Short-term gains, long-term gaps: Unpacking the role of learning analytics in student engagement in social annotation

Social annotation is a collaborative learning practice that engages students in active reading, in-text commenting, and peer dialogue around shared digital texts. While it holds promise for supporting deep discourse and learning, challenges persist, including inconsistent, superficial, and fragmented participation. This study investigates how analytics-based suggestions influence students' social annotation engagement (defined multi-dimensionally as their contributions, peer interaction, and reading activity) in a fully asynchronous undergraduate course. Using a within-subject phased design over nine weeks, trace and textual data were collected from 94 students to analyze the patterns of their engagement across three phases: pre-intervention, intervention, and post-intervention. Quantitative analyses, including linear mixed-effects modeling and social network analysis, revealed that analytics-based suggestions significantly enhanced student engagement during the intervention phase: students produced more frequent, longer, and higher-quality annotation contributions, participated more actively and densely in peer discussions, and spent more time reading. However, these improvements were not sustained after the intervention ended, pointing to a reliance on the suggestions rather than the development of lasting practices. Findings highlight both the potential of analytics-informed support to catalyze social annotation learning while underscoring the challenges for sustaining this engagement over time. The study contributes both empirical evidence and design considerations for integrating learning analytics into social annotation learning contexts in ways that promote deeper, more consistent, and sustained engagement.