Driving performance after an extended period of travel in an automated highway system.

Federal Highway Administration, Office of Safety and Traffic Operations, Washington, D.C.

Systems studies of automated highway systems: appendix I - conceptual automated highway system designs. Final report.

Federal Highway Administration, Traffic Systems Division, Washington, D.C.

Practicality of automated highway systems. Volume I - summary report. Final report.

Federal Highway Administration, Office of Research and Development, Washington, D.C.

Guidelines for desing and evaluation of human factors aspects of automated guideway transit systems. Final report.

Urban Mass Transportation Administration, Washington, D.C.

Systems studies of automated highway systems: appendix II - analysis of automated highway systems. Final report.

Federal Highway Administration, Traffic Systems Division, Washington, D.C.

Workshop on Methodology for Evaluating the Effectiveness of Transit Crime Reduction Measures in Automated Guideway Transit Systems. Proceedings. Final report.

Urban Mass Transportation Administration, Washington, D.C.

Alternative mass transportation technologies technical data. Interim report.

Texas State Department of Highways and Public Transportaion, Transportation Planning Division, Austin

Risk assessment in fixed guideway transit system construction. Final report.

Federal Transit Administration, Washington, D.C.

Fundamental studies in automatic vehicle control. Interim report.

Federal Highway Administration, Office of Research and Development, Washington, D.C.

Fundamental studies in automatic vehicle control. Final report.

Federal Highway Administration, Office of Research, Washington, D.C.

Factors contributing to the retention of seated passengers during emergency stops. Final report.

Urban Mass Transportation Administration, Washington, D.C.

Automated Feedback for 'Fill in the Gap' Programming Exercises

Timely feedback is a vital component in the learning process. It is especially important for beginner students in Information Technology since many have not yet formed an effective internal model of a computer that they can use to construct viable knowledge. Research has shown that learning efficiency is increased if immediate feedback is provided for students. Automatic analysis of student programs has the potential to provide immediate feedback for students and to assist teaching staff in the marking process. This paper describes a “fill in the gap” programming analysis framework which tests students’ solutions and gives feedback on their correctness, detects logic errors and provides hints on how to fix these errors. Currently, the framework is being used with the Environment for Learning to Programming (ELP) system at Queensland University of Technology (QUT); however, the framework can be integrated into any existing online learning environment or programming Integrated Development Environment (IDE)