Prediction of difficulty for video-based learning materials : an initial approach

This work is intended to have an initial approach towards the video-based learning materials difficulty prediction using machine learning techniques. First, we address the possible features that can be extracted from the video resources, propose a scheme of 7 domains of features that a video can hav...

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Autores:: Venegas Bernal, Tomás Felipe
Lovera Lozano, Juan Manuel Alberto

Tipo de recurso:: Trabajo de grado de pregrado

Fecha de publicación:: 2018

Institución:: Universidad de los Andes

Repositorio:: Séneca: repositorio Uniandes

Idioma:: eng

Description
Summary:	This work is intended to have an initial approach towards the video-based learning materials difficulty prediction using machine learning techniques. First, we address the possible features that can be extracted from the video resources, propose a scheme of 7 domains of features that a video can have and extract the domains' features. After that, we introduce a data recollection platform that is used to obtain video class labels. We later treat this problem as a binary class problem using majority vote and use SMOTE to address class imbalance. We then use supervised learning algorithms to build difficulty prediction models. Later we perform experiments to determine the best feature subset and best algorithm to solve the problem. Finally, we obtain a prediction model using Random Forest which has an average accuracy of 90.3%.

Prediction of difficulty for video-based learning materials : an initial approach

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