Research

Traditional Research vs Action Research

Qualitative Methods, Quantatative Methods, and Mixed Methods

Quantative Methods

Focus:

Quantitative research focuses on measuring the “what,” “how much,” and “how many,” often testing hypotheses and identifying relationships between variables.

What Data Looks Like:

Data is numerical and statistical, such as test scores, survey ratings, and performance metrics.

Tools/Methods Used:

Common tools include surveys, questionnaires, tests, rating scales (e.g., Likert scales), and statistical analyses like descriptive statistics.

Sample Sizes:

Typically involves large, often randomly selected samples to allow for generalization to a broader population.

Outcomes:

Results are presented as statistical findings, trends, or relationships, often used to make predictions, evaluate effectiveness, or support data-driven decisions.

Qualitative Methods

Focus:

Qualitative research centers on understanding the “why” and “how” of human experiences, emphasizing meaning, context, and participants’ perspectives. It seeks deep, interpretive insights rather than generalizable laws.

What Data Looks Like:

Data is non-numerical and descriptive, including interview transcripts, observations, journals, and documents. These data are rich, narrative, and contextual.

Tools/Methods Used:

Common methods include interviews, observations, focus groups, document analysis, and case studies, often analyzed through coding and thematic analysis.

Sample Sizes:

Typically small, purposeful samples are used to gain in-depth understanding rather than broad generalization.

Outcomes:

Findings are presented as themes or narratives that provide insight into lived experiences and inform context-specific decisions.

Mixed Methods

Focus:

Mixed methods research integrates both qualitative and quantitative approaches to provide a more comprehensive understanding of a research problem.

What Data Looks Like:

Data includes both numerical (quantitative) and narrative (qualitative) forms collected within the same study.

Tools/Methods Used:

Combines tools such as surveys and tests (quantitative) with interviews, focus groups, or observations (qualitative), often using designs like sequential or convergent approaches.

Sample Sizes:

May involve both large samples (for quantitative data) and smaller, targeted samples (for qualitative data) within one study.

Outcomes:

Produces integrated findings, allowing researchers to both measure outcomes and explain underlying reasons, resulting in more holistic and actionable insights.

AI Prompt Engineering Training Research

Over the course of ETEC 5430 and ETEC 6440, I developed a strong interest in AI prompt engineering and its potential to support student learning, particularly at the university level. In my role as a STEM counselor, I observed that many students in my caseload were struggling in their courses. It wasn't necessarily due to a lack of effort, but because they had difficulty aligning course content with their individual learning processes.

Recognizing this gap, I began exploring how existing resources available to CSUSB students, such as ChatGPT, could be used more intentionally. My focus was on how students might leverage these tools to better structure, interpret, and enhance their course notes in ways that align with their learning needs.

Based on this work, I designed a course that provides students with accessible strategies and tools to support their academic success. Beyond immediate coursework, the goal is for students to carry these skills forward, using them to support continued learning and problem-solving in future academic and professional contexts.

ETEC 5430 Presentation

Research Proposal

Data Collection Plan

Reflection

When I began this program, my experience with data was mostly rooted in quantitative methods. Such as working with graphs, p-values, and boxplots. As I became more immersed in the field of education, I was introduced to qualitative and mixed methods, which expanded my perspective on how research can be conducted. Mixed methods, in particular, allowed me to build on my background in statistics while incorporating more reflective approaches such as interviews, surveys, and observations. This combination gave me the opportunity to explore not only what was happening in a learning environment, but also how and why it was happening. By working with multiple forms of data, I was able to develop a more well-rounded understanding of how to support learners and where to focus my efforts.

One of the most meaningful parts of this experience was taking my research beyond the classroom and presenting it at the Grad Slam. This opportunity challenged me to translate my findings in a way that was accessible to a broader audience, including those who may not have been familiar with topics like AI prompt engineering. Being able to share my work in that setting was both inspiring and motivating, as it reinforced the value of the research process and its potential impact. It encouraged me to continue exploring new questions and remain engaged in research that can inform and improve learning experiences.

Caroline Beatty Grad Slam

References

Bronack, S. (2026, January 27). Module 2 - Quantitative Research Methods Summary and Review. [Google Docs]. ETEC 6430: Technology and Learning II, California State University, San Bernardino. Google Classroom.

Bronack, S. (2026, February 10). Module 3 - Qualitative Research Methods Summary. [Google Docs]. ETEC 6430: Technology and Learning II, California State University, San Bernardino. Google Classroom.

Bronack, S. (2026, March 3). Module 4 - Mixed Methods Research Summary. [Google Docs]. ETEC 6430: Technology and Learning II, California State University, San Bernardino. Google Classroom.

Bronack, S. (2026, March 4). Module 4.18: Action Research. [Google Docs]. ETEC 6430: Technology and Learning II, California State University, San Bernardino. Google Classroom.

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