A Data-Driven Contextual Learning Model for Developing Students’ Algebraic and Analytical Thinking

Dimas Safitri; Mira Maharani; Teguh Rohman

Authors

Dimas Safitri Universitas Negeri Malang Author
Mira Maharani Universitas Negeri Malang Author
Teguh Rohman Universitas Negeri Jakarta Author

Keywords:

Algebraic thinking, Analytical thinking, Contextual learning, Data-driven learning

Abstract

This qualitative study aims to analyze how a data-driven contextual learning model develops students’ algebraic and analytical thinking in mathematics learning. The study responds to the problem that many students still understand algebra procedurally and struggle to connect symbols, variables, and patterns with real-life contexts. A qualitative case study design was used at a public junior high school in Indonesia from December 2025 to February 2026. Participants consisted of Grade VIII students, one mathematics teacher, and 9 to 12 selected student informants representing high, medium, and low initial mathematical ability. Data were collected through semi-structured interviews, limited participatory observation, documentation of student worksheets, teacher reflection notes, and classroom learning artifacts. Thematic analysis was used to identify patterns of meaning from the data. The findings reveal four main themes: the shift from procedural algebra to contextual algebraic meaning, the growth of analytical reasoning through data interpretation, collaborative meaning-making during classroom interaction, and remaining conceptual obstacles in translating data into algebraic representation. The study contributes to mathematics education by showing that contextual data can help students interpret variables, identify relationships, and justify algebraic expressions more meaningfully. These findings imply that teachers should use familiar data, structured scaffolding, and reflective discussion to support algebra learning. Further research can examine this model across different topics, grade levels, and school contexts.

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