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Participants in the study were upper elementary school students (n = 64) aged 13–14 (41 females, 23 males) enrolled in their compulsory five-week physics course. In each lesson, the students collaborated in the same groups of three to four students. The students were grouped by the teacher based on the prior achievement of the student’s previous grades in physics. The groups were made as homogenous as possible to ensure that each group had low- and high-achieving students. The collaborative tasks were designed together between the science teachers and researchers. The science teachers ensured that the topics covered the required subjects and contents, and the researchers ensured that tasks promoted regulation of learning and required genuine collaboration. The topics of the lessons and collaborative tasks focused on sound and light, light and vision, lenses, and reflection. For example, when studying reflection, the students were asked to use different types of lenses and hypothesize how the beam of light would pass through different types of lenses and examine that by doing real experiments with the lenses. Altogether, the students had five 90-minute physics lessons. Each lesson followed the flipped classroom principles due to its potential to facilitate the regulation of learning (Jovanovic et al., 2019 ). According to the flipped classroom principles, the students were asked to familiarize themselves with the topic of the day as homework prior to the lesson. Prior to the physics lesson, the students were instructed on how to use Shimmer3 GSR (Burns et al., 2010) sensors independently to measure their physiological arousal. Students were instructed to fit the Shimmer3 sensors at the beginning of each physics lesson, and they were informed that they could be taken off if they were uncomfortable. After that, the lesson started with a short moment of teacher-led teaching and instruction to ensure that students had familiarized themselves with the topic. This was followed by the collaborative learning tasks with the aim of co-constructing a more profound and shared understanding of the topic. Before starting the collaborative task, the students were asked to rate their situation-specific perceptions related to task difficulty and task understanding. The same questions were asked after the collaborative task. In addition, after the collaborative task, the students were asked to individually fill in a quiz included in Qridi® to measure their learning about the contents of the task. The Qridi® platform ( https://kokoa.io/products/qridi ) was used and tailored to coordinate the students’ collaborative learning. Qridi® included a 6Q tool (authors et al., 2020), which was tailored to increase the students’ awareness of collaborative learning and to support their awareness of the regulation of learning. Particularly, the 6Q tool provided the means to explore situational variations in individuals’ task perceptions and task difficulty. In this study, two metacognitive dimensions of the 6Q tool were investigated. Namely, the students’ evaluation of the task difficulty and task understandings were investigated before and after each collaborative task. This study collected (1) students’ situation-specific perceptions related to task understanding and task difficulty before and after each collaborative task, (2) quiz scores to measure students’ learning about the contents after each collaborative task and (3) continuous physiological data from each student measuring their physiological arousal in five collaborative learning tasks. Specifically, the 6Q tool implemented in the Qridi® environment measured the students’ task understanding (Schraw & Dennison, 1994) with the single question “How well do you understand the task?” The students used a slider scale from 0 to 100 (0 being not at all and 100 being very well). The tool also measured task difficulty (Efklides et al., 1998 ) with the single question “How difficult is the task?” (0 being very easy and 100 being very difficult). Single item questions that draw on existing literature were used in order to ensure minimal interference to group interaction (Dindar et al., 2020 ). The quiz was also implemented in Qridi® and involved five to six multiple choice questions. All the activities performed in the Qridi® environment were recorded with time-stamped information for the external server. The students’ situation-specific ratings related to task understanding consisted altogether of 283 responses before the collaborative task and 270 responses after the collaborative task. The students’ situation-specific ratings related to task difficulty consisted altogether of 281 responses before the collaborative task and 269 responses after the collaborative task. Students’ learning outcomes were measured for each task from students’ answers in the quiz. This consisted altogether of 201 responses. The quiz involved five to six questions, in which each correct answer was scored as one point. The mean value of the quiz score was 4.23 (Std = 1.26). Physiological arousal was measured via Shimmer 3 sensors, resulting altogether in 222 continuous recordings of physiological data. First, parts of the data before the attachment and after the de-attachment of the sensor were cut out from the dataset. Second, data was down-sampled from 128hz to 16hz to speed uphe further analysis. Physiological data recordings indicating a missing contact of the electrode were removed from the data set. The Butterworth low pass filter with frequency 1 and order 5 was used to remove the small movement artefacts from the signal. Non-specific skin conductance responses with a minimum amplitude of 0.05 µS were detected from the signal with the trough-to-peak method (Benedek & Kaernbach, 2010 ; Dawson et al., 2017 ). This resulted in a mean of 10.53 (Std = 2.18) peaks per minute. For the first research question, a paired-sample t-test was used to evaluate changes in students’ perceptions of the task and its difficulty. For the second research question, the independent sample t-test was used to evaluate whether there was a relationship between learners’ physiological arousal in terms of the change between situational task perceptions and task difficulty before and after each collaborative learning session. First, the learning sessions were divided into two categories based on the change in students’ perceptions of the task and task difficulty. The categorization for task difficulty resulted in 86 learning sessions where learners perceived the collaborative task as more difficult after completing the task and 166 learning sessions where learners perceived the collaborative task as less difficult after completing the task. The categorization for task understanding resulted in 98 learning sessions where students’ task understanding was lower after completing the task and 176 learning sessions where students’ task understanding was higher after completing the learning task. For the third and fourth research question, Generalized Estimating Equations (GEE) models were fitted to determine the students’ perceptions as predictors for physiological arousal and learning outcomes. GEE can be used to estimate regression parameters for clustered data, such as repeated measures coming from the same students, as it statistically adjusts the standard error estimates based on the dependency in clusters (McNeish et al., 2017 ). It allows inferences on how the mean result obtained in the whole population changes according to the changes in the independent variable(s). To estimate which factors predict learning outcomes, the students’ perceptions of the task understanding and task difficulty before and after the collaborative task and measures of physiological arousal were used as independent variables. Regarding perceptions of task understanding and task difficulty, pre- and post-measures were considered as separate variables as the pre-measure is more future oriented and post-measure is more of an evaluation considering the past. Collinearity diagnostics indicated that multicollinearity was not a concern in the predictors (Task perceptions before, Tolerance = 0.542, VIF = 1.84; Task perceptions after, Tolerance = 0.375, VIF = 2.66; Task difficulty before, Tolerance = 0.505, VIF = 1.97; Task difficulty after, Tolerance = 0.347, VIF = 1.84). In order to answer the first research question, a paired samples t-test was conducted to evaluate the ratings of students’ perceptions of task difficulty before and after each collaborative task. A value of 0 indicated that the task was easy and a value of 100 indicated that the task was difficult. There was a significant difference in the ratings for task difficulty before the collaborative task ( M = 50,94, SD = 22,89) and after the collaborative task ( M = 42,54, SD = 26,25, t(262) = 6,047, p = .00), d = 0.373. This means that the students perceived the task as less difficult after completing it (Fig. 1 ). Mean values of task difficulty ratings before and after the collaborative task Similarly, the paired sample t-test was conducted to evaluate the ratings of the students’ perceptions of task understanding before and after each collaborative task. A value of 0 indicated that the students did not understand the task, whereas a value of 100 indicated that the students were confident in their task understanding. There was a significant difference in the ratings for task understanding before the collaborative task (M = 53.78, SD = 20.79) and after the collaborative task (M = 62.31, SD = 25.17, t(266) = -6.06, p = .00), d = 0.37. This means that the students were more confident about what the task was about after completing it (Fig. 2 ). Mean values of task perceptions before and after the collaborative task The second research question focused on investigating whether there was a relationship between the level of physiological arousal and students’ perception of task understanding and task difficulty. Independent t-tests showed a significant difference in students’ physiological arousal in learning situations where the students perceived the task as less difficult at the end of the learning session than before the learning session. However, the results were not significant for changes in students’ task understanding. Students demonstrated significantly fewer EDA peaks, t(196) = -2.15, p = .032 in the 68 learning sessions where the task was perceived as less difficult at the end of the learning session (M = 10.06, S D = 2.41), when compared to the 130 learning sessions where the task was perceived as more difficult at the end of the learning session (M = 10.7, SD = 1.88) d = 0.32. This means that the level of physiological arousal was higher when the student’s perceived the task as being easier at the end of the collaborative learning session. Since the dependent variable was continuous, gamma with logarithmic (log) and identity link functions to select the best fitting models (Garson, 2012 ) were tested. Those two specifications were selected because they yielded the lowest quasi-likelihood under the independence criterion (QIC) values, meaning they were the best fitting models. The results indicate that higher values in students’ perceptions of task difficulty at the beginning and at the end of the collaborative learning task predicted higher physiological arousal (EDA peaks in one minute) (p < .05). That is to say, higher ratings of task difficulty predict physiological arousal. The more difficult the students perceived the task as being both before and after the collaborative task, the higher the level of arousal. Table 1 summarizes these findings and the results of the GEE analysis. With regard to the fourth research question, GEE was used to examine the effects on learning outcomes of task difficulty and task perceptions before and after the collaborative task. In addition, the effect of physiological arousal (number of EDA peaks in one minute) was also used. Since the dependent variable was continuous, gamma with logarithmic (log) and identity link functions to select the best fitting models (Garson, 2012 ) were tested. Those two specifications were selected because they yielded the lowest quasi-likelihood under the independence criterion (QIC) values, meaning they were the best fitting. Table 2 summarizes the results of the GEE analysis. The results of the GEE indicate that the student’s improved perceptions of task understanding after the collaborative task positively predicted the learning outcomes ( p < .005). In addition, the level of physiological arousal predicts the students’ learning outcomes (p = < 0.000). The results also indicate that higher values in students’ perceptions of task difficulty at the beginning (p < .05 and at the end of collaborative learning task predict higher learning outcomes (p < .005).
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