Module 4: Evaluation Model & Data Source Justification

Module 4: Comparing Evaluation Models – CIPP vs. Kirkpatrick

In Module 4, I designed an infographic using Canva that visually compares the CIPP Model (Context, Input, Process, Product) with Kirkpatrick’s Four-Level Model to determine which evaluation approach best aligns with my educational technology initiative: the Magic Student AI Platform. A comic strip format inspired the layout of the infographic, helping to make a complex comparison more engaging and digestible.

Both models offer value in educational evaluation. Kirkpatrick’s model is strong for measuring participant response and training effectiveness, especially in professional development contexts. However, I selected the CIPP model because it offers a broader, systems-oriented approach that is ideal for evaluating educational programs embedded in a school setting. The CIPP model allows for flexibility and ongoing assessment, which is critical for a program like Magic Student, as it is still in its pilot phase. It helps evaluators examine the context of implementation, determine necessary inputs, monitor processes, and assess both short- and long-term outcomes.

My infographic clearly outlined these distinctions while justifying how CIPP better supports the formative and summative evaluation needs tied to writing instruction, especially in my 6th-grade classroom, where many students are below grade level. I selected data sources that prioritize both quantitative validity (e.g., STAAR writing scores) and qualitative insights (e.g., student writing reflections and teacher observations), while also addressing limitations such as potential bias and inconsistent student access to technology.

Feedback from Maria helped me realize the importance of specifying the target age group for the program—6th-grade students in a middle school setting. By clarifying this, I can better explain how Magic Student’s features can be tailored to meet specific developmental and academic needs, ultimately increasing program effectiveness.

This module helped me deepen my understanding of how evaluation models influence both the scope of data collection and the clarity of program impact, reinforcing the value of selecting a model that aligns closely with project goals.