Proposed S-Algo+ data mining algorithm for web platforms course content and usage evaluation

Kazanidis, Ioannis; Valsamidis, Stavros; Gounopoulos, Elias; Kontogiannis, Sotirios

doi:10.1007/s00500-020-04841-8

Cited by 17 publications

(12 citation statements)

References 44 publications

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“…On the other hand, Kazanidis et al pointed out that there are many methods of data mining, including classification, regression analysis, cluster analysis, feature analysis, and web data mining. Different methods are applied in different occasions, and they have different characteristics [9]. Jeffery et al suggested predicting user access requests.…”

Section: Literature Reviewmentioning

confidence: 99%

Construction of User Behavior Web Data Mining Model for Internet Marketing

Wei

2022

Wireless Communications and Mobile Computing

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With the rapid development of the Internet and the rapid change of the era of big data, data mining has played a great role in exploring and discovering potential valuable information from the vast sea of data and has become one of the most popular research and practice fields. At the same time, the emerging marketing model of network marketing is sweeping the market, and network marketing is increasingly replacing the traditional marketing model with its unique advantages, such as wide coverage, fast dissemination, and being more flexible and targeted. Preserving, researching, and analyzing the behavior of network marketing users are the difficulties of current network marketing. It is the focus of this paper to explore how to use Web data mining technology to model the user behavior of Internet marketing. For the above problems, this paper used the weight calculation algorithm of users’ daily behavior feature items and the sequential pattern discovery algorithm based on multiple factor constraints to scientifically construct the online marketing user behavior model. The experimental results have shown that the sequential pattern discovery algorithm based on multiple factor constraints could save 30%-40% of the running cost when building a user behavior model. At the same time, the user behavior model constructed by this algorithm has improved the simulation and prediction accuracy of user behavior by 37%, which showed that the use of Web data mining to build network marketing user behavior model could provide an objective basis for the strategic development of network marketing.

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Section: Literature Reviewmentioning

confidence: 99%

Construction of User Behavior Web Data Mining Model for Internet Marketing

Wei

2022

Wireless Communications and Mobile Computing

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“…is shows that there are correlations between students' model of learning behavior and the final grade of the course. Learning classification algorithm i with U (6) for j in P (7) Evaluating the results of i with j (8) end ( 9) end (10) Choose the best classification algorithm by using TOPSIS (11) Select classification algorithm with highest performance (12) Use the algorithm to predict course grade (13) return prediction of students' course grade ALGORITHM 2: Prediction of students' course grade according to their model. 6 Complexity Complexity Table 5 similarly illustrates the performance measures of classification methods for a course called "carrying out and writing a research" with different feature numbers.…”

Section: Analysis Of Correlation Between Student Models and Coursementioning

confidence: 99%

“…Educational data mining (EDM) seeks ways to understand students' behavior in such learning environments and accordingly enable students and teachers to take initiatives, where and when necessary [11,12]. For instance, it can offer individualized learning paths, recommendations, and feedback by predicting students' performance or risk of failure in courses, contributing to their academic achievement [5,7].…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern

Hooshyar

Yang

2021

Complexity

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While modelling students’ learning behavior or preferences has been found as a crucial indicator for their course achievement, very few studies have considered it in predicting achievement of students in online courses. This study aims to model students’ online learning behavior and accordingly predict their course achievement. First, feature vectors are developed using their aggregated action logs during a course. Second, some of these feature vectors are quantified into three numeric values that are used to model students’ learning behavior, namely, accessing learning resources (content access), engaging with peers (engagement), and taking assessment tests (assessment). Both students’ feature vectors and behavior model constitute a comprehensive students’ learning behavioral pattern which is later used for prediction of their course achievement. Lastly, using a multiple criteria decision-making method (i.e., TOPSIS), the best classification methods were identified for courses with different sizes. Our findings revealed that the proposed generalizable approach could successfully predict students’ achievement in courses with different numbers of students and features, showing the stability of the approach. Decision Tree and AdaBoost classification methods appeared to outperform other existing methods on different datasets. Moreover, our results provide evidence that it is feasible to predict students’ course achievement with a high accuracy through modelling their learning behavior during online courses.

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“…To find and remove outliers, we need to find more important features and reduce the burden of data mining steps. Sometimes, we need to realize the transformation between high-dimensional and low-dimensional [7][8].…”

Section: Figure 1data Mining Technology Processing Processmentioning

confidence: 99%

Sub Magnetic Environmental Biology based on Data Mining Algorithm

2022

AJEB

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With the development of manned space technology, the scope of human activities has expanded rapidly, and astronauts will stay in space longer and longer. Space is an extreme environment, including microgravity, cosmic radiation and sub magnetic field. There have been many reports on the effects of microgravity and cosmic rays on human body, but there are few studies on the effects of sub magnetic field on life. This paper will study and analyze the biological effect (BE) of sub magnetic environment based on data mining algorithm(DMA). Taking Chlorella as the research object, the sub magnetic field of algal cells is processed by using data mining technology, and the effects of sub magnetic field on the cell cycle and algal cell activity of Chlamydomonas reinhardtii(CR) mediated by blue light are explored. The results of the combined action of sub magnetic field and organism constitute the BE of magnetic field. Among them, the different sources of magnetic field, the size and direction of field strength, the action time, as well as the species and sensitivity of organism itself, the action position and other factors will affect the biological magnetic BE, which initially provides a reference basis for the possible BE of green algae in the process of sub magnetic field culture.

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Proposed S-Algo+ data mining algorithm for web platforms course content and usage evaluation

Cited by 17 publications

References 44 publications

Construction of User Behavior Web Data Mining Model for Internet Marketing

Construction of User Behavior Web Data Mining Model for Internet Marketing

[Retracted] Predicting Course Grade through Comprehensive Modelling of Students’ Learning Behavioral Pattern

Sub Magnetic Environmental Biology based on Data Mining Algorithm

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