Low arousal states (especially boredom) have been shown to be more deleterious to learning than high arousal states, though the latter have received much more attention (e.g., test anxiety, confusion, and frustration). Aiming at profiling arousal in the classroom (how active students are) and examining how activation levels relate to achievement, we studied sympathetic arousal during two runs of an elective advanced physics course in a real classroom setting, including the course exam. Participants were high school students (N = 24) who were randomly selected from the course population. Arousal was indexed from electrodermal activity, measured unobtrusively via the Empatica E4 wristband. Low arousal was the level with the highest incidence (60% of the lesson on average) and longest persistence, lasting on average three times longer than medium arousal and two times longer than high arousal level occurrences. During the course exam, arousal was positively and highly correlated (r = .66) with achievement as measured by the students' grades. Implications for a need to focus more on addressing low arousal states in learning are discussed, together with potential applications for biofeedback, teacher intervention, and instructional design.
This article explores the dynamics of collaborative learning in the classroom from the perspective of the commonalities and interdependence in the degree of physiological activation from the sympathetic nervous system (i.e., sympathetic arousal) of group members. Using Empatica E4 wristbands, electrodermal activity to derive arousal was measured in 24 high school students working in groups of three (i.e., triads) during two runs of an advanced physics course. The participants met three times a week over six weeks for lessons of 75 min each. Most of the time (60 95% of the lesson) the triad members were at different arousal levels, and, when they were on the same level, it was mainly the low arousal (or deactivated) level. Less than 4% of the time were the triad members simultaneously in high arousal. Possible within-triad arousal contagion cases (71.3%) occurred mostly on a one-to-one basis and with a latency from within a few seconds up to ten min, but usually within one min. This study supports the view that only small parts of group work are collaborative, as far as the synchronicity and coordination which collaboration presupposes. Although exploratory, results also illustrate the affordances of physiological measures to characterize collaborative processes.
Collaborative learning is considered a critical 21 st century skill. Much is known about its contribution to learning, but still investigating a process of collaboration remains a challenge. This paper approaches the investigation on collaborative learning from a psychophysiological perspective. An experiment was set up to explore whether biosensors can play a role in analysing collaborative learning. On the one hand, we identified five physiological coupling indices (PCIs) found in the literature: 1) Signal Matching (SM), 2) Instantaneous Derivative Matching (IDM), 3) Directional Agreement (DA), 4) Pearson's correlation coefficient (PCC) and the 5) Fisher's z-transform (FZT) of the PCC. On the other hand, three collaborative learning measurements were used: 1) collaborative will (CW), 2) collaborative learning product (CLP) and 3) dual learning gain (DLG). Regression analyses showed that out of the five PCIs, IDM related the most to CW and was the best predictor of the CLP. Meanwhile, DA predicted DLG the best. These results play a role in determining informative collaboration measures for designing a learning analytics, biofeedback dashboard.
This systematic review on data modalities synthesises the research findings in terms of how to optimally use and combine such modalities when investigating cognitive, motivational, and emotional learning processes. ERIC, WoS, and ScienceDirect databases were searched with specific keywords and inclusion criteria for research on data modalities, resulting in 207 relevant publications. We provide findings in terms of target journal, country, subject, participant characteristics, educational level, foci, type of data modality, research method, type of learning, learning setting, and modalities used to study the different foci. In total, 18 data modalities were classified. For the 207 multimodal publications, 721 occurrences of modalities were observed. The most popular modality was interview followed by survey and observation. The least common modalities were heart rate variability, facial expression recognition, and screen recording. From the 207 publications, 98 focused exclusively on the cognitive aspects of learning, followed by 27 publications that only focused on motivation, while only five publications exclusively focused on emotional aspects. Only 10 publications focused on a combination of cognitive, motivational, and emotional aspects of learning. Our results plea for the increased use of objective measures, highlight the need for triangulation of objective and subjective data, and demand for more research on combining various aspects of learning. Further, rather than researching cognitive, motivational, and emotional aspects of learning separately, we encourage scholars to tap into multiple learning processes with multimodal data to derive a more comprehensive view on the phenomenon of learning.
The accuracy of students’ relative comprehension judgments when reading texts is typically rather low. This has been ascribed to students grounding their comprehension judgments on cues that are not diagnostic of their actual comprehension level. Asking students to complete causal diagrams—a diagramming scaffold—before judging comprehension has proved effective in providing them with more diagnostic cues and thereby fostered metacomprehension accuracy and self-regulated learning. However, there is still room for improvement. We investigated experimentally whether adding the instruction to students to self-assess their causal diagrams: (1) would lead to more accurate judgments than comprehension judgments, (2) would boost their utilization of diagnostic diagram cues by increasing the saliency of those cues, and (3) would enhance metacomprehension accuracy. Participants (N = 427 secondary students in The Netherlands) were randomly assigned to one of three conditions, namely (1) only diagram completion, (2) diagram completion plus diagram self-assessment, or a (3) filler task after reading (control). Self-assessments were more accurate than comprehension judgments, while both correlated strongly. However, no significant differences were found between diagramming conditions concerning diagram cue utilization and metacomprehension accuracy. Apparently, students self-assess their diagrams even without instruction to do so. Nonetheless, the effect of the diagramming scaffold for improving relative metacomprehension accuracy was replicated and extended to absolute metacomprehension accuracy.
Massive open online courses (MOOCs) have recently emerged as a revolution in education. Due to the huge amount of users, it is difficult for teachers to provide personalized instruction. Learning analytics computer applications have emerged as a solution. At present, MOOC platforms provide low support for learning analytics visualizations, and a challenge is to provide useful and effective visualization applications about the learning process. At this paper we review the learning analytics functionality of Open edX and make an overview of our learning analytics application ANALYSE. We present a usability and effectiveness evaluation of ANALYSE tool with 40 students taking a Design of Telematics Applications course. The survey obtained very positive results in a system usability scale (SUS) questionnaire (78.44/100) in terms of the usefulness of visualizations (3.68/5) and the effectiveness ratio (92/100) of the actions required for the respondents. Therefore, we can conclude that the implemented learning analytics application is usable and effective.
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