Mapping students’ probabilistic reasoning and literacy through solo taxonomy in discrete random variable problem solving among preservice mathematics teachers
DOI:
https://doi.org/10.58524/jasme.v6i2.1249Keywords:
Discrete Random Variables, Probabilistic Reasoning, Preservice Mathematics Teachers, SOLO TaxonomyAbstract
Background: Probability learning requires not only procedural computation but also probabilistic reasoning and probabilistic literacy to interpret uncertainty meaningfully. However, many mathematics education students still experience difficulties in understanding discrete random variable concepts and communicating probabilistic meaning contextually. Previous studies have generally examined probabilistic reasoning and probabilistic literacy separately, with limited integration using SOLO Taxonomy as a cognitive framework.
Aims: This study aimed to investigate students’ probabilistic reasoning and probabilistic literacy in solving discrete random variable problems through SOLO Taxonomy.
Method: A mixed-methods design was employed involving 20 fifth-semester mathematics education students enrolled in an Introduction to Probability Theory course. Data were collected through probabilistic reasoning tests, probabilistic literacy questionnaires, and semi-structured interviews. Students’ responses were classified into five SOLO Taxonomy levels and analyzed using descriptive and thematic approaches.
Results: The findings revealed that most students were categorized at the Unistructural (45%) and Prestructural (30%) levels, indicating fragmented conceptual understanding and limited probabilistic interpretation. Only a small proportion achieved Relational (5%) and Extended Abstract (5%) levels. Higher probabilistic literacy was associated with more integrated reasoning, coherent interpretation, and stronger logical justification.
Conclusion: Probabilistic literacy and probabilistic reasoning are closely interconnected competencies in solving discrete random variable problems. SOLO Taxonomy provides an effective framework for identifying students’ probabilistic thinking structures and supporting higher-order probabilistic learning development.
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