Factors affecting perceived learning in asynchronous online foreign language courses - Peammetta, Phanurut; Luenam, Pramote (Journal of Applied Statistics and Information Technology, 2024)
As the world becomes increasingly interconnected, the demand for asynchronous online language learning has surged, highlighting the need for engaging and effective instructional design in this area. Online educators are challenged to meet diverse learner needs and optimize learning outcomes in asynchronous environments. Learners face various challenges, such as limited real-time interaction, which can lead to feelings of isolation and delays in feedback. Additionally, learners must develop strong self-discipline and time management skills. Other complicating factors include technology accessibility, cultural and linguistic barriers, and maintaining motivation throughout the learning process. To address these issues, it is essential to examine factors like online learner motivation and other determinants of learning outcomes. This study, drawing on Garrison, Anderson, and Archer’s Community of Inquiry (CoI) framework and Deci and Ryan’s intrinsic motivation theory, proposed and tested a model of perceived learning in online language courses. Data were collected from 140 learners with prior experience in asynchronous courses through an online questionnaire and analyzed using Structural Equation Modeling to test hypotheses and assess model fit. The results demonstrated that the model had an excellent fit (p-value = 1.000, SRMR = 0.0680, CFI = 1.0000, GFI = 0.977, RMSEA = 0.00004). The findings supported the mediating effect of the CoI framework’s three interactions – social presence, cognitive presence, and teaching presence – on perceived learning through intrinsic motivation. These interactions, when mediated by intrinsic motivation, positively influenced perceived learning, though they did not directly predict learning outcomes. This study suggests a new model for developing effective asynchronous online language courses and recommends further research to explore additional constructs and refine this model.