Triangulate physiological data with other CSCL data streams ( such as video or logfile traces ) to uncover otherwise unidentifiable psychophysiological reactions¶

Strategy · All strategies
Evidence · 2 claims (1 for, 1 mixed) · 2 studies (1 review, 1 associational), q2 · 1 of 2 report an effect size · 2 claims rest on one study

Description¶

The article recommends combining physiological sensors with other data sources to interpret bodily reactions in collaborative learning. It states that "the use of physiological data in triangulation with other data (e.g., video) has the potential to uncover otherwise unidentifiable psychophysiological reactions". It further suggests connecting physiological data with logfile traces to gather bigger datasets, and notes that physiological sensors are "seamless" and record reactions "on the fly".

Design Implications¶

Context¶

Requirements¶

• Sensors capable of continuous, unobtrusive recording of electrodermal activity alongside independent data streams such as video or logs

Constraints¶

• Due to the small size and qualitative nature of these findings, it is not possible to generalize them
• more research that has explanatory power is required to further explore and understand the interplay between metacognition, regulation, and physiological signals

Target Learners¶

• collaborative learners in CSCL settings

Target Learning Goals¶

• recognising regulation-relevant events and difficulties in collaborative learning

Claims¶

• Students' monitoring frequency during the collaborative exam was significantly and positively correlated with their number of EDA peaks. [+W]
• Combining more modalities does not always improve detection of learner mental states [~W]

Related Strategies¶

• Use multimodal data channels combining subjective and objective measures to overcome single-channel constraints in SSRL research
• Combine system logs, eye-tracking, and speech recognition to capture professional performance, communication, and collaboration for automated feedback in full-mission simulator training
• Use multimodal data and time-sensitive analyses to capture situational motivational processes
• Employ multimodal methods to capture the multifaceted feedback process

Examples¶

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Key Sources¶

• Jonna Malmberg, Eetu Haataja, Tapio Seppänen, Sanna Järvelä. (2019). Are we together or not? The temporal interplay of monitoring, physiological arousal and physiological synchrony during a collaborative exam. International Journal of Computer-Supported Collaborative Learning 14:467–490. https://doi.org/10.1007/s11412-019-09311-4