Knowledge Graph-Based Teacher Support for Learning Material Authoring

Grévisse, Christian; Manrique, Rubén; Mariño, Olga; Rothkugel, Steffen

doi:10.1007/978-3-319-98998-3_14

Cited by 12 publications

(12 citation statements)

References 15 publications

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“…In order to evaluate the relation extraction, the algorithm XGBoost was used for binary classification. In several other investigations, entity extraction was cast as a classification problem which was addressed using algorithms like Support Vector Machine (SVM) [31] and Conditional Random Field classifier (CRFClassifier) [32]. Other methodologies focused on entity extraction using semantic similarity [33] or Stack Long Short Term Memory (Stack-LSTM) with Spacy 1 [34].…”

Section: A Knowledge Acquisitionmentioning

confidence: 99%

“…Knowledge management using KGs explored tools such as SoLeMiO ( [31]) to integrate learning material in popular authoring softwareThe study focused on semantic representation by exploiting information from open KGs through expansion and filtering strategies in order to assist teachers in the authoring process of learning material through semi-automatic annotations. The authors used the identified concepts to extract and retrieve related learning resources from an open corpus.…”

Section: ) Knowledge Managementmentioning

confidence: 99%

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Knowledge Graphs in Education and Employability: A Survey on Applications and Techniques

2022

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Studies on the relationship between education and employability are of paramount importance for policy makers, training institutions, companies and students. The availability of large scale data on the internet, such as online job ads, has been leveraged to better investigate this relationship. Recent studies have shown that representing this data using knowledge graphs can be useful in identifying the job market needs and establishing better assessment methods. Such studies also highlight the skill mismatch between education and the job market and help mitigate it. In this survey, we provide a comprehensive review of the applications of knowledge graphs in education and employability. We go through the knowledge graph based frameworks that have been proposed in previous work on different aspects of education and employability. The survey introduces new taxonomies for these applications and their methodologies, and provides a thorough outlook on promising research directions. We also present a use case to illustrate how knowledge graphs can be used to investigate the relationship between education and the job market.

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Section: A Knowledge Acquisitionmentioning

confidence: 99%

Section: ) Knowledge Managementmentioning

confidence: 99%

Knowledge Graphs in Education and Employability: A Survey on Applications and Techniques

2022

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“…While fully automatic semantic annotation can be incomplete or incorrect, semiautomatic support suggests a set of initial concepts that the teacher can choose to add, while still giving him the possibility to add any missing one. Therefore, we developed SoLeMiO, an Add-in for Office 365 (Grévisse et al 2018a). This popular suite is available at many educational institutions free of charge, and its modern add-in architecture enables their use on any platform the suite is available for.…”

Section: Authoring Supportmentioning

confidence: 99%

“…From the recognized concepts, a semantic representation is built using additional information from Open Knowledge Graphs through expansion and filtering strategies. A detailed description of this process was presented in Grévisse et al (2018a). Annotation can happen either at document level, or in a fine-grained way, assigned to a text selection in a Word document or a slide in PowerPoint.…”

Section: Authoring Supportmentioning

confidence: 99%

Scaffolding support through integration of learning material

Grévisse

Rothkugel

Reuter

2019

Smart Learn. Environ.

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The ever-growing amount of learning material poses a challenge to both learners and teachers. In order to perform a learning task, students often need to search for additional information. Inquiry might be hampered if they do not know what exactly to search for, especially if they lack prior knowledge in a domain. Furthermore, they might need to recall meta-information, e.g., the implicit links between learning resources or the location of an explanation within a document. In addition, their performance in a learning task might suffer from the constant need for further information. As the authoring of learning material is a time-intensive task, reusability of resources in different contexts should be enabled. In this paper, we present the ALMA-Yactul ecosystem, a holistic approach for student-centered integration of learning material. Based on semantic annotations, learners are provided with scaffolding support for retrieving material related to their current study task. This integration of resources is showcased in a variety of applications and domains, such as a plugin for an IDE or an enhanced sketchnoting app. Moreover, we show how Yactul, a gamified student response platform, can benefit from semantic annotations and integration of learning material. Additionally, teachers are provided with semi-automatic annotation support in the popular Office 365 suite to foster the reusability of their content. Apart from presenting the results of its usage in university classes, we also show how high school students without prior knowledge benefitted from this scaffolding support. A significantly better performance in resolving programming-related tasks could be observed compared to a traditional search process. Finally, the ecosystem is evaluated against a state-of-the-art technology integration model.

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“…Alternatively, leveraging semantically structured representations of knowledge, such as Wikidata's COVID-19 Knowledge Graph [58,61], enables the design of computational methods to explore, analyze, and integrate COVID-19-related information in decision-support systems with ease. Such semantic resources allow both manual explorations by domain experts and automatic processing by computational methods, which makes them useful in a variety of scenarios, from tracking epidemiological evolution [45] to generating public health recommendations [20,62], and supporting different informative [21] or didactic [22] needs.…”

Section: Introductionmentioning

confidence: 99%

Data Models for Annotating Biomedical Scholarly Publications: the Case of CORD-19

Turki

Taieb

Piad-Morffis

et al. 2022

Companion Proceedings of the Web Conference 2022

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Semantic text annotations have been a key factor for supporting computer applications ranging from knowledge graph construction to biomedical question answering. In this systematic review, we provide an analysis of the data models that have been applied to semantic annotation projects for the scholarly publications available in the CORD-19 dataset, an open database of the full texts of scholarly publications about COVID-19. Based on Google Scholar and the screening of specific research venues, we retrieve seventeen publications on the topic mostly from the United States of America. Subsequently, we outline and explain the inline semantic annotation models currently applied on the full texts of biomedical scholarly publications. Then, we discuss the data models currently used with reference to semantic annotation projects on the CORD-19 dataset to provide interesting directions for the development of semantic annotation models and projects.

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Knowledge Graph-Based Teacher Support for Learning Material Authoring

Cited by 12 publications

References 15 publications

Knowledge Graphs in Education and Employability: A Survey on Applications and Techniques

Knowledge Graphs in Education and Employability: A Survey on Applications and Techniques

Scaffolding support through integration of learning material

Data Models for Annotating Biomedical Scholarly Publications: the Case of CORD-19

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