Using Smartphones as Experimental Tools—a Follow-up: Cognitive Effects by Video Analysis and Reduction of Cognitive Load by Multiple Representations

Abstract

Mobile devices (smartphones or tablets) as experimental tools (METs) offer inspiring possibilities for science education, but until now, there has been little research studying this approach. Previous research indicated that METs have positive effects on students' interest and curiosity. The present investigation focuses on potential cognitive effects of METs using video analyses on tablets to investigate pendulum movements and an instruction that has been used before to study effects of smartphones' acceleration sensors. In a quasi-experimental repeated-measurement design, a treatment group uses METs (TG, N TG = 23) and a control group works with traditional experimental tools (CG, N CG = 28) to study the effects on interest, curiosity, and learning achievement. Moreover, various control variables were taken into account. We suppose that pupils in the TG have a lower extraneous cognitive load and higher learning achievement than those in the CG working with traditional experimental tools. ANCOVAs showed significantly higher levels of learning achievement in the TG (medium effect size). No differences were found for interest, curiosity, or cognitive load. This might be due to a smaller material context provided by tablets, in comparison to smartphones, as more pupils possess and are familiar with smartphones than with tablets. Another reason for the unchanged interest might be the composition of the sample: While previous research showed that especially originally less-interested students profited most from using METs, the current sample contained only specialized courses, i.e., students with a high original interest, for whom the effect of METs on their interest is presumably smaller.

Investigating Dynamic Visualizations of Multiple Representations Using Mobile Video Analysis in Physics Lessons

“…The learner thus has more free cognitive resources available for active knowledge construction, which increases the effectiveness of the learning process and ultimately leads to a deeper conceptual understanding. This supports the theoretical foundation of effectiveness of tablet-based video analysis derived from multimedia learning theories and contributes to an expansion of the research basis by providing a possible explanation for the positive effects on learning achievement already proven in several studies (Becker et al 2018(Becker et al , 2019Hochberg et al 2020;Klein et al 2018). Furthermore, it could also be shown that basic theoretical design principles for multimedia learning environments can also have a learning-promoting effect in real multirepresentational teaching scenarios.…”

“…Due to the increasing use of mobile technologies both in everyday life and in the education sector, Mutlu-Bayraktar et al (2019) recommend that the influence of mobile technologies on cognitive load in teaching-learning situations should also be systematically investigated in empirical studies. In this respect, initial studies have shown positive effects of using mobile devices to enhance inquiry-based experimental learning with multiple representations on conceptual understanding (e.g., Becker et al 2018Becker et al , 2019Becker et al , 2020Klein et al 2018;Kuhn and Vogt 2015;Hochberg et al 2020) and motivation (e.g., Hochberg et al 2018).…”

The doctrine of normal tendency in active learning teaching methodology: investigations into probability distributions and averages

“…Since passive blocking does not involve active signal jamming, it will not require Federal Communications Commission (FCC) approval (in case jamming is preferred permission must be obtained from the FCC). The professor must be given access to a switch to turn on wifi when it is time to conduct some high technology higher learning activity that makes use of the internet or other smart phone experiment (Hochberg et al 2020 ). The wifi can be turned off after the internet exercise.…”

Changes in Students’ Understanding of and Visual Attention on Digitally Represented Graphs Across Two Domains in Higher Education: A Postreplication Study

“…Line graphs, in particular, are frequently used in higher education. For example, the relationships between distance and speed in physics or between time and stock prices in finance can both be illustrated with a line graph ( Bowen and Roth, 1998 ; Benedict and Hoag, 2012 ; Susac et al, 2018 ; Becker et al, 2020a , b ; Hochberg et al, 2020 ; Klein et al, 2020 ). More recently, a number of studies investigated and compared university students’ understanding of graphs in mathematics, physics, and other contexts using parallel (isomorphic) tasks ( Christensen and Thompson, 2012 ; Planinic et al, 2012 , 2013 ; Wemyss and van Kampen, 2013 ; Bollen et al, 2016 ; Ivanjek et al, 2016 , 2017 ).…”

“…However, there are currently only few studies with a pretest–posttest assessment design focusing on the changes in graph understanding and how to foster this understanding. Digital learning environments, learning from examples, and using instructional material showed an impact on students’ graph comprehension ( Bell and Janvier, 1981 ; Bergey et al, 2015 ; Becker et al, 2020a , b ; Hochberg et al, 2020 ). For example, the impact of instruction on graph construction conventions (e.g., on legends and labels) on students’ graph understanding was confirmed in a control group design ( Miller et al, 2016 ).…”