Towards a feature-based didactic framework for generating individualized programming tasks for an e-learning environment
Nico Willert, J. Eriksson · 2023
Adaptive programming tasks are a promising approach for personalized learning that adapts to each student’s unique needs and abilities. However, developing effective adaptive programming tasks can be challenging, particularly when it comes to selecting the appropriate changes and adapting the difficulty of the exercise. In this paper, we propose a model for tracking student knowledge and adapting programming exercises to guide the selection and implementation of task features. Our model combines aspects of cognitive load, computational thinking and feature-oriented software product line engineering to identify core and optional features, so that they can be used in conjunction to adapt to the specific needs and abilities of each student. We provide an overview over the insights gained from an exploratory study with students. To support the creation process of feature-based programming tasks, we present an approach using a template-based generator.