A Methodology showing correlational analysis in online learning

Journal citation

Kuo, Y. C., & Belland, B. R. (2016). An exploratory study of adult learners’ perceptions of online learning: Minority students in continuing education. Educational Technology Research and Development, 64(4), 661-680. https://doi.org/10.1007/s11423-016-9442-9

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Summary of Article

The interaction and the presence in online learning are essential to dig deep into when searching for quality in online education. Quantitative research using the survey and correlation design was conducted by Kuo & Belland (2016) to examine the interaction of African American students (including factors of performance, satisfaction, internet self-efficacy, interaction, and student/course related variables). In their article, “An exploratory study of adult learner’s perception of online learning: Minority students in continuing education” the authors followed a framework suggested by Moore (1989), which focus on three main components of interaction in online education: Learner instructor, learner-learner and learner content interaction. The authors surveyed 199 non-traditional working adult students that attended six online courses (asynchronous courses using blackboard) taught by the same professor. Only 167 answered the online survey — data analysis using SPSS 17.0. The correlation, regression, and ANOVAs analysis were utilized to answer the five research questions. The authors concluded that “Learner-content interaction was found to be the most important predictor of student satisfaction, and learner-instructor interaction followed” (Kuo & Belland, 2016, p.676).

Methodology

While the focus of this assignment is on the correlation analysis methodology, this article methodology contains three statistical analysis procedures; correlation analysis, regression, and ANOVA. The authors used a survey questionnaire as well while collecting data for the study.

Note that “One sixty-seven participants are sufficient for the researcher to examine a regression with three independent variables”(Kuo and Belland 2016, p. 667).

The purpose of the study was to “explore relationships among interaction, learning outcomes, Internet self-efficacy, and student and course-related variables” (Kuo and Belland 2016, p. 665).

Kuo and Belland (2016) analyzed data using correlation analysis, regression, and ANOVA on PSS 17.0 to answer the study question as shown below (p. 668):

1. Is Internet self-efficacy associated with the three types of interaction?
2. To what extent are the three types of interaction associated with student satisfaction?
3. Do the three types of interaction predict student satisfaction?
4. Is student satisfaction related to student performance?
5. Do student characteristics and course-related variables have an influence on the three types of interaction, Internet self-efficacy and student satisfaction?

Table 3 on page 668 showed that the authors used the correlation analysis for questions one, two, and four. Regression analysis was used for question three and ANOVA analysis for question five. Therefore, Kuo and Belland (2016) used the correlation analysis to examine the relationship among a set of variables, which lead to the goal of their research as to “examine the effect of interaction on satisfaction and the correlations of several proposed variables among minority students in online settings” (p. 671).

Usefulness of Article

This study has shown that through the asynchronous experience, the African American undergraduate population experienced moderate satisfaction. Moreover, the learner-content interaction is the most predictable for student’s satisfaction than learner-faculty, which came in the second place. On the other hand, online students’ Internet self-efficacy was significantly related with interaction types. The authors found as well that “Student- and course-related variables had a significant effect on learner-human interaction…but not on learner-non-human interaction (i.e., learner-content interaction)” (Kuo and Belland, 2016. P. 676).

Therefore, this study is critical and useful as it gives a good understanding of any faculty member who is teaching online and having the same student population. So, they can design the online course accordingly and improve their teaching style. Also, administrators and the university can benefit from reading this research as it offers statistical data that confirm the significant role of interaction on student satisfaction.

Researchers mentioned that this study is useful because it added to the limited literature around the correlations among two learning outcomes.

Finally, as a current doctoral student interested in researching the effectiveness of online learning, this study has added new knowledge to my understanding of online learning. It would allow me to build on existing statistical data for my future dissertation.

 Future areas of Investigation

At the end of the study, Kuo and Belland (2016) provided information about the limitations and suggestions for future research. Authors noted that the use of self-report in assessing Internet self-efficacy, learners’ interaction, and satisfaction is a limitation to the study because self-report “provides participants’ own perspectives, it does pose some potential validity problems” (Bakers el al. 2005). Also, the study was limited by the type of participants; adult learner undergraduate African American and the focus on working adults is different than the traditional students.

So, it would be more beneficial for future research to investigate this topic with diverse students and not also the undergraduates working full-time adults, minority students which results would be generalizable to other universities other than the HBCUs.

Also, Kuo and Belland (2016) stated that “future studies should investigate further how levels of technology confidence course-related variables influence the three types of interaction” (p. 677).