🔍 Informal Evaluation

• Usage Data Tracking: The app monitors engagement metrics like module completion rates, time spent on activities, frequency of logins, and how consistently learners interact with different features. These analytics help identify patterns in user behavior and potential barriers to engagement.
• AI-Generated Feedback: After each cooking module or recipe tutorial, the AI assistant provides tailored suggestions—e.g., “Try a smaller dice on the carrots next time,” or “Well done following the timing steps!” This immediate, relevant feedback helps reinforce good habits while correcting mistakes in a non-judgmental manner.
• Weekly Progress Dashboards: Learners receive a personalized summary showing their progress across different skill domains, including recipe completion, challenge participation, and goal alignment. This visual reinforcement helps build motivation and shows clear milestones achieved.

🧠 Self-Assessment

• Reflective Prompts: After completing key activities, learners respond to prompts like “What did you find challenging about this task?” or “How would you improve your result next time?” This encourages metacognition and a growth mindset.
• Confidence and Skill Rating: Learners rate themselves using a 5-point scale (e.g., 1 = not confident, 5 = very confident) across categories such as “chopping,” “meal planning,” and “budget cooking.” This data informs future learning paths and helps learners see personal development over time.
• Visual Dashboard: Progress and reflection data are displayed on a personal dashboard, giving learners a quick overview of their growth and helping them set new goals. Optional notifications may prompt learners to revisit skills that haven’t been practiced recently.

Ruolin:

After the last presentation, I re-examined and modified parts of my previous task analysis. Visualizing each phase and mapping activities onto the timeline helped clarify what this stage should truly focus on—breaking down the learner’s tasks and aligning them with outcomes, instead of jumping directly to activities or assessments. This process provided me with a clearer direction for the next steps.

Working with George on the website layout made me realize how important collaboration is between different roles. I had to balance detailed content with how it would visually flow on the screen. This helped me prioritize clarity, consistency, and modularity—which are important.

George:

Creating the implementation plan pushed me beyond simply listing steps—it required me to critically analyze the logic and learner flow behind each phase. I realized that what sounds good on paper doesn’t always translate into actionable, learner-centered experiences. For example, I initially underestimated how difficult it is to balance learner autonomy with structured guidance. Planning this out forced me to think more deeply about sequencing: why a task must come first, what cognitive or motivational state it builds, and how it scaffolds the next step.

I also grappled with feasibility—imagining technical limitations, time constraints, and learner bandwidth, which are often overlooked in idealized designs. Through this process, I developed a stronger understanding of instructional practicality and how to design with both user behavior and system limitations in mind.

Nicole:

I really enjoyed this phase, as it gave us the opportunity to start ideating potential directions for our learning experience. The activities we worked on over the past few weeks, especially exploring different types of learning activities, assessments, and media tools, really pushed me to think more deeply and intentionally about each idea, rather than jumping to conclusions too quickly.

Creating logic models was especially helpful in evaluating the effectiveness of our ideas and identifying ways to improve them. Overall, it was exciting to see the range of ideas my group and I came up with, and interesting to notice how aligned we were in the end. It made it easy for us to come to a shared decision on what we felt was the strongest solution to address our learning problem.

Merry:

I realized the importance of putting learning theories into practice. For example, by applying DCT for the short video activity, it helps to give our design a more clarified path, and makes us think deeper from the user's side. It connected our ideas to learning theories that felt more rational and solid.

I liked making logic models the most, especially because of the activity we did in class. It gave me a clear structure of what I am doing and what should be done as the next step. For the next phase, I think it will be helpful to set small deadlines for specific tasks so we can move forward faster.