Abstract
<title>Abstract</title> <p> Students often experience difficulties in critically evaluating statistical evidence during data-driven decision making. This study investigated changes in students’ critical thinking skills and the development of their thinking processes within a mindtools-based learning environment. A quasi-experimental study employing a single-group repeated-measures design was conducted with 32 undergraduate students enrolled in an introductory statistics course in one of private university, East Java, Indonesia. The learning environment was implemented through a spreadsheet-based learning game designed to support data exploration, statistical interpretation, and evidence-based decision making under conditions of uncertainty. Quantitative data were obtained from critical thinking pretest and posttest scores, while qualitative data were collected through observations, written justifications, reflective responses, and learning artifacts generated across three progressively designed learning missions. Quantitative data were analyzed using a paired-samples <italic>t</italic> -test, whereas qualitative data were analyzed using deductive content analysis based on Facione’s critical thinking framework. The results revealed a significant improvement in students’ critical thinking skills following the intervention. Qualitative findings further showed that students’ thinking processes evolved from reliance on single statistical indicators toward more reflective evaluation of evidence and revision of decisions. The development of critical thinking dimensions was not uniform across missions; while interpretation and analysis were more evident during initial interactions with data, evaluation and self-regulation emerged more prominently when students encountered ambiguity and statistical uncertainty. These findings suggest that spreadsheet-based learning games, when designed as mindtools, can facilitate the development of critical thinking through evidence evaluation, reflection, and decision revision in data-driven statistical decision-making contexts. </p>