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

arXiv: https://arxiv.org/abs/2301.08826 · HTML: https://mlsanity.com/paper/2301.08826/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 173 references that are in the map.
- First posted on arXiv 20 Jan 2023.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [GlossBERT: BERT for Word Sense Disambiguation with Gloss Knowledge](https://mlsanity.com/paper/1908.07245/) (2019)
- [Learning to Extract Coherent Summary via Deep Reinforcement Learning](https://mlsanity.com/paper/1804.07036/) (2018)
- [Leveraging BERT for Extractive Text Summarization on Lectures](https://mlsanity.com/paper/1906.04165/) (2019)
- [Text Summarization Techniques: A Brief Survey](https://mlsanity.com/paper/1707.02268/) (2017)
- [Code and Named Entity Recognition in StackOverflow](https://mlsanity.com/paper/2005.01634/) (2020)
- [Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review](https://mlsanity.com/paper/1905.02845/) (2019)
- [Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences](https://mlsanity.com/paper/2009.11795/) (2020)
- [Generating Knowledge Graphs by Employing Natural Language Processing and Machine Learning Techniques within the Scholarly Domain](https://mlsanity.com/paper/2011.01103/) (2020)
- [Sarcasm Detection in Tweets with BERT and GloVe Embeddings](https://mlsanity.com/paper/2006.11512/) (2020)
- [Math Operation Embeddings for Open-ended Solution Analysis and Feedback](https://mlsanity.com/paper/2104.12047/) (2021)
- [Extractive Research Slide Generation Using Windowed Labeling Ranking](https://mlsanity.com/paper/2106.03246/) (2021)
- [Unsupervised Sentence Representations as Word Information Series: Revisiting TF--IDF](https://mlsanity.com/paper/1710.06524/) (2017)

## Built on by (most cited first, 12 of 12)

- [Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2305.06360/) (2023)
- [A Large Language Model Approach to Educational Survey Feedback Analysis](https://mlsanity.com/paper/2309.17447/) (2023)
- [Is My Text in Your AI Model? Gradient-based Membership Inference Test applied to LLMs](https://mlsanity.com/paper/2503.07384/) (2025)
- [Sentiment analysis and opinion mining on educational data: A survey](https://mlsanity.com/paper/2302.04359/) (2023)
- [Using Generative Text Models to Create Qualitative Codebooks for Student Evaluations of Teaching](https://mlsanity.com/paper/2403.11984/) (2024)
- [Dealing with Data for RE: Mitigating Challenges while using NLP and Generative AI](https://mlsanity.com/paper/2402.16977/) (2024)
- [Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2502.16708/) (2025)
- [Exploring a Large Language Model for Transforming Taxonomic Data into OWL: Lessons Learned and Implications for Ontology Development](https://mlsanity.com/paper/2504.18651/) (2025)
- [A Controlled Synthetic Benchmark for Educational Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2605.25502/) (2026)
- [RescueLens: LLM-Powered Triage and Action on Volunteer Feedback for Food Rescue](https://mlsanity.com/paper/2511.15698/) (2025)
- [Measuring Curriculum Alignment across Topical Coverage, Competency, and Cognitive Depth: A Longitudinal Framework Applied to CS2013 and CS2023](https://mlsanity.com/paper/2606.19469/) (2026)
- [A Durability and Cross-Language Transfer Benchmark for a Validated Teaching-Feedback Classification Protocol](https://mlsanity.com/paper/2607.11873/) (2026)

## Abstract (arXiv)

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 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.