Discovery and evaluation of student's profiles with machine learning

Trandafili, Evis; Allkoçi, Alban; Kajo, Elinda; Xhuvani, Aleksander

doi:10.1145/2371316.2371350

Cited by 15 publications

(5 citation statements)

References 5 publications

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“…In another study [49]and [50] have applied Expectation-Maximization (EM) clustering algorithm to discover student profiles from course evaluation data and for finding associations between subjects that was based on student performance.…”

Section: K-means Clustering Methods Has Been Usedmentioning

confidence: 99%

Clustering Algorithms Applied in Educational Data Mining

M.A

2015

IJIEE

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Fifty years ago there were just a handful of universities across the globe that could provide for specialized educational courses. Today Universities are generating not only graduates but also massive amounts of data from their systems. So the question that arises is how can a higher educational institution harness the power of this didactic data for its strategic use? This review paper will serve to answer this question. To build an Information system that can learn from the data is a difficult task but it has been achieved successfully by using various data mining approaches like clustering, classification, prediction algorithms etc. However the use of these algorithms with educational dataset is quite low. This review paper focuses to consolidate the different types of clustering algorithms as applied in Educational Data Mining context.

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Section: K-means Clustering Methods Has Been Usedmentioning

confidence: 99%

Clustering Algorithms Applied in Educational Data Mining

M.A

2015

IJIEE

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“…Trandafili et al [32] conducted experiments on real datasets, and revealed the relationship between student profiles and course performance through clustering and association rule mining. Park et al [33] proposed a student profile system using learner multidimensional feature analysis to extract learner feature information.…”

Section: Student Profilementioning

confidence: 99%

Exploration and Visualization of Learning Behavior Patterns From the Perspective of Educational Process Mining

Feng

Fan

2022

IEEE Access

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The data in modern educational information systems are not given enough attention and are not fully utilized. Therefore, the motivations of our study are to preliminarily explore learning behavior patterns by applying process mining to educational datasets, and construct prediction models based on previous learning behavior. The data in modern educational information systems can be used by teaching managers to analyze various aspects of the educational process from different perspectives. We prepare to choose three datasets randomly which include student number, courses and grades attributes from a university's educational information systems. This paper firstly applies system clustering to give an overview of students' academic performance, and roughly determines clustering number. In order to ensure the accuracy which is relevant to the analysis of students' learning behavior patterns, a semi-biased statistic is proposed to quantitatively determine clustering number. Then, the data are clustered by fast clustering algorithm, and the clustering effect is cross-validated which is aimed at accurately analyzing the behavior patterns of student groups and using data visualization technology to visualize different student groups. Finally, the support vector machine is used to construct a classifier for predicting the learning behavior pattern, and the parameters in the support vector machine are optimized by Bayesian optimization, genetic algorithm optimization and whale optimization respectively. The research found that: 1) In the equal test of the group mean, when the significance of most courses is less than 0.05, it means that there is a significant difference among different categories. In this case, using the semi-biased statistic proposed in the paper is helpful to improve clustering effect. 2) The better the students learn, the better the clustering effect of the category which they belong to is. 3) Whale optimization algorithm works best.INDEX TERMS Educational process mining, learning behavior patterns, student profiles, support vector machines, system clustering, the semi-biased statistic.

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“…This has led to an increasingly complex analysis process of the growing volume of data and to the incapability to take decisions regarding curricula reform and restructuring. On the other side, educational data mining is a growing field aiming at discovering knowledge from student's data in order to thoroughly understand the learning process and take appropriate actions to improve the student's performance and the quality of the courses delivery [1].…”

Section: Introductionmentioning

confidence: 99%

Data Mining for Education Decision Support: A Review

Suhirman

Herawan

Chiroma

et al. 2014

Int. J. Emerg. Technol. Learn.

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Management of higher education must continue to evaluate on an ongoing basis in order to improve the quality of institutions. This will be able to do the necessary evaluation of various data, information, and knowledge of both internal and external institutions. They plan to use more efficiently the collected data, develop tools so that to collect and direct management information, in order to support managerial decision making. The collected data could be utilized to evaluate quality, perform analyses and diagnoses, evaluate dependability to the standards and practices of curricula and syllabi, and suggest alternatives in decision processes. Data minings to support decision making are well suited methods to provide decision support in the education environments, by generating and presenting relevant information and knowledge towards quality improvement of education processes. In educational domain, this information is very useful since it can be used as a base for investigating and enhancing the current educational standards and managements. In this paper, a review on data mining for academic decision support in education field is presented. The details of this paper will review on recent data mining in educational field and outlines future researches in educational data mining.

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Discovery and evaluation of student's profiles with machine learning

Cited by 15 publications

References 5 publications

Clustering Algorithms Applied in Educational Data Mining

Clustering Algorithms Applied in Educational Data Mining

Exploration and Visualization of Learning Behavior Patterns From the Perspective of Educational Process Mining

Data Mining for Education Decision Support: A Review

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