Design and Implementation of Early Warning System Based on Educational Big Data

Wang, Zhuping; Zhu, Chenjing; Ying, Zelin; Zhang, Ying; Wang, Ben; Jin, Xinyu; Yang, Huansong

doi:10.1109/icsai.2018.8599357

Cited by 18 publications

(4 citation statements)

References 2 publications

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“…A study by a Chinese research group led by Wang [34] included information relative to grades and credits enrolled, as well as usage logs of university buildings, such as the library and dorms. Principal component analysis confirmed the existence of a correlation between book borrow patterns from the library and academic achievement.…”

Section: E Resource Managementmentioning

confidence: 99%

Profiling Students’ Self-Regulation With Learning Analytics: A Proof of Concept

et al. 2022

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The ability to regulate one's own learning processes is a key factor in educational scenarios. Self-regulation skills notably affect students' efficacy when studying and academic performance, for better or worse. However, neither students or instructors generally have proper understanding of what selfregulated learning is, the impact that it has or how to assess it. This paper has the purpose of showing how learning analytics can be used in order to generate simple metrics related to several areas of students' selfregulation, in the context of a first-year university course. These metrics are based on data obtained from a learning management system, complemented by more specific assessment-related data and direct answers to self-regulated learning questionnaires. As the end result, simple self-regulation profiles are obtained for each student, which can be used to identify strengths and weaknesses and, potentially, help struggling students to improve their learning habits.

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Section: E Resource Managementmentioning

confidence: 99%

Profiling Students’ Self-Regulation With Learning Analytics: A Proof of Concept

et al. 2022

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“…The objective of an AEWS is to discover and identify existing and potential academic problems of students in the early stages of education and inform students so that remedial actions can be taken to mitigate the risks. The authors in [81] proposed an AEWS based on Big education data collected from different departments of the university such as the academic affairs, library and other departments. The authors used principal component analysis (PCA) to locate the key predictors and utilized three machine learning algorithms to train and test their classifiers from their sample data.…”

Section: ) Dropout Prediction and Academic Early Warning Systemsmentioning

confidence: 99%

Big Educational Data & Analytics: Survey, Architecture and Challenges

Ang

Ge²,

Seng

2020

IEEE Access

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The proliferation of mobile devices and the rapid development of information and communication technologies (ICT) have seen increasingly large volume and variety of data being generated at an unprecedented pace. Big data have started to demonstrate significant values in higher education. This paper gives several contributions to the state-of-the-art for Big data in higher education and learning technologies research. Currently, there is no comprehensive survey or literature review for Big educational data. Most literature reviews from a few authors have focused on one of these fields: educational mining, learning analytics with discussions on one or two aspects such as Big data technologies without educational focus, social media data in education, etc. Most of these literature reviews are short and insufficient to provide more inclusive reviews for Big educational data. In this paper, we present a comprehensive literature review of the current and emerging paradigms for Big educational data. The survey is presented in five parts: (1) The first part presents an overview and classification of Big education research to show the full landscape in this field, which also gives a concise summary of the overall scope of this paper; (2) The second part presents a discussion for the various data sources from education platforms or systems including learning management systems (LMS), massive open online courses (MOOC), learning object repository (LOR), OpenCourseWare (OCW), open educational resources (OER), social media, linked data and mobile learning contributing to Big education data; (3) The third part presents the data collection, data mining and databases in Big education data; (4) The fourth part presents the technological aspects including Big data platforms and architectures such as Hadoop, Spark, Samza and Big data tools for Big education data; and (5) The fifth part presents different approaches of data analytics for Big education data. This part provides a more inclusive discussion on data analytics which is beyond traditional forms of learning analysis in higher education. This includes predictive analytics, learning analytics including collaborative, behavior, personal learnings and assessment, followed by recommendation systems, graph analytics, visual analytics, immersive learning and analytics, etc. The final part of the paper discusses social (e.g. privacy and ethical issues) and technological challenges for Big data in education. This part also illustrates the technological challenges faced by giving an example for utilizing graph-based analytics for a cross-institution learning analytics scenario. INDEX TERMS Big data, learning technologies, educational data, learning analytics.

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“…Therefore, on the basis of understanding the current situation of financial data statistics and risk early warning analysis in the era of big data, this paper mainly explores how to use advanced technologies to build financial data statistics and risk early warning analysis system, and then plays an important role in the financial field. [4][5][6] 2. Method…”

Section: Introducionmentioning

confidence: 99%

Design and implementation of financial data statistics and risk early warning analysis system in the era of big data

Luo¹

2023

AEMR

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In the era of big data, the financial field is facing more and more data statistical risk, in order to ensure the continuous improvement of the financial economic development level, Chinese scholars put forward the transformation of the traditional non-empirical analysis method and artificial subjective evaluation method, reasonable use of experts system and information technology as the core management mode. From the perspective of overall development, traditional assessment methods still play an important role in financial data statistics and risk early warning analysis, while the application level of relevant information systems and expert systems is not high. Therefore, in the future innovation and development of the financial field, it is necessary to continue to discuss the design and implementation of financial data statistics and risk early warning analysis system in the era of big data. After understanding the statistical characteristics of financial data and the design principles of risk early warning system in the era of big data, this paper mainly studies the design structure and implementation of financial data statistics and risk early warning analysis system, which will play a positive role in enhancing the competitiveness of financial institutions in the new era.

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Design and Implementation of Early Warning System Based on Educational Big Data

Cited by 18 publications

References 2 publications

Profiling Students’ Self-Regulation With Learning Analytics: A Proof of Concept

Profiling Students’ Self-Regulation With Learning Analytics: A Proof of Concept

Big Educational Data & Analytics: Survey, Architecture and Challenges

Design and implementation of financial data statistics and risk early warning analysis system in the era of big data

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