Designing an R-supported statistical reasoning learning environment model - Tri Astuti Arigiyati; Dhoriva Urwatul Wutsqa; Kana Hidayati; Sony Yunior Erlangga (COMPTON: Jurnal Ilmiah Pendidikan Fisika, 2026)
This study reports the define phase of a development study aimed at designing an R-supported Statistical Reasoning Learning Environment (SRLE) model for university statistics learning. The study was motivated by a persistent instructional problem: students were able to perform statistical procedures but had difficulty interpreting results, evaluating data representations, and explaining conclusions in context. A qualitative exploratory design was employed with eight Mathematics Education students and two statistics lecturers selected through purposive sampling. Data were collected through semi-structured interviews, classroom observations, students’ learning artefacts, and field notes during a limited R-assisted SRLE trial. The data were analysed thematically using the Miles, Huberman, and Saldaña interactive model, supported by triangulation, member checking, and an audit trail. Five themes were identified: the need to shift learning from formula execution to contextual interpretation; the value of R-mediated visualisation for connecting data, graphs, and meaning; the emergence of reflective statistical habits of mind through inquiry and peer discussion; the changing role of lecturers as facilitators of statistical reasoning; and the persistence of syntax-related barriers among novice R users. The findings indicate that an R-supported SRLE model should combine real-data tasks, structured R scaffolds, multiple representations, collaborative interpretation, and explicit reflection prompts. The study contributes an empirically grounded set of design principles for the subsequent design and development stages, while avoiding premature claims about model effectiveness before larger-scale validation.