Cognitive Collaboration Found in Cardiac Physiology: Study in Classroom Environment

Cowley, Benjamin Ultan; Torniainen, Jari; Ukkonen, Antti; Vihavainen, Arto; Puolamäki, Kai

doi:10.1371/journal.pone.0159178

Cited by 36 publications

(45 citation statements)

References 42 publications

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“…In addition, understanding learners' self-perceptions, self-evaluations, and reflections is critical for disclosing regulatory activities in SRL (Hadwin et al 2011). Considering this, some scholars have asserted that the repeated utilization of single-item questionnaires can be useful in capturing on-task states and transitory learning processes (Ainley and Patrick 2006). Further, combining self-reports with process-oriented measures (e.g.…”

Section: Measuring Srlmentioning

confidence: 99%

“…Third, single-item self-report measures were used to capture the perceived transitory changes in the behavioral, cognitive, motivational, and emotional aspects of each learning session. Single-item scales have been found to be reliable and useful in several SRL studies (e.g., Ainley et al 2002;Ainley and Patrick 2006). However, their psychometric attributes are questionable since common statistical analyses (e.g.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

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Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning

et al. 2019

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This study investigated the interplay of temporal changes in self-regulated learning processes (i.e., behavioral, cognitive, motivational and emotional) and their relationship with academic achievement in computer-supported collaborative learning. The study employed electrodermal activity and self-report data to capture the dynamicity of selfregulated learning processes during 15 sessions of collaborative learning activities. Our findings revealed that the changes in motivational regulation was related to academic achievement. However, academic achievement was not related to behavioral regulation, cognitive regulation or emotional regulation. Physiological synchrony among the collaborating students was found to be related only to cognitive regulation. The results also showed that the concordance of self-report data among the collaborating students was related to higher physiological synchrony among them in the behavioral, cognitive, and motivational dimensions of self-regulated learning. The findings reflect the complexity of the relationships between self-regulated learning constructs and demonstrates the potential value of physiological measures in self-regulated learning research.

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Section: Measuring Srlmentioning

confidence: 99%

Section: Limitations and Future Directionsmentioning

confidence: 99%

Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning

et al. 2019

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“…The cardiovascular parameters HR and HRV are often used in driving simulation and on-road driving studies as indicators of mental effort, stress, workload, and task demands (see the overview of studies in Backs and Boucsein, 2000 ; Mulder et al, 2005 ; Brookhuis and de Waard, 2011 ; Mehler et al, 2012 ; Ahonen et al, 2016 ; Schmidt et al, 2016 ). A common finding is that with higher invested effort and stress, HR increases and HRV decreases.…”

Section: Introductionmentioning

confidence: 99%

Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving

Beggiato

Hartwich

Krems

2018

Front. Hum. Neurosci.

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As technological advances lead to rapid progress in driving automation, human-machine interaction (HMI) issues such as comfort in automated driving gain increasing attention. The research project KomfoPilot at Chemnitz University of Technology aims to assess discomfort in automated driving using physiological parameters from commercially available smartbands, pupillometry and body motion. Detected discomfort should subsequently be used to adapt driving parameters as well as information presentation and prevent potentially safety-critical take-over situations. In an empirical driving simulator study, 40 participants from 25 years to 84 years old experienced two highly automated drives with three potentially critical and discomfort-inducing approaching situations in each trip. The ego car drove in a highly automated mode at 100 km/h and approached a truck driving ahead with a constant speed of 80 km/h. Automated braking started very late at a distance of 9 m, reaching a minimum of 4.2 m. Perceived discomfort was assessed continuously using a handset control. Physiological parameters were measured by the smartband Microsoft Band 2 and included heart rate (HR), heart rate variability (HRV) and skin conductance level (SCL). Eye tracking glasses recorded pupil diameter and eye blink frequency; body motion was captured by a motion tracking system and a seat pressure mat. Trends of all parameters were analyzed 10 s before, during and 10 s after reported discomfort to check for overall parameter relevance, direction and strength of effects; timings of increase/decrease; variability as well as filtering, standardization and artifact removal strategies to increase the signal-to-noise ratio. Results showed a reduced eye blink rate during discomfort as well as pupil dilation, also after correcting for ambient light influence. Contrary to expectations, HR decreased significantly during discomfort periods, whereas HRV diminished as expected. No effects could be observed for SCL. Body motion showed the expected pushback movement during the close approach situation. Overall, besides SCL, all other parameters showed changes associated with discomfort indicated by the handset control. The results serve as a basis for designing and configuring a real-time discomfort detection algorithm that will be implemented in the driving simulator and validated in subsequent studies.

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“…Nowadays, the functionality of many IDEs can be extended with plugins to collect data with additional features, lifting the previous technological restrictions. Examples include student-IDE interactions (Brown, Kolling, McCall, & Utting, 2014;Hundhausen, Olivares, & Carter, 2017), asynchronous discussion posts , automatic testing against test cases (Edwards & Perez-Quinones, 2008), survey/quiz data to gain insights into learner attitudes and conceptual understanding of tasks (Ihantola, Sorva, & Vihavainen, 2014), studies collecting data from eye-trackers (Busjahn et al, 2014;Kevic et al, 2015;Mangaroska, Sharma, Giannakos, Traetteberg, & Dillenbourg, 2018), mouse and keyboard pressure (Arapakis, Lalmas, & Valkansas, 2014;Begel, 2016), heart rate (Ahonen et al, 2016), and electro-dermal activity (Müller, 2015). Although data with additional features can help explain how students learn to program, to our knowledge, none of the hardware devices used to collect data from learners engaged in programming tasks is directly integrated with the IDE, which again imposes limitations on the range of data that can be collected.…”

Section: Ide-based Learning Analyticsmentioning

confidence: 99%

Gaze-Driven Design Insights to Amplify Debugging Skills: A Learner-Centered Analysis Approach

et al. 2018

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This study investigates how multimodal user-generated data can be used to reinforce learner reflection, improve teaching practices, and close the learning analytics loop. In particular, the aim of the study is to utilize user gaze and action-based data to examine the role of a mirroring tool (i.e., Exercise View in Eclipse) in orchestrating basic behavioural regulation during debugging. The results demonstrated that students who processed the information presented in the Exercise View and acted upon it, improved their performance and achieved a higher level of success than those who failed to do so. The findings shed light on what constitutes relevant data within a particular learning context in programming using gaze patterns. Moreover, these findings could guide the collection of essential learner-centred analytics for designing usable, modular learning environments based on data-driven approaches. Notes for Practice• Mirroring tools could regulate learner behaviour depending on the contextual set up of the programming environment.• This study demonstrated that students who improved their performance and achieved a higher level of debugging success have gaze patterns that corresponded with attention shifts with the mirroring tool and other areas of interest (AOIs).• Visual attention strategies among novices are not as well developed as they are among experts. This is shown in the successful and unsuccessful debugging behaviour patterns calculated from two-and three-way transition probabilities that we observed.• IDE-based (integrated development environment) learning analytics combined with gaze data could aid researchers and educators in designing and delivering interventions to enhance student progress by guiding them to attend to the right information at the right time so as to maximize student understanding of relevant concepts.

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Cognitive Collaboration Found in Cardiac Physiology: Study in Classroom Environment

Cited by 36 publications

References 42 publications

Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning

Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning

Using Smartbands, Pupillometry and Body Motion to Detect Discomfort in Automated Driving

Gaze-Driven Design Insights to Amplify Debugging Skills: A Learner-Centered Analysis Approach

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