Exploring Document Retrieval Features Associated with Improved Short- and Long-term Vocabulary Learning Outcomes

Abstract

A growing body of information retrieval research has studied the potential of search engines as effective, scalable platforms for self-directed learning. Towards this goal, we explore document representations for retrieval that include features associated with effective learning outcomes. While prior studies have investigated different retrieval models designed for teaching, this study is the first to investigate how document-level features are associated with actual learning outcomes when users get results from a personalized learning-oriented retrieval algorithm. We also conduct what is, to our knowledge, the first crowdsourced longitudinal study of long-term learning retention, in which we gave a subset of users who participated in an initial learning and assessment study a delayed post-test approximately nine months later. With this data, we were able to analyze how the three retrieval conditions in the original study were associated with changes in long-term vocabulary knowledge. We found that while users who read the documents in the personalized retrieval condition had immediate learning gains comparable to the other two conditions, they had better long-term retention of more difficult vocabulary.