Skip to content

DEVELOPMENT OF CLASSROOM WELL-BEING STRATEGIES FOR MANAGING BEHAVIOURAL ISSUES AMONG PRIMARY SCHOOL PUPILS USING THE FUZZY DELPHI METHOD

Oct 2026 · PUPIL International Journal of Teaching Education and Learning
Behavioral and Psychological Studies

Abstract

Behavioural issues among primary school pupils such as hyperactivity, excessive classroom noise, low motivation, lack of focus, and disruptive behaviour have become increasingly challenging for teachers in creating an effective learning environment. These behavioural challenges affect pupils’ engagement, classroom well-being, and teaching effectiveness. Therefore, this study aims to develop classroom well-being strategies for managing behavioural issues among primary school pupils using the Fuzzy Delphi Method (FDM). The study is grounded in the concepts of positive classroom management, pupil engagement, and classroom well-being. Employing a quantitative design through the Fuzzy Delphi Method, the study involved five experts from the fields of primary education, counselling, educational psychology, behavioural management, and classroom management. The experts were selected based on their professional experience and direct involvement in managing pupil behaviour in schools. The research instrument was developed through literature review and preliminary needs analysis. The findings demonstrated a high level of expert consensus, with all items fulfilling the threshold value (d ≤ 0.2), expert agreement exceeding 75%, and α-cut values above 0.5. The highest-ranked strategies included interactive learning activities, positive reinforcement, emotional support, movement-based activities, brain breaks, and positive teacher–pupil relationships. The study contributes practically by providing a structured framework to support teachers in managing behavioural challenges and improving classroom well-being in primary schools.

View source

Similar papers

#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54
#machine learning Review Open access Jun 2014

Why Early-Stage Software Startups Fail: A Behavioral Framework

This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.

Carmine Giardino, Xiaofeng Wang, P. Abrahamsson · 175 citations · ⚡19
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#machine learning Review Open access May 2016

Key Challenges in Software Startups Across Life Cycle Stages

It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.

Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al. · 62 citations · ⚡6

Related blog posts

MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.