A Framework for Reinforced Student Learning Using Concept Maps

This research set up a basis for automated assessment of concept maps, measuring the structural understanding of students for a specific topic. This framework uses a novel scoring algorithm to identify strong and weak concepts for each student, as well as the entire class. The students are clustered on the basis of the concept scores-so that the tutors can target remedials effectively to help reinforce learning. In the iterations that followed, showed a decrease in the number of clusters suggesting that students were comprehending various aspects of topic learned consecutively better over time (remediation). The framework was implemented with an experimental group of students. In a subsequent class test on the topic, the experimental group performed significantly better than control group. The reinforcement model not only rationalizes the assessment, but also allows instructors to help the students to reconstruct their knowledge in each iteration.