Bridging Error Monitoring and Metacognition: Testing the variability and consistency of the two concepts in educational tasks with the impact of feedback

Making mistakes is crucial for learning, as recognising errors enables subsequent correction, potentially enhancing performance and achieving goals. The process of error monitoring aligns with metacognition—a higher-order thinking ability to reflect on one’s own cognitive processes. Despite their similarity and importance in learning, few studies directly con- nect error monitoring and metacognition, with the former typically explored in cognitive neuroscience and seldom tested within educational contexts. This PhD thesis addresses this research gap, examining the impact of two pivotal learning factors: task difficulty and feedback on error monitoring, and exploring the inte- gration of error monitoring within metacognitive theory. Conducted online, the experiment involved 585 adult participants, divided into feedback and control (no feedback) groups. Participants simultaneously engaged in a Flanker task, a Numerical task, and a Science judgement task. Error monitoring was measured using post-error slowing (PES), indicating extended response times following an error. Metacognition was assessed both online, through cali- bration, and offline, using the State Metacognition Inventory. This comprehensive approach aims to enrich our understanding of the interplay between error awareness and metacognitive processes in learning contexts. The central research question, aiming to connect error monitoring with metacognition across various tasks and feedback conditions, was divided into three subsidiary questions: 1) What are the task and feedback effect on error monitoring? 2) What are the task and feedback effect on metacognition measured by calibration and a self-report questionnaire? and 3) What is the correlation between error monitoring and metacognition varied by tasks and feedback? For the first subsidiary research question, I observed that error monitoring, as gauged by PES, was diminished in the Flanker task but augmented in educational tasks, including the Numerical and Science tasks. Feedback did not directly influence PES. Regarding the sec- ond research question, participants tended to overestimate their performance in difficult tasks and underestimate it in easier ones. Confidence, alongside the accuracy of their performance judgements, enhanced as they practised the tasks. A positive correlation emerged between self-reported metacognitive awareness and the precision of performance recall. Lastly, ad- dressing the third question, the Numerical task revealed a positive link between self-reported awareness and PES, with feedback intensifying the correlation between error monitoring and metacognition. The findings for the first time elucidate how the connection between error monitoring and metacognition varies by task and with the provision of feedback, providing insights into how errors are managed within metacognitive awareness. This deepens our understanding of metacognition, a crucial component of higher-order thinking. In terms of theoretical implications, this research bolsters the theories of error mon- itoring, offering supportive evidence for the adaptive orienting theory of PES. This theory posits that the functional role of PES depends on the stage of error processing. Practically, PES tends to be non-functional in easy tasks with a low error rate, while it may marginally improve performance in demanding tasks. In conclusion, this thesis enhances our understanding of error monitoring and metacog- nition, contributing to both fields’ theoretical frameworks. It also provides evidence support- ing the measurement consistency of online and offline metacognition. The findings could inform the content design of digital learning spaces where learners’ behavioural indices are measurable and informative, especially regarding behavioural adjustments following errors. Furthermore, this study paves the way for future research to explore nuanced error monitor- ing across different types of educational tasks.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2023

Author dc:creator

• Yang, Yiqiong

Advisors dc:contributor.advisor

• Michelle, Ellefson
• Hale, Ögel-Balaban

• Cognitive processing
• Error monitoring
• Metacognition

Licence

• https://www.repository.cam.ac.uk/bitstreams/9e77e2a5-2363-48f2-a7dd-8fdebc847d4b/download
• http://purl.org/NET/rdflicense/allrightsreserved

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.118862

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/385102