Personal Generative Libraries for Personalised Learning: A Case Study

With the ever-growing types and amount of educational content, its retrieval from conventional digital libraries to ensure the specific learner’s needs, e.g., for personalised learning or course specificity, encounters many issues, such as time, quality, or even no search result. One way to overcome those issues and enforce learning performance is to apply a specialised approach, such as personalised digital libraries with personalised content. This chapter introduces the Personal Generative Library (PGL) concept. It describes the automated content design and automated management for personalised learning. Based on this concept, we have built an experimental system that integrates conventional repositories, the teacher’s PGL, the students’ PGLs, their individual repositories, and personalised learning processes using the previously developed framework. The main contribution of this research is (i) a distributed architecture of the proposed system, (ii) generative capabilities of its constituents (e.g., Metadata Generator, Query Generator, LO List Generator), and (iii) proposed methodology. The basis of this methodology is a deep separation of concepts at the component level (i.e., content items) and the sub-system level (i.e., student’s PGL/repository, teacher’s PGL/repository, and their tools) along with generative technology applied. Either the content within the student’s PGL/repository is a direct product of personalised learning obtained during the classroom activities or is a by-product created during outside activities. We have presented a survey provided by students to evaluate the personalised content of the teacher’s PGL/repository. This survey, constructed on the well-known methodology, gave a good evaluation in one high school, though this approach is independent neither of the teaching course nor the teaching environment.