In the era of personalized education, the provision of comprehensible explanations for learning recommendations is of great value to enhance the learner's understanding and engagement with the recommended learning...
详细信息
ISBN:
(数字)9798350394023
ISBN:
(纸本)9798350394030
In the era of personalized education, the provision of comprehensible explanations for learning recommendations is of great value to enhance the learner's understanding and engagement with the recommended learning content. Large language models (LLMs) and generative AI have recently opened new doors for generating human-like explanations, for and along learning recommendations. However, their precision is still far away from acceptable in a sensitive field like education. To harness the abilities of LLMs, while still ensuring a high level of precision towards the intent of the learners, this paper proposes an approach to utilize knowledge graphs (KG) as a source of factual context for LLM prompts, reducing the risk of model hallucinations, and safeguarding against.wrong or imprecise information, while maintaining an application-intended learning context. We utilize the semantic relations in the knowledge graph to offer curated knowledge about learning recommendations. With domain-experts in the loop, we design the explanation as a textual template, which is filled and completed by the LLM. Domain experts were integrated in the prompt engineering phase as part of a study, to ensure that explanations include information that is relevant to the learner. We evaluate our approach quantitatively using Rouge-N and Rouge-L measures, as well as qualitatively with experts and learners. Our results show an enhanced recall and precision of the generated explanations compared to those generated solely by the GPT model, with a greatly reduced risk of generating imprecise information in the final learning explanation.
Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and analysis objective....
Modelling learning objects (LO) within their context enables the learner to advance from a basic, remembering-level, learning objective to a higher-order one, i.e., a level with an application- and analysis objective. While hierarchical data models are commonly used in digital learning platforms, using graph-based models enables representing the context of LOs in those platforms. This leads to a foundation for personalized recommendations of learning paths. In this paper, the transformation of hierarchical data models into knowledge graph (KG) models of LOs using text mining is introduced and evaluated. We utilize custom text mining pipelines to mine semantic relations between elements of an expert-curated hierarchical model. We evaluate the KG structure and relation extraction using graph quality-control metrics and the comparison of algorithmic semantic-similarities to expert-defined ones. The results show that the relations in the KG are semantically comparable to those defined by domain experts, and that the proposed KG improves representing and linking the contexts of LOs through increasing graph communities and betweenness centrality.
Physical skills and language skills are both fundamental intelligent abilities of human being. In this paper, we focus our attention to such sophisticated physical skills as playing sports and playing inst.uments and ...
详细信息
Physical skills and language skills are both fundamental intelligent abilities of human being. In this paper, we focus our attention to such sophisticated physical skills as playing sports and playing inst.uments and introduce research activities aiming at elucidating and verbalizing them. This research area has been launched recently. We introduce approaches from physical modeling, measurements and data analysis, cognitive science and human interface. We also discuss such issues as skill acquisition and its support systems. Furthermore, we consider a fundamental issue of individual differences occurring in every application of skill elucidation. Finally we introduce several attempts of skill elucidation in the fields of dancing, manufacturing, playing string inst.uments, sports science and medical care.
In early design phases an effective information exchange among CAD (Computer Aided Design) tools depends on a standardized representation for the product data in all PLM (Product Lifecycle management) tools. The NIST ...
详细信息
In early design phases an effective information exchange among CAD (Computer Aided Design) tools depends on a standardized representation for the product data in all PLM (Product Lifecycle management) tools. The NIST Core Product Model (CPM) and its extension are proposed to provide the required base-level product model that is open, non-proprietary, generic, extensible, independent of any one product development process and capable of capturing the full engineering context commonly shared in product development. The Open Assembly Model (OAM) Model extends CPM to provide a standard representation and exchange protocol for assembly. The assembly information model emphasizes the nature and information requirements for part features and assembly relationships. The model includes both assembly as a concept and assembly as a data structure. For the latter it uses the model data structures of ISO 10303, informally known as the Standard for the Exchange of Product model data (STEP). The objective of the paper is to show how the OAM can be used to realize seamless integration of product information, with an emphasis on assembly, throughout all phases of a product design. A gearbox design example is used to illustrate the process.
Planning and calculating adequate nutritional support for sick or premature newborn infants in an intensive care unit is tedious, time-consuming work. Besides requiring considerable expert knowledge and practical expe...
详细信息
Planning and calculating adequate nutritional support for sick or premature newborn infants in an intensive care unit is tedious, time-consuming work. Besides requiring considerable expert knowledge and practical experience, this task is prone to inherent, possibly fatal, calculation errors. To automate this process, we built several versions of the Vienna Expert System for Parenteral Nutrition of Neonates (VIE-PNN) knowledge-based system for prescribing parenteral (intravenous) nutrition supply. Unfortunately, clinicians did not employ these initial versions in their daily routine. This article describes our recent redesign of VIE-PNN using an HTML-based client-server architecture. We have integrated this tool into the intranet of workstations that runs a clinic's patient-data-management system. Its acceptance among physicians has been immediate. Two neonatal ICUs at the University of Vienna have put this integrated version into daily use. Factors underlying this knowledge-based system's successful operation include its ease of use, minimal required input, robustness, explanation facilities, and, most important, time savings compared to hand calculation.
暂无评论