From Exposure to Elaboration: A Longitudinal Dataset on Short-Video Learning and Conceptual Retention

Description 1. Overview This dataset contains longitudinal data designed to examine how short-video microlecture exposure contributes to conceptual retention through cognitive and motivational learning processes. The study adopts a process-oriented framework in which learning outcomes are understood as the result of dynamic interactions between behavioral engagement, perceived instructional alignment, motivational states, and deep cognitive processing over time. 2. Research Context Data were collected from undergraduate students across three measurement waves within a single academic semester. The dataset integrates both self-reported measures and performance-based assessments, allowing for the simultaneous analysis of subjective learning experiences and objective learning outcomes in digital learning environments. 3. Core Constructs The dataset includes six primary constructs capturing different dimensions of the learning process: Short-video Exposure (EXP): Behavioral engagement with microlecture videos, including viewing frequency, completion, and attentional focus. Content Personalization (PER): Perceived alignment between instructional content and individual learning needs, prior knowledge, and learning preferences. Digital Media Self-Efficacy (DMSE): Learners’ confidence in navigating, evaluating, and managing digital learning environments. Situational Interest (INT): Immediate motivational engagement during learning activities, including attention, enjoyment, curiosity, and perceived meaningfulness. Elaboration (ELAB): Deep cognitive processing strategies, including self-explanation, prior knowledge integration, and conceptual comparison. Conceptual Retention (RET): Performance-based learning outcomes measured through recall, explanation, integration, and transfer tasks. Each retention dimension is scored from 0 to 25, with total retention ranging from 0 to 100. 4. Longitudinal Structure The dataset follows a three-wave structure: T1: Baseline measures, including exposure, personalization, and self-efficacy T2: Intermediate learning outcomes and cognitive processing T3: Final learning outcomes and retention This structure enables the examination of temporal dynamics, including lagged relationships and developmental learning processes across time. 5. Measurement Approach The dataset combines: Likert-scale survey responses (1–7 scale) capturing perceptions and experiences Objective performance-based assessments evaluating conceptual understanding This dual-measurement approach supports more robust analysis of learning processes by linking internal states with observable outcomes. 6. Analytical Potential The dataset is suitable for advanced statistical analyses, including: Structural Equation Modeling (SEM) Longitudinal mediation analysis Cross-lagged panel modeling Growth and repeated-measures analysis The design allows researchers to investigate how early exposure and engagement translate into long-term learning outcomes through intermediate cognitive mechanisms. 7. Research Significance This dataset provides a comprehensive empirical foundation for studying learning processes in digitally mediated environments, particularly within short-video microlearning formats. It supports research on how engagement must be transformed into sustained cognitive processing in order to produce durable and transferable learning outcomes over time.