OBE Oriented Constructing Method of Educational Knowledge Graph for Automatic Control Discipline

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With the rapid development of education informatization and the exponential growth of education big data, Educational Knowledge Graph (EKG) has received significant attention in the field of smart education. However, current research of constructing Chinese EKG still faces enormous challenges, such as incomplete education ontology, low recognition performance of educational entities and relations, and the scarce dataset. This paper proposes an OBE (Outcome-Based Education) oriented constructing method of Chinese EKG for automatic control discipline. We first design an OBE based multilevel education ontology for EKG to guide the knowledge modeling of automatic control discipline. And then, we propose a DWBERT-BiLSTM-CRF model to recognize more specific educational entities, and combines BERT-wwm with BiGRU and attention mechanism to efficiently extract the entity relations. In addition, we construct a Chinese dataset for identifying educational entity and its relations of automatic control discipline (ACEER). We evaluate the effectiveness and generalizations of our EKG model by extensive experiments on our ACEER dataset and four benchmark datasets, which demonstrates that our proposed models can achieve outstanding performance on all datasets.

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  • Jan 1, 2024
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  • Ting You

With the development of educational informatization, a large amount of data has been generated in the field of education through various platforms, which prompted research on educational big data among numerous researchers. The quasi-experimental research method has certain controllability. This study adopts the method of quasi-experimental research to conduct a semester of educational big data visualization experiments teaching students through the learning platform. The purpose is to explore the impact of educational big data visualization on students’ learning initiatives. Participants were divided into two different groups, namely an experimental group (52 people) and a control group (40 people) for a 4-month teaching experiment from February 2023 to June 2023. The results indicated that there was no significant difference in the mean value of the learning initiative between these two groups. However, the results for the pre-and post-tests showed one stronger effect on the experimental group and much less compared to the control group. The size effect of the experimental group was 0.314, but the control group was only 0.167. This means that the effect has been stronger for the experimental group than for the control group.

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