Students' Orientation Using Machine Learning and Big Data

Ouatik, Farouk; Erritali, Mohammed; Ouatik, Fahd; Jourhmane, Mostafa

doi:10.3991/ijoe.v17i01.18037

Cited by 14 publications

(12 citation statements)

References 15 publications

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“…Numerous studies have shown that Twitter sentiment analysis is more efficient when used in certain domains including healthcare [2][3], banking sector [4], marketing [5][6], tourism [7], and politics [8]. Moreover, sentiment analysis is used in the education field to improve the quality of the educational institution's services [9][10], enhance the learning process by analyzing students' feelings [11] and orientation [12], and extract useful information about the teaching methodology of a teacher. In particular, recent studies have shifted their focus to sentiment analysis to detect the strengths and weaknesses of courses in higher education by analyzing students' online opinions [13] and measuring specific university indicators such as university reputation from social media for constructing a ranking mechanism [14].…”

Section: Introductionmentioning

confidence: 99%

Real-time Twitter Sentiment Analysis for Moroccan Universities using Machine Learning and Big Data Technologies

Lasri

Riadsolh

Elbelkacemi

2023

Int. J. Emerg. Technol. Learn.

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In recent years, sentiment analysis (SA) has raised the interest of researchers in several domains, including higher education. It can be applied to measure the quality of the services supplied by the higher education institution and construct a university ranking mechanism from social media like Twitter. Hence, this study presents a novel system for Twitter sentiment prediction on Moroccan public universities in real-time. It consists of two phases: offline sentiment analysis phase and real-time prediction phase. In the offline phase, the collected French tweets about twelve Moroccan universities were classified according to their sentiment into ‘positive’, ‘negative’, or ‘neutral’ using six machine learning algorithms (random forest, multinomial Naive Bayes classifier, logistic regression, decision tree, linear support vector classifier, and extreme gradient boosting) with the term frequency-inverse document frequency (TF-IDF) and count vectorizer feature extraction techniques. The results reveal that random forest classifier coupled with TF-IDF has obtained the best test accuracy of 90%. This model was then applied on real-time tweets. The real-time prediction pipeline comprises Twitter streaming API for data collection, Apache Kafka for data ingestion, Apache Spark for real-time sentiment analysis, Elasticsearch for real-time data exploration, and Kibana for data visualization. The obtained results can be used by the Ministry of higher education, scientific research and innovation of Morocco for decision-making process.

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Section: Introductionmentioning

confidence: 99%

Real-time Twitter Sentiment Analysis for Moroccan Universities using Machine Learning and Big Data Technologies

Lasri

Riadsolh

Elbelkacemi

2023

Int. J. Emerg. Technol. Learn.

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“…For the nature of data, some models are designed based solely on learners' grades and others are designed based not only on grades but also on learners' social environment variables [22].…”

Section: B Discussionmentioning

confidence: 99%

“…In [21], authors insist on correctly detecting the relevant variables involved in the process and their relationships with each other. In [22], authors used learners' scores to make predictions. In addition to grades, they used the number of absence per subject of the student.…”

Section: A Related Workmentioning

confidence: 99%

An Effective Decision-Making Support for Student Academic Path Selection using Machine Learning

HOUNGUE¹,

HOUNTONDJI²,

DAGBA³

2022

IJACSA

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In Benin, after the GCSE (General Certificate of Secondary Education), learners can either enroll in a Technical and Vocational Education and Training (TVET), or further their studies in the general education. Majority of those who take the latter path enroll in Senior High School by choosing the Biology stream or field of study. However, most of them do not have the abilities required to succeed in this field. For instance, for the last edition of the Senior Secondary Education Certificate (French baccalaureate) held in June 2022 in Benin, the Biology field of study had a low success rate of 42%. Therefore, one may consider that there is a problem in the orientation of the students. In recent years, Machine Learning has been used in almost every field to optimize processes or to assist in decision-making. Improving academic performance has always been of general interest. And, good academic performance implies good academic orientation. The goal of this study is to optimally help learners who have just obtained their GCSE to select their field of study. For this purpose, two major elements are predicted: i) Scientific or Literary ability of students, ii) Literature or Mathematics and Physical Sciences (MPS) or Biology stream of learners. More precisely, the average marks in Mathematics, Physics and Chemistry Technology (PCT) and Biology from 6 th to 9 th grade for 325 students are used. Machine Learning algorithms such as Decision Tree, Random Forest, Linear Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), and Logistic Regression are used to predict learners' ability and the stream. As a result, for learners' ability prediction, we obtained the best accuracy of 99% with the random forest algorithm for a split that reserved around 21% of the dataset for testing. As for the learners' stream prediction, we obtained the best accuracy of 95% with the Linear SVC algorithm for a split that reserved around 20% of the dataset for testing. This study contributes to Educational Data Mining (EDM) by performing academic data exploration using numerous methods. Furthermore, it provides a tool to ease students academic path selection, which may be used by educational institutes to ensure student performance. This paper presents the steps and the outputs of the study, we performed with some recommendations for future research.

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“…Predictions are taken by taking a simple majority decision by dividing the individual nodes into a subset of predictors using the best distribution. At that node, the predictors are selected randomly and this randomness makes it robust against overfitting [22] [23].…”

Section: Random Forestmentioning

confidence: 99%

Predicting Employee Turnover in IT Industries using Correlation and Chi-Square Visualization

Priambodo¹,

Jumaryadi²,

Rahayu³

et al. 2022

IJACSA

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Employee turnover in the IT industry is among the highest compared to other industries. Knowing factors that influence the turnover may help reduce this issue in future. One of these factors is job satisfaction, which are composed of two important factors, status and seniority. In this study, the correlation and chi-square visualization are utilized to determine the factors that affect employee turnover. The experiment was carried out to predict turnover using a private IT consultant dataset comparing three classification algorithms (decision tree, Naïve Bayes, and Random Forest). The result shows that job duration and positioning are factors that influence employee turnover in a software company.

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Students' Orientation Using Machine Learning and Big Data

Cited by 14 publications

References 15 publications

Real-time Twitter Sentiment Analysis for Moroccan Universities using Machine Learning and Big Data Technologies

Real-time Twitter Sentiment Analysis for Moroccan Universities using Machine Learning and Big Data Technologies

An Effective Decision-Making Support for Student Academic Path Selection using Machine Learning

Predicting Employee Turnover in IT Industries using Correlation and Chi-Square Visualization

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