Abstract

The COVID-19 pandemic and quarantine have forced students to use distance learning. Modern information technologies have enabled global e-learning usage but also revealed a lack of personalization and adaptation in the learning process when compared to face-to-face learning. While adaptive e-learning methods exist, their practical application is slow because of the additional time and resources needed to prepare learning material and its logical adaptation. To increase e-learning materials’ usability and decrease the design complexity of automated adaptive students’ work evaluation, we propose several transformations from a competence tree-based structure to a graph-based automated e-evaluation structure. Related works were summarized to highlight existing e-evaluation structures and the need for new transformations. Competence tree-based e-evaluation structure improvements were presented to support the implementation of top-to-bottom and bottom-to-top transformations. Validation of the proposed transformation was executed by analyzing different use-cases and comparing them to the existing graph-to-tree transformation. Research results revealed that the competence tree-based learning material storage is more reusable than graph-based solutions. Competence tree-based learning material can be transformed for different purposes in graph-based e-evaluation solutions. Meanwhile, graph-based learning material transformation to tree-based structure implies material redundancy, and the competence of the tree structure cannot be restored.

Highlights

  • IntroductionMuch attention is currently being devoted to the personalization of e-learning systems [1,2,3]

  • This modification allowed for the adaptation of a contextual graph transformation to a tree structure for adaptive knowledge e-evaluation purposes

  • The existing Brezillon graph transformation to tree structure has only one possible result—there are no variations of the transformation

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Summary

Introduction

Much attention is currently being devoted to the personalization of e-learning systems [1,2,3]. There are some challenges in developing personalized learning systems. One of the challenges is the adaptation of learning material to fit learners’ needs and the improvement of learning efficiency [1,3]. This includes task identification and selection of the right difficulty level [4,5]. Tasks of the right complexity increase a learner’s motivation and cause a state of flow. Flow is “the state in which people are so intensely involved in an activity that nothing else seems to matter; the experience itself is so enjoyable that people will do it even at great cost, for the sheer sake of doing it” [4]

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