<![CDATA[Purpose: This study aimed to develop and preliminarily evaluate ImageStat EDU, an interactive digital image-based tool designed to support statistical visualization and learning, particularly in descriptive statistics, histogram interpretation, and introductory probability distributions.Method: The study employed a Research and Development approach using the ADDIE model. Product evaluation involved three expert validators representing mathematics/statistics content, instructional media and ICT, and pedagogy, followed by a small-scale implementation with 15 undergraduate mathematics education students enrolled in a statistics course. Data were collected using expert validation sheets, a user response questionnaire, and pretest–posttest assessments. Expert and student responses were analyzed descriptively, while changes in student performance were examined using a paired-sample t-test, a Wilcoxon signed-rank test, and Cohen’s.Findings: Expert evaluation showed substantial improvement following prototype revision, with the final overall evaluation reaching 96.67%. Students also reported high perceived practicality and usefulness, with an overall response of 92.00%. The mean score increased from 36.88 on the pretest to 65.17 on the posttest, with a large within-participant effect size ( ). Parametric and nonparametric analyses consistently indicated higher posttest performance. However, these findings represent preliminary changes within a small, uncontrolled sample and should not be interpreted as evidence of causal effectiveness.Significance: ImageStat EDU demonstrates the potential of using image-derived pixel data to connect visual information, numerical summaries, histograms, and distributional representations within an interactive statistics learning environment. The study provides preliminary evidence supporting further investigation of image-based statistical exploration in mathematics teacher education using larger and comparative research designs.]]>
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