EXPERIENCES OF AND PREFERENCES FOR INTERACTIVE INSTRUCTIONAL ACTIVITIES IN ONLINE LEARNING ENVIRONMENT

Abstract

Given the lack of research on the pedagogical issues of online interactions, this study is conducted to deepen current understanding about student experiences of and preferences for instructional activities that promote learner-instructor, learner-learner, and learner-self interactions in online education. Four overarching research questions were examined in this study: (1) What instructional activities are used to promote online course interactions? (2)What are learner preferences for these instructional activities? (3) Is there a relationship between learner's gender, age, prior educational level, online experience, work status, marital status, or personality and preferences for the instructional activities that promote online class interactions? (4) Why do learners prefer some interactive activities over others? Using survey analyses from 188 online MBA students and 11 follow-up interviews, the study revealed findings at both the technological and pedagogical levels. For instance, learner preferences toward class-level asynchronous discussion ranked quite low even though it was used often in online learning. Results further revealed that the relatively low preferences toward class-level discussion were related to large class size, repetitive postings, and unclear rules and expectations. In general, online learners prefer to engage in all three types of interactions. The preferences decline in the order of learner-instructor, learner-learner, and learner-self interaction. Age is positively related to learner preferences for learner-self interactions, while raising kids is negatively related to learner preference for learner-learner interactions. Other individual characteristics (such as gender, work status, and personality traits) did not demonstrate a significant effect on learner preferences for all three types of interactions. The results of this research suggested the existence of other variables that could better predict learner preferences for online learning interactions.