Proposal for a computer-based learning environment (CBLE) to improve the decision-making skills of general aviation pilots

2002 ◽  
Author(s):  
N. Barrette-Sabourin ◽  
D. Dodds-Nagy ◽  
K. Staudinger
Keyword(s):  
Decision Making ◽  
Learning Environment ◽  
General Aviation ◽  
Computer Based Learning ◽  
Computer Based ◽  
Aviation Pilots

Weatherwise: Evaluation of a Cue-Based Training Approach for the Recognition of Deteriorating Weather Conditions during Flight

2003 ◽  
Vol 45 (2) ◽  
pp. 337-345 ◽  
Author(s):  
Mark Wiggins ◽  
David O'Hare
Keyword(s):  
Decision Making ◽  
Significant Proportion ◽  
Evaluation Process ◽  
Weather Conditions ◽  
The United States ◽  
Training System ◽  
General Aviation ◽  
Self Report ◽  
Computer Based Training ◽  
Computer Based

Inappropriate and ineffective weather-related decision making continues to account for a significant proportion of general aviation fatalities in the United States and elsewhere. This study details the evaluation of a computer-based training system that was developed to provide visual pilots with the skills necessary to recognize and respond to the cues associated with deteriorating weather conditions during flight. A total of 66 pilots were assigned to one of two groups, and the evaluation process was undertaken at both a self-report and performance level. At the self-report level, the results suggested that pilots were more likely to use the cues following exposure to the training program. From a performance perspective, there is evidence to suggest that cue-based training can improve the timeliness of weather-related decision making during visual flight rules flight. Actual or potential applications of this research include the development of computer-based training systems for fault diagnosis in complex industrial environments.

Estimating student ability and problem difficulty using item response theory (IRT) and TrueSkill

2019 ◽  
Vol 47 (2) ◽  
pp. 67-75 ◽  
Author(s):  
Youngjin Lee
Keyword(s):  
Data Mining ◽  
Item Response Theory ◽  
Problem Solving ◽  
Learning Environment ◽  
Item Response ◽  
Predictive Power ◽  
Response Theory ◽  
Content Type ◽  
Computer Based Learning ◽  
Computer Based

Purpose The purpose of this paper is to investigate an efficient means of estimating the ability of students solving problems in the computer-based learning environment. Design/methodology/approach Item response theory (IRT) and TrueSkill were applied to simulated and real problem solving data to estimate the ability of students solving homework problems in the massive open online course (MOOC). Based on the estimated ability, data mining models predicting whether students can correctly solve homework and quiz problems in the MOOC were developed. The predictive power of IRT- and TrueSkill-based data mining models was compared in terms of Area Under the receiver operating characteristic Curve. Findings The correlation between students’ ability estimated from IRT and TrueSkill was strong. In addition, IRT- and TrueSkill-based data mining models showed a comparable predictive power when the data included a large number of students. While IRT failed to estimate students’ ability and could not predict their problem solving performance when the data included a small number of students, TrueSkill did not experience such problems. Originality/value Estimating students’ ability is critical to determine the most appropriate time for providing instructional scaffolding in the computer-based learning environment. The findings of this study suggest that TrueSkill can be an efficient means for estimating the ability of students solving problems in the computer-based learning environment regardless of the number of students.

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