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Classification of strategies for solving programming problems using AoI sequence analysis
Author(s) -
Unaizah Obaidellah,
Michael Raschke,
Tanja Blascheck
Publication year - 2019
Publication title -
hal (le centre pour la communication scientifique directe)
Language(s) - English
Resource type - Conference proceedings
ISBN - 978-1-4503-6709-7
DOI - 10.1145/3314111.3319825
Subject(s) - computer science , eye tracking , sequence (biology) , set (abstract data type) , granularity , reading (process) , problem statement , statement (logic) , artificial intelligence , machine learning , programming language , management science , economics , political science , biology , law , genetics
This eye tracking study examines participants' visual attention when solving algorithmic problems in the form of programming problems. The stimuli consisted of a problem statement, example output, and a set of multiple-choice questions regarding variables, data types, and operations needed to solve the programming problems. We recorded eye movements of students and performed an Area of Interest (Aol) sequence analysis to identify reading strategies in terms of participants' performance and visual effort. Using classical eye tracking metrics and a visual Aol sequence analysis we identified two main groups of participants---effective and ineffective problem solvers. This indicates that diversity of participants' mental schemas leads to a difference in their performance. Therefore, identifying how participants' reading behavior varies at a finer level of granularity warrants further investigation.

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