A Predictive Personalization Approach to Enhance Foreign Language Learning and Teaching

This project aims to develop an outcome-prediction-guided personalized approach to enhance foreign language learning and teaching. The team constructs and refines prediction models capable of forecasting individual learners’ future classroom-based foreign language learning performances. The team designs a customized language remediation protocol based on learners’ predictive outcomes and profiles, then assess its effectiveness on students who are identified by the model as potentially having difficulty in foreign language learning. This project plans to collect multi-site longitudinal learner data from Hong Kong and the Mainland, incorporating more heterogeneous samples to improve and validate the team’s existing prediction models, thereby enhancing model prediction and generalizability to unseen samples. The prediction models strive to identify learners who may face difficulties in foreign language learning and reveal their prospective profiles for designing effective personalized instruction.

Funding Source: RGC - Collaborative Research Fund (CRF)