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

In plain words

Schools and universities collect a lot of written student feedback, and reading all of it by hand takes a long time. This review asks which language-processing techniques could help: turning comments into numbers a computer can use, picking the most useful word signals, finding the main topics, shortening long comments, tagging how students feel about teaching or facilities, and organising facts in a knowledge graph.

The authors also list the awkward cases that make this hard, such as sarcasm, words with several meanings, emoticons and symbols, opinions about one aspect of a course rather than the whole, and labels that are heavily unbalanced. The paper runs no experiments of its own. It sorts existing methods and the results other studies report, then discusses which ones might carry over from other domains such as product reviews and Twitter.

TL;DR

• A literature review, not a new method. It has no code, no training setup and no experiments of its own §1 §5.
• It covers feature extraction (bag of words, TF-IDF, word embeddings), feature selection, topic modelling, text summarisation, document categorisation, entity extraction, knowledge graphs and sentiment annotation §2.
• It also covers six challenges: domain-specific language, sarcasm, ambiguity, emoticons and special characters, aspect-based sentiment analysis, and data imbalance §3.
• Use it as a map of the field and its open problems. Do not use its numbers as benchmarks: they come from different datasets and setups §2.

Problem

Monitoring and tracking student feedback by hand is slow and resource-heavy. Feedback can be numeric ratings or free-text comments, and the authors focus on the text §1. They ask four questions: which NLP methods exist, which challenges apply to education, what current trends are in student-feedback analysis, and which methods from other fields could transfer §1.

Method

This is a review, so the "method" is the review procedure and the taxonomy it uses.

Procedure as reported. The review searched Google Scholar, which covers Wiley, Scopus, Springer, ACM Digital Library, IEEE Xplore, PubMed, ScienceDirect and MDPI. The search results were checked manually for relevance §5. The paper does not give the search strings, date range or number of papers kept. Figure 9 shows a year-wise distribution of references fig. 9 §5. The authors state that most references are from the last five years, and that more than 90% are journal articles or conference papers §5.

# Review procedure as reported  [§5]
# Input:  queries (not stated)
# Output: kept papers (count not stated)

def review(queries):
    # Search Google Scholar, which indexes the listed databases  [§5]
    results = search_scholar(queries)
    # Check each result by hand for relevance to NLP in feedback analysis  [§5]
    kept = [paper for paper in results if is_relevant(paper)]
    # Sections 2 and 3 organise the kept papers by technique  [§2] [§3]
    return kept

Taxonomy used in the body.

1. Feature extraction turns text into vectors: bag of words, TF-IDF, and word embeddings (Word2Vec, GloVe, Doc2Vec, BERT) §2.1. Table 2 lists the algorithms and cited works per technique tab. 2.
2. Feature selection is split into filter, wrapper and embedded methods. The paper gives 2^n as the number of feature subsets for n features §2.2. It names sequential forward selection as a wrapper example §2.2.2 and L1 regularisation as an embedded example §2.2.3. Figures 1–3 illustrate the three families fig. 1 fig. 2 fig. 3.
3. Topic modelling is split into non-probabilistic models (LSA and NMF) §2.3.1 and probabilistic models (PLSA and LDA) §2.3.2. The paper says LDA is the most used topic model in the works it reviews §2.3.2 §4.
4. Text evaluation covers: summarisation as extractive, abstractive or hybrid §2.4.1 fig. 4 fig. 5 fig. 6; document categorisation §2.4.2; entity extraction §2.4.3; knowledge graphs §2.4.4 fig. 7; and sentiment annotation §2.4.5.
5. Challenges: domain-specific language §3.1, sarcasm §3.2, ambiguity §3.3, emoticons and special characters §3.4, aspect-based sentiment analysis §3.5, and data imbalance §3.6.
6. Discussion covers Gartner's 2021 hype-cycle diagram for AI, which places decision intelligence, deep learning and knowledge graphs near the peak §4 fig. 8. It also compares Python, Java and R NLP packages §4 tab. 1.

No equations, algorithms or pseudocode appear in the paper. The pseudocode above is the review procedure as the paper describes it, not a method from the paper.

Hyperparameters

This is a review: it reports no training hyperparameters.

Results

Author-reported. The paper runs no experiments; the numbers below are the results of the cited studies, as quoted in its text. Each row gives the setup the paper states, and "not stated" where it does not say. The paper does not claim the numbers are comparable.

The paper gives the Li et al. figures as two pairs without stating which pair belongs to which dataset; see Pitfalls.

Paper vs code

No official code. The paper links no code, and its source has no project page. A GitHub repository search for the full title (2026-10-18) returned zero results. The paper describes no implementation, so nothing is compared against code.

Pitfalls

• Not an implementable method. Nothing here is a training recipe. Agents that need an implementation should follow the cited original papers.
• Numbers are not comparable. The reported accuracies and F1 scores come from different datasets, languages, labels, baselines and metrics. Some are Twitter or e-commerce data, not student feedback §2.1 §2.3.2. Several studies name no baseline values at all.
• Ambiguous figure pairing (Li et al.). The text gives two pairs of accuracies (91.73/94.88 and 91.11/96.23) and does not say which pair is which dataset §2.4.2.
• Scope limitation contradicts the body. The conclusion says data scarcity and class imbalance were not discussed §5. Section 3.6 does discuss data imbalance and sampling fixes §3.6. Read §3.6 as the authors' position, and §5 as their statement of scope.
• Table 1 does not match the text. The text says the table gives each package's documentation source §4. Table 1 lists packages, languages and features only, with no source column tab. 1.
• Table 2 may not match the text. In the TeX source, the challenge rows of Table 2 are commented out, so the compiled table shows only methodology and text-evaluation rows tab. 2. The Topic Modelling row lists Wang et al. and Fowler et al., whose paragraphs are commented out in the source. The source therefore lists them without discussing them §2.3.2 tab. 2. Feature Extraction in Table 2 also lists LSA and LDA, which the text places under topic modelling §2.3.1 §2.3.2.
• Search is under-specified. The paper gives no search strings, date range, inclusion criteria beyond manual relevance, or number of studies included §5. The review cannot be reproduced from the text.
• Forecasts are cited, not verified. The claim that 70% of organisations will shift from big to small and wide data by 2025 is attributed to another source and is not evaluated here §4.
• Third-party figures. Figure 7 (a knowledge graph) and figure 8 (Gartner's hype cycle) come from other sources, as their captions credit fig. 7 fig. 8. This card describes them in words only.

Lineage

• Builds on: none stated. The review builds on the cited studies in §2 and §3.
• Compared against: none. It does not benchmark methods against each other.
• Superseded by: not known from content in this wiki.