A Word Embeddings Based Clustering Approach for Collaborative Learning Group Formation

Wu, Yongchao; Nouri, Jalal; Li, Xiu; Weegar, Rebecka; Afzaal, Muhammad; Zia, Aayesha

doi:10.1007/978-3-030-78270-2_70

Cited by 2 publications

(1 citation statement)

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“…With this approach, 59% of the feedback text was reduced at a cost if mean average error increased to 0.06 while predicting course ratings from student feedback. Wu et al [38] proposed pre-trained word embeddings to automatically create clusters such as homogeneous and heterogeneous student groups based on students' knowledge. Homogeneous groups can assist teachers to provide collective feedback, and heterogeneous groups can support and improve collaborative learning.…”

Section: A Feature Extractionmentioning

confidence: 99%

A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis

Shaik

Tao

et al. 2022

IEEE Access

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Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI's impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.

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Section: A Feature Extractionmentioning

confidence: 99%