Citation: Katona, J. Measuring
Cognition Load Using Eye-Tracking
Parameters Based on Algorithm
Description Tools. Sensors 2022, 22,
912. https://doi.org/10.3390/
s22030912
Academic Editors: Enrico Vezzetti,
Andrea Luigi Guerra, Gabriele
Baronio, Domenico Speranza and
Luca Ulrich
Received: 2 January 2022
Accepted: 22 January 2022
Published: 25 January 2022
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Article
Measuring Cognition Load Using Eye-Tracking Parameters
Based on Algorithm Description Tools
Jozsef Katona
CogInfoCom Based LearnAbility Research Team, Department of Software Development and Application,
Institute of Computer Engineering, University of Dunaujvaros, 2400 Dunaujvaros, Hungary;
katonaj@uniduna.hu; Tel.: +36-25-551-605
Abstract:
Writing a computer program is a complex cognitive task, especially for a new person in
the field. In this research an eye-tracking system was developed and applied, which allows the
observation of eye movement parameters during programming as a complex, cognitive process,
and the conclusions can be drawn from the results. The aim of the paper is to examine whether the
flowchart or Nassi–Shneiderman diagram is a more efficient algorithm descripting tool for describing
cognitive load by recording and evaluating eye movement parameters. The results show that the case
of the interpreting flowchart has significantly longer fixation duration, more number of fixations, and
larger pupil diameter than the case of the Nassi–Shneiderman diagram interpreting. Based on the
results of the study, it is clear how important it is to choose the right programming tools for efficient
and lower cost application development.
Keywords: cognitive task; cognition load; eye-tracking; programming; algorithm description tools
1. Introduction
Writing a computer program is a complex cognitive task, especially for a new person
in the field [
1
]. In order to facilitate the process and to understand the algorithms to be
implemented a higher level of algorithmic thinking and problem solving is required [
2
–
4
].
Algorithm description tools have been introduced to describe the algorithm to be imple-
mented independently of the programming language in order to make the algorithms
more transparent and understandable. The commonly used algorithm description tool is
the flowchart (FCh), which describes the algorithm as a directed graph, thus illustrating
the steps of execution, while the Nassi–Shneiderman diagram (NSD), also called NS dia-
gram or structogram, represents the algorithm as a graph without edges as a “structured
flow charts”. Additional algorithm description tools have recently been applied like the
pseudocode, Jackson diagram, the sentence-like description and the description with tree;
however, the current study performs the analysis of the previously mentioned FCh and
NS diagrams.
In the visual programming described in the article by Charntaweekhun and Wangsirip-
itak [
1
], students learning programming can simply compile and run a program using the
FCh without any coding and allow for easy debugging and detection. Thus, the presented
system is excellent for teaching structural programming, as it avoids the learning difficulty
caused by the individual syntax of programming languages, thus providing an opportunity
to develop problem-solving skills [1,5].
In the study, Xinogalos [
5
] provides an overview of FCh-based programming environ-
ments and makes suggestions for making software engineering education more effective.
In the article in Cabo [
6
], it was found that students who use FCh to solve problems effec-
tively will learn Python or similar programming languages more easily (
r-squared = 0.68
).
Therefore, the use of flowcharts in programming education could appear as a kind of
supportive tool in the development of various cognitive processes [
6
]. Hooshyar et al. [
7
]
Sensors 2022, 22, 912. https://doi.org/10.3390/s22030912 https://www.mdpi.com/journal/sensors
