Feedback Analysis in Outcome Base Education Using Machine Learning

Lwin, Htar Htar; Oo, Sabai; Lin, Kyaw Kyaw; Aung, Wai Phyo; Ko, Phyo Paing

doi:10.1109/ecti-con49241.2020.9158328

Cited by 24 publications

(12 citation statements)

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“…6) Machine or manual translation to English: Three of the studies translated the data into English using Google Translate, (Sadriu et al [19], Lwin et al [20] and Lalata et al [23]) sometimes later revised by human whereas another two studies (Marcu and Danubianu [15] and Nikolovski et al [22]) manually translated the data into English. In total, 20.8% of the studies translated the comments that are written in a language rather than English.…”

Section: According To Data Language Size and Labelsmentioning

confidence: 99%

“…quality rating scaled from 1 to 5 is the label for the review written by students about the class. Some researchers such as Lwin et al [20] use machine labelling for the numeric data by clustering with k-means algorithm while using human labelling for textual data. 9) Machine labelling: The rest (4 papers, 17.4%) of the studies carried out the labelling with the aid of a machine, which implies that the algorithms used in the models automatically label the data.…”

Section: According To Data Language Size and Labelsmentioning

confidence: 99%

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Natural Language Processing of Student's Feedback to Instructors: A Systematic Review

Sunar,

Khalid

2024

IEEE Trans. Learning Technol.

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Section: According To Data Language Size and Labelsmentioning

confidence: 99%

Section: According To Data Language Size and Labelsmentioning

confidence: 99%

Natural Language Processing of Student's Feedback to Instructors: A Systematic Review

Sunar,

Khalid

2024

IEEE Trans. Learning Technol.

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“…Supervised [7], [8], [9], [10], [11], [12], [13], [2], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29]. Unsupervised [30], [31], [32], [33], [34].…”

Section: Learning Techniques Papersunclassified

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning (RETRACTED)

Oghu,

Ogbuju,

Abiodun

et al. 2023

Bull.Socinf.The.App

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The education industry considers quality to be a crucial factor in its development. Nevertheless, the quality of many institutions is far from perfect, as there is a high rate of systemic failure and low performance among students. Consequently, the application of digital computing plays an increasingly important role in assuring the overall quality of an educational institution. However, the literature lacks a reasonable number of systematic reviews that classify research that applied natural language processing and machine learning solutions for students’ sentiment analysis and quality assurance feedback. Thus, this paper presents a systematic literature review that structure available published papers between 2014 and 2023 in a high-impact journal-indexed database. The work extracted 59 relevant papers from the 3392 initially found using exclusion and inclusion criteria. The result identified five (5) prevalent techniques that are majorly researched for sentiment analysis in education and the prevalent supervised machine learning algorithms, lexicon-based approaches, and evaluation metrics in assessing feedback in the education domain.

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“…Table 8 shows papers that reported the sources of the datasets used for conducting experiments along with their corresponding categories and description. Here, the data were mostly collected by conducting surveys among students and teachers or by providing questioners to collect feedback from the students Education/research platforms [14,31,36,40,[44][45][46]48,58,61,70,78,82,84,86,93,95,99,101] This category contains the data extracted from online platforms providing different courses such as Coursera, edX, and research websites such as ResearchGate, LinkedIn, etc.…”

Section: Rq5 What Are the Most Common Sources Used To Collect Students' Feedback?mentioning

confidence: 99%

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

et al. 2021

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In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and processing students’ opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students’ feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection.

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Feedback Analysis in Outcome Base Education Using Machine Learning

Cited by 24 publications

References 1 publication

Natural Language Processing of Student's Feedback to Instructors: A Systematic Review

Natural Language Processing of Student's Feedback to Instructors: A Systematic Review

A Review of Sentiment Analysis Approaches for Quality Assurance in Teaching and Learning (RETRACTED)

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

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