Smart Library Instruction using Adaptive Learning Analytics Tools

Thesis Abstract

This study investigates the effectiveness of adaptive learning analytics tools in enhancing library instruction for graduate students in information science programs, addressing the persistent gap between traditional library pedagogy and diverse learner needs in digital literacy and information skills. The problem centers on whether integrating adaptive analytics that tailor instructional content to individual behavior and performance can improve information literacy competencies, perceived self-efficacy, and subsequent academic research outcomes. The aim is to evaluate the impact of an adaptive library instruction framework on learning outcomes and engagement, with three specific objectives (1) to measure changes in information literacy competencies using the ACRL Framework post-instruction; (2) to assess student engagement, satisfaction, and perceived self-efficacy through validated scales; and (3) to examine how adaptive analytics features influence instructional decisions and librarian workload. The study hypothesizes that learners exposed to adaptive, analytics-driven instruction will show greater gains in information literacy, higher engagement levels, and more favorable attitudes toward self-directed learning compared with a control group receiving standard instruction. The research adopts a mixed-methods design, combining quasi-experimental comparison with qualitative insights to illuminate mechanisms of change. The population comprises graduate students enrolled in library science and information studies master’s and doctoral programs at three universities. A total of 320 students will be recruited and randomly assigned to either the experimental group (n=160) or the control group (n=160). The experimental group will receive library instruction enhanced by adaptive learning analytics that monitor navigation patterns, time-on-task, quiz performance, and resource usage to dynamically adjust content, sequencing, and pacing. Instruments include a validated information literacy assessment aligned with the ACRL Framework, a Student Engagement Scale, a Computer Self-Efficacy Scale, and a User Experience Survey. Data collection will occur at three time points pre-instruction, immediately post-instruction, and a 8-week follow-up. For quantitative analysis, multivariate analysis of covariance (MANCOVA) will compare post-instruction outcomes between groups while controlling for baseline scores, with supplementary hierarchical linear modeling to account for nested data by course. Regression analyses will identify the predictive value of engagement and self-efficacy on information literacy gains. Qualitative data will be gathered via semi-structured interviews with 20 librarians and 30 students from the experimental group, and thematic analysis will be employed to identify emergent patterns related to adaptive feedback, perceived usefulness, and workflow implications for librarians. The study is anchored in constructivist learning theory and social cognitive theory, with the Technology Acceptance Model guiding the interpretation of user adoption, and the diffusion of innovations framework informing implications for scale-up. Expected findings include statistically significant improvements in information literacy scores, higher self-reported engagement, and more positive attitudes toward autonomous learning in the experimental group, along with richer instructional data leading to more timely and personalized guidance from librarians. The study anticipates that adaptive analytics will enable librarians to tailor scaffolds, reduce cognitive overload, and provide targeted resource recommendations, thereby enhancing the quality and efficiency of library instruction. It is anticipated that the follow-up assessment will show sustained gains in information literacy and greater self-efficacy in information-seeking practices. The contribution to knowledge lies in empirical evidence on the efficacy of adaptive learning analytics to personalize library instruction, the operationalization of an analytics-informed instructional framework, and insights into the impact on librarian workload, instructional design, and student learning trajectories in higher education. Recommendations include developing scalable, privacy-conscious analytics dashboards for library instruction, integrating adaptive modules into standard curricula, and professional development programs for librarians focused on data-informed pedagogy and ethical use of learner data. The study concludes that adaptive learning analytics tools can meaningfully augment library instruction by aligning instructional pacing and content with learner needs, while maintaining instructional quality and educator control.