Dissertations Analysis on Distance Education

Sun, C. Y. J. (2009). Motivational influences in distance education: The role of interest, self-efficacy, and self-regulation. University of Southern California.

The study was aimed at examining how motivational and learning variables in distance education affect learner engagement. Therefore, the primary research question that the study was meant to address is: How do motivational and learning factors of interest, self-efficacy, and self-regulation influence engagement of distance education learners? (Sun, 2009, p.4). The researchers utilized a quantitative research methodology. Data collection was conducted using four instruments: the Engagement Scale (Fredricks, et al., 2005), the Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich & De Groot, 1990); the Web Users Self-Efficacy Scale (WUSE) (Eachus & Cassidy, 2006); and the Situational Interest Scale (Chen, et al., 1999). These instruments were used to collect data on three independent variables (self-regulation, computer self-efficacy, and situational interest). Specifically, MSLQ scale was used to assess participants self-regulation, respondents computer self-efficacy was measured using WUSE while their situational interest was assessed using the Situational Interest Scale. On the other hand, the dependent variable (engagement) was measured using the Engagement Scale. Three types of engagement were examined: cognitive, emotional, and behavioral (Sun, 2009).

The authors established a statistically significant relationship between self-regulation and interest and all the three types of engagement. However, no statistically significant relationship between computer self-efficacy and engagement was found. Further, a statistically significant relationship between participants socio-demographic factors (degree objective, age, FT, and school enrolled in) and emotional engagement was found. Additionally, self-regulation, self-efficacy, and interest predicted the engagement variables in hierarchical regression analyses. Interest was found to be the only predict emotional engagement. But using Pearson correlation, interest was found to correlate with cognitive, emotional, and behavioral levels of engagement. Unexpectedly, computer self-efficacy did not predict cognitive, emotional, and behavioral types of engagement (Sun, 2009).

Hamane, A. C. (2014). Student engagement in an online course and its impact on student success. Pepperdine University.

Hamanes (2014) study was guided by three research questions. In the first research question, the author sought to examine if participants perceived level of engagement and participants actual level of engagement. The second research question addressed the association between respondents perceived level of engagement and their success in online courses. Lastly, the third research question was meant to examine if participants actual level of engagement correlated with their success in an online course. The variables were measured using different research instruments. First, perceived level of engagement was assessed using OSES Instrument by Author Marcia Dixson. OSES has 19 questionnaire items categorized as either performance engagement, participation engagement, emotional engagement, or skills engagement. Second, students actual levels of engagement were measured using Learning Management System (LMS) datasets. Lastly, students success rates were assessed by examining the total points obtained by the students versus the total points possible. The point earned by the students reflected their level of achievement of course objectives.

The first research question was addressed using correlation and regression analysis. From the analysis, it was found out that a weak positive relationship exists between participants perceived level of engagement and participants actual level of engagement. The results of analysis of the second research question indicated a weak but statistically significant positive relationship between respondents perceived level of engagement and their success. Therefore, higher levels of perceived engagement may lead to better success. Lastly, data analysis used to answer the third research question showed that there are moderate positive relationships between participants actual level of engagement and their success. Therefore, it can be concluded that increased actual level of engagement leads to higher success (Hamane, 2014).

References

Hamane, A. C. (2014). Student engagement in an online course and its impact on student success. Pepperdine University.

Sun, C. Y. J. (2009). Motivational influences in distance education: The role of interest, self-efficacy, and self-regulation. University of Southern California.