Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis

Pair programming is a model of collaborative learning. It has become a well-known pedagogical practice in teaching introductory programming courses because of its potential benefits to students. This study aims to investigate pair patterns in the context of pair program tracing and debugging to dete...

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Main Authors: Villamor, Maureen M, Rodrigo, Maria Mercedes T
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Published: Archīum Ateneo 2022
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Online Access:https://archium.ateneo.edu/discs-faculty-pubs/344
https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1344&context=discs-faculty-pubs
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spelling ph-ateneo-arc.discs-faculty-pubs-13442022-12-07T01:21:37Z Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis Villamor, Maureen M Rodrigo, Maria Mercedes T Pair programming is a model of collaborative learning. It has become a well-known pedagogical practice in teaching introductory programming courses because of its potential benefits to students. This study aims to investigate pair patterns in the context of pair program tracing and debugging to determine what characterizes collaboration and how these patterns relate to success, where success is measured in terms of performance task scores. This research used eye-tracking methodologies and techniques such as cross-recurrence quantification analysis. The potential indicators for pair success were used to create a model for predicting pair success. Findings suggest that it is possible to create a model capable of predicting pair success in the context of pair programming. The predictors for the pair success model that can obtain the best performance are the pairs' proficiency level and degree of acquaintanceship. This was achieved using an ensemble algorithm such as Gradient Boosted Trees. The performance of the pairs is largely determined by the proficiency level of the individuals in the pairs; hence, it is recommended that the struggling students be paired with someone who is considered proficient in programming and with whom the struggling student is comfortable working with. 2022-01-01T08:00:00Z text application/pdf https://archium.ateneo.edu/discs-faculty-pubs/344 https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1344&context=discs-faculty-pubs Department of Information Systems & Computer Science Faculty Publications Archīum Ateneo Forecasting Program debugging Students Trees (mathematics) Collaborative learning Cross recurrences Eye-tracking Introductory programming course Pair-programming Pedagogical practices Performance Potential benefits Proficiency level Recurrence quantification analysis Eye tracking Computer Engineering Computer Sciences Education Educational Technology Electrical and Computer Engineering Engineering Physical Sciences and Mathematics
institution Ateneo De Manila University
building Ateneo De Manila University Library
continent Asia
country Philippines
Philippines
content_provider Ateneo De Manila University Library
collection archium.Ateneo Institutional Repository
topic Forecasting
Program debugging
Students
Trees (mathematics)
Collaborative learning
Cross recurrences
Eye-tracking
Introductory programming course
Pair-programming
Pedagogical practices
Performance
Potential benefits
Proficiency level
Recurrence quantification analysis
Eye tracking
Computer Engineering
Computer Sciences
Education
Educational Technology
Electrical and Computer Engineering
Engineering
Physical Sciences and Mathematics
spellingShingle Forecasting
Program debugging
Students
Trees (mathematics)
Collaborative learning
Cross recurrences
Eye-tracking
Introductory programming course
Pair-programming
Pedagogical practices
Performance
Potential benefits
Proficiency level
Recurrence quantification analysis
Eye tracking
Computer Engineering
Computer Sciences
Education
Educational Technology
Electrical and Computer Engineering
Engineering
Physical Sciences and Mathematics
Villamor, Maureen M
Rodrigo, Maria Mercedes T
Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis
description Pair programming is a model of collaborative learning. It has become a well-known pedagogical practice in teaching introductory programming courses because of its potential benefits to students. This study aims to investigate pair patterns in the context of pair program tracing and debugging to determine what characterizes collaboration and how these patterns relate to success, where success is measured in terms of performance task scores. This research used eye-tracking methodologies and techniques such as cross-recurrence quantification analysis. The potential indicators for pair success were used to create a model for predicting pair success. Findings suggest that it is possible to create a model capable of predicting pair success in the context of pair programming. The predictors for the pair success model that can obtain the best performance are the pairs' proficiency level and degree of acquaintanceship. This was achieved using an ensemble algorithm such as Gradient Boosted Trees. The performance of the pairs is largely determined by the proficiency level of the individuals in the pairs; hence, it is recommended that the struggling students be paired with someone who is considered proficient in programming and with whom the struggling student is comfortable working with.
format text
author Villamor, Maureen M
Rodrigo, Maria Mercedes T
author_facet Villamor, Maureen M
Rodrigo, Maria Mercedes T
author_sort Villamor, Maureen M
title Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis
title_short Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis
title_full Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis
title_fullStr Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis
title_full_unstemmed Predicting Pair Success in a Pair Programming Eye Tracking Experiment Using Cross-Recurrence Quantification Analysis
title_sort predicting pair success in a pair programming eye tracking experiment using cross-recurrence quantification analysis
publisher Archīum Ateneo
publishDate 2022
url https://archium.ateneo.edu/discs-faculty-pubs/344
https://archium.ateneo.edu/cgi/viewcontent.cgi?article=1344&context=discs-faculty-pubs
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