An intelligent graph mining algorithm to analyze student performance in online learning

Munshi, M.; Shrimali, Tarun; Gaur, Sanjay

doi:10.1007/s10639-022-11447-0

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…The work of Munshi et al (2022) presents a new hybrid approach based on Elman neural and apriori mining with the purpose of predicting student performance in online education. The incorporation of the Elman neural system aims to eliminate the noisy data present in the databases.…”

Section: Analysis Of Research Questionmentioning

confidence: 99%

Educational data mining and learning analytics: a review of educational management in e-learning

Rabelo

Rodrigues

Nobre

et al. 2023

IDD

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Purpose The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective. Design/methodology/approach This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM. Findings From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles. Originality/value This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

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Section: Analysis Of Research Questionmentioning

confidence: 99%

Educational data mining and learning analytics: a review of educational management in e-learning

Rabelo

Rodrigues

Nobre

et al. 2023

IDD

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“…strong AI system 33 , attempt future development forecasting 34,35 , and further explore the application of education big data and AI technology in educational assessment.…”

Section: Declarations Data Availabilitymentioning

confidence: 99%

Research and Application of a User Academic Profile Analysis Platform Based on AHP-EW

Yu,

Wang,

et al. 2023

Preprint

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Academic achievement is a crucial factor in evaluating the academic level of students. However, there is a problem in that academic performance solely reflects grades and cannot assess students' comprehensive abilities and qualities. To address this issue, we have developed and implemented a platform for the analysis of a user's academic profile based on the Analytic Hierarchy Process-Entropy Weight (AHP-EW) method. The platform comprises input and calculation modules for academic performance, construction and evaluation of quality evaluation indicators, diagnostic analysis reports, and knowledge graphs. It constructs students’ academic performance portraits, delivers academic diagnosis and analysis services for various educational regions and schools, and generates academic performance portrait across different levels. It improves the clarity, coherence, and comprehensibility of academic performance and plays a vital role in diagnosing, analyzing, evaluating, and providing feedback on academic performance.

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“…Then, they used Bayes classifier to classify learners' learning performance, and completed the system's adaptive recommendation mechanism via personalized page sorting algorithm. In order to further explore the evaluation method of students' learning performance in the intelligent learning system, Munshi et al [6] analyzed the user feature model from the perspective of learning performance, and then used Elman neural and Apriori mining hybrid method to extract student characteristic information for achieving intelligent learning effect. Of course, many other researchers explored the field of intelligent learning systems from the perspectives of intelligent navigation [7], recommendation algorithm [8], semantic ontology [9], and deep learning [10], and their research results will serve as our important reference.…”

Section: Introductionmentioning

confidence: 99%

A study on the personalized learning path recommendation of intelligent learning system based on user behavior characteristic analysis

Zhao,

Deng

2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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At present, the rapid development of Internet information technology has brought convenience to education and teaching. E-learning based on network has become an important way of learning. However, the traditional E-learning learning environment usually only provides course learning resources by the fixed preset program, which is not friendly to the user's personalized learning experience. Intelligent learning system (ILS) is a support environment that can dynamically provide personalized and intelligent learning services by diagnosing the individual differences of learners' users. It mainly provides current users with suitable learning paths or learning resources via personalized recommendation technology, so as to meet users' learning needs and improve their learning experience. However, the current research on intelligent learning system is still in the exploratory stage. Many researchers have studied intelligent learning system from different perspectives, but their research achievements still need to be explored and improved on the effect of intelligent recommendation. Based on this, this paper will further explore the key technology of personalized recommendation of intelligent learning system on the basis of the related research case analysis. In order to improve the recommendation accuracy of intelligent learning system, we will model and analyze the user behavior characteristics, and further explore the recommendation solution of personalized learning path of the system. The simulation experiment shows that the research results have a good personalized recommendation effect, which can effectively diagnose and analyze the current user's learning state characteristics, and then dynamically provide a suitable personalized learning path to meet the user's learning needs.

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An intelligent graph mining algorithm to analyze student performance in online learning

Cited by 5 publications

References 44 publications

Educational data mining and learning analytics: a review of educational management in e-learning

Educational data mining and learning analytics: a review of educational management in e-learning

Research and Application of a User Academic Profile Analysis Platform Based on AHP-EW

A study on the personalized learning path recommendation of intelligent learning system based on user behavior characteristic analysis

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