS) approach, data were collected from 158 engineering students across multiple universities. The research model incorporates six constructs: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Social Influence (SI), Facilitating Conditions (FC), Self-Efficacy (SE), and Perceived Risks (PR), each operationalized through seven measurement indicators. The results indicate that PU, PEOU, SI, and SE have significant positive effects on AI acceptance, while PR exerts a significant negative influence. Conversely, FC does not demonstrate a significant impact. These findings offer theoretical and practical implications for fostering AI adoption in engineering education, including strategies for educators, policymakers, and developers of AI-based tools to enhance user acceptance. This study extends the literature on technology acceptance in educational settings, providing actionable insights for improving the integration of AI in higher education.
