Enhancing Student Learning Outcomes through a Station Rotation Model with Hypercontent Materials: A Research and Development Study
Main Article Content
Abstract
Learning in the Introduction to Education course at a private university in Banten, Indonesia, remains lecturer-centered and reliant on conventional teaching materials. Institutional records for 2021–2023 show that 17.32% of enrolled students failed or required remedial study, despite an 82% positive evaluation of the university's cloud-based learning management system (LMS). This study aimed to design, validate, and test the effectiveness of a Station Rotation-based learning model supported by Hypercontent teaching materials for this course. A Research and Development (R&D) design was employed, integrating the Dick, Carey and Carey instructional design model with the Rowntree model for developing hypercontent materials. Formative evaluation proceeded through four sequential stages: expert review by nine validators (three each in instructional design, content, and media), one-to-one trials with three students, a small-group trial with eight students, and a field trial with 28 first-semester Physical Education and Health students using a one-group pretest–posttest design. Data were analyzed using expert validation scoring, the normalized gain (N-Gain) index, and the paired-samples t-test (α = 0.05). Expert validation produced very high feasibility scores on a 4.00-point scale: instructional design 3.73, content 3.85, and media 3.76, with an average practicality score of 3.76. In the field trial, mean scores rose from 66.11 (pretest) to 91.46 (posttest), yielding an average N-Gain of 0.799 (high category) and a paired t-test result of t(27) = 13.512, p < .001. The model integrates a station-rotation blended-learning structure with multimodal hypercontent materials specifically for a conceptually dense, first-semester foundation course, offering a validated and practical alternative to lecturer-centered instruction in university settings with adequate technological infrastructure.


