The Effects of Student Response Systems on Student Learning and Attitudes in Undergraduate Psychology Courses

Abstract

Student response systems (SRSs) are reported to increase student interest, interaction, and engagement and result in ''deeper'' learning. However, the effects on learning are still suspect. Here, outcomes were compared for students in SRS and traditional sections of 200-level and 500-level psychology courses. Results showed SRS students reported more engagement, gave higher evaluations, and had fewer absences. There were also main effects of course and interaction effects of SRS and course on engagement and absenteeism. The SRS effect on student achievement varied by course. These findings add to our understanding that the effects of SRS are the result of a mix of learner, teaching, and course characteristics.

Enriching feedback in audience response systems: Analysis and implications of objective and subjective metrics on students' performance and attitudes

“…On the other side, however, there are also studies showing that nonclicker classes were comparable (Caldwell, ; Elicker & McConnell, ) or even better than the clicker ones (Fortner‐Wood, Armistead, Marchand, & Morris, ). Although Shapiro () cites methodological issues and technology‐related factors as the reason for little or negative effects in some studies, the fact remains that the discussion on the impact of ARS on actual student performance is still ongoing.…”

Statistical Models for Analyzing Learning Data

“…Most statistical tests are robust to moderate violations of these assumptions (e.g., ANOVA tests are robust to moderate violations of normality such as a K-S statistic p value close to, but not less than, .05; Elliot and Woodward 2007), especially when your sample size is large. However, smaller sample sizes may be a reason to pay more attention to data screening since small sample sizes are less robust to violations of assumptions (Field 2013). If your sample size is small (e.g., a class size of 20) and your initial data screening shows that your dependent variable of exam scores is very skewed, you may consider addressing skew with a data transformation before further analyses (see Field 2013 for information about data transformation).…”

“…After creating a composite score, it is crucial to check and report the internal consistency or reliability of that created scale. The most common method is to calculate Cronbach' s alpha, a statistic that measures the average inter-correlation between every item in a composite score (for interpretation see Field 2013). A Cronbach' s alpha of greater than .7 is typically considered to represent good reliability (Field 2013).…”

“…Type II error refers to the failure to reject a false null hypothesis, or failure to detect a significant result when one existed (e.g., finding that there is not a significant relationship between time spent studying and exam scores when, in the population of students, a meaningful relationship existed). Commonly regarded acceptable probability of Type II error is .20 or 20 percent of the time (Field 2013).…”

Comparing Four Touch-Based Interaction Techniques for an Image-Based Audience Response System

“…An ARS can make lectures more engaging and fun for the attendees, and potentially improves the information transfer between the presenter and the audience. Various positive effects of using an ARS have been demonstrated, including increased lecture attendance (Cain, Black, & Rohr, 2009;FortnerWood, Armistead, Marchand, & Morris, 2012;Greer & Heaney, 2004;Preszler, Dawe, Shuster, & Shuster, 2007), increased attention (Latessa & Mouw, 2005;Miller, Ashar, & Getz, 2003) and engagement (Fortner-Wood et al, 2012;Siau, Sheng, & Nah, 2006) during lectures, and improved learning performance (Pradhan, Sparano, & Ananth, 2005;Preszler et al, 2007;Rubio, Bassignani, White, & Brant, 2008).…”