The Importance of Automated Real-Time Performance Feedback in Virtual Reality Temporal Bone Surgery Training

Davaris, Myles T.; Wijewickrema, Sudanthi; Zhou, Yun; Piromchai, Patorn; Bailey, James E.; Kennedy, Gregor; O’Leary, Stephen

doi:10.1007/978-3-030-23204-7_9

Cited by 9 publications

(11 citation statements)

References 55 publications

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“…( 2017 ) adapted the feedback based on students' affective states, reduced boredom and off-task behavior. Similarly, Davaris et al . ( 2019 ) used hand movement data and game logs in a VR setting not only to help the trainee surgeons understand basic anatomy, but also to provide feedback on the students' mistakes.…”

Section: Student Feedbackmentioning

confidence: 65%

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Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Sharma¹,

Giannakos²

2020

Brit J Educational Tech

119

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Most research on learning technology uses clickstreams and questionnaires as their primary source of quantitative data. This study presents the outcomes of a systematic literature review of empirical evidence on the capabilities of multimodal data (MMD) for human learning. This paper provides an overview of what and how MMD have been used to inform learning and in what contexts. A search resulted in 42 papers that were included in the analysis. The results of the review depict the capabilities of MMD for learning and the ongoing advances and implications that emerge from the employment of MMD to capture and improve learning. In particular, we identified the six main objectives (ie, behavioral trajectories, learning outcome, learning‐task performance, teacher support, engagement and student feedback) that the MMLA research has been focusing on. We also summarize the implications derived from the reviewed articles and frame them within six thematic areas. Finally, this review stresses that future research should consider developing a framework that would enable MMD capacities to be aligned with the research and learning design (LD). These MMD capacities could also be utilized on furthering theory and practice. Our findings set a baseline to support the adoption and democratization of MMD within future learning technology research and development. What is already known about this topic Capturing and measuring learners’ engagement and behavior using MMD has been explored in recent years and exhibits great potential. There are documented challenges and opportunities associated with capturing, processing, analyzing and interpreting MMD to support human learning. MMD can provide insights into predicting learning engagement and performance as well as into supporting the process. What this paper adds Provides a systematic literature review (SLR) of empirical evidence on MMD for human learning. Summarizes the insights MMD can give us about the learning outcomes and process. Identifies challenges and opportunities of MMD to support human learning. Implications for practice and/or policy Learning analytics researchers will be able to use the SLR as a guide for future research. Learning analytics practitioners will be able to use the SLR as a summary of the current state of the field.

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Section: Student Feedbackmentioning

confidence: 65%

“…( 2017 ) used students' speech and interaction data to adaptively scaffold their affective states so that the off-task behavior could be reduced and the learning gains could be increased. Finally, Davaris et al . ( 2019 ) used logs and movement data to facilitate support that reduced students' mistakes.…”

Section: Challenges and Future Directionsmentioning

confidence: 98%

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Sharma¹,

Giannakos²

2020

Brit J Educational Tech

119

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“…Our study adds new knowledge for several reasons: first, it is the first study to investigate automated summative feedback in temporal bone training because all previous studies have used continuous feedback (real-time feedback), through green-lighting for example. 12,34,35 Next, we have studied the effect in a prolonged, distributed training programme, which is closer to real-life training conditions. Also, we included retention testing after two to three months to study the effect on longer term performance.…”

Section: Discussionmentioning

confidence: 99%

Automated summative feedback improves performance and retention in simulation training of mastoidectomy: a randomised controlled trial

et al. 2021

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Objective This study aimed to investigate the effects of automated metrics-based summative feedback on performance, retention and cognitive load in distributed virtual reality simulation training of mastoidectomy. Method Twenty-four medical students were randomised in two groups and performed 15 mastoidectomies on a distributed virtual reality simulator as practice. The intervention group received additional summative metrics-based feedback; the control group followed standard instructions. Two to three months after training, participants performed a retention test without learning supports. Results The intervention group had a better final-product score (mean difference = 1.0 points; p = 0.001) and metrics-based score (mean difference = 12.7; p < 0.001). At retention, the metrics-based score for the intervention group remained superior (mean difference = 6.9 per cent; p = 0.02). Also at the retention, cognitive load was higher in the intervention group (mean difference = 10.0 per cent; p < 0.001). Conclusion Summative metrics-based feedback improved performance and lead to a safer and faster performance compared with standard instructions and seems a valuable educational tool in the early acquisition of temporal bone skills.

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“…Similar systems have been designed for general skills assessment in surgery (Levin et al, 2019). For example, they produce adaptive feedback orthopaedic surgery using haptic interfaces (Luengo & Mufti-Alchawafa, 2013) or within virtual reality during temporal-bone surgery (Davaris et al, 2019).…”

Section: Multimodal Intelligent Tutorsmentioning

confidence: 99%

Keep Me in the Loop: Real-Time Feedback with Multimodal Data

Mitri

Schneider

Drachsler

2021

Int J Artif Intell Educ

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This paper describes the CPR Tutor, a real-time multimodal feedback system for cardiopulmonary resuscitation (CPR) training. The CPR Tutor detects training mistakes using recurrent neural networks. The CPR Tutor automatically recognises and assesses the quality of the chest compressions according to five CPR performance indicators. It detects training mistakes in real-time by analysing a multimodal data stream consisting of kinematic and electromyographic data. Based on this assessment, the CPR Tutor provides audio feedback to correct the most critical mistakes and improve the CPR performance. The mistake detection models of the CPR Tutor were trained using a dataset from 10 experts. Hence, we tested the validity of the CPR Tutor and the impact of its feedback functionality in a user study involving additional 10 participants. The CPR Tutor pushes forward the current state of the art of real-time multimodal tutors by providing: (1) an architecture design, (2) a methodological approach for delivering real-time feedback using multimodal data and (3) a field study on real-time feedback for CPR training. This paper details the results of a field study by quantitatively measuring the impact of the CPR Tutor feedback on the performance indicators and qualitatively analysing the participants’ questionnaire answers.

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The Importance of Automated Real-Time Performance Feedback in Virtual Reality Temporal Bone Surgery Training

Cited by 9 publications

References 55 publications

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

Automated summative feedback improves performance and retention in simulation training of mastoidectomy: a randomised controlled trial

Keep Me in the Loop: Real-Time Feedback with Multimodal Data

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