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A Real-Life Machine Learning Experience for Predicting University Dropout at Different Stages Using Academic Data

Antonio Jesus Fernandez-Garcia, Juan Carlos Preciado, Fran Melchor, Roberto Rodriguez-Echeverria, Jose Maria Conejero, Fernando Sanchez-Figueroa
2021 IEEE Access  
along with an Ensemble of them at different stages: prior to enrolment, at the end of the first semester, at the end of the second semester, at the end of the third semester, and at the end of the fourth  ...  The difficulty of accessing personal data and privacy issues that it entails force the institutions to rely on the Academic Data of their students to create accurate and reliable predictive systems.  ...  Citation information: DOI 10.1109/ACCESS.2021.3115851, IEEE Access Author et al.: A real-life machine learning experience for predicting school dropout at different stages Datasets Feature Source Data  ... 
doi:10.1109/access.2021.3115851 fatcat:tcmfabrnmbbdfoquqphpsv46km

Student Dropout Prediction [chapter]

Francesca Del Bonifro, Maurizio Gabbrielli, Giuseppe Lisanti, Stefano Pio Zingaro
2020 Lecture Notes in Computer Science  
Our experiments have been performed by considering real data of students from eleven schools of a major University.  ...  To address this problem we developed a tool that, by exploiting machine learning techniques, allows to predict the dropout of a first-year undergraduate student.  ...  Another work on the University dropout phenomenon was proposed in [7] . The proposed solution aim at predicting the student dropout but using a completely different representation for the students.  ... 
doi:10.1007/978-3-030-52237-7_11 fatcat:rpybdstdjrhjrnjzhhzxlu2knu

Significance of Non-Academic Parameters for Predicting Student Performance Using Ensemble Learning Techniques

Deepti Aggarwal, Sonu Mittal, Vikram Bali
2021 International Journal of System Dynamics Applications  
Prediction models are designed to predict the performance of students at a very early stage so that preventive measures can be taken beforehand.  ...  Various parameters (academic as well as non-academic) are considered to predict the student performance using different classifiers.  ...  This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.  ... 
doi:10.4018/ijsda.2021070103 dblp:journals/ijsda/AggarwalMB21 fatcat:uaus7wfdangrhbqnqcpjcl5v54

Precision education with statistical learning and deep learning: a case study in Taiwan

Shuo-Chang Tsai, Cheng-Huan Chen, Yi-Tzone Shiao, Jin-Shuei Ciou, Trong-Neng Wu
2020 International Journal of Educational Technology in Higher Education  
A statistical learning method and a machine learning method based on deep neural networks were used to predict their probability of dropping out.  ...  Consequently, a large number of students have been admitted to universities regardless of whether they have an aptitude for academic studies.  ...  Our gratitude also goes to the staff of the Center of Institutional Research and Development of Asia University for their support and assistance with data sorting and analysis.  ... 
doi:10.1186/s41239-020-00186-2 fatcat:wqpfhhoj75dn5nh47q2ks4a25i

College Student Retention: When Do We Losing Them? [article]

Mehrdad J. Bani, Mina Haji
2017 arXiv   pre-print
In this paper, we provide a detailed analysis of the student attrition problem and use statistical methods to predict when students are going to dropout from school using real case data.  ...  We take advantage of multiple kinds of information about different aspects of student's characteristic and efficiently utilize them to make a personalized decision about the risk of dropout for a particular  ...  In another word, we implement different statistical methods on real student retention data to predict when a student is going to dropout from school based on the pre-school data which is available at the  ... 
arXiv:1707.06210v1 fatcat:2q2alfpvvng6xobrwac72qdkoq

STUDENTS' ACADEMIC PERFORMANCE AND DROPOUT PREDICTION

Ahmed O. Ameen, Moshood Alabi Alarape, Kayode S. Adewole
2019 MALAYSIAN JOURNAL OF COMPUTING  
To group the studies, this review proposes taxonomy of the methods and features used in the literature for SAP and dropout prediction.  ...  A number of researches in Educational Psychology (EP), Learning Analytics (LA) and Educational Data Mining (EDM) has been carried out to study and predict SAP, most especially in determining failures or  ...  In the study, a data-driven system was developed to predict the academic grades of students in different courses and the dropout.  ... 
doi:10.24191/mjoc.v4i2.6701 fatcat:a6wjepeczjehlhibj3bbxy2qly

Educational Anomaly Analytics: Features, Methods, and Challenges

Teng Guo, Xiaomei Bai, Xue Tian, Selena Firmin, Feng Xia
2022 Frontiers in Big Data  
We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation  ...  This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field.  ...  A survey of machine learning approaches and techniques for student dropout prediction. Data Sci.  ... 
doi:10.3389/fdata.2021.811840 pmid:35098114 pmcid:PMC8795666 fatcat:2cejzd6kkvesbly6ytxfy5lb2e

Selección de tutores académicos en la educación superior usando árboles de decisión

Argelia B. Urbina Nájera, Jorge De la Calleja
2018 Revista Española de Orientación y Psicopedagogía  
The method includes identifying the main skills of tutors in an automated manner using decision trees, one of the most used algorithms in the machine learning community for solving several real-world problems  ...  Experiments were carried out using a data set of 277 students and 19 tutors, which were selected by random sampling and voluntary participation, respectively.  ...  This validation technique is commonly used in the machine learning community for performing experiments, that is, the original data set is randomly divided into ten equally sized subsets and 10 experiments  ... 
doi:10.5944/reop.vol.29.num.1.2018.23297 fatcat:d2xgds6ljjhibhsqfctrhipioi

Predictive Model for Taking Decision to Prevent University Dropout

Argelia B. Urbina-Nájera, Luis A. Méndez-Ortega
2022 International Journal of Interactive Multimedia and Artificial Intelligence  
This document presents an experimental study to obtain a predictive model that allows anticipating a university dropout.  ...  Dropout is an educational phenomenon studied for decades due to the diversity of its causes, whose effects fall on society's development.  ...  Model to Predict Dropout Finally, the model with the best metrics (accuracy, sensitivity, precision, among others) is chosen after performing the different experiments with test and training data.  ... 
doi:10.9781/ijimai.2022.01.006 fatcat:razm2wcxszdy5jq2aj2vsi4mpu

Literature Survey on Educational Dropout Prediction

Mukesh Kumar, A.J. Singh, Disha Handa
2017 International Journal of Education and Management Engineering  
After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction.  ...  In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student.  ...  In his experiment, they apply machine learning algorithm to extract the knowledge from existing student data for making a predictive model for future.  ... 
doi:10.5815/ijeme.2017.02.02 fatcat:hzou7dpynbg6rpor36recw563q

Guiding the Students in High School by Using Machine Learning

Mustafa Ababneh, Aayat Aljarrah, Damla Karagozlu, Fezile Ozdamli
2021 TEM Journal  
Machine learning is considered the most significant technique that processes and analyses educational big data.  ...  In this research paper, many previous papers related to analysing the educational big data that uses a lot of artificial intelligence techniques were studied.  ...  academic direction at this stage, while only two studies were found that guide students in the university ( [22] , [24] ), and that it is a big gap in the development of the process of academic success  ... 
doi:10.18421/tem101-48 fatcat:qyzlj3zqrbebjpwx63hkdfdlce

Survival Analysis based Framework for Early Prediction of Student Dropouts

Sattar Ameri, Mahtab J. Fard, Ratna B. Chinnam, Chandan K. Reddy
2016 Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16  
We evaluate our method on real student data collected at Wayne State University.  ...  In this paper, we develop a survival analysis framework for early prediction of student dropout using Cox proportional hazards model (Cox).  ...  Acknowledgments This work was supported in part by the US National Science Foundation grants IIS-1231742, IIS-1527827 and IIS-1646881.  ... 
doi:10.1145/2983323.2983351 dblp:conf/cikm/AmeriFCR16 fatcat:dsurd7odyza3tmq2unydwrnjue

A survey on Prediction and Analysis of Students Academic Performance Using Machine Learning Technique

Ashmina Khan, Prof. K. N. Hande
2022 International Journal for Research in Applied Science and Engineering Technology  
However, it remains challenging to combine these data to obtain a holistic view of a student, use these data to accurately predict academic performance, and use such predictions to promote positive student  ...  Second, machine learning based classification algorithms are developed to predict academic performance.  ...  grades and increasing dropout rates even at world-class universities.  ... 
doi:10.22214/ijraset.2022.43192 fatcat:oqagyn4sarbtxj36gicc5xaafu

A Conceptual Framework to Aid Attribute Selection in Machine Learning Student Performance Prediction Models

Ijaz Muhammad Khan, Abdul Rahim Ahmad, Nafaa Jabeur, Mohammed Najah Mahdi
2021 International Journal of Interactive Mobile Technologies  
The machine learning algorithm's performance demotes with using the entire attributes and thus a vigilant selection of predicting attributes boosts the performance of the produced model.  ...  One of the important key applications of learning analytics is offering an opportunity to the institutions to track the student's academic activities and provide them with real-time adaptive consultations  ...  For instance, forecasting the dropout rate [79, 80] at an institute may consider different levels of latent attributes.  ... 
doi:10.3991/ijim.v15i15.20019 fatcat:hmvp6i7d5bhzznss2icvd7us5e

Who will dropout from university? Academic risk prediction based on interpretable machine learning [article]

Shudong Yang
2021 arXiv   pre-print
It predicts academic risk based on the LightGBM model and the interpretable machine learning method of Shapley value.  ...  Second, the introduction of Shapley value calculation makes machine learning that lacks a clear reasoning process visualized, and provides intuitive decision support for education managers.  ...  A Unified Approach to Interpreting Model Predictions[C]// Nips. 2017.  ... 
arXiv:2112.01079v1 fatcat:5v5nfj7fdvctharoprbontz65m
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