Changes in science attitudes, beliefs, knowledge and physiological arousal after implementation of a multimodal, cooperative intervention in primary school science classes

Abstract

Purpose Student competency in science learning relies on students being able to interpret and use multimodal representations to communicate understandings. Moreover, collaborative learning, in which students may share physiological arousal, can positively affect group performance. This paper aims to observe changes in student attitudes and beliefs, physiology (electrodermal activity; EDA) and content knowledge before and after a multimodal, cooperative inquiry, science teaching intervention to determine associations with productive science learning and increased science knowledge. Design/methodology/approach A total of 214 students with a mean age of 11 years 6 months from seven primary schools participated in a multimodal, cooperative inquiry, science teaching intervention for eight weeks during a science curriculum unit. Students completed a series of questionnaires pertaining to attitudes and beliefs about science learning and science knowledge before (Time 1) and after (Time 2) the teaching intervention. Empatica E3 wristbands were worn by students during 1 to 3 of their regularly scheduled class sessions both before and after the intervention. Findings Increases in EDA, science knowledge, self-efficacy and a growth mindset, and decreases in self-esteem, confidence, motivation and use of cognitive strategies, were recorded post-intervention for the cohort. EDA was positively correlated with science knowledge, but negatively correlated with self-efficacy, motivation and use of cognitive strategies. Cluster analysis suggested three main clusters of students with differing physiological and psychological profiles. Practical implications First, teachers need to be aware of the importance of helping students to consolidate their current learning strategies as they transition to new learning approaches to counter decreased confidence. Second, teachers need to know that an effective teaching multimodal science intervention can not only be associated with increases in science knowledge but also increases in self-efficacy and movement towards a growth mindset. Finally, while there is evidence that there are positive associations between physiological arousal and science knowledge, physiological arousal was also associated with reductions in self-efficacy, intrinsic motivation and the use of cognitive strategies. This mixed result warrants further investigation. Originality/value Overall, this study proposes a need for teachers to counter decreased confidence in students who are learning new strategies, with further research required on the utility of monitoring physiological markers.

Detecting Emotions through Electrodermal Activity in Learning Contexts: A Systematic Review

“…Six studies used both tonic and phasic components to further analyze the EDA signal [ 52 , 53 , 56 , 58 , 61 , 69 ]. Additionally, six studies used tonic components, i.e., skin conductance level (SCL) only [ 39 , 40 , 49 , 55 , 63 , 65 ], and seven studies used phasic components, i.e., skin conductance response (SCR) only [ 33 , 36 , 50 , 60 , 64 , 66 , 68 ]. Six studies used the raw EDA signal for feature extraction and analysis, and two studies provided no information on processing [ 54 , 62 ].…”

“…These studies used different cut-off frequencies, ranging from 1 to 5 hertz [ 58 , 69 ]. Other studies combined a low-pass Butterworth filter with a high-pass filter (cut-off at 0.01 hertz) or only used a high-pass filter [ 39 , 63 ]. The actual filtering was done with different tools, such as the Matlab toolkit Ledalab [ 49 , 58 ], the EDA-Explorer tool [ 56 ], Brain Vision Analyser Software [ 66 ], or Acqknowledge [ 59 ].…”

“…As shown in Table 3 , descriptive features such as the mean, standard deviation, minimum, maximum, and range were extracted in 19 of the 27 studies. The most commonly used feature ( n = 18) is the mean EDA of an individual participant [ 36 , 39 , 40 , 46 , 49 , 50 , 52 , 53 , 54 , 56 , 57 , 58 , 59 , 63 , 65 , 67 , 69 , 70 , 71 ]. The mean EDA is the average of EDA in a specific time period, but how this is calculated differs between studies.…”

“…Three of these studies did not report why they chose the time window of one minute [ 58 , 60 , 61 ], but Malmberg et al [ 64 ] explained they focus on event-based SCR, which occurs from 3 (low arousal) to 25 (high arousal) times a minute. Carroll et al [ 39 ] calculated a weighted mean (root-mean-square) in their study. The root-mean-square embodies the mean SCL per participant weighted by the variability in the signal [ 39 ].…”

“…As mentioned above, the emerging group of educational researchers who use EDA focus on two different research strands. The first one investigates the relationship between physiological arousal as measured by EDA and learning outcomes [ 39 , 40 ]. These learning outcomes generally refer to students’ performance on a learning task or a test.…”