Predicting Student Performance to Improve Academic Advising Using the Random Forest Algorithm

Nachouki, Mirna; Naaj, Mahmoud Abou

doi:10.4018/ijdet.296702

Cited by 20 publications

(18 citation statements)

References 29 publications

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“…However, there are still some deficiencies in this field, which requires a perfect mechanism and a more reasonable system. It mainly includes the following aspects: the government and non-governmental institutions should improve the ecological compensation mechanism and play a good role in the market mechanism; Give play to the role of experts and technicians; Strengthen the knowledge and ability of ecological environment protection [17,18].…”

Section: Research On the Status Quo Of Natural Protection Environmentmentioning

confidence: 99%

Nature Conservation Environment Based on Public Participation of Random Forests

2023

NEP

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With the development of social economy, the public's awareness of participating in the natural protection of the environment has gradually increased, and a certain organizational structure and interest expression mechanism have gradually formed in the natural protection of the environment. The public's participation in natural environment protection is also increasingly concerned by government departments, social organizations and enterprises. Based on the random forest model, combined with public participation activities and management practice experience, this paper explores the problems and solutions of public participation in natural protection environment. The results show that: in the questionnaire survey of public environmental awareness, 164 people said "very important", 92.1%; Thirteen respondents thought it was "important", 7.3% of all respondents. There are 177 items in total, about 99.4% of all respondents. This shows that most people are still aware of environmental protection, but lack guidance.

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Section: Research On the Status Quo Of Natural Protection Environmentmentioning

confidence: 99%

Nature Conservation Environment Based on Public Participation of Random Forests

2023

NEP

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“…The first level combined three ensemble learning including Random Forests, Extreme Gradient Boosting, and Light Gradient Boosted Machine for base learners, and the second level used Multilayer Perceptron (MLP). The best result of the student success rates was 98.86%, and dropout rate declined in class Nachouki and Abou Naaj (2022) studied Random Forests to improve predict the student performance. The Pearson Correlation was used to evaluate the relationship between two variables and all variables.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It helps researchers to improve the educational process and learning outcomes of students (Xu et al, 2021). Ensemble learning techniques have been used to enhance the predicting model of them (Badal & Sungkur, 2022;Karalar et al, 2021;Nachouki & Abou Naaj, 2022;Smirani et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Predictive Model for Clustering Learning Outcomes Affected by COVID-19 Using Ensemble Learning Techniques

Nuankaew¹,

Nuankaew²

2023

INT J EDUC METHODOL

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<p style="text-align: justify;">The influence of COVID-19 has caused a sudden change in learning patterns. Therefore, this research studied the learning achievement modified by online learning patterns affected by COVID-19 at Rajabhat Maha Sarakham University. This research has three objectives. The first objective is to study the cluster of learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. The second objective is to develop a predictive model using machine learning and data mining technique for clustering learning outcomes affected by COVID-19. The third objective is to evaluate the predictive model for clustering learning outcomes affected by COVID-19 at Rajabhat Maha Sarakham University. Data collection comprised 139 students from two courses selected by purposive sampling from the Faculty of Information Technology at the Rajabhat Maha Sarakham University during the academic year 2020-2021. Research tools include student educational information, machine learning model development, and data mining-based model performance testing. The research findings revealed the strengths of using educational data mining techniques for developing student relationships, which can effectively manage quality teaching and learning in online patterns. The model developed in the research has a high level of accuracy. Accordingly, the application of machine learning technology obviously supports and promotes learner quality development.</p>

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“…2) Spark RDD Resilient Distributed Dataset (RDD), a RDD [7][8]. Specifically, it is a distributed, fault-tolerant, scalable and partitioned data structure [9][10]. In addition, Spark provides a full set of supporting arithmetic for RDDs, allowing users to perform very rich logical computations [11][12].…”

Section: ) Spark Ecosystemmentioning

confidence: 99%

An Improved Random Forest Algorithm Based on Spark

2022

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Classification algorithms are an important branch of data mining, and are also of great importance in the era of BD. The random forest algorithm (RFA) is one of the classification algorithms and is widely used in various industries for its good classification performance. However, the performance of RFA is not so good when dealing with high-dimensional data and unbalanced data. The main objective of this paper is to improve the RFA based on Spark. In this paper, we read a large amount of relevant algorithm literature in terms of algorithm research, and gain a comprehensive understanding of what feature selection and unbalanced classification are, as well as what characteristics they have and how these problems should be solved. It then focuses on how some domestic and international scholars have solved these problems. This paper focuses on studying and analysing the strengths and weaknesses of the RFA, and makes relevant improvements to address the two weaknesses of the RFA. In order to solve the problems of the RFA in the field of feature selection and the field of unbalanced classification, the optimization is improved respectively, and the parallelized design of the optimized algorithm is finally implemented on Spark.

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Predicting Student Performance to Improve Academic Advising Using the Random Forest Algorithm

Cited by 20 publications

References 29 publications

Nature Conservation Environment Based on Public Participation of Random Forests

Nature Conservation Environment Based on Public Participation of Random Forests

Predictive Model for Clustering Learning Outcomes Affected by COVID-19 Using Ensemble Learning Techniques

An Improved Random Forest Algorithm Based on Spark

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