Oculearn Poster for BIOE Day_AlmostFinal (1)

Problem Need Statement Training Based on Expert Movement Data Oculearn Solution Real-Time Matching to Training Module Student View of Training Module Error: 0.09 First Person View Guided to Correct Motion Statistics and Analytics Capture the Student Register the Expert Model Determine the Difference Record Expert Actions Extract Expert Model Build Training Module Practice Time Practice Time Proficiency Proficiency Ideal Learning Curve Current Learning Curve Wasted Time Oculearn: First Person Motion Learning for Healthcare Ryan Rightmer Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA Scott Thompson Tim Folts Anisha Butala Piyusha Gade Oculearn trains healthcare students to perform new techniques by providing information derived from expert actions from a first person perspective Key Benefits - Student view from instructor perspective - In-depth performance metrics - Compatible with other tools - Reduces early learning confusion Training of dental students requires the development of fine motor skills. Training simulators provide very good practice scenarios but do not provide sufficient feedback informing students how to correct mistakes. As it exists right now, the training feedback loop is simply practicing the procedure and being informed of how well they performed afterwards leading to inefficient learning. A need exists to instruct students in how to correct their mistakes and learn the appropriate techniques without overly monopolizing the time of the instructor.

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Transcript of Oculearn Poster for BIOE Day_AlmostFinal (1)

Page 1: Oculearn Poster for BIOE Day_AlmostFinal (1)

Problem

Need Statement

Training Based on Expert Movement Data

Oculearn Solution

Real-Time Matching to Training Module

Student View of Training Module

Error: 0.09

First Person View Guided to Correct Motion Statistics and Analytics

Capture the Student Register the Expert Model Determine the Di�erence

Record Expert Actions Extract Expert Model Build Training Module

Practice Time Practice Time

Pro�

cien

Pro�

cien

Ideal Learning Curve Current Learning Curve Wasted Time

Oculearn: First Person Motion Learning for HealthcareRyan Rightmer

Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USAScott ThompsonTim FoltsAnisha Butala Piyusha Gade

Oculearn trains healthcare students to perform new techniques by providing

information derived from expert actions from a �rst person perspective

Key Bene�ts - Student view from instructor perspective - In-depth performance metrics - Compatible with other tools - Reduces early learning confusion

Training of dental students requires the development of �ne motor skills. Training simulators provide very good practice scenarios but do not provide su�cient feedback informing students how to correct mistakes. As it exists right now, the training feedback loop is simply practicing the procedure and being informed of how well they performed afterwards leading to

ine�cient learning.

A need exists to instruct students in how to correct their mistakes and learn the appropriate techniques without overly

monopolizing the time of the instructor.