# WEREC: An Unsupervised Method for Educational Concept Extraction Based on Word Embedding Refinement
## 6 Conclusions and Future Work  
A vast volume of educational data has emerged out of massive online learning and new learning media. More useful information must be extracted from educational big data to inform artificial intelligence-empowered teaching and learning. We intended to exploit semantic information for educational concept extraction (i.e., knowledge mining and information processing). Hence WERECE was proposed, a simple yet powerful unsupervised educational concept extraction method based on word embedding refinement. To the best of our knowledge, this effort represents an early attempt to refine pre-trained word embeddings for educational concept extraction in an unsupervised manner. Various experiments on two educational datasets substantiated WERECE's feasibility and effectiveness. We also observed how its parameter settings influenced the model's performance. Experimental results related to educational concept extraction demonstrate promise in three regards. First, two core components of WERECE--word embedding refinement and the discriminant function--could enrich educational concept extraction. Second, WERECE showed robust precision and sensitive recall, even with varied parameter settings. Third, it significantly outperformed the chosen baselines with respect to educational concept extraction. As a conclusion, WERECE provides valuable insight into the integration of pretrained word embedding models in the unsupervised extraction of educational concepts.  
In the future, we intend to apply WERECE to corpora beyond education and educational technology. Genetic algorithms can be used to identify the model's optimal parameter settings to achieve the best performance. WERECE's output could also act as a springboard for further educational applications. For instance, the model can be used to derive discipline-specific concepts from students' online discussions. The concepts' semantic similarity can then be computed to uncover their semantic relationships (e.g., subordinate relations). Such work can inform a discipline-oriented knowledge graph suitable for various educational tasks, such as learning assessment and feedback, learning interest prediction, and learning resource recommendation.  
**Author Contributions:** Conceptualization, Y.Z. and J.H.; methodology, J.H. and Y.Z.; software, R.D.; validation, R.D. and X.W.; formal analysis, Y.Z. and J.H.; investigation, S.C., R.D., J.Z. and L.L.; resources, X.W., S.C., J.Z. and L.L.; data curation, S.C.; writing--original draft preparation, J.H., R.D. and X.W.; visualization, J.H.; supervision, Y.Z.; project administration, Y.Z.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China: 62207015, and the Humanities and Social Sciences Youth Foundation of the Chinese Ministry of Education: 22 YJC880021.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflict of interest.