A method for assessing students' interpretations of contextualized data.

Area of Science:

• Educational Psychology
• Data Interpretation Pedagogy

Background:

• Interpreting data within its context is a crucial educational objective.
• Assessing this skill requires evaluating the integration of contextual understanding and statistical reasoning.
• Existing methods may have limitations in fully capturing the nuances of student data interpretation.

Purpose of the Study:

• To present a research methodology for assessing students' ability to interpret data in context.
• To explore the synergy between contextual and statistical reasoning in student data interpretation.
• To demonstrate how Toulmin's model and the Structure of the Observed Learning Outcome (SOLO) can be combined for enhanced analysis.

Main Methods:

• Employs a two-stage qualitative data analysis approach.
• Stage 1: Utilizes Toulmin's model of argumentation to identify student justifications and uncertainty in data interpretation.
• Stage 2: Applies the multi-modal conceptualization of the Structure of the Observed Learning Outcome (SOLO) to rank the quality of student arguments.

Main Results:

• The combined Toulmin and SOLO approach provides a robust framework for analyzing student data interpretations.
• This tandem method effectively identifies the quality of reasoning and justification in student arguments.
• The integrated approach addresses limitations inherent in using either Toulmin's or SOLO's model independently.

Conclusions:

• The proposed research method offers a powerful tool for educators and researchers evaluating data literacy.
• Combining Toulmin's model and SOLO enhances the assessment of students' contextual and statistical reasoning.
• This integrated methodology facilitates a deeper understanding of student learning in data interpretation.