Exploring Learner Engagement Patterns in Teach-Outs Using Topic, Sentiment and On-topicness to Reflect on Pedagogy

Yan, Wenfei; Dowell, Nia; Holman, Caitlin; Welsh, Stephen S.; Choi, Heeryung; Brooks, Christopher

doi:10.1145/3303772.3303836

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…However, most of the identified research papers that evaluated the effectiveness of the sentiment analysis models were published during 2019 and 2020 ( Cobos et al, 2019a ; Cobos et al, 2019b ; Yan et al, 2019 ; Capuano and Caballé, 2020 ; Capuano et al, 2020 ; Estrada et al, 2020 ; Hew et al, 2020 ; Onan, 2021 ). ( Cobos et al, 2019a ; Cobos et al, 2019b ) compared and measured the evaluation effectiveness of machine learning (SVM, NB, ANN) and NLP approaches (VADER, TextBlob) to extract features and perform text analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Their prototype was based on a content analyser system for edX MOOCs. Another group of researchers conducted a relevant study by applying unsupervised natural language processing techniques to explore students’ engagement in Coursera MOOCs ( Yan et al, 2019 ). Further, they evaluated the performance of LDA, LSA (Latent Semantic Analysis) and topic modelling to discover the emerging topics in discussion forums and to investigate the sentiments associated with the discussions.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, there is also a lack of standardized datasets available for the evaluation of sentiment analysis models in MOOCs. Most of the researchers have used publicly available datasets of Coursera, edX, FutureLearn, and even datasets from their own institutions ( Ezen-Can et al, 2015 ; Moreno-Marcos et al, 2018 ; Cobos et al, 2019a ; Yan et al, 2019 ; Estrada et al, 2020 ; Lee et al, 2020 ). The absence of standardized datasets plays a negative role when benchmarking or comparing algorithmic solutions of different researchers.…”

Section: Discussionmentioning

confidence: 99%

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Sentiment Analysis of Students’ Feedback in MOOCs: A Systematic Literature Review

Dalipi

Zdravkova

Ahlgren

2021

Front. Artif. Intell.

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In recent years, sentiment analysis (SA) has gained popularity among researchers in various domains, including the education domain. Particularly, sentiment analysis can be applied to review the course comments in massive open online courses (MOOCs), which could enable instructors to easily evaluate their courses. This article is a systematic literature review on the use of sentiment analysis for evaluating students’ feedback in MOOCs, exploring works published between January 1, 2015, and March 4, 2021. To the best of our knowledge, this systematic review is the first of its kind. We have applied a stepwise PRISMA framework to guide our search process, by searching for studies in six electronic research databases (ACM, IEEE, ScienceDirect, Springer, Scopus, and Web of Science). Our review identified 40 relevant articles out of 440 that were initially found at the first stage. From the reviewed literature, we found that the research has revolved around six areas: MOOC content evaluation, feedback contradiction detection, SA effectiveness, SA through social network posts, understanding course performance and dropouts, and MOOC design model evaluation. In the end, some recommendations are provided and areas for future research directions are identified.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Sentiment Analysis of Students’ Feedback in MOOCs: A Systematic Literature Review

Dalipi

Zdravkova

Ahlgren

2021

Front. Artif. Intell.

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“…Since their emergence as a popular mode of learning in 2012 [2], they have been delivering learning opportunities to a wide range of learners free or at low cost across different domains around the world [3], attracting thousands of learners to take advantage of the offered opportunities [4]. Amongst these, MOOC online discussion forums offer opportunities for learners to ask questions and express their feelings about course content and their learning progress, via posts.…”

Section: Introductionmentioning

confidence: 99%

A Multidimensional Deep Learner Model of Urgent Instructor Intervention Need in MOOC Forum Posts

Alrajhi

Alharbi

Cristea

2020

Lecture Notes in Computer Science

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In recent years, massive open online courses (MOOCs) have become one of the most exciting innovations in e-learning environments. Thousands of learners around the world enroll on these online platforms to satisfy their learning needs (mostly) free of charge. However, despite the advantages MOOCs offer learners, dropout rates are high. Struggling learners often describe their feelings of confusion and need for help via forum posts. However, the often-huge numbers of posts on forums make it unlikely that instructors can respond to all learners and many of these urgent posts are overlooked or discarded. To overcome this, mining raw data for learners' posts may provide a helpful way of classifying posts where learners require urgent intervention from instructors, to help learners and reduce the current high dropout rates. In this paper we propose, a method based on correlations of different dimensions of learners' posts to determine the need for urgent intervention. Our initial statistical analysis found some interesting significant correlations between posts expressing sentiment, confusion, opinion, questions, and answers and the need for urgent intervention. Thus, we have developed a multidimensional deep learner model combining these features with natural language processing (NLP). To illustrate our method, we used a benchmark dataset of 29598 posts, from three different academic subject areas. The findings highlight that the combined, multidimensional features model is more effective than the text-only (NLP) analysis, showing that future models need to be optimised based on all these dimensions, when classifying urgent posts.

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“…Student engagement is one of the key pedagogical concepts in education. Yan et al[70] evaluated learner engagement in Teach-Outs, which aims to drive meaningful discus-sions around topics of social urgency without any formal assessment. The authors used opinion-mining techniques including sentiment analysis, topic modelling, and similarity analysis to investigate the topics discussed by learners in the online forum.…”

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confidence: 99%

Sentiment analysis and opinion mining on educational data: A survey

Shaik,

Tao,

Dann

et al. 2023

Preprint

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Sentiment analysis AKA opinion mining is one of the most widely used NLP applications to identify human intentions from their reviews. In the education sector, opinion mining is used to listen to student opinions and enhance their learning-teaching practices pedagogically. With advancements in sentiment annotation techniques and AI methodologies, student comments can be labelled with their sentiment orientation without much human intervention. In this review article, 1) we consider the role of emotional analysis in education from four levels: document level, sentence level, entity level, and aspect level, 2)sentiment annotation techniques including lexicon-based and corpus-based approaches for unsupervised annotations are explored, 3)the role of AI in sentiment analysis with methodologies like machine learning,

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Exploring Learner Engagement Patterns in Teach-Outs Using Topic, Sentiment and On-topicness to Reflect on Pedagogy

Cited by 8 publications

References 9 publications

Sentiment Analysis of Students’ Feedback in MOOCs: A Systematic Literature Review

Sentiment Analysis of Students’ Feedback in MOOCs: A Systematic Literature Review

A Multidimensional Deep Learner Model of Urgent Instructor Intervention Need in MOOC Forum Posts

Sentiment analysis and opinion mining on educational data: A survey

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