Sentimental Analysis on E-learning Videos for Learner’s Opinion Using Machine Learning Methodology - Support Vector Machine

Rajesh, P.; Akila, D.

doi:10.1007/978-981-19-2538-2_18

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…SA helps users to easily classify their emotional input information. Students' anti-course feelings can be tracked that serves as www.ijacsa.thesai.org feedback from online learning sites [18]. In the teachinglearning process virtual learning environments (VLE) deliver a set of communication and interaction tools utilized by learners and educators.…”

Section: Literature Reviewmentioning

confidence: 99%

Multifaceted Sentiment Detection System (MSDS) to Avoid Dropout in Virtual Learning Environment using Multi-class Classifiers

T¹,

J²

2023

IJACSA

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Sentiment analysis with machine learning plays a vital role in Higher Educational Institutions (HEI) for decision making. Technology-enabled interactions can only be successful when a strong student-teacher link is established, and the emotions of students are clearly comprehended. The paper aims at proposing Multifaceted Sentiment Detection System (MSDS) for detecting sentiments of higher education students participating in virtual learning and to classify the comments posted by them using Machine Learning (ML) algorithms. Present research evaluated a total of n=1590 students' comments with the presence of three specific multifaceted characteristics each providing 530 comments to perform Sentiment Analysis (SA) for monitoring their sentiments, opinions that facilitate predicting dropout in virtual learning environment (VLE). This begins with the phrase extraction; then data pre-processing techniques namely digits, punctuation marks and stop-words removal, spelling correction, tokenization, lemmatization, ngrams, and POS (Part of Speech) are applied. Texts are vectorized using two feature extraction techniques with count vectorization and TF-IDF metrics and classified with four multiclass supervised ML techniques namely Random Forest, Linear SVC, Multinomial Naive Bayes, and Logistic Regression for multifaceted sentiment classification. Analyzing students' feedback using sentiment analysis techniques classifies their positive, negative, or even more refined emotions that enables dropout prediction. Experimental results reveal that the highest mean accuracy result for device efficiency, cognitive behavior, technological expertise with cloud learning platform usage were achieved by Logistic Regression with 98.49%, Linear SVC with 93.58% and Linear SVC with 92.08% respectively. Practically, results confirm feasibility for detecting students' multifaceted behavioral patterns and risk of dropout in VLE.

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Section: Literature Reviewmentioning

confidence: 99%

Multifaceted Sentiment Detection System (MSDS) to Avoid Dropout in Virtual Learning Environment using Multi-class Classifiers

T¹,

J²

2023

IJACSA

View full text Add to dashboard Cite

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“…To be interpreted, open responses have to be processed with ML techniques, such as sentiment analysis [46]. More specifically, in the context of research into learning on virtual platforms, analysing students' feelings allows us to test the perceived effectiveness in relation to the use of different digital resources [47]. The ultimate aim of sentiment analysis is to determine the emotions behind the words.…”

Section: Use Of Machine Learning Techniques In Analysis Of Perceived ...mentioning

confidence: 99%

Nursing Students’ Perceived Satisfaction with Flipped Learning Experiences: A Mixed-Methods Study

et al. 2022

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Nowadays, technological teaching tools, such as virtual labs, have become essential, especially in nursing degrees. These resources help implement practical learning based on self-regulation and it is important to know how satisfied students are with them. This means it is important to study students’ perceived satisfaction with virtual labs in flipped learning experiences, which was the general objective of the present study. The aims of the study were: (1) to determine whether there were significant differences in nursing students’ perceived levels of satisfaction according to the type of subject or gender; (2) to ascertain what strengths and weaknesses nursing students perceived about using virtual labs; and (3) to determine the kinds of feelings (positive, negative or neutral) nursing students had using virtual labs. A mixed research methodology was applied, with a sample of 222 undergraduate nursing students at Burgos University (Spain). There were significant differences in the satisfaction perceived by students depending on the subject. Students reported medium-high satisfaction with virtual labs, although they did note that digital skills are needed to use them and suggested incorporating intelligent assistants. Virtual labs seem to be effective, although further studies are needed.

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Sentimental Analysis on E-learning Videos for Learner’s Opinion Using Machine Learning Methodology - Support Vector Machine

Cited by 2 publications

References 19 publications

Multifaceted Sentiment Detection System (MSDS) to Avoid Dropout in Virtual Learning Environment using Multi-class Classifiers

Multifaceted Sentiment Detection System (MSDS) to Avoid Dropout in Virtual Learning Environment using Multi-class Classifiers

Nursing Students’ Perceived Satisfaction with Flipped Learning Experiences: A Mixed-Methods Study

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