CritiqueTwo

Tiffany Hoefer May 23, 2011 EDET 780 Critique #2 Kim K, Frick T. Changes in Student Motivation during Online Learning. //Journal of Educational Computing Research// [serial online]. January 1, 2011;44(1):1-23. Available from: ERIC, Ipswich, MA. Accessed May 11, 2011.

Introduction

The purpose of this study was to investigate how changes to motivational levels alter through SDEL (Self-directed E-Learning) courses. SDEL is utilized by adult learners in a variety of settings (corporate training, web-based distance education) and is readily available. In SDEL a human instructor is rarely available to answer questions or enhance student motivation. The researchers hoped to determine how learner motivation changes as they work through an SDEL class and to determine factors related to the change (if the change occurs at all).

The researchers specifically examined the following questions: -Which factors best predict learner motivation in the beginning, during and end of self-directed e-learning (SDEL)? -Does learner motivation changes as he or she goes through instruction in SDEL? -What factors are related to learner motivational change during SDEL?

A major U.S. e-learning vendor helped the authors to identify learners who had used their product(s) to participate in e-learning solutions. The company was unwilling to be indentified for the article and the authors have no affiliation with the vendor.

The sample of participants was drawn from the population identified by the vendor. The students had taken an SDEL course from a variety of education or training areas. Approximately 800 adult learners were chosen for the study. A purposive sampling method was used to try and pull a sample with diverse backgrounds and contexts. Approximately 400 additional learners were selected from working professionals from varying types of industry.

Survey respondents indicated that 43% took the e-learning course as college students and 52% took the course as working professionals. The working professionals came from various backgrounds – 45% from business, 39% from higher education and 13% from the other areas, such as non-profit or government. Of the college students 30% were undergraduates and 70% were graduate students. Gender was broken down as 46% female and 54% male. 18.2% were 24 or younger, 43.5% were between 25 and 34 years of age, 21.7% were between 35 and 44, and 16.6% were 45 years or older. Various questions related Internet usage and number of software programs commonly utilized indicated that most of the respondents has moderate to high levels of computer and Internet technologies.

The research instrument from which data was analyzed was a questionnaire constructed to collect quantitative data. Reliability and validity of the questionnaire was measured using a three step process. First, the survey was written based on theoretical framework as determined from the literature review. Then the preliminary survey was modified after an initial qualitative study revealed gaps. Finally, the survey was modified again being tested in a pilot study, of which the sole purpose was to determine how to improve reliability.

Prospective survey respondents were sent a message via e-mailing lists or listservs inviting them to participate in the survey. They were provided with study information and a URL to the survey site. 368 individuals completed the survey (a 46% response rate). Surveys completed anonymously and responses stored on a web server. The data was analyzed using SPSS 12 for Windows.

Student motivation and background related to e-learning was measured using a series of Likert-scale items. Fours scales were identified and named. Motivation during was self-reported by respondents, with initial motivational levels relatively high, with continued high levels as they progressed through some of the course. The modal response to changes in motivational level as the course progressed as neutral.

The best predictor of positive change in learner motivation was the respondents’ motivation during the course. Motivation during the course was measured directly affected by the results found in the next section, creating a relationship.

The best predictor of motivation during the course was the level of motivation with which the respondent started. Data was analyzed using a stepwise multiple regression analysis to determine motivation after completion of some parts of the class. Independent variables included survey responses to “e learning is not for me, I don’t want to learn by myself”, level of motivation at the beginning of the course and how often the student interacted with an instructor or technical support staff.

The best predictor of motivation when beginning the course was its perceived relevance. Data was analyzed using a stepwise multiple regression analysis to determine learner motivation at the beginning of the course. Independent variables were all demographic factors, perceived relevance and prior computing experience.

Critique

The authors of this article clearly stated their research objective and questions. The writing was clear and concise. The statistical explanations in the article were quite extensive, but appropriate for the type of journal and audience for which they were presenting the material. An non-academic would probably skim the reporting of chi-square and multiple R findings, but the authors explained clearly if they located significance or not. So, I believe that this article could be useful to someone without a firm grasp of statistical analysis.

The literature review was extensive without overwhelming the article. The researchers clearly showed some gaps in the previous research, which validated the need for this study. The authors did an outstanding job of linking theoretical framework and previous research findings to their article. As the research was devoted to individual learner motivations within the learning context, the authors showed the ties to effective motivational design, cognitive load theory, academic learning time (ALT) and social-cognitive theory.

I was pleased to study an article directed at corporate and adult learning, as that is my focus. As one of the biggest factors in student motivation was deemed to be perceived relevance, I am concerned about the practical application of this information. Pre-conceived notions about training or educational relevance may be hard to change, and subsequent improvement may be difficult to actualize. However, I do think that from an educational recruiting standpoint, this information is invaluable. I taught in an adjunct position at Virginia College last quarter, and student perception of relevance plays a large part in their continued involvement, even in a more traditional environment. So, I think it is important to address relevance issues early on to help students be more successful. Since on-line learning and computer-based applications are still relatively new and evolving, any research regarding behaviors and participation could be helpful and welcome.

The results of this study are clearly presented in the article and, the authors clearly outlined finding and any statistical significance. The authors did not really provide any practical use of the study. They re-iterated early suppositions that educators need to be vigilant to design curriculum and instruction that promotes motivation and retention, but they did not supply many ideas to help realize that goal or how to adjust for the variables affecting success.

The authors recognized that sample method was non-random because of limitations to their access of the entire population. However they did attempt to address that issue by attempting to collect data from the a large sample of the population. Additionally, the authors caution against trying to generalize these findings to non-SDEL environments, as this study was specifically geared toward SDEL.

Conclusion

This study provides some insight into the student thoughts and abilities affecting motivation during on-line learning, specifically SDEL. The article provided a detailed understanding of the theoretical background and framework related to learning in general, and how those theories specifically apply to what factors influence student motivation and success in SDEL courses and, I personally will find this study helpful. The findings may help instructors and educators to be more aware of the reasons why students perform poorly, slowly or do not finish SDEL activities. If educators are aware of the characteristics affecting how students perceive and perform in SDEL, they can monitor activity and work to improve retention and motivation. Further research is warranted to try and see if pre-conceived attitudes toward relevance can by changed early on and improve success. Additional research regarding motivation should continue in non-SDEL fields, such as CSCL and traditional classroom.

Screencast narration: []

PowerPoint presentation: