Following the COVID-19 pandemic, online learning has become a new mode of learning that students must adapt to. However, the mechanisms by which students receive and grasp knowledge in the online learning mode remain unknown. Cognitive load theory (CLT) offers instructions to students considering the knowledge of human cognition. Therefore, this study considers the CLT to explore the internal mechanism of learning under the online mode in an experimental study. We recruited 76 undergraduates and randomly assigned them to four groups in which they will watch videos at four different kinds of speed (1.0× or 1.25× or 1.5× or 2× speed). The study observed and analyzed how video playback speed affected students' learning and cognitive load to obtain the following results: (1) Video playback speed significantly influenced the students' learning effect. The best effect was observed at the speed of 1.25× and 1.5×. (2) The speed that affected the learning effect best differed according to the students' learning abilities. High-level group students performed best at the speed of 1.5×, whereas low-level group students performed best at the speed of 1.25×. (3) The 1.5× speed showed significant differences in the learning effect by students' majors. This indicates that the cognitive load of liberal arts students increased greatly at this speed. (4) A change in playback speed has a significant impact on the cognitive load. Accelerated playback speed increases the cognitive load of students. The highest learning effect is observed under medium cognitive load.
Against the backdrop of the deep integration of the Internet with learning, blended learning offers the advantages of combining online and face-to-face learning to enrich the learning experience and improve knowledge management. Therefore, the objective of this present study is twofold: a. to fill a gap in the literature regarding the adoption of blended learning in the post-pandemic era and the roles of both the technology acceptance model (TAM) and the theory of planned behavior (TPB) in this context and b. to investigate the factors influencing behavioral intention to adopt blended learning. For that purpose, the research formulates six hypotheses, incorporates them into the proposed conceptual model, and validates them using model-fit indices. Based on data collected from Chinese university students, the predicted model’s reliability and validity are evaluated using structural equation modeling (SEM). The results of SEM show that (a) the integrated model based on the TAM and the TPB can explain 67.6% of the variance in Chinese university students’ adoption of blended learning; (b) perceived usefulness (PU), perceived ease of use (PEU), and subjective norms (SN) all have positive impacts on learning attitudes (LA); (c) PEU has a positive influence on PU, and SN has a positive influence on perceived behavioral control (PBC); and (d) both PU and LA have a positive influence on the intention to adopt blended learning (IABL). However, PEU, SN, and PBC have little effect on IABL; e. LA mediates the effect of PU on IABL, and PU mediates the effect of PEU on IABL. This study demonstrated that an integrated conceptual framework based on the TAM and the TPB as well as the characteristics of blended learning offers an effective way to understand Chinese university students’ adoption of blended learning.
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