A Time-Aware Approach to Detect Help-Seeking Behaviour from Student-Platform Interaction

Seeking help when needed is a crucial skill, especially in unsupervised learning scenarios. It is known that some students do not ask for assistance when they need it, which can lead to counterproductive learning sessions. This work addresses the challenge of detecting help-seeking behaviour by lear...

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Detalles Bibliográficos
Autor: Horta Bartomeu, Raquel
Tipo de recurso: tesis de maestría
Fecha de publicación:2023
País:España
Institución:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/23781
Acceso en línea:https://hdl.handle.net/20.500.14468/23781
Access Level:acceso abierto
Palabra clave:1203.17 Informática
help-seeking behaviour
student behaviour
adaptive learning
event prediction
sequence classification
recurrence plots
Descripción
Sumario:Seeking help when needed is a crucial skill, especially in unsupervised learning scenarios. It is known that some students do not ask for assistance when they need it, which can lead to counterproductive learning sessions. This work addresses the challenge of detecting help-seeking behaviour by learning from student-platform interaction events using deep learning models. This is the first work to predict help-seeking by considering the temporal nature of student behaviour while being independent of the topic being taught and the task at hand. We depict student-platform interaction as sequences of actions and evaluate five distinct approaches alongside various data representation techniques. Our research yields a model for detecting help-seeking behaviour solely from action sequences. We hypothesise that this approach has the potential for further improvement, especially when combined with pedagogical data and personalised features. Furthermore, we introduce a novel knowledge representation technique for categorical sequence analysis.