A review on sentiment discovery and analysis of educational big‐data

Han, Zhongmei; Wu, Ji; Huang, Changqin; Huang, Qionghao; Zhao, Meihua

doi:10.1002/widm.1328

Cited by 23 publications

(15 citation statements)

References 126 publications

(159 reference statements)

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“…For obtaining the statistics to solve or optimize model parameters, the training data are scanned using existing data mining algorithms. However, big data mining needs intensive computing to frequently access large-scale data (Han et al, 2020;Wu et al, 2013). For controlling the emerging nature of large-scale data, general purpose parallel processing algorithms are extended to large number of machine learning algorithms based on the simple MapReduce programming model on multicore processors.…”

Section: Privacy-preserving Methods For Big Datamentioning

confidence: 99%

Privacy preserving big data analytics: A critical analysis of state‐of‐the‐art

Pramanik

Lau

Hossain

et al. 2020

WIREs Data Min & Knowl

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In the era of “big data,” a huge number of people, devices, and sensors are connected via digital networks and the cross‐plays among these entities generate enormous valuable data that facilitate organizations to innovate and grow. However, the data deluge also raises serious privacy concerns which may cause a regulatory backlash and hinder further organizational innovation. To address the challenge of information privacy, researchers have explored privacy‐preserving methodologies in the past two decades. However, a thorough study of privacy preserving big data analytics is missing in existing literature. The main contributions of this article include a systematic evaluation of various privacy preservation approaches and a critical analysis of the state‐of‐the‐art privacy preserving big data analytics methodologies. More specifically, we propose a four‐dimensional framework for analyzing and designing the next generation of privacy preserving big data analytics approaches. Besides, we contribute to pinpoint the potential opportunities and challenges of applying privacy preserving big data analytics to business settings. We provide five recommendations of effectively applying privacy‐preserving big data analytics to businesses. To the best of our knowledge, this is the first systematic study about state‐of‐the‐art in privacy‐preserving big data analytics. The managerial implication of our study is that organizations can apply the results of our critical analysis to strengthen their strategic deployment of big data analytics in business settings, and hence to better leverage big data for sustainable organizational innovation and growth. This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy Fundamental Concepts of Data and Knowledge > Big Data Mining Fundamental Concepts of Data and Knowledge > Data Concepts

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Section: Privacy-preserving Methods For Big Datamentioning

confidence: 99%

Privacy preserving big data analytics: A critical analysis of state‐of‐the‐art

Pramanik

Lau

Hossain

et al. 2020

WIREs Data Min & Knowl

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“…Another review study [19] provided an overview of sentiment analysis techniques for education. The authors of this study provided a sentiment discovery and analysis (SDA) framework for multimodal fusions.…”

Section: Related Workmentioning

confidence: 99%

“…The authors of this study provided a sentiment discovery and analysis (SDA) framework for multimodal fusions. Rather than the text, audio, and visual signals focused in [19], our review article aims to present all aspects related to the sentiment analysis of educational information with a focus on textual information only in a systematic way. Furthermore, we also provide a long list of current approaches employed for sentiment discoveries and the results obtained by them.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

et al. 2021

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In the last decade, sentiment analysis has been widely applied in many domains, including business, social networks and education. Particularly in the education domain, where dealing with and processing students’ opinions is a complicated task due to the nature of the language used by students and the large volume of information, the application of sentiment analysis is growing yet remains challenging. Several literature reviews reveal the state of the application of sentiment analysis in this domain from different perspectives and contexts. However, the body of literature is lacking a review that systematically classifies the research and results of the application of natural language processing (NLP), deep learning (DL), and machine learning (ML) solutions for sentiment analysis in the education domain. In this article, we present the results of a systematic mapping study to structure the published information available. We used a stepwise PRISMA framework to guide the search process and searched for studies conducted between 2015 and 2020 in the electronic research databases of the scientific literature. We identified 92 relevant studies out of 612 that were initially found on the sentiment analysis of students’ feedback in learning platform environments. The mapping results showed that, despite the identified challenges, the field is rapidly growing, especially regarding the application of DL, which is the most recent trend. We identified various aspects that need to be considered in order to contribute to the maturity of research and development in the field. Among these aspects, we highlighted the need of having structured datasets, standardized solutions and increased focus on emotional expression and detection.

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“…These works examined the application of EDM/LA in multimodal educational data, but which barely touched on data fusion, focusing instead on complex learning tasks (Blikstein & Worsley, 2016), the study of LA architectures (Shankar et al, 2018), and the study of learning environments where multimodal LA is usually applied (Ochoa, 2017). There are also a few review papers more focused in the specific application of data fusion in EDM/LA (Dewan et al, 2019; Han et al, 2020; Nandi et al, 2020). However, they only focused on some specific aspects, including emotion recognition (Nandi et al, 2020), engagement detection (Dewan et al, 2019), or sentiment analysis (Han et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…There are also a few review papers more focused in the specific application of data fusion in EDM/LA (Dewan et al, 2019; Han et al, 2020; Nandi et al, 2020). However, they only focused on some specific aspects, including emotion recognition (Nandi et al, 2020), engagement detection (Dewan et al, 2019), or sentiment analysis (Han et al, 2020). Finally, the survey that is most closely related with our current review is from Mu et al (2020), which focused only on LA, without examining EDM bibliography.…”

Section: Introductionmentioning

confidence: 99%

A review on data fusion in multimodal learning analytics and educational data mining

Chango

Lara²,

Cerezo

et al. 2022

WIREs Data Min & Knowl

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The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye-tracking, user logs, and click-stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area.

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A review on sentiment discovery and analysis of educational big‐data

Cited by 23 publications

References 126 publications

Privacy preserving big data analytics: A critical analysis of state‐of‐the‐art

Privacy preserving big data analytics: A critical analysis of state‐of‐the‐art

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

A review on data fusion in multimodal learning analytics and educational data mining

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