Measuring Cognition Load Using Eye-Tracking Parameters Based on Algorithm Description Tools.

Area of Science:

• Computer Science
• Cognitive Science
• Human-Computer Interaction

Background:

• Programming is a complex cognitive task, particularly for novices.
• Effective algorithm description tools are crucial for efficient software development.
• Understanding cognitive load during programming can optimize development processes.

Purpose of the Study:

• To compare the efficiency of flowchart and Nassi-Shneiderman diagrams as algorithm description tools.
• To evaluate cognitive load during programming using eye movement parameters.
• To determine which diagrammatic tool leads to reduced cognitive effort.

Main Methods:

• Development and application of an eye-tracking system to monitor programmers.
• Recording and analyzing eye movement parameters such as fixation duration, number of fixations, and pupil diameter.
• Comparative analysis of these parameters when interpreting flowcharts versus Nassi-Shneiderman diagrams.

Main Results:

• Interpreting flowcharts resulted in significantly longer fixation durations.
• A higher number of fixations was observed when using flowcharts.
• Larger pupil diameters indicated increased cognitive load with flowchart interpretation compared to Nassi-Shneiderman diagrams.

Conclusions:

• Nassi-Shneiderman diagrams are a more efficient tool for algorithm description, leading to lower cognitive load.
• Choosing appropriate programming tools significantly impacts application development efficiency and cost.
• Eye-tracking provides valuable insights into the cognitive processes involved in programming.