Informing Science Institute

Title of Paper: Dynamic Adaptive Representation Calibration for Keypoint-Based Sign Language Recognition

DOI: https://doi.org/10.28945/5859
Journal: Interdisciplinary Journal of Information, Knowledge, and Management
Authors: Ying Ma, Nan Lin
Authors’ Email: [email protected], [email protected]

Aim/Purpose:
Individual differences in signers’ speed and amplitude cause variations in keypoint sequences, leading to redundant features that degrade the classification performance of sign language recognition models.
Findings:
The proposed method achieved a Top-1 accuracy of 56.86% on the WLASL2000 dataset (a 3.2% relative improvement over the state-of-the-art DSTA-SLR) and 65.88% on the MSASL1000 dataset. Visualizations confirm that the proposed module significantly improves feature discriminability and clustering clarity. Additionally, multi-stream training validations demonstrate consistent performance improvements across spatial and temporal dynamic streams, effectively raising the model’s overall representation ceiling.

IJIKM - Dynamic Adaptive Representation Calibration for Keypoint-Based Sign Language Recognition An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

Title of paper: Integrating Generative AI and WebAR Into Environmental Design Education: A Project-Based Classroom Case Study
DOI: https://doi.org/10.28945/5868

Journal: Journal of Information Technology Education: Innovations in Practice
Authors: Yang Liang, Kexin Hao, Sirui He, Peng Lei, Yiyuan Li, Bing Yang
Authors’ Email: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected]

Aim/Purpose:
This study examines how generative artificial intelligence (GenAI) and web-based augmented reality (WebAR) can be integrated into a project-based introductory environmental design course and how students perceive the usefulness, feasibility, and demands of the resulting workflow.
Findings:
All 11 groups submitted the required types of project output during the course. During subsequent verification, complete archived deliverable sets could be opened and inspected for 10 groups. Component-level fulfillment varied across site-report completeness, correspondence with the original redesign, map or site positioning, and technical ex*****on. Students generally perceived the workflow positively, particularly the continuing need for human design judgment, the connection between software learning and real design problems, and the contextual communication value of WebAR. However, perceived support from ComfyUI for rapid initial ideation was comparatively lower, while technical complexity and time pressure remained practical concerns.

JITE:IIP - Integrating Generative AI and WebAR Into Environmental Design Education: A Project-Based Classroom Case Study An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

Title of Paper: Evaluating the Enactment of Teacher Digital Competence: A Convergent Mixed-Methods Approach

DOI: https://doi.org/10.28945/5853
Journal: Journal of Information Technology Education: Research
Authors: Nidal Abounajem
Authors’ Email: [email protected]

Aim/Purpose:
This study examines the relationship between teachers’ self-reported digital competence and its enactment in classroom practice, addressing limitations in how education systems evaluate the patterns of ICT integration.
Findings:
Results indicated a statistically significant increase in self-reported digital competence from pre-intervention (M = 1.85, SD = 0.83) to post-intervention (M = 3.19, SD = 0.93), t(149) = 18.62, p < .001, Cohen’s d = 1.52. The proportion of teachers at the Intermediate stage or above increased from 20.0% (n = 30) to 78.7% (n = 118). Classroom observations documented instructional ICT practices that were broadly consistent with teachers’ reported post-intervention competence levels, with 65.5% alignment between perceived competence and documented classroom practice.

JITE:Research - Evaluating the Enactment of Teacher Digital Competence: A Convergent Mixed-Methods Approach An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

Title of Paper: Enhancing Organizational Sensemaking and Evidence-Based Decision-Making: An Explainable AI (XAI) Approach to Higher Education Graduate Employability

DOI: https://doi.org/10.28945/5856
Journal: Interdisciplinary Journal of Information, Knowledge, and Management
Authors: Damla Karagozlu, Ahmet Adalier, Kian Jazayeri
Authors’ Email: [email protected], [email protected], [email protected]

Aim/Purpose:
The use of machine learning models in graduate employability prediction is constrained by limited transparency. This limitation weakens their value for organizational decision-making in higher education.
Findings:
Education level and language proficiency are identified as dominant predictors of employability outcomes. Substantial influence is also exerted by GPA, university ranking, and internship experience, while demographic attributes such as gender make a negligible contribution. Both aggregate feature importance and heterogeneous individual effects are exposed through the explainability analysis.

IJIKM - Enhancing Organizational Sensemaking and Evidence-Based Decision-Making: An Explainable AI (XAI) Approach to Higher Education Graduate Employability An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.

Title of Paper: SHELF: A Gender-Sensitive Hybrid AI Framework for Advancing Educational Equity in Underserved Regions

DOI: https://doi.org/10.28945/5817
Journal: Journal of Information Technology Education: Innovations in Practice
Authors: Ayesha Nawaz, Muhammad Hamid, Aisha Shabbir, Aisha Blfgeh, Muhammad Saleem
Authors’ Email: [email protected], [email protected], [email protected], [email protected], [email protected]

Aim/Purpose:
AI-based recommendation systems frequently fail to serve women in low-resource educational environments effectively, as they neglect critical realities, such as time constraints, limited connectivity, and socio-cultural barriers. This paper proposes a hybrid ensemble AI framework that directly addresses these contextual realities by integrating algorithmic fairness, gender-aware personalization, and low-bandwidth adaptability into a unified, women-focused framework.
Findings:
Results show that 48% of studies focus on AI-based recommender systems, 19% on adaptive and fair conceptual models, 11% on gender-specific education, and 12% on emerging technologies such as IoT and edge computing. Systematic reviews and meta-analyses of educational recommendation systems account for 7% of studies, while only 3% address transparency and explainability, highlighting a major research gap.

JITE:IIP - SHELF: A Gender-Sensitive Hybrid AI Framework for Advancing Educational Equity in Underserved Regions An international association advancing the multidisciplinary study of informing systems. Founded in 1998, the Informing Science Institute (ISI) is a global community of academics shaping the future of informing science.