Measuring Student Emotions in an Online Learning Environment

Alencar, Márcio Aurélio dos Santos; Netto, José Francisco de Magalhães

doi:10.5220/0008956505630569

Cited by 7 publications

(8 citation statements)

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“…The use of suggestion boxes is becoming outdated and not necessary in the 21 st century with the digital revolution and the flow of big data of information in a couple of seconds. Illustratively from table (5), it is clear that a lot of published works of previous researchers' findings of student feedback showed 65% remarks is skewed in favour of lecturers which highlight the key impacts of quality of education lies with the caliber of lecturers employed by any institutions., while 10% student feedbacks are skewed in favour of the institutions and 25% are targeted at their courses and institutions combined. These findings are vital information for universities Management and head of institutions to see the need for training and retraining of lecturers, updating of the course's curriculum on regular basis, renovation of universities classrooms and buildings, toilets, hostels, street cleaning, removal of endemic cobweb encroachment and all indexes of dirty environment should be taken care of.…”

Section: Resultsmentioning

confidence: 94%

Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning

Oghu,

Ogbuju,

Abiodun

et al. 2023

AIMS Research Publication

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The idea of modernization has become a norm in society, and the quality of the education sector that is been delivered to society is worth being assessed, and this will reflect the caliber of graduands these institutions are producing. As the key to this assessment is to x-ray clear-cut metrics used in the assessment of these institutions of higher learning by regulating authorities, parents, and other stakeholders These institutions' academic deliverables in terms of availability of experienced teachers, modern teaching and learning amenities, open access to learning materials, open access to assessment results, and timely responses to transcripts application to mention just a few needs proper evaluation. A lot of institutions' indices of performance are not encouraging, which translates to a high level of students dropping out, poor student performance, and low productivity which affects the standard of living and overall planned change that education is supposed to bring to the generality of the society at large. We must appreciate the efforts of previous researchers who have spent precious time using various sentiment analysis tools to unveil these challenges facing our education institutions and providing far-reaching measures that will make these institutions improve all indexes of measurement approved by regulating authorities globally as a mark of global competitiveness. Literature reviews by researchers using natural language processing and other machine learning techniques to get students and other stakeholders’ feedback by applying sentiment analysis to improve the quality of teaching and learning in the education sector have yielded the required results. Given the above this research effort aimed at reviewing published papers geared towards improving learners' experiences and enhancing quality assurance in the education system from published articles between 2016 to 2023 in referenced databases from high-impact journals. The research study collated 80 useful articles from the pool of 4595 that were earlier surfed using eligibility criteria which entailed employing exclusion and inclusion characteristics. The outcome of the literature reviews showed that 70% of researchers have used sentiment analysis as feedback and improvement analysis tools in enhancing the student learning experience and quality assurance in the higher education sector. The highly used sentiment analysis algorithms are lexicon-based, Support Vector Machine (SVM), Naïve Bayes(NB), Linear Regression(LR), and Artificial Neural Network(ANN). Keywords: Sentiment Analysis, Education Sector, Supervised Machine Learning Algorithms, social media, Questionnaires, Lexicon-based, Natural Language Processing(NLP) Emughedi Oghu, Emeka Ogbuju, Taiwo Abiodun & Francisca Oladipo.. (2023): Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning. Journal of Advances in Mathematical & Computational Science. Vol. 10, No. 4. Pp 1-20 Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V11N3P1

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Section: Resultsmentioning

confidence: 94%

Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning

Oghu,

Ogbuju,

Abiodun

et al. 2023

AIMS Research Publication

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“…All the resources of Sentic computing in the field of sentiment analysis and affective computing are publicly available and used in different domains such as utilizing aspect-based affective computing and sentiment analysis for summarizing opinion (Kumar et al, 2021), recommender systems-based sentiment analysis (Pasquier et al, 2020) and e-learning for measuring the learner sentiment (Alencar and Netto, 2020).…”

Section: Sentiment Analysismentioning

confidence: 99%

Fine-grained Sentiment-enhanced Collaborative Filtering-based Hybrid Recommender System

Alatrash,

Priyadarshini

2024

JWE

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Developing online educational platforms necessitates the incorporation of new intelligent procedures in order to improve long-term student experience. Presently, e-learning recommender systems rely on deep learning methods to recommend appropriate e-learning materials to the students based on their learner profiles. Fine-grained sentiment analysis (FSA) can be leveraged to enrich the recommender system. User-posted reviews and rating data are vital in accurately directing the student to the appropriate e-learning resources based on posted comments by comparable learners. In this work, a new e-learning recommendation system is proposed based on individualization and FSA. A hybrid framework is provided by integrating alternating least square (ALS) based collaborative filtering (CF) with FSA to generate an effective e-content recommendation named HCFSAR. ALS attempts to capture the learner’s latent factors based on their selections of interest to build the learner profile. Three FSA models based on attention mechanisms and bidirectional long short-term memory (bi-LSTM) are suggested and used to train twelve models in order to predict new ratings from learner-posted book reviews based on the extracted learner profile. HCFSAR used multiplication word embeddings for stronger corpus representation that were trained on a dataset generated for an educational context and showed a better accuracy of 93.39% for the best model entitled MHAM based ABHR-2 with multiplication (MHAAM), which performed better than other models. A tailored dataset that has been created by scraping reviews of different e-learning resources is leveraged to train different proposed models and validate against public datasets.

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“…To predict the sentiment in a given sentence the latter approaches aggregate the sentiments of the individual words or phrases. These so-called lexicon-or dictionary-based SA approaches are popular since they are easy to implement and do not require labeled training data (Alencar and Netto 2020). One commonly used example dictionary approach is VADER (Valence Aware Dictionary for Sentiment Reasoning) (Hutto and Gilbert 2014).…”

Section: Background and Related Workmentioning

confidence: 99%

Practical Sentiment Analysis for Education: The Power of Student Crowdsourcing

Kasumba,

Neumman

2024

AAAI

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Sentiment analysis provides a promising tool to automatically assess the emotions voiced in written student feedback such as periodically collected unit-of-study reflections. The commonly used dictionary-based approaches are limited to major languages and fail to capture contextual differences. Pretrained large language models have been shown to be biased and online versions raise privacy concerns. Hence, we resort to traditional supervised machine learning (ML) approaches which are designed to overcome these issues by learning from domain-specific labeled data. However, these labels are hard to come by -- in our case manually annotating student feedback is prone to bias and time-consuming, especially in high-enrollment courses. In this work, we investigate the use of student crowdsourced labels for supervised sentiment analysis for education. Specifically, we compare crowdsourced and student self-reported labels with human expert annotations and use them in various ML approaches to evaluate the performance on predicting emotions of written student feedback collected from large computer science classes. We find that the random forest model trained with student-crowdsourced labels tremendously improves the identification of reflections with negative sentiment. In addition to our quantitative study, we describe our crowdsourcing experiment which was intentionally designed to be an educational activity in an introduction to data science course.

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Measuring Student Emotions in an Online Learning Environment

Cited by 7 publications

References 0 publications

Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning

Baseline Study of Different Sentiment Analysis Computing Methods to Enhance Quality Assurance In Teaching and Learning

Fine-grained Sentiment-enhanced Collaborative Filtering-based Hybrid Recommender System

Practical Sentiment Analysis for Education: The Power of Student Crowdsourcing

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