Research

Educational Design Research

This page highlights two classroom-based research projects conducted during my IDT Program. Both studies followed a systematic research process: problem identification, literature review, design, data collection, analysis, findings and reflection. They both address authentic challenges in secondary English instruction: AI-Assisted Feedback and Student Elaboration in Analytical Writing. Click on the Collapsible Groups below to learn more about the types of research and data we learned about.

OR, you can scroll down to my Research Projects!

Traditional Research vs. Action Research

Qualitative vs. Quantitative Data

My Research in the IDT Program

ETEC 6430 Research: Improving Student Elaboration with Sentence Stems

This study explored whether using graphic organizers with embedded sentence stems as exit tickets could help 11th-grade students improve their ability to elaborate on textual evidence in analytical writing. Over six weeks, students completed scaffolded ACE-based writing tasks, and results showed significant growth in elaboration skills, especially for multilingual learners.

ETEC 6540.pdf

A.I. Project The Efficacy of Brisk Teaching in Providing Effective and Actionable Feedback to High School English Students.pdf

ETEC 5430 Evaluating Brisk Teaching for AI Feedback in High School Writing

This study examined the Brisk Teaching Chrome extension, an AI tool designed to provide feedback on student writing. Thirty-two AP Language students’ work was scored for feedback clarity, actionability, and encouragement. Results showed moderate effectiveness with giving feedback on lower-order skills such as in syntax and surface-level revisions, but weaker for higher-order feedback such as argumentation and analysis. Findings suggest Brisk Teaching can supplement, but not replace, teacher guidance.

REFLECTION

Across my projects, I have explored two different but connected challenges in teaching writing: how to strengthen student elaboration and how to make feedback more effective through AI tools. Together, these studies shaped my growth as both a teacher and a researcher.

From the Sentence Stems/Exit Ticket study, I learned how powerful scaffolds and structured practice can be. Students showed measurable growth in elaboration skills when given clear frameworks (ACE, ACECES) and repeated opportunities to practice with gradually reduced supports. This confirmed to me that small and intentional instructional strategies can create significant gains, especially for multilingual learners who often need language scaffolding tools to express their ideas.

From the Brisk Teaching AI Feedback study, I learned that the technology has some promise but at its currently level is a bit shallow. The AI could handle surface-level revisions and offer encouraging comments, but it could not replace the nuance, cultural responsiveness, or contextual understanding of a teacher, especially for higher-order thinking such as argumentation and analysis. This reminded me that while AI may save time and provide some consistency, human judgment and guidance remain essential in feedback.

Together, these projects highlight two important themes in my practice:

Scaffolding matters. Students benefit most when supports are deliberate, gradual, and targeted at known weaknesses (like elaboration).
Technology is a tool, not a solution. AI can supplement instruction but must be implemented critically, with awareness of its limitations.

Personally, these experiences taught me the full research cycle - from defining a problem, reviewing literature, and designing interventions, to collecting and analyzing data and considering next steps. More importantly, they helped me see myself as a teacher-researcher, as somebody who can investigate authentic classroom problems, test solutions, and share findings that inform both my own practice and the wider educational community

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