Automated text detection from big data scene videos in higher education: a practical approach for MOOCs case study

Devi, Mortha Manasa; Maddala, Seetha; Raju, Somalaraju Viswanadha

doi:10.1007/s12528-021-09294-y

Cited by 4 publications

(3 citation statements)

References 101 publications

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“…MSER features can improve the effectiveness of e-learning by providing personalized feedback to students. MSER is known for its stability and robustness, making it valuable for various computer vision applications [61].…”

Section: E Mser Featuresmentioning

confidence: 99%

Automating Assessment and Providing Personalized Feedback in E-Learning: The Power of Template Matching

Alhalalmeh,

Fouda,

Rushdi

et al. 2023

Sustainability

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This research addressed the need to enhance template-matching performance in e-learning and automated assessments within Egypt’s evolving educational landscape, marked by the importance of e-learning during the COVID-19 pandemic. Despite the widespread adoption of e-learning, robust template-matching feedback mechanisms should still be developed for personalization, engagement, and learning outcomes. This study augmented the conventional best-buddies similarity (BBS) approach with four feature descriptors, Harris, scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and maximally stable extremal regions (MSER), to enhance template-matching performance in e-learning. We systematically selected algorithms, integrated them into enhanced BBS schemes, and assessed their effectiveness against a baseline BBS approach using challenging data samples. A systematic algorithm selection process involving multiple reviewers was employed. Chosen algorithms were integrated into enhanced BBS schemes and rigorously evaluated. The results showed that the proposed schemes exhibited enhanced template-matching performance, suggesting potential improvements in personalization, engagement, and learning outcomes. Further, the study highlights the importance of robust template-matching feedback in e-learning, offering insights into improving educational quality. The findings enrich e-learning experiences, suggesting avenues for refining e-learning platforms and positively impacting the Egyptian education sector.

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Section: E Mser Featuresmentioning

confidence: 99%

Automating Assessment and Providing Personalized Feedback in E-Learning: The Power of Template Matching

Alhalalmeh,

Fouda,

Rushdi

et al. 2023

Sustainability

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“…Sustainability 2022, 14, 11199 7 of 17 Educational Privacy in the Online Classroom [49], Automated text detection [50], Privacy in MOOC [51], MOOC video watching behavior [52], Topic-oriented learning assistance [53], etc. Table 3 summarizes the articles of Big Data in Online Education/MOOCs.…”

Section: Big Data In Moocsmentioning

confidence: 99%

“…The projection resulted in a considerable improvement in student test scores [47] Trends in Learning Analytics A larger variety of learning-related characteristics [50] Automatic text recognition in MOOC videos Assessment of ICDAR Benchmark datasets for video text results in high recall…”

Section: [42]mentioning

confidence: 99%

MOOC 5.0: A Roadmap to the Future of Learning

et al. 2022

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Industry 4.0 has created a whole new world for us to explore, and its effects can be seen in every facet of our lives, especially in the workplace where it calls for technology-driven employment. There is a growing need to teach individuals and assist them in transitioning to longer-term employment prospects to execute Industry 4.0 effectively. Although MOOCs revolutionized the way learners study, it is critical to investigate teaching techniques using Education 4.0 at this time. This article explores how the technologies of Industry 4.0 can be incorporated into MOOCs. This paper proposes MOOCs 5.0, whose features include better universal access, better learner engagement, adaptive learning, greater collaboration, security, and curiosity, which is being developed using Industry 4.0 technologies of the Internet of Things, Cloud Computing, Big Data, Artificial Intelligence/Machine Learning, Blockchain, Gamification Technologies, and the Metaverse and would incorporate the zones of ethics and humanism, while at the same time providing learners with a richer and more individualized experience.

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Big Data and Data Mining in Education: A Bibliometrics Study from 2010 to 2022

Wang

Dai

2022

2022 7th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)

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Automated text detection from big data scene videos in higher education: a practical approach for MOOCs case study

Cited by 4 publications

References 101 publications

Automating Assessment and Providing Personalized Feedback in E-Learning: The Power of Template Matching

Automating Assessment and Providing Personalized Feedback in E-Learning: The Power of Template Matching

MOOC 5.0: A Roadmap to the Future of Learning

Big Data and Data Mining in Education: A Bibliometrics Study from 2010 to 2022

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