Big Data Application in Education: Dropout Prediction in Edx MOOCs

Liang, Jiajun; Yang, Jiandong; Wu, Yongji; Li, Chao; Liu, Zheng

doi:10.1109/bigmm.2016.70

Cited by 86 publications

(74 citation statements)

References 7 publications

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“…Data learning mining is all those techniques and procedures used to extract useful and relevant information from the large amount of data reported from educational platforms (Menon et al 2017). On the other hand, learning analytics is a construct that is derived from data mining and alludes to the management, processing, and analysis of students' educational data, which are studied with the purpose of improving and optimizing the learning process (Liang et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Big Data in Education. A Bibliometric Review

et al. 2019

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The handling of a large amount of data to analyze certain behaviors is reaching a great popularity in the decade 2010–2020. This phenomenon has been called Big Data. In the field of education, the analysis of this large amount of data, generated to a greater extent by students, has begun to be introduced in order to improve the teaching–learning process. In this paper, it was proposed as an objective to analyze the scientific production on Big Data in education in the databases Web of Science (WOS), Scopus, ERIC, and PsycINFO. A bibliometric study was carried out on a sample of 1491 scientific documents. Among the results, the increase in publications in 2017 and the configuration of certain journals, countries and authors as references in the subject matter stand out. Finally, potential explanations for the study findings and suggestions for future research are discussed.

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Section: Introductionmentioning

confidence: 99%

Big Data in Education. A Bibliometric Review

et al. 2019

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“…Over the past few years, researchers have made substantial progress in mining data from MOOCs, such as mining texts from discussion forums [7], mining video behaviors [8,9], performance prediction [10,13,21,23], plagiarism detection [11,12], and dropout detection [18,19]. These data mined from MOOCs have been widely used for predicting student achievement.…”

Section: Prediction In Moocsmentioning

confidence: 99%

“…Researchers are committed to improving the effectiveness of learning in many ways. Previous work has tried to predict and improve student achievement in MOOCs by considering courses [5,6], forums [7], watching behaviors on video [8,9], quizzes [10], plagiarism [11,12], and so on.…”

Section: Introductionmentioning

confidence: 99%

Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs

Zhang

et al. 2019

Applied Sciences

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With the development of data mining technology, educational data mining (EDM) has gained increasing amounts of attention. Research on massive open online courses (MOOCs) is an important area of EDM. Previous studies found that assignment-related behaviors in MOOCs (such as the completed number of assignments) can affect student achievement. However, these methods cannot fully reflect students’ learning processes and affect the accuracy of prediction. In the present paper, we consider the temporal learning behaviors of students to propose a student achievement prediction method for MOOCs. First, a multi-layer long short-term memory (LSTM) neural network is employed to reflect students’ learning processes. Second, a discriminative sequential pattern (DSP) mining-based pattern adapter is proposed to obtain the behavior patterns of students and enhance the significance of critical information. Third, a framework is constructed with an attention mechanism that includes data pre-processing, pattern adaptation, and the LSTM neural network to predict student achievement. In the experiments, we collect data from a C programming course from the year 2012 and extract assignment-related features. The experimental results reveal that this method achieves an accuracy rate of 91% and a recall of 94%.

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“…The first of them is based on lack of data due to many countless activities, especially in class education and communications with lecturers and other students. It is easy to collect data on computer-based education, but it definitely is not so in case of words of mouth communication [5]. The term words of mouth mean words that are said by one person to another or to a group directly in personal conversation without any media resource usage.…”

Section: A Education Policymentioning

confidence: 99%

“…Machine learning is used to make predictive analytics with the process of classification, clustering and regression. Common algorithms include KMeans, decision tree, random forest, logistic regression, SVM, naïve Bayes, neural networks and so on [5].…”

Section: Machine Learning Applicationsmentioning

confidence: 99%

Big Data Analysis in General Education: Opportunities and Concerns

Lyu¹,

Zhu²

2018

Proceedings of the 2018 International Seminar on Education Research and Social Science (ISERSS 2018)

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Abstract-General education, as important part of higher education, is putting increasing attention on big data courses in universities. This paper discusses the opportunities and concerns about big data analysis in general education by stating predictive analytics, personalized learning planning and introducing machine learning techniques in these aspects. A basic framework of opportunities, an integrated application process, machine learning applications are introduced and performance prediction as well as course recommendation system are given combining the specialty of general education. Concerns are suggested from open online platfo rm, universities, governments and institutions that more security is hard needed in cross-disciplinary learning environment as opportunities are multifaceted.

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Big Data Application in Education: Dropout Prediction in Edx MOOCs

Cited by 86 publications

References 7 publications

Big Data in Education. A Bibliometric Review

Big Data in Education. A Bibliometric Review

Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs

Big Data Analysis in General Education: Opportunities and Concerns

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