Data analysis techniques are essential tools for educators conducting classroom inquiry, as they enable a systematic examination of collected evidence to draw meaningful conclusions. By employing a variety of qualitative and quantitative methods, you can uncover patterns, relationships, and insights that inform your teaching practice. Whether analyzing survey responses, student work products, or observational data, these techniques help illuminate the impact of instructional strategies on student learning. Ultimately, effective data analysis fosters a deeper understanding of educational processes and enhances the overall learning experience. Below is a non-exhaustive list of data sources and possible corresponding data analysis techniques.
Student Work Products
Rubric-Based Assessment: Develop and apply rubrics to evaluate student work artifacts based on predetermined criteria, facilitating consistent and objective assessment.
Content Analysis: Analyze the content of written work or projects to identify key themes, concepts, or trends in students’ understanding and application of knowledge.
interviews + focus groups
Qualitative Content Analysis: Systematically categorize and interpret the content of responses to identify key themes, patterns, and insights from participants’ discussions.
Narrative Analysis: Examine the stories and narratives shared by participants to understand how they make sense of their experiences and the meanings they attribute to them.
Think Aloud Protocols
Verbal Protocol Analysis: Transcribe and analyze the verbalizations of participants to uncover cognitive processes, strategies, and decision-making patterns while they engage in tasks.
Coding for Cognitive Strategies: Develop a coding scheme to categorize different cognitive strategies used by participants, such as problem-solving techniques or comprehension strategies.
Written Reflections
Thematic Analysis: Identify themes and patterns in students’ written reflections to understand their perceptions, insights, and emotional responses to learning experiences.
Reflective Journaling Analysis: Analyze the depth and quality of reflections to assess the level of critical thinking and self-awareness demonstrated by students.
Surveys/pre- and post-tests
Descriptive Statistics: Use measures such as mean, median, mode, and standard deviation to summarize and describe the basic features of the data.
Cross-tabulation: Analyze the relationship between two or more categorical variables to identify patterns and differences in responses among different groups.
Paired t-test: Compare the means of the pre-test and post-test scores to determine if there is a statistically significant difference in performance after an intervention.
Effect Size Calculation: Calculate effect sizes (e.g., Cohen’s d) to quantify the magnitude of the change in scores, providing insight into the practical significance of the intervention.
Onlooker/Participant Observations
Thematic Analysis: Identify and analyze recurring themes or patterns in observed behaviors to understand interactions and engagement in the learning environment.
Descriptive Coding: Use coding to categorize observed behaviors, allowing for a systematic analysis of specific actions or interactions during the observation.
Visual Reflections
Visual Analysis: Examine visual artifacts (e.g., concept maps, drawings) to interpret the relationships and concepts represented, gaining insights into students’ understanding and thought processes.
Content Mapping: Use content mapping techniques to categorize and analyze the elements present in visual reflections, identifying key concepts and their connections.
Sharing your findings
Sharing your findings is one of the defining characteristics of SoTL work. Now that you’ve documented/collected evidence and learned some things about teaching and learning in your course or program, it’s time to share with others to get feedback and contribute insights that may help others in their teaching practice and our greater knowledge of teaching and learning processes.
Investigating our teaching can be a vulnerable experience, particularly if our findings are that our teaching practices “didn’t work” or didn’t show the learning gains we predicted, but sharing doesn’t have to be scary.
Share with:
- a discipline-based colleague in your department or at another institution
- a few discipline-based colleagues through your promotion and tenure or teaching review meetings
- a wider audience of local colleagues across disciplines at your institution (e.g., the annual Innovations in Teaching and Learning conference at GMU; check with your center for teaching and learning or faculty affairs office to learn what opportunities there are at your institution)
- a wider audience of discipline-based colleagues at a discipline conference
- a wider audience of colleagues from across disciplines at a regional or national pedagogy conference (see our list on our conferences page)
- a wider audience of colleagues from across disciplines and the public through publishing an article about your work (see our list on our publishing page)