Research Article
Ngoc Dan Nguyen, Nguyen-Ai-Thu Duong, Minh Dung Tang
CONT ED TECHNOLOGY, Volume 18, Issue 4, Article No: ep692
ABSTRACT
Digital games can support mathematics learning when their features are aligned with mathematical content and instructional goals. This methodological study adapted the technological pedagogical content knowledge-practical rubric to examine game pedagogical content knowledge in digital game-based mathematics lesson plans. The seven dimensions and four performance levels of the original rubric were retained, while the descriptors were revised for game mechanics, mathematical representation, instructional guidance, and game-supported assessment. The adapted descriptors underwent qualitative review by three experts. Two trained raters then used the rubric to score 50 lesson plans, and agreement for each dimension was estimated using weighted Cohen’s kappa. The coefficients ranged from .710 to .885, indicating substantial to almost perfect agreement. These coefficients describe agreement between the two raters for this sample; they do not establish construct or criterion-related validity. The rubric may provide teacher educators and professional development facilitators with a structured basis for reviewing and revising digital game-based mathematics lesson plans. Further studies should test the rubric with an independent expert panel, additional raters, new samples from multiple institutions, and evidence from classroom practice.
Keywords: digital game-based learning, rubric, assessment, teacher, TPACK-G, mathematics education
Research Article
Areej ElSayary, Ghadah Al Murshidi, Karim Ragab, Ahmed Al Zaabi
CONT ED TECHNOLOGY, Volume 18, Issue 1, Article No: ep636
ABSTRACT
Artificial intelligence (AI) is transforming educational systems by enhancing teaching, assessment, and learning personalization. This study investigated teachers’ perceptions of AI integration using an integrated technology acceptance model (TAM), technological pedagogical content knowledge (TPACK), and generative artificial intelligence (GenAI) framework. The main constructs used are perceived usefulness (PU), attitudes toward use (ATU), and behavioral intention (BI), with GenAI dimensions (agency, amplification, adaptivity, and authenticity) embedded within them. The study employed a cross-sectional design with 332 teachers in the emirate of Al Ain, United Arab Emirates. Results showed that PU was the strongest predictor of both ATU and BI, while ATU did not significantly mediate the PU-BI relationship. Amplification and adaptivity were positively perceived, whereas concerns about authenticity and agency tempered attitudes. Teachers aged 30-49 and those with 1-10 years of experience reported higher BI, and teachers of grades 4-9 showed greater PU. The findings highlight the need for professional development that fosters both practical integration and ethical understanding of AI in education.
Keywords: AI in education, technology acceptance model, TPACK, TPACK-GenAI