Wardono
Universitas Negeri Semarang, Indonesia

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Meaningful learning to enhance conceptual understanding, engagement, and mathematical reasoning in elementary statistics education Ema Butsi Prihastari; Wardono; Scolastika Mariani; Masrukan
Journal of Advanced Sciences and Mathematics Education Vol. 6 No. 2 (2026): Journal of Advanced Sciences and Mathematics Education
Publisher : CV. FOUNDAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/jasme.v6i2.1254

Abstract

Background: Elementary school statistics education faces challenges due to students’ low conceptual understanding, often resulting from abstract and procedural teaching methods. Aims: This study aimed to examine the implementation of meaningful learning in elementary statistics, assess its effects on students’ conceptual understanding and engagement, and identify supporting and inhibiting factors. Method: A mixed-method explanatory sequential design was employed. Quantitative data were collected through perception questionnaires from 60 fifth-grade students, while qualitative data were obtained via participatory observations, semi-structured interviews, and classroom documentation involving one teacher and 15 students in a purposively selected class. Quantitative data were analyzed descriptively, and qualitative data were analyzed using the Miles and Huberman model. Results: Meaningful learning positively influenced statistics education. Mean scores were 3.42 for conceptual understanding (SD=0.51), 3.67 for material relevance (SD=0.48), 3.38 for motivation (SD=0.55), 3.25 for engagement (SD=0.62), and 3.35 for satisfaction (SD=0.58). All students (100%) agreed that the material was relevant to real-life contexts. Mini-project activities, such as the “Our Body Statistics” project, enhanced active participation. Supporting factors included teacher creativity, student enthusiasm, and a supportive learning environment; inhibiting factors included time constraints, varied student readiness, and limited contextual resources. Conclusion: Implementing meaningful learning improves conceptual understanding and engagement in elementary statistics. Contextual, experience-based approaches should be expanded with adequate teacher training and time allocation to optimize learning outcomes.
Statistical Literacy of University Students: An Analysis of Decision-Making, Evaluating, Communicating, and Interpreting Skills Zulqoidi Habibie; Kartono` Kartono; Wardono; Iqbal Kharisudin
Hipotenusa: Journal of Mathematical Society Vol. 8 No. 1 (2026): Hipotenusa : Journal of Mathematical Society
Publisher : Program Studi Tadris Matematika Universitas Islam Negeri (UIN) Salatiga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/hipotenusa.v8i1.7271

Abstract

This study aimed to analyze university students’ statistical literacy based on four indicators: decision-making, evaluating, communicating, and interpreting, as well as to examine differences in statistical literacy according to gender. A descriptive survey design was employed. The participants comprised 144 students from six universities across five provinces in Indonesia: Banten, Jakarta, East Java, Jambi, and Bengkulu, who were selected using non-probability sampling with a convenience sampling technique. Data were collected using an open-ended statistical literacy test developed based on response-skill indicators from the perspective of data consumers. The collected data were analyzed using descriptive statistics and the Mann–Whitney U test to determine whether significant differences existed between male and female students. The results revealed no statistically significant differences in statistical literacy between male and female students. Among the four indicators (decision-making (average 22.9), evaluation (75), communication (55.6), and interpretation (22.2)) students demonstrated relatively stronger performance in evaluating and communicating statistical information than in decision-making and interpreting. Analysis of students’ responses indicated that the main difficulties were related to understanding the context of statistical information, accurately interpreting data, integrating multiple statistical measures when making decisions, and effectively communicating information derived from graphs and datasets. These findings highlight the importance of integrating statistical knowledge with contextual understanding to support the development of statistical literacy among university students. The limitations of this study are evident in the data collected, namely the imbalance in the number of male and female students, which means the results cannot yet be generalized; further research is needed.