Designing with Data: From Templates to Custom Solutions
This assignment challenged me to think beyond simply completing a task and instead engage in a thoughtful design process. My first step was to search for existing digital rubric templates that I could adapt for my CNC machining context. Rather than starting from scratch, I explored several online options and ultimately settled on a publicly shared Google Sheets rubric template I found on Reddit.
You can view the inspiration source here:
👉 Original Shared Google Sheet Template
The template was functional and well-structured, and it provided a helpful starting framework. However, as I began adapting it to my CNC Lathe Programming Project rubric, I realized it did not fully support the level of control and reliability I wanted. Specifically, the checkbox format allowed multiple selections within a single criterion, which introduced the possibility of inaccurate or inflated scoring.
Rubric Version 1: Checkbox-Based Adaptation
Using the Reddit template as inspiration, I created my own checkbox-based rubric tailored to CNC machining performance criteria.
👉 CNC Lathe Programming Project Rubric (Checkbox Version)
This version incorporated automated scoring formulas and structured performance levels. While it functioned technically, testing revealed that the lack of selection restriction could lead to user error. That discovery led me to refine the design further.
Rubric Version 2: Dropdown-Based Refinement
To improve scoring integrity, I redesigned the rubric using dropdown validation instead of checkboxes.
👉 CNC Lathe Programming Project Rubric (Dropdown Version)
The dropdown format ensures that only one performance level can be selected per criterion. This change significantly strengthened reliability and usability. What began as a template adaptation evolved into a fully customized digital assessment tool built to meet the specific needs of my instructional context.
Exploring Google Sheets as a Design Platform
Although I am very comfortable working in Excel, this project required me to work extensively in Google Sheets. I found that many functions operate similarly, but navigating validation rules, formulas, and layout options required hands-on exploration. Writing formulas, experimenting with logic, and refining data validation felt surprisingly similar to light programming. While the original goal was simply to create a rubric, I gained additional technical experience working within a new digital ecosystem.
Using Polling to Inform Career Alignment
For the feedback tool component, I designed a career interest survey rather than a traditional quiz. The goal was to explore whether precision machining might be a good fit for respondents based on their interests in problem-solving, technology use, 3D modeling, and preferred work environments.
👉 CNC Career Interest Survey (Microsoft Forms)
👉 Could CNC Machining Be a Good Fit for You? – Results
👉 Excel Data Summary & Charts
Analyzing the results using PivotTables and data visualization tools reinforced the importance of organizing information effectively. I experimented with reshaping data vertically and horizontally—not because it was required, but because understanding how data structure affects interpretation felt important for long-term application. The survey results indicated a strong preference for blended work environments combining cognitive and hands-on tasks, aligning closely with modern CNC machining roles.
Final Reflection
This assignment reinforced that effective digital assessment design is iterative. Templates can provide helpful starting points, but meaningful refinement requires evaluation, testing, and adaptation. By exploring platform limitations, refining rubric functionality, and analyzing real response data, I strengthened both my technical skills and my understanding of how digital tools can support instructional and workforce development goals.
Designing with data is not simply about creating charts—it is about building reliable systems that support better decision-making.
Comments
Post a Comment