Past research has debated whether learning outcomes are dependent on the format of instruction and its design. More specifically, most have compared video-based learning with the traditional lecture environment. Although both possess advantages and disadvantages, certain factors can influence whether the instruction is effective or not. Cognitive load theory posits that three types of load (extraneous, intrinsic, and germane) can affect learning through the design of instructional materials. If not properly designed, instruction can hinder performance. Moreover, the usability of such materials can influence cognitive load and students’ perception of the instruction. The present study examined if video-based and instructor-led online training showed differences in performance outcomes on website accessibility evaluation tasks. In addition, participants’ ratings of usability, enjoyability, perceived usefulness, and intention to use were subjectively measured and compared. Participants completed a training session online by watching a set of videos or a live instruction and were asked to complete quizzes and an accessibility evaluation task on a website for four checkpoints. Results showed that participants performed significantly better on easy tasks than on more difficult ones; however, the type of instruction did not appear to impact learning outcomes. Instead, both video and lecture-based training were equally effective in teaching participants how to conduct evaluations, especially when the material is considered to be easy. We concluded that instructional design alone might not be enough to aid students in learning more difficult subjects. Instructors must consider other potential factors that may influence how well a student understands the material in an online learning environment. Limitations and future directions are discussed.
CITATION STYLE
Apatiga, Y., & Vu, K. P. L. (2022). Comparing the Effectiveness of Instructor-Led Versus Video-Based Learning Methods for Online Website Accessibility Training. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13517 LNCS, pp. 189–206). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-22131-6_14
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