Effectiveness of particulate nature of matter (PNM)-based intervention studies in improving academic performance: a meta-analysis study
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Abstract
Through a meta-analysis, this study examines how effective particulate nature of matter (PNM)-based intervention studies are at improving academic performance. Well-known databases (e.g., ERIC, Springer Link, Taylor & Francis, and ScienceDirect) were used to look for the PNM-based intervention studies via specific keyword patterns. Also, a manual search of related journals and dissertations was conducted to find any missing papers. Subsequently, this meta-analysis included 66 papers (44 dissertations, 21 articles, and one proceeding) published from 1992 to 2022. All statistical data from the papers were initially inserted into an Excel sheet and then imported into comprehensive meta-analysis (CMA) statistics software to calculate Hedges’ g values. The findings indicated that the overall effect-size for the random-effects model was 0.90, which means that the PNM-based intervention studies have a large effect for academic performance. Furthermore, it was revealed that moderator variables, namely educational level and type of intervention, positively affected participants’ PNM-based academic performance (p o 0.05). In light of the findings, it can be concluded that the PNM-based intervention studies are effective at improving the participants’ academic performance. Moreover, given the findings regarding educational level, it can be deduced that K–8 students are able to learn the fundamental features or dimensions of the PNM. Since the meta-analysis includes few extreme values, further research should be undertaken to test the effectiveness of the intervention types on academic performance. Furthermore, the current study excluded a notable number of papers because they lacked sufficient data; therefore, science researchers should take care to include sufficient data or effect-size value for their papers to facilitate dissemination, generalization and comparison of their results.