TiTela: Enhancing Teacher Inquiry with Fine-Grained E-book Logs

Abstract

While data-driven educational practices are increasingly required in K-12, teachers often struggle to bridge the gap between learning analytics (LA) data and instructional improvement due to insufficient data literacy and heavy workloads. To address this, we developed TiTela, a system based on Teacher Inquiry (TI) and Teaching and Learning Analytics (TLA) frameworks utilizing fine-grained e-book log data. This paper presents functional refinements of TiTela based on a formative evaluation with high school teachers. Results led to essential improvements, including a shift from real-time monitoring to post-lesson analysis reports and an architectural change from a material-based to a question-based approach. These enhancements facilitate more effective evidence-based reflection, allowing teachers to correlate student reactions directly with their instructional intentions.