Abstract
The difficulties of retrieving the educational content from conventional digital libraries for personalised learning (PL) are well known. In this paper, to overcome those issues and enforce learning performance, we propose the concept personal generative library (PGL). We will discuss an experimental system that integrates conventional repositories, the teacher’s PGL, the students’ PGLs, their individual repositories along with the personalised learning processes using the developed framework. The teacher’s individual repository stores the personalised content for all students along with assessment tasks for each type of the content. The teacher’s and students’ PGLs have the identical structure. The student’s content is a direct product of PL obtained during the classroom activities by modifying the teacher’s content due to the needs of personalisation or is a by-product created or searched during outside learner’s activities. We have approved this approach in one high school. We will present experimental results of the PGLs usage and the quality evaluation. Our approach enables enforcing the PL significantly in terms of higher flexibility, efficient search and more efficient procedures to form the personalised learning paths for smart CS education.
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Štuikys, V., Burbaitė, R., Kubiliūnas, R., Valinčius, K. (2020). Personal Generative Libraries for Smart Computer Science Education. In: Uskov, V., Howlett, R., Jain, L. (eds) Smart Education and e-Learning 2020. Smart Innovation, Systems and Technologies, vol 188. Springer, Singapore. https://doi.org/10.1007/978-981-15-5584-8_18
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