Key factors in MOOC pedagogy based on NLP sentiment analysis of learner reviews: What makes a hit

Li, Lingyao; Johnson, John B.; Aarhus, William; Shah, Dhawal

doi:10.1016/j.compedu.2021.104354

Cited by 53 publications

(22 citation statements)

References 31 publications

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“…The authors extracted techrelated skills using named entity recognition (NER) and built a personalized multi-level course recommendation system. This is a domain-specific NER designed to scrape data like job postings, course descriptions, and MOOCs online courses [131] information from multiple websites and enhance the system with annotated corpus from StackOverflow and GitHub [132]. The annotated StackOverflow data were embedded and split into train, test and validation datasets.…”

Section: A Domain-specific Languagementioning

confidence: 99%

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

Shaik

Tao

et al. 2022

IEEE Access

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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's 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.

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Section: A Domain-specific Languagementioning

confidence: 99%

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

Shaik

Tao

et al. 2022

IEEE Access

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“…Mais recentemente, análises de comentários de 116 MOOCs da plataforma Coursera foram feitas por Li et al (2022), a fim de capturar os sentimentos expressados por estudantes, quando emitem opiniões/avaliam o curso por meio de comentários. Os autores mencionam que utilizaram uma ferramenta de análise de sentimentos nomeada "Vader" -Valence Aware Dictionary and Sentiment Reasoner, e utilizaram uma métrica de atribuição de pontuação ao sentimento.…”

Section: Estudos Sobre Sentimentos Em Moocsunclassified

“…ocorrem no contexto de MOOCs, especialmente os termos considerados positivos. Também foi possível saber a quais elementos os sentimentos estão direcionados, em semelhança aos resultadosLi et al (2022), a maioria dos sentimentos são dirigidos ao professor e aos materiais. Assim comoYan et al (2021), também identificamos elogios sobre atuação do professor e referente a qualidade dos conteúdos.…”

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Análise de Comentários em Vídeos de um MOOC: o Curso É Aberto e os Sentimentos Também

Souza¹,

Perry²

2022

EaD en Foco

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A abertura é uma das principais qualidades incorporadas aos Cursos Online Massivos e Abertos (MOOCs). A partir deste atributo os estudantes ganharam espaços e liberdade para criar, comunicar e se expressar em plataformas de aprendizagem. Nesse contexto, é comum a exposição textual em MOOCs, possibilitando aos estudantes o compartilhamento de seus pensamentos de forma aberta. Esses pensamentos representados textualmente estão acompanhados de sentimentos que muitas vezes não são notados por professores. Diante disso, esta pesquisa foi guiada pelo objetivo de identificar os sentimentos expostos de maneira aberta em comentários de vídeos de um MOOC, disponíveis no YouTube. O conceito de pensamento aberto foi a abordagem teórica que ofereceu suporte ao estudo. A análise de sentimentos foi efetivada por meio de uma composição metodológica fundamentada no uso de dicionário, e um processo de mineração de mídias sociais. O MOOC foi composto por 11 vídeos, e teve o total de 111 comentários. Os resultados levaram a identificação de quatro grupos de expressão de sentimentos: agradecimento, apreciação, indagação e reconhecimento. Os sentimentos prevalentes são positivos e direcionados principalmente para o professor e ao conteúdo do curso. Palavras-chave: MOOC. Análise de sentimentos. Pensamento aberto.

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“…Interestingly, the researchers found students' ratings of difficulty increased when new terms were introduced in the narration, which suggests that lexical complexity contributes to learners' perception of lecture difficulty. A recent study adopted crowdsouring and sentiment analysis of reviews of the top-rated MOOCs in Coursera.org to ascertain what makes a good MOOC (L. Li, Johnson, Aarhus, & Shah, 2021). Their results showed that the quality of course design and materials along with the instructor's ability to lecture are key contributors to learners' perception of good online courses.…”

Section: Video Difficulty Assessmentmentioning

confidence: 99%

Leveraging ensemble machine learning and multimodal video complexity for better prediction of video difficulty in second language

Alghamdi¹

2022

Preprint

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Research into multimodal language learning has shown that video materials are engaging and effective in teaching and learning language skills. While previous studies have predominantly focused on how to make learning from video more effective, little research has been directed at estimating video content difficulty. This study, therefore, aims to examine the efficacy of developing predictive models using different configurations of ensemble machine learning approaches (averaging, bagging, boosting, and stacking) and video complexity features (acoustic, phonological, lexical, syntactic, and visual complexity features). A corpus of 640 videos that were subjectively rated for content difficulty by 322 English language learners was used to build and evaluate predictive models. The results demonstrated that ensemble machine learning models substantially outperformed baseline regression models in predicting video difficulty. The findings are discussed in terms of their applications in second language learning and assessment.

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Key factors in MOOC pedagogy based on NLP sentiment analysis of learner reviews: What makes a hit

Cited by 53 publications

References 31 publications

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

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

Análise de Comentários em Vídeos de um MOOC: o Curso É Aberto e os Sentimentos Também

Leveraging ensemble machine learning and multimodal video complexity for better prediction of video difficulty in second language

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