The Construction of Distance Education Personalized Learning Platform Based on Educational Data Mining

Xu, Yanqiang; Zhang, Miao; Gao, Zhigang

doi:10.1007/978-3-030-25128-4_134

Cited by 12 publications

(5 citation statements)

References 3 publications

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“…Therefore, it is essential to utilize machine learning methods to automatically assess the features and similarities inherent in the data to partition the data objects into distinct subsets or clusters [2,8]. The clustering process requires that the intra-cluster data similarity be as large as possible while the inter-cluster data similarity be as small as possible [11]. Clustering aims to discover the characteristics of the data or to process the data through the obtained classes or clusters [10].…”

Section: Clusteringmentioning

confidence: 99%

Educational data mining: Methods and applications

Wang

2023

ACE

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Educational data mining is a rapidly growing field that applies various statistical and data mining techniques to analyze educational data. This paper provides a general review of the literature on educational data mining, focusing on the methods and applications. Methods used in education data mining include classification and clustering. A classification algorithm is a supervised learning technique that seeks to categorize a given set of data objects into specified categories, build a classification model using the input data that already exists, and then apply the model to categorize new data items. The Naive Bayes, Decision Tree, Neural Network, and K-Nearest Neighbors have commonly employed classification algorithms in educational data mining. Clustering is unsupervised learning, whose objective is to divide a collection of data objects into various groups, where samples within a cluster exhibit a high level of resemblance and those between clusters are dissimilar. In educational data mining, the K-means Clustering Algorithm, Grid-Based Clustering, and Hierarchical Clustering are common clustering techniques. Those data mining algorithms are used in education such as student behavior prediction, student bad behavior detection, and student grouping. Overall, this research demonstrates that education data mining has a significant potential to improve educational programmers and student results. To solve the legal and privacy issues associated with the collecting and use of educational data, however, more research and solutions are required.

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Section: Clusteringmentioning

confidence: 99%

Educational data mining: Methods and applications

Wang

2023

ACE

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“…With regard to the individual characteristics of student learning, personalized learning is a breakthrough in the generally accepted teaching methods. Clearly, the digital development of society has influenced cognitive styles and methods of distance learning (Xu et al, 2019).…”

Section: Conceptual or Theoretical Frameworkmentioning

confidence: 99%

Personalized distance-learning experience in determining students’ performance

Mejanova

Лебедева²,

Assylbekova³

et al. 2022

WJET

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One way or another, students face certain difficulties and disadvantages of distance learning. This can affect students physically and emotionally if appropriate measures are not taken. The solution might be the personalization of distance learning process, which implies the application of individual approaches to learning. The article aims to determine the academic performance of students who use the system of personalized distance learning Quiz Guide. For this study, 140 students were selected. The results of the performance analysis showed that the experimental group scored higher on the final test, indicating that students using the Quiz Guide learning system had significantly better academic achievement than those who took the traditional approach to learning. According to the post-test results, the control group scored (average) 65.14 points, the experimental group, 74.71. Keywords: Academic performance, computer learning system, individualized approach, personalized distance learning, Quiz Guide system.

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“…В последнее время интеллектуальный анализ образовательных данных (Educational Data Mining, EDM) все активнее применяется в информационной среде университета и внедряется в качестве новых сервисов для улучшения образовательного процесса [3]. Одним из таких примеров является разработка рекомендательной системы в Калифорнийском университете в Беркли, которая направлена на помощь студентам при принятии решения о выборе элективных курсов [4].…”

Section: Introductionunclassified

Use of artificial intelligence technologies for building individual educational trajectories of students

Kupriyanov

Борисович²,

Agranat

et al. 2021

Vestn. Ross. univ. družby nar., Ser. Informatiz. obraz.

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Problem and goal. Developed and tested solutions for building individual educational trajectories of students, focused on improving the educational process by forming a personalized set of recommendations from the optional disciplines. Methodology. Data mining and machine learning methods were used to process both numeric and textual data. The approaches based on collaborative and content filtering to generate recommendations for students were also used. Results. Testing of the developed system was carried out in the context of several periods of elective courses selection, in which 4,769 first- and second-year students took part. A set of recommendations was automatically generated for each student, and then the quality of the recommendations was evaluated based on the percentage of students who used these recommendations. According to the results of testing, the recommendations were used by 1,976 students, which was 41.43% of the total number of participants. Conclusion. In the study, a recommendation system was developed that performs automatic ranking of subjects of choice and forms a personalized set of recommendations for each student based on their interests for building individual educational trajectories.

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The Construction of Distance Education Personalized Learning Platform Based on Educational Data Mining

Cited by 12 publications

References 3 publications

Educational data mining: Methods and applications

Educational data mining: Methods and applications

Personalized distance-learning experience in determining students’ performance

Use of artificial intelligence technologies for building individual educational trajectories of students

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