Authors:
Mario Garcia Valdez
1
;
Amaury Hernandez Aguila
1
;
Juan-J. Merelo
2
and
Alejandra Mancilla Soto
1
Affiliations:
1
Instituto Tecnológico de Tijuana, Mexico
;
2
University of Granada, Spain
Keyword(s):
Affective Computing, Neural Networks, Learning Analytics.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Learning to program is often regarded as a difficult task. When selecting an appropriate programming exercise,
experienced instructors gauge a student´s affective state and skills to then assign an activity with the
appropriate level of difficulty. This work is focused on the prediction of the affective states of programmers
with different levels of expertise when learning a new programming language. For this, an interactive webbased
programming platform is proposed. The platform is designed to collect data from the studentsínteraction
for data analysis. Current work is focused on the prediction of affective states using non-obtrusive sensors.
Specifically, the aim of this research is to evaluate the use of keyboard and mouse dynamics as an appropriate
sensory input for an affective recognition system. The proposed method uses feature vectors obtained
by mining data generated from both keyboard and mouse dynamics of students as they work in basic Python
programming assignmen
ts, which were used to train different classification algorithms to classify learners
into five different affective states: boredom, frustration, distraction, relaxation and engagement. Accuracy
achieved was around 75% with J48 obtaining the best results, proving that data gathered from non-obtrusive
sensors can successfully be used as another input to classification models in order to predict an individual´s
affective states.
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