The functional roles of metacognitive judgement and emotion in predicting clinical reasoning performance with a computer simulated environment

ABSTRACT

Monitoring one’s learning activities is a key component of self-regulated learning (SRL) leading to successful learning and performance outcomes across settings. Achievement emotions also play an important part in SRL and consequently student learning outcomes. However, there is little research on how specific types of monitoring (i.e. Feeling-of-Knowing, Judgement-of-Learning) and achievement emotions jointly affect performance in clinical reasoning within computer simulated environments. In this study, we explored the joint predictive value of monitoring judgements and achievement emotions in medical students’ ability to make accurate and efficient clinical diagnoses using BioWorld, a computer-based learning environment designed to help medical students practice clinical reasoning skills. Multiple types of data (log files, verbal protocols, self-report questionnaires) from 27 students were analyzed using text mining and linear mixed-effects models. We found that feeling-of-knowing judgements positively predicted diagnostic efficiency whereas judgement-of-learning and the achievement emotion of anger negatively predicted diagnostic efficiency. Achievement emotion of pride positively predicted confidence in diagnosing the case correctly. This study not only provides theoretical and methodological insights but also alert medical instructors to the many dimensions of diagnostic performances and effective intervention strategies in clinical reasoning.

KEYWORDS:

• Computer simulated environment
• metacognitive judgement
• achievement emotion
• self-regulated learning
• linear mixed-effects model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Ethical approval

This study was approved by the Research Ethics Board of McGill University.

Additional information

Funding

This research was made possible by the Social Sciences and Humanities Research Council of Canada.

Notes on contributors

Susanne P. Lajoie

Professor Susanne Lajoie is a Canada Research Chair in the Department of Educational and Counselling Psychology (ECP), a member of the Institute for Health Sciences Education at McGill University, and a Fellow of the Royal Society of Canada. She explores how theories of learning and affect can be used to guide the design of advanced technology rich learning environments in different domains, i.e. medicine, mathematics, history, etc.

Shan Li

Shan Li is a PhD candidate in the ECP department at McGill University, and is currently a member of the ATLAS (Advanced Technologies for Learning in Authentic Settings) Lab. His research focuses on medical simulations in interactive learning environments, self-regulated learning, and learning analytics.

Juan Zheng

Juan Zheng is a PhD candidate in the ECP department of McGill University with background in educational technology and learning sciences. Her research interests are self-regulated learning, academic emotions, and educational data mining.