Back for a third time today, folks. As I said before, it’s crunch time and there are many readings to be reflected upon in these last few days before the final deadline for this class. Once this outpouring of blog posts ends, I will be attempting to post regularly on topics of my own choosing. Some will come from my next class in the masters program, some will be topics and articles that I just genuinely wish to spend time discussing. No matter what this blog turns its attention to, I hope that it continues to be interesting and thought-provoking to the readers who are kind enough to spend their time reading and considering my thoughts and those of the authors whose articles I present here. Now, let us move on to the topic of this blog post.
Today I will be discussing cyber assessment. Cyber assessment is a broad term and can be used to describe anything from using technology to assess whether writing is the result of plagiarism to the more literal usage of e-learning and digital tests/quizzes. In my current course, our dear instructor allows us to submit our quizzes for the class in a typed format. This is a fine example of the more literal definition of cyber assessment. While we did read articles about data mining being used to detect plagiarism, as well as a comparison of the response rates between online and paper-based surveys, these two topics were not particularly interesting. The article that I did find intriguing was the one by Terzis and Economides, which explores the differences between men and women in their perceptions and acceptance of computer based assessment (CBA).
Terzis and Economides (2011) had previously created a Computer Based Assessment Acceptance Model (CBAAM) that uses eight factors in order to define behavioral intention (BI) to use a CBA. These factors are Perceived Playfulness (PP), Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Computer Self Efficacy (CSE), Social Influence (SI), Facilitating Conditions (FC), Goal Expectancy (GE) and Content (pg. 2110). For those that read my previous post and remember Davis’ research, you know that PEOU and PU were the main factors that he said determined acceptance of any type of information technology by a user. Here, Terzis and Economides state that their model shows that behavioral intention to use a CBA is strongly correlated to PP and PEOU. They also state which of the other aforementioned factors significantly explains Perceived Usefulness and Perceived Playfulness. According to the research, PU is significantly explained by Goal Expectancy, Content, Social Influence and Perceived Ease of Use while Perceived Playfulness is defined by Usefulness, Content, Ease of Use and Goal Expectancy. The authors then add that Computer Self Efficacy and Facilitating Conditions also help to determine Perceived Ease of Use (p. 2110).
In this study, Terzis and Economides (2011) wanted to determine the differences between men and women in the factors that affected their perception and acceptance of a CBA. They formulated hypotheses based on a vast amount of research in a variety of areas. I do not want to elaborate on all of it, but one example is their hypothesis that PP influences behavioral intention to use a CBA more in men than in women. They based this on research that determined that men expressed more positive feelings toward multiple choice assessments than females and also because previous studies found that men enjoy playing computer games more than women, meaning that men’s characteristics are more aligned with CBA’s game orientation (p. 2112).
Ultimately, the research done by Terzis and Economides showed some very interesting things. Before we talk about gender differences, I would like to highlight some of the general observations. First, the authors state that Social Influence is a strong determinant of PU. Remember when I previously talked about those who might use Facebook just because everyone else is? As it turns out, there is correlations with Davis’ PU factor after all. In other words, even if a user would not necessarily perceive a site as useful on their own, they may already have that perception in their mind upon their first visit if they know that a lot of other people are using the same system (p. 2116).
The authors also noted that Goal Expectancy shows that students who are prepared and expect to be successful in a course are more likely to find a CBA useful as well as playful. They also noted that while Content has no direct effect on BI, it does influence PP, PU, and GE. This indirect effect on BI means that a CBA that contains content that is clear and interesting is more likely to be used. Finally, the research did not show any correlation between BI and PU like previous research, but an indirect effect exists here as the correlation between PP and PU was high. As mentioned above, this research showed that BI is most strongly correlated to PEOU and PP, not PU like with Davis’ study. Since PU has a high correlation with PP though, it must be considered when designing the CBA (p. 2216).
As for the gender differences, the authors found that the male students are more influenced to use a CBA through Playfulness, Usefulness, Content and Social Influence. This means that if the creators of a CBA want to influence male students to use the CBA, they have to make the CBA playful, they must make it useful for enhancing male students’ knowledge and performance, they must ensure that it delivers both appropriate and clear content, and they must make the CBA something that will be recommended and suggested by their peers and their teachers (p. 2119).
The female students are mainly influenced to use a CBA through Playfulness, Ease of Use, Content and Goal Expectancy. This means that Playfulness and Content are important for both males and females, but the authors note that these factors do not matter to the same degree as they do for males. On the other hand, female students are influenced by Ease of Use and Goal Expectancy, unlike the male students, and not by Usefulness and Social Influence as the male students were. This means that creators of a CBA who want to influence female students to use it must create a CBA that is easy to use with a simple design. The CBA also has to pique the interest of the female students in a way that maximizes their preparation and raises their Goal Expectancy (p. 2119).
Using this research, educators who wish to create and utilize CBA’s in their classrooms can understand the factors that affect students as a whole, as well as the individual factors that matter more to men than women and vice versa. This understanding can lead them to create better CBA’s that all of their students want to use. Without this knowledge, utilization of a CBA might be fractured amongst a given student population, resulting in less than optimal outcomes and perhaps even a reduction in willingness to use a CBA in the future by the instructor themselves.
Terzis, V. & Economides, A. A. (2011). Computer based assessment: Gender differences in perceptions and acceptance. Computers in Human Behavior, 27, 2108–2122.