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The Spinning Box: An Innovative Educational Game to Stimulate Early Childhood Development a Single-group Pre-test and Post-test Research Design Hasani, Ruslan; Putri, Besse Sherly Aulia; Simunati, Simunati; Yulianto, Yulianto; Agussalim, Agussalim; Lorica, Josephine D.; Abas, Hafiza; Shiratuddin, Mohd Fairuz
IJECA (International Journal of Education and Curriculum Application) Vol 8, No 3 (2025): December
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/ijeca.v8i3.35483

Abstract

Early childhood is a critical period for cognitive development, socialization and independence, motor skills, and language. This article introduces the Spinning Box, an innovative educational game designed to stimulate children's holistic development through gross motor skills, fine motor skills, speech and language skills, and socialization and independence. The research method is a quasi-experimental study using a single-group pre-test and post-test design proposed to evaluate its effectiveness. Analysis was descriptive and used paired t-tests to detect differences in mean scores before and after the intervention. Participants consisted of four children aged 3–4 years. Inclusion criteria: healthy children without major developmental disorders, with parental consent. The Spin Box consists of a cube with four sides, each corresponding to a developmental task (motor skills, speech and language skills, and socialization and independence skills). Children took turns spinning the box and completing the assigned tasks. Sessions lasted for 20 minutes, three times a week, for eight weeks. Measurements were conducted using the Pre-Screening Developmental Questionnaire. The results showed significant improvements in all four domains of child development following the Spinning Box intervention. All p-values were below 0.05. Furthermore, Cohen's d effect sizes across all domains were very large (d > 0.8), with most even far exceeding this value. This shows that the Spinning Box educational game has a very strong influence in stimulating development in early childhood. However, with only four participants from one center and a single-group pre-post design, these findings are preliminary and should be interpreted with caution, as they cannot be broadly generalized.Parental involvement is widely recognized as a key determinant of children’s academic achievement, motivation, and socio-emotional development. Yet despite decades of research, evidence on concrete and scalable strategies remains fragmented. This study synthesizes recent innovations by conducting a structured literature review of 63 Scopus-indexed articles published between 2020 and 2025, complemented by earlier foundational works to provide historical and conceptual grounding. The objective was to identify effective approaches, examine barriers to implementation, and highlight emerging opportunities for strengthening parent–school partnerships in primary and secondary education. Findings converge into five clusters. First, programmatic interventions such as parenting workshops and structured sharing sessions enhanced parental knowledge, confidence, and collaboration with teachers, leading to improvements in children’s learning outcomes. Second, technology-enabled access—including internet provision, device distribution, and digital platforms—expanded opportunities for engagement, although inequalities in digital literacy and access remained evident. Third, multi-channel communication systems such as logbooks, bulletins, and social media groups supported continuous dialogue between home and school, with positive effects on student behavior and motivation. Fourth, shared governance through parental participation in school committees, decision-making, and community events fostered inclusivity, reduced dropout risks, and enhanced school–community relationships. Fifth, targeted outreach, including home visits and culturally responsive practices, proved especially effective in reaching disadvantaged families and improving trust, equity, and sustained participation. Overall, the review demonstrates that parental involvement is highly adaptable and can be strengthened through a mix of low-cost innovations and contextually sensitive strategies. These findings offer educators and policymakers actionable insights for institutionalizing parental engagement and addressing persistent equity gaps in education.
Artificial intelligence in writing: unveiling a research landscape Salehudin, Wan Rusydiah; Saari, Zilal; Abas, Hafiza
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp66-75

Abstract

This study examines the expanding research landscape of artificial intelligence (AI) in writing, a field that continues to reshape the way ideas are produced, refined, and communicated. While AI has been widely examined in education and technology, limited research has mapped its thematic evolution and ethical dimensions in writing. To address this gap, 1,596 publications indexed in Scopus between 2021 and 2024 were analyzed using bibliometric mapping tools such as Scopus analyzer and VOSviewer. The analysis covers publication patterns, collaboration networks, and keyword relationships to trace the intellectual structure of the field. The results indicate a sharp increase in scholarly output over the past four years, supported by contributions from multiple disciplines, including computer science, social sciences, and education. Several thematic clusters were identified, centering on AI-assisted creative writing, authorship ethics, educational use, and cross-sector innovation. Despite these advances, ethical frameworks and responsible AI applications in writing remain underexplored. This paper offers a comprehensive overview of current trends and presents a foundation for future research on how AI can be integrated into writing practices responsibly and in ways that uphold human creativity and academic integrity.
Forecasting Rice Prices in Indonesia Using a Hybrid HWES-MLP Time Series Prediction Model Supriadin, Supriadin; Haris, M. Al; Amri, Saeful; Abas, Hafiza; Fadugba, Sunday Emmanuel
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 10, No 2 (2026): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v10i2.35445

Abstract

Rice is the main staple food for the majority of the Indonesian population. However, the fluctuation in rice prices and future uncertainty emphasize the importance of forecasting rice prices, thus requiring a forecasting model capable of providing accurate predictions. Various previous forecasting methods have been limited in capturing the combination of linear and non-linear patterns in rice price data, spurring the need for a more comprehensive hybrid approach. This research applies a quantitative approach by utilizing secondary data sourced from publications of the Central Statistics Agency (BPS) of Indonesia. This study aims to forecast rice prices in Indonesia using a hybrid approach combining Holt–Winters Exponential Smoothing (HWES) with Multilayer Perceptron (MLP). The hybrid model is designed to overcome the limitations of the Holt-Winters Exponential Smoothing method, which can only capture linear patterns such as trend and seasonality, by adding the Multilayer Perceptron method to capture non-linear patterns that cannot be handled by the linear approach. The dataset comprises monthly rice prices in Indonesia from January 2010 to December 2024, while the period of January–December 2025 is used as the prediction period. The data analysis process was carried out using the software R-Studio and Minitab, which provide a variety of features to support time series modeling. The results indicate that the most effective method for forecasting rice prices in Indonesia is the Hybrid Holt Winters Exponential Smoothing (α = 0.5; β = 0.3; γ = 0.3)-Multilayer Perceptron (12-12-1), which achieved the highest accuracy with a MSE of 9666.12, a RMSE of 310.9117, and a MAPE of 1.9949%. This finding indicates that the Hybrid HWES-MLP approach is highly capable of capturing rice price data patterns. Thus, this model holds significant potential to be utilized as a benchmark supporting government policy in maintaining rice price stability, market intervention, and optimizing the management of national rice reserves stock.