Using optimized clustering to identify students' science learning paths to knowledge integration

Abstract

Background: This study captured students' repertoire of science ideas and determined the varied paths students take to integrate their disconnected ideas as they studied a web-based Genetic Inheritance unit. Method: We analyzed 6^th graders' responses to embedded items and activities to establish progress in knowledge integration in two different learning conditions: revisiting and critiquing. Learning paths were established by measuring students' idea dissimilarities using Levenshtein edit distance, clustering using silhouette coefficient and K-means, and determining the most representative path via generalized median method. Results: Four learning paths emerged from the revisit condition (isolated links, partial links, valid links, integrated links) and three learning paths emerged from the critique condition (isolated links, partial links, and integrated links). Conclusion: We found that by providing opportunities for students to revisit or critique ideas, the curriculum supported them to follow multiple paths in building their repertoire of ideas and integrating initial and new information.