Validity and Reliability of the E-Aishi Interactive Professional Quality Module: A Scoping Review Using Cohen’s Kappa with Expert Evaluation

This scoping review examines the validity and reliability of the e-AISHI Interactive Professional Quality Module, a digital intervention designed to enhance the character and professional competencies of preschool teachers. Guided by the PRISMA-ScR framework and Arksey & O’Malley’s six-stage methodology. This study maps existing literature on module validation specifically focusing on expert evaluation using the Cohen’s Kappa statistical technique. Content validity was established by three subject matter experts in module development, counseling and ICT. The findings indicate that the e-AISHI module achieved a perfect inter-rater agreement (Kappa=1) demonstrating excellent initial validity and reliability compared to international studies that reported moderate to substantial agreement. While this perfect consensus underscores the clarity of the module's constructs, the study acknowledges that the score may reflect the homogeneity of a limited expert panel. Consequently, this validation serves as a foundational step within a broader Design and Development Research (DDR) framework with future phases intended to assess internal consistency and construct validity through empirical field testing. This review synthesizes methodological trends and gaps providing a rigorous framework for future validation studies in educational module development. Ultimately, the e-AISHI module serves as a scientifically grounded tool to support teacher professionalism and the achievement of Sustainable Development Goal (SDG) 4.

e-AISHI Module, Validity, Reliability, Preschool Teachers, Cohen’s Kappa, Scoping Review.

1. Badhrianawati, H. B., & Romarzila, O. (2025). The Design Of E-Aishi Interactive Professional Quality Module To Preschool Teacher Character Development Using Design And Development Research (DDR) In Early Childhood Education. Universiti Pendidikan Sultan Idris (UPSI), Perak, Malaysia. [Google Scholar] [Crossref]

2. D’Abbronzo, G., et al. (2025). Development Of An Automated Artificial Intelligence Based Tool For Reticulin Fibrosis Assessment In Bone Marrow Biopsies. Università Degli Studi Della Campania Luigi Vanvitelli, Via Luciano Armanni 5, 80138 Naples, Italy. Journal of Clinical Pathology. Web of Science. [Google Scholar] [Crossref]

3. Dale, N., et al. (2024). Modification and Validation of an Autism Observational Assessment Including ADOS-2® for Use with Children with Visual Impairment. Great Ormond Street Hospital NHS Trust, Great Ormond Street, London WC1 3JH, UK. Scopus. [Google Scholar] [Crossref]

4. Geara et al. (2025). Assessment of the internal validity of the Swedish National Quality Register in Gynecological Surgery (GynOp). Division of Obstetrics and Gynecology at Södersjukhuset, Stockholm, Sweden. Scopus. [Google Scholar] [Crossref]

5. Johar, R. (2025). Development Of Algebraic Expressions E-Modules Through Realistic Mathematics Education Approach. Department Of Mathematics Education, Universitas Syiah Kuala, Aceh, Indonesia Scopus. [Google Scholar] [Crossref]

6. Lin, Y., et al. (2025). Transperineal ultrasound versus digital palpation: Identifying key parameters for objective pelvic floor muscle contraction assessment. Fuzhou University Affiliated Provincial Hospital, Fuzhou, China. Web of Science. [Google Scholar] [Crossref]

7. Messer, M., et al. (2025). How Consistent Are Humans When Grading Programming Assignments?. King’s College London, London, United Kingdom of Great Britain and Northern Ireland. ERIC. [Google Scholar] [Crossref]

8. Moon Flip, et al. (2025). Innovative Digital Evaluation Methodologies: Checkbox Grading For Large-Scale Mathematics Examinations. Freudenthal Institute, Utrecht University, the Netherlands. Scopus. [Google Scholar] [Crossref]

9. Radović, S., & Seidel, N. (2024). Development And Validation Of The Self-Regulated Learning Support (SRL-S) Rubric In Technology-Enhanced Environments. Center of Advanced Technology for Assisted Learning and Predictive Analytics (CATALPA), Fern Universität in Hagen, Germany. ERIC. [Google Scholar] [Crossref]

10. Wang, J. X., Zhang, Q. T., et al. (2024). Enhanced Dual-Channel Model-Based With Improved Unet++ Network For Landslide Monitoring And Region Extraction. [Google Scholar] [Crossref]

11. Faculty of Arts and Sciences, Beijing Normal University, Zhuhai 519087, China. Web of Science. [Google Scholar] [Crossref]

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