A Sentiment Analysis Based Approach to Fight MOOCs’ Drop Out

Sraidi, Soukaina; Smaili, El Miloud; Azzouzi, Salma; Charaf, My El Hassan

doi:10.1007/978-981-16-3637-0_36

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Studies likeDalipi et al (2021) and Xavier and Meneses (2020) have found the various themes but these articles are based on literature reviews. Although there have been numerous studies conducted on the topic, such as those bySraidi et al (2022) andGreenland & Moore…”

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confidence: 99%

Thematic and Sentiment Analysis of Learners’ Feedback in MOOCs

Pant

Manoj

Pande³

2023

JL4D

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In recent years, sentiment analysis has gained popularity among researchers in various domains, including the education domain. Sentiment analysis can be applied to review the course comments in Massive Open Online Courses (MOOCs), which could enable course designers’ to easily evaluate their courses. The objective of this study is to explore the influential factors that affect the completion rate of MOOCs and unravel the sentiments of dropout learners by evaluating learners’ feedback. In the present study, sentiment analysis was performed using Python programming and NVivo tools on the feedback of the learners enrolled in three MOOCs entitled Introduction to Cyber Security, Digital Forensics and Development of Online Courses for SWAYAM, which was hosted on the SWAYAM platform (www.swayam.gov.in). Two instruments were used for data collection: (1) a structured questionnaire using a 5-point Likert scale was administrated using Google Forms — the questionnaires have also some additional open-ended questions — and (2) semi-structured interview schedules with the domain experts. The feedback was collected using Google Forms and a total of 324 responses were received between April 23, 2022 to May 31, 2022. The non-probability sampling method served as the sampling approach in the quantitative phase in this study. During analysis, the findings of the feedback uncovered important dimensions of some peculiar factors that may be responsible for retention of learners, i.e., content localisation, credit mobility and latest trend courses that were less explored in the earlier literature.

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confidence: 99%

Thematic and Sentiment Analysis of Learners’ Feedback in MOOCs

Pant

Manoj

Pande³

2023

JL4D

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An Optimized Adaptive Learning Approach Based on Cuckoo Search Algorithm

Smaili

Azzouzi

Charaf

2022

2022 8th International Conference on Optimization and Applications (ICOA)

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Enhancing Innovation Through Bayesian Networks in Recommender Learning Paths

Tagdimi,

Amzil,

Jdidou

et al. 2023

Technological Tools for Innovative Teaching

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In an era characterized by rapidly evolving educational paradigms, the imperative to foster innovation and facilitate personalized learning experiences cannot be overstated. Traditional recommendation algorithms, such as collaborative filtering and content-based approaches, have played a pivotal role in tailoring educational content to individual needs. However, these methods, though effective in many respects, tend to perpetuate established preferences, inadvertently stifling innovative thinking. This chapter explores the transformative potential of Bayesian networks within recommender systems to address these limitations. It begins by examining the restrictions inherent in conventional recommendation algorithms, emphasizing the significance of fostering innovation in education. Bayesian networks are introduced as a revolutionary solution capable of bridging these gaps. This chapter offers a comprehensive overview of how Bayesian networks can reshape the landscape of personalized learning.

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A Sentiment Analysis Based Approach to Fight MOOCs’ Drop Out

Cited by 3 publications

References 16 publications

Thematic and Sentiment Analysis of Learners’ Feedback in MOOCs

Thematic and Sentiment Analysis of Learners’ Feedback in MOOCs

An Optimized Adaptive Learning Approach Based on Cuckoo Search Algorithm

Enhancing Innovation Through Bayesian Networks in Recommender Learning Paths

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