Longitudinal Study of Online Engagement in Education Programs

1 The Longitudinal Trajectories of Online Engagement Over a Full Program Ashley Whitely Woody Mason EDU -525 Grand Canyon University 9/9/2023

2 Introduction Learner’s involvement has a timeline which discloses over a period of time and can be wrought by different influences. This paper will be summarizing the Longitudinal Trajectories of Online Engagement over a Full Program. Among the key issues that shall be considered includes the research problem, research question, method employed in facilitating the research, findings that were realized and the implications as they were discussed by the author. Research questions The research was seeking to understand the following questions; By describing in detail, which engagement conditions are renowned at course level and existent defining aspects? How stable are the learners' involvement conditions at course level and if unstable how does this state change? How is the trajectory of student involvement in the entire program? Which temporal associations exist between involvement patterns and dropping out of the system? Methods To achieve the answer for the questions above, the research employed various methods in each particular quiz. To attain the riposte for question one above, LCA method was used to group students in different clusters as per their course levels. The method was majorly employed in order to recognize the arrangement of human behaviors and also establish the subcategories as per the discernible faces.

3 For question two, sequence mining was first implemented in order to comprehend and study course involvement trends. A total number of 15 trainees were sampled where every student was represented by a sequence comprising utmost 15 engagements. The traminer R package was then deployed to conduct a condition sequence from the already chosen course that was in chronological format. Frequent sequences were worked out in order to comprehend patterns of engagement. In question 3, sequence mining and HMM was used. “LR library seqHMM” was used to assemble trends of course involvement states into similar clusters. For question four, a “Kaplan-Meier curve” was deployed. This approximates the fraction of learners still active in the program at every point with the equivalent confidence interval ( Aldridge et al., 2023 ). A Kruskal-Wallis ANOVA test was performed in order for approximating ways through which the trajectory associates with performance as a measured grade point average. Results Grouping with LCA identified three groups of students having different behaviors. The active group had students who were very active in online research, browsing and frequently used online course material ( Saqr et al., 2023 ). The second group “average” which was in the 5 th decile for frequent use of online course materials. Results in question two shows that by engaging students no major changes that were common as portrayed by the entropy values. HMM method also revealed three trajectories i.e. most engaged trajectory, an intermediate trajectory and the troubled trajectory. All these three trajectories had distinct behaviors.

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4 Implication The study has various implications especially to all the stakeholders together with trainers. That students who portrays the stage of undertaking in the final three deciles are at risk of being disengaged and dropping out may be often ( Saqr, 2021). Disengagement in one course has an effect on the next learning activity. Also longitudinal learning trajectories of learning activities have to be researched on and should be treated with keenness it calls for.

5 References Aldridge, W. A., Roppolo, R. H., Chaplo, S. D., Everett, A. B., Lawrence, S. N., DiSalvo, C. I., ... & Boothroyd, R. I. (2023). Trajectory of external implementation support activities across two states in the United States: A descriptive study. Implementation Research and Practice , 4 , 26334895231154285. Saqr, M., & López-Pernas, S. (2021). The longitudinal trajectories of online engagement over a full program . Computers & Education , 175 , 104325. https://doi.org/10.1016/j.compedu.2021.104325 Saqr, M., López-Pernas, S., Helske, S., & Hrastinski, S. (2023). The longitudinal association between engagement and achievement varies by time, students’ profiles, and achievement state: A full program study. Computers & Education , 199 , 104787.

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