Assessing Whether Students Seek Constructive Criticism: The Design of an Automated Feedback System for a Graphic Design Task

2016 ◽  
Vol 27 (3) ◽  
pp. 419-447 ◽  
Author(s):  
Maria Cutumisu ◽  
Kristen P. Blair ◽  
Doris B. Chin ◽  
Daniel L. Schwartz
Keyword(s):  
Graphic Design ◽  
Feedback System ◽  
Design Task ◽  
Constructive Criticism ◽  
Automated Feedback

System interaction influences on cognitive-affective states to enhance performance, workload, and knowledge acquisition in teams conducting close air support simulations.

2016 ◽  
Vol 60 (1) ◽  
pp. 1260-1263
Author(s):  
Baron C. Summers ◽  
Herbert Hauser
Keyword(s):  
Knowledge Acquisition ◽  
Affective State ◽  
Feedback System ◽  
Affective States ◽  
Feedback Systems ◽  
Training Environment ◽  
Team Members ◽  
Human Computer Interfaces ◽  
Automated Feedback ◽  
Aerial Systems

The purpose of this research is to shed light on the effects of an automated feedback system to optimize cognitive-affective states and increase effectiveness of using remotely piloted aerial system team members training to conduct Close Air Support missions in a simulation training environment. Feedback manipulations in this study utilize attributes of engagement as an optimal cognitive-affective state in order to assess state and effectiveness differences. Understanding these effects could enable predictions of aspects that might be adapted to optimize future approaches in training teams in complex situations. If states of learners can be impacted via feedback experiences to an engagement like state and thereby benefit from increased learning and effectiveness, then training approaches utilizing feedback may advance in capability. Thus, designs of automated feedback systems in human-computer interfaces may help advance training of complex military tasks such as close air support with remotely piloted aerial systems through decreasing workload, increasing knowledge acquisition, and enabling better performance.

A new adaptive automated feedback system for Barkhausen signal measurement

2006 ◽  
Vol 129 (1-2) ◽  
pp. 112-117 ◽  
Author(s):  
H.V. Patel ◽  
S. Zurek ◽  
T. Meydan ◽  
D.C. Jiles ◽  
L. Li
Keyword(s):  
Feedback System ◽  
Signal Measurement ◽  
Automated Feedback

The Enterprise Oriented Computer Graphic Design Courses Construction of Practice Teaching System

2014 ◽  
Vol 989-994 ◽  
pp. 5237-5240 ◽  
Author(s):  
Er Jing Bai ◽  
De Li Lin
Keyword(s):  
Computer Graphics ◽  
Graphic Design ◽  
Computer Graphic ◽  
Feedback System ◽  
Teaching Quality ◽  
Teaching Staff ◽  
Employment Rate ◽  
Teaching System ◽  
Practice Teaching ◽  
Computer Students

Nowadays, the computer graphics technology is widely used in the area of design. The author puts forward the enterprise oriented computer graphic design courses construction of practice teaching system, which is for improving the employment rate of the computer students and making the students can directly blend in the enterprise after graduation. This system includes four big modules. They are the construction of teaching staff, the construction of practical condition and cooperation with enterprise, practice teaching system, teaching quality supervision and feedback system[1]. Each module is decomposed into several small modules.

University Implementation of TEACHActive – An Automated Classroom Feedback System and Dashboard

2021 ◽  
Vol 65 (1) ◽  
pp. 375-379
Author(s):  
Jameel Kelley ◽  
Dana AlZoubi ◽  
Stephen B. Gilbert ◽  
Evrim Baran ◽  
Aliye Karabulut-Ilgu ◽  
...  
Keyword(s):  
Open Source Software ◽  
Implementation Process ◽  
Feedback System ◽  
Lessons Learned ◽  
State University ◽  
Iowa State University ◽  
Similar System ◽  
Faculty Recruitment ◽  
It Support ◽  
Automated Feedback

Computer vision has the potential to play a significant role in capacity building for classroom instructors via automated feedback. This paper describes the implementation of an automated sensing and feedback system, TEACHActive. The results of this paper can enable other campuses to replicate a similar system using open-source software and consumer-grade hardware. Some of the challenges discussed include faculty recruitment, IRB procedures, camera-based classroom footage privacy, hardware setup, software setup, and IT support. The design and implementation of the TEACHActive system is being carried out at Iowa State University and is being tested with faculty in classrooms pilots. Preliminary interviews with instructors show a desire to include more active learning methods in their classrooms and overall interest in a system that can perform automated feedback. The primary results of this paper include lessons learned from the institutional implementation process.

scholarly journals Deep Learning Assisted Detection of Abdominal Free Fluid in Morison's Pouch During Focused Assessment With Sonography in Trauma

2021 ◽  
Vol 8 ◽  
Author(s):  
Chi-Yung Cheng ◽  
I-Min Chiu ◽  
Ming-Ya Hsu ◽  
Hsiu-Yung Pan ◽  
Chih-Min Tsai ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Acquisition ◽  
Model Performance ◽  
Feedback System ◽  
Training Model ◽  
Free Fluid ◽  
Patient Records ◽  
Validation Set ◽  
Automated Feedback ◽  
Fluid Detection

Background: The use of focused assessment with sonography in trauma (FAST) enables clinicians to rapidly screen for injury at the bedsides of patients. Pre-hospital FAST improves diagnostic accuracy and streamlines patient care, leading to dispositions to appropriate treatment centers. In this study, we determine the accuracy of artificial intelligence model-assisted free-fluid detection in FAST examinations, and subsequently establish an automated feedback system, which can help inexperienced sonographers improve their interpretation ability and image acquisition skills.Methods: This is a single-center study of patients admitted to the emergency room from January 2020 to March 2021. We collected 324 patient records for the training model, 36 patient records for validation, and another 36 patient records for testing. We balanced positive and negative Morison's pouch free-fluid detection groups in a 1:1 ratio. The deep learning (DL) model Residual Networks 50-Version 2 (ResNet50-V2) was used for training and validation.Results: The accuracy, sensitivity, and specificity of the model performance for ascites prediction were 0.961, 0.976, and 0.947, respectively, in the validation set and 0.967, 0.985, and 0.913, respectively, in the test set. Regarding feedback prediction, the model correctly classified qualified and non-qualified images with an accuracy of 0.941 in both the validation and test sets.Conclusions: The DL algorithm in ResNet50-V2 is able to detect free fluid in Morison's pouch with high accuracy. The automated feedback and instruction system could help inexperienced sonographers improve their interpretation ability and image acquisition skills.

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