iLeaf
iLeaf (previously Bloom) is a web application that automatically generates customized, individualized assessment feedback reports. By providing exam feedback based on the student’s performance on levels of Bloom’s taxonomy, coupled with study suggestions for each level, the app aims to improve students’ critical thinking skills. The automatic generation of these reports takes the burden off of instructors who would have had to manually and tediously create individual feedback forms.
I worked on this project as the back-end developer among a team of three for a client who was a Brown University professor. I took an active role in organizing both the technical and design specs before building the web app as a Node.js project that connects to a MongoDB database.
The professor view of the app allows creating and uploading new exam data, which would contain information about each question’s mapping to levels of Bloom’s taxonomy and whether each student got the question correct. Based on this data, the summary page shows aggregated visualizations for the class’s performance on each assessment. Individual reports for each student are also available. On the other hand, the students will only see their own performance breakdown on each of the exams along with study tips for the areas that they lack in.
Professor view of iLeaf.
Student view of iLeaf.