A framework for predicting academic orientation using supervised machine learning

Mrabet, Hicham El; Moussa, Abdelaziz Ait

doi:10.1007/s12652-022-03909-7

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…The two models, together, allow achieving an optimal orientation for the learners. However, it is worth to emphasize that in the paper [26] published in June 2022, authors went in the same direction. But, the major difference between both proposed approaches lies in the fact that they based themselves only on personality variables whereas the study performed in this paper is based on learner' average marks.…”

Section: Contributions and Limitationsmentioning

confidence: 87%

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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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Section: Contributions and Limitationsmentioning

confidence: 87%

“…In addition, the authors of [26] implemented a framework which predicts academic orientation using supervised machine learning. They had a dataset of 350 individuals and compared the performance of decision tree, KNN, SVM and logistic regression by cross-validation and by a split that reserved 30% of the data for testing.…”

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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“…Career planning is a strategic process through which individuals map out their professional goals and aspirations, as well as the steps needed to achieve them [5]. It involves self-assessment to identify strengths, interests, values, and skills, which serve as the foundation for determining suitable career paths.…”

Section: Introductionmentioning

confidence: 99%

Career Interest Assessment: College Students Career Planning Based On Machine Leaning

Cui

2024

jes

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College students' career planning based on career interest assessment and machine learning integrates advanced technological tools with traditional career counseling methods to provide personalized and data-driven guidance. By utilizing career interest assessment tools, students can explore their strengths, preferences, and aspirations across various career domains. Machine learning algorithms analyze this data to generate tailored recommendations, matching students with career paths that align with their interests and aptitudes. This paper presents a novel approach to career assessment and planning by leveraging the Sugeno Fuzzy Optimized Weighted Machine Learning (SFOwML) framework. The proposed framework integrates fuzzy logic principles with machine learning techniques to provide personalized and accurate career recommendations tailored to individuals' skills, preferences, and values. Through the incorporation of optimization techniques, the framework refines the career assessment model, enhancing its accuracy in predicting suitable career paths for individuals. The practical application of the SFOwML framework offers a valuable tool for career counselors, educators, and individuals seeking guidance in their career choices. The framework incorporates numerical values to quantify individuals' skills, preferences, and values on a scale from 0 to 100, allowing for a more precise evaluation of their career profiles. Through the integration of fuzzy logic principles with machine learning techniques, personalized career recommendations are generated based on these numerical assessments.

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“…A Multi-Layer Perceptron Neural Network (MLPNN) was used by Hamadneh et al [18] to predict students' performance in blended learning and reduce academic failure rates. In the study [19], the authors aimed to predict a student's performance and provide support for academic guidance using a supervised ML.…”

Section: Introductionmentioning

confidence: 99%

Predicting Student Performance Using Mental Health and Linguistic Attributes with Deep Learning

Venkatachalam,

Sivanraju

2023

RIA

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Early prediction of students' academic performance is a critical research topic in educational data mining. ML models have been developed to predict academic performance, but it has become difficult to extract high-quality handcrafted attributes due to the large dataset. To solve this issue, a Deep Neural Network (DNN) was presented to automatically extract attributes from students' multi-source data. However, it didn't deal with the student's mental health and their mood changes (i.e., physiological attributes), which were also essential to increase the prediction performance. Thus, this article proposes a Student Accomplishment prediction using the Distinctive Deep Learning (SADDL) model. First, the student's academic and demographic attributes are collected along with the posts shared by them about academic performance on online social networks. A Latent Dirichlet Allocation (LDA) is applied to extract the physiological attributes from the online posts data. Then, all the attributes of students' data are given to the SADDL network, which comprises three modules: (i) a Long Short-Term Memory (LSTM) module to learn the temporal dependency; (ii) a multidimensional Deep Convolutional Neural Network (DCNN) module to learn the correlation attributes and (iii) a Multi-Layer Perceptron (MLP) module to predict the students' academic performance. Finally, the experimental results show that the SADDL can predict students' academic performance with an accuracy of 91.71% while using 80% training and 20% test dataset, which is 9.91% improved than the existing ML models. Similarly, the SADDL has 89.3% while using 70% training and 30% test dataset, which is 10.36% higher than the existing ML models.

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A framework for predicting academic orientation using supervised machine learning

Cited by 8 publications

References 26 publications

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

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

Career Interest Assessment: College Students Career Planning Based On Machine Leaning

Predicting Student Performance Using Mental Health and Linguistic Attributes with Deep Learning

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