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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, NTG = 23) and a control group works with traditional experimental tools (CG, NCG = 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.
This contribution presents the results of a replication study on the learning effect of tablet-supported video analysis compared to traditional teaching sequences using non-digital experimental materials in the subject areas of uniform and accelerated motion in high school physics lessons. In addition to the replication of the preliminary study results recently published in this journal (Becker et al 2018, 2019), the investigation of the effect on the cognitive load as well as the emotional state of the students is another focal point. Compared to the preliminary study, the sample size was significantly increased from N = 109 to N = 294. The individual effects of the preliminary study could be replicated in this way. For both topics, a significant reduction of extraneous cognitive load and a positive effect on intervention-induced emotions could be demonstrated. Moreover, the theoretically founded causal relationship between emotion, cognitive load, and learning achievement could be empirically verified by means of structural equation modeling.
Visual–graphical representations are used to visualise information and are therefore key components of learning materials. An important type of convention-based representation in everyday contexts as well as in science, technology, engineering, and math (STEM) disciplines are vector field plots. Based on the cognitive theory of multimedia learning, we aim to optimize an instruction with symbolical-mathematical and visual-graphical representations in undergraduate physics education through spoken instruction combined with dynamic visual cues. For this purpose, we conduct a pre-post study with 38 natural science students who are divided into two groups and instructed via different modalities and with visual cues on the graphical interpretation of vector field plots. Afterward, the students rate their cognitive load. During the computer-based experiment, we record the participants’ eye movements. Our results indicate that students with spoken instruction perform better than students with written instruction. This suggests that the modality effect is also applicable to mathematical-symbolical and convention-based visual-graphical representations. The differences in visual strategies imply that spoken instruction might lead to increased effort in organising and integrating information. The finding of the modality effect with higher performance during spoken instruction could be explained by deeper cognitive processing of the material.
Cognitive Load Theory is considered universally applicable to all kinds of learning scenarios. However, instead of a universal method for measuring cognitive load that suits different learning contexts or target groups, there is a great variety of assessment approaches. Particularly common are subjective rating scales, which even allow for measuring the three assumed types of cognitive load in a differentiated way. Although these scales have been proven to be effective for various learning tasks, they might not be an optimal fit for the learning demands of specific complex environments like technology-enhanced STEM laboratory courses. The aim of this research was therefore to examine and compare existing rating scales in terms of validity for this learning context and to identify options for adaptation, if necessary. For the present study, the two most common subjective rating scales that are known to differentiate between load types (the Cognitive Load Scale by Leppink et al. and the Naïve Rating Scale by Klepsch et al.) were slightly adapted to the context of learning through structured hands-on experimentation where elements like measurement data, experimental setups, and experimental tasks affect knowledge acquisition. N=95 engineering students performed six experiments examining basic electric circuits where they had to explore fundamental relationships between physical quantities based on observed data. Immediately after experimentation, students answered both adapted scales. Various indicators of validity, which considered the scales’ internal structure and their relation to variables like group allocation as participants were randomly assigned to two conditions with contrasting spatial arrangement of measurement data, were analyzed. For the given data set, the intended three-factorial structure could not be confirmed and most of the a priori defined subscales showed insufficient internal consistency. A multitrait-multimethod analysis suggests convergent and discriminant evidence between the scales which could not be confirmed sufficiently. The two contrasted experimental conditions were expected to result in different ratings for extraneous load, which was solely detected by one adapted scale. As a further step, two new scales were assembled based on the overall item pool and the given data set. They revealed a three-factorial structure in accordance with the three types of load and seem to be promising new tools, although their subscales for extraneous load still suffer from low reliability scores.
The coordination of multiple external representations is important for learning, but yet a difficult task for students, requiring instructional support. The subject in this study covers a typical relation in physics between abstract mathematical equations (definitions of divergence and curl) and a visual representation (vector field plot). To support the connection across both representations, two instructions with written explanations, equations, and visual representations (differing only in the presence of visual cues) were designed and their impact on students’ performance was tested. We captured students’ eye movements while they processed the written instruction and solved subsequent coordination tasks. The results show that students instructed with visual cues (VC students) performed better, responded with higher confidence, experienced less mental effort, and rated the instructional quality better than students instructed without cues. Advanced eye-tracking data analysis methods reveal that cognitive integration processes appear in both groups at the same point in time but they are significantly more pronounced for VC students, reflecting a greater attempt to construct a coherent mental representation during the learning process. Furthermore, visual cues increase the fixation count and total fixation duration on relevant information. During problem solving, the saccadic eye movement pattern of VC students is similar to experts in this domain. The outcomes imply that visual cues can be beneficial in coordination tasks, even for students with high domain knowledge. The study strongly confirms an important multimedia design principle in instruction, that is, that highlighting conceptually relevant information shifts attention to relevant information and thus promotes learning and problem solving. Even more, visual cues can positively influence students’ perception of course materials.
Relating mathematical concepts to graphical representations is a challenging task for students. In this paper, we introduce two visual strategies to qualitatively interpret the divergence of graphical vector field representations. One strategy is based on the graphical interpretation of partial derivatives, while the other is based on the flux concept. We test the effectiveness of both strategies in an instruction-based eye-tracking study with N = 41 physics majors. We found that students’ performance improved when both strategies were introduced (74% correct) instead of only one strategy (64% correct), and students performed best when they were free to choose between the two strategies (88% correct). This finding supports the idea of introducing multiple representations of a physical concept to foster student understanding.Relevant eye-tracking measures demonstrate that both strategies imply different visual processing of the vector field plots, therefore reflecting conceptual differences between the strategies. Advanced analysis methods further reveal significant differences in eye movements between the best and worst performing students. For instance, the best students performed predominantly horizontal and vertical saccades, indicating correct interpretation of partial derivatives. They also focused on smaller regions when they balanced positive and negative flux. This mixed method research leads to new insights into student visual processing of vector field representations, highlights the advantages and limitations of eye-tracking methodologies in this context, and discusses implications for teaching and for future research. The introduction of saccadic direction analysis expands traditional methods, and shows the potential to discover new insights into student understanding and learning difficulties.