A Technology-enhanced Smart Learning Environment based on the Combination of Knowledge Graphs and Learning Paths

Abstract

In our position paper on a technology-enhanced smart learning environment, we propose the innovative combination of a knowledge graph representing what one has to learn and a learning path defining in which order things are going to be learned. In this way, we aim to identify students' weak spots or knowledge gaps in order to individually assist them in reaching their goals. Based on the performance of different learning paths, one might further identify the characteristics of a learning system that leads to successful students. In addition, by studying assessments and the different ways a particular problem can be solved, new methods for a multi-dimensional classification of assessments can be developed. The theoretical findings on learning paths in combination with the classification of assessments will inform the design and development of a smart learning environment. By combining a knowledge graph with different learning paths and the corresponding practical assessments we enable the creation of a smart learning tool. While the proposed approach can be applied to different educational domains and should lead to more effective learning environments fostering deep learning in schools as well as in professional settings, in this paper we focus on the domain of mathematics in primary and high schools as the main use case.

Modeling Personalized Smart Teaching for Learner Needs

“…The current teaching activities have entered the stage of emphasizing the personalization of instruction, diversification of learning forms, and quantification of resource base by means of computer network platforms [1][2]. Due to the specificity of computer teaching leads to the problems of information overload, tailored instruction, environmental isolation, etc., which existed in the traditional educational environment, are particularly prominent and have become one of the key issues affecting the development of modern network education [3][4][5].…”

EduCOR: An Educational and Career-Oriented Recommendation Ontology

“…Although many schemata and vocabularies were suggested in the past for the educational domain, only a few of them are still available online and can accommodate particularities of OERs, and related personalised recommendation systems' features. Furthermore, recent works revealed the increased interest in educational Knowledge Graphs [10,20], which, however, often lack an underlying ontology or schema [5]. Commercial products seem to follow a similar direction, as they usually do not use or do not publish their underlying knowledge schema 1 .…”

EduCOR: An Educational and Career-Oriented Recommendation Ontology

“…Although many schemata and vocabularies were suggested in the past for the educational domain, only a few of them are still available online and can accommodate particularities of OERs, and related personalised recommendation systems' features. Furthermore, recent works revealed the increased interest in educational Knowledge Graphs [10,20], which, however, often lack an underlying ontology or schema [5]. Commercial products seem to follow a similar direction, as they often do not use or do not publish their underlying knowledge schema 4 .…”