Xufei Liu, PhD Candidate in Operations, Information and Decisions, The Wharton School; Gad Allon, Operations, Information and Decisions, The Wharton School; Ken Moon, Operations, Information and Decisions, The Wharton School
Abstract: Long-term memory is key to deeper learning, yet students differ drastically in how they acquire and retain knowledge. Current education platforms offer individualized practice at scale to address student heterogeneity, but most approaches optimize engagement, with long-term learning as an afterthought. To address this, we partner with a Chinese language-learning flashcard platform with thousands of active students and ~6 million card reviews per month, where each word is practiced across four skills (reading, writing, definition, and tone).
Using large-scale behavioral data, we (i) structurally estimate individual learning/forgetting dynamics (including differences across skills and content difficulty) and (ii) design policies that personalize review timing and challenge to improve retention and learning efficiency, while managing the risk of student attrition as sessions become more difficult. We are able to capture learning across related contextual skills, making it possible to measure both skill-specific learning and skill transference learning. Through this, we are able to suggest review sessions which target words that have the largest leverage in both types of learning, leading to greater improvement of student skill without increasing difficulty and decreasing engagement.