Supporting academic decision making at higher educational institutions using machine learning-based algorithms

Nieto, Yuri Vanessa; García‐Díaz, Vicente; Montenegro, Carlos; Crespo, Rubén González

doi:10.1007/s00500-018-3064-6

Cited by 81 publications

(30 citation statements)

References 27 publications

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“…In [8], data that include student enrolment details as well as the activity generated from the university learning management system are used to predict the students' performance in an Australian university. Similarly, in [9], Machine Learning algorithms are used to predict graduation rates in a Colombian university. In [10], four types of mathematical models designed to predict students' academic performance in engineering dynamics (a Higher Education subject) are developed and compared.…”

Section: B Related Workmentioning

confidence: 99%

Creating a Recommender System to Support Higher Education Students in the Subject Enrollment Decision

et al. 2020

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Higher Education plays a principal role in the changing and complex world of today, and there has been rapid growth in the scientific literature dedicated to predicting students' academic success or risk of dropout thanks to advances in Data Mining techniques. Degrees such as Computer Science in particular are in ever greater demand. Although the number of students has increased, the number graduating is still not enough to provide society with as many as it requires. This study contributes to reversing this situation by introducing an approach that not only predicts the dropout risk or students' performance but takes action to help both students and educational institutions. The focus is on maximizing graduation rates by constructing a Recommender System to assist students with their selection of subjects. In particular, the challenge is addressed of constructing reliable Recommender Systems on the basis of data which are both sparse and few in quantity, imbalanced, and anonymized, and which might have been stored under imperfect conditions. This approach is successfully applied to create a Recommender System using a realworld dataset from a public Spanish university containing performance data of a Computer Science degree course, demonstrating its successful application in real environments. The construction of a support system based on that approach is described, its results are evaluated, and its implications for students' academic achievement, and for institutions' graduation rates are discussed. Through the construction of this decision support system for students, we intend to increase the graduation rates and lower the dropout rate.

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Section: B Related Workmentioning

confidence: 99%

Creating a Recommender System to Support Higher Education Students in the Subject Enrollment Decision

et al. 2020

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“…It's able to learn without specific coding instantly. A Deep Neural Network (DNN) model, although it has a long-developed history in computing and data science, is attracting the interest [13] and implementations of a wide variety. DNN extends the ability to analyze complex data sets that conventional statistical methods cannot easily explain.…”

Section: Figure 1 Student Performance Analysismentioning

confidence: 99%

Education 4.0 using artificial intelligence for students performance analysis

Chen

Zhang²,

Jiang³

et al. 2020

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Nowadays, predicting students' performance is one of the most specific topics for learning environments, such as universities and schools, since it leads to the development of effective mechanisms that can enhance academic outcomes and avoid destruction. In education 4.0, Artificial Intelligence (AI) can play a key role in identifying new factors in students' performance and implementing personalized learning, answering routine student questions, using learning analytics, and predictive modeling. It is a new challenge to redefine education 4.0 to recognize the creative and innovative intelligent students, and it is difficult to determine students' outcomes. Hence, in this paper, Hybridized Deep Neural Network (HDNN) to predict student performance in Education 4.0. The proposed HDNN method is utilized to determine the dynamics that likely influence the student's performance. The deep neural network monitors predict, and evaluate students' performance in an education 4.0 environment. The findings show that the proposed HDNN method achieved better prediction accuracy when compared to other popular methods.

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“…Setelah kategori yang akan digunakan telah ditentukan, langkah selanjutnya yaitu melakukan data collection and preparation [4]. Setelah pengumpulan dan persiapan data selesai, maka dilanjutkan dengan data pre-processing menggunakan tahapan sebagai berikut [3…”

Section: Koleksi Dan Persiapan Dataunclassified

Deteksi Dini Status Keanggotaan Industri Kebugaran Menggunakan Pendekatan Supervised Learning

Narabel¹,

Budi²

2020

JuTISI

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In the fitness industry, the number of members is a major factor for the sustainability of its business. The ability of managers and trainers to detect members who represent traits to quit membership is critical. Four supervised learning classification methods like Support Vector Machine, Random Forest, K-Nearest Neighbor, and Artificial Neural Network were used to generate early detection using two variants of datasets that have different amounts of data. Classification results are separated into three different zones, which are Green Zone, Yellow Zone, and Red Zone. Artificial Neural Network methods using backpropagation training give 99.90% of accuracy on a dataset which has more amount of data. The evaluation has been done using the confusion matrix and AUC-ROC curves.

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Supporting academic decision making at higher educational institutions using machine learning-based algorithms

Cited by 81 publications

References 27 publications

Creating a Recommender System to Support Higher Education Students in the Subject Enrollment Decision

Creating a Recommender System to Support Higher Education Students in the Subject Enrollment Decision

Education 4.0 using artificial intelligence for students performance analysis

Deteksi Dini Status Keanggotaan Industri Kebugaran Menggunakan Pendekatan Supervised Learning

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