LAUSR

Engagement is an important factor influencing the effectiveness and quality of online learning programs and how satisfied online learners are with the online learning experience. Therefore, when developing online learning programs, ways to increase engagement should be one of the top priorities. To determine the parameters that increase engagement of independent online learners, this paper selects three types of quantitative data by which to assess online learning engagement: video playing options, video lecture viewing time, and concurrent learning behaviors. This data, collected from real-time observation and analysis of authentic online learning of 14,000 adult learners, was used to run a real-time growth algorithm that determined a few key parameters that increase independent-learning engagement. A few primary results of this study are: 1) real-time, dynamically calculated data representing general or individual engagement parameters of independent learning can help both teachers and learners be aware of, recognize, and adjust learning status accurately and effectively; 2) the algorithm can identify optimal parameters for online learning by analyzing numerical values of engagement, rules, and characteristics of online learning that are difficult to be observed directly; and 3) it is necessary to dynamically analyze learners’ engagement based on their learning processes and behaviors in response to different video lectures to know the effectiveness and feasibility of such materials and to support the design, production, and modification of optimal learning materials.