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Kim, Sangyun, Euteum Choi, Yong-Kee Jun, and Seongjin Lee. "Student Dropout Prediction for University with High Precision and Recall." Applied Sciences 13, no. 10 (2023): 6275. http://dx.doi.org/10.3390/app13106275.
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Since a high dropout rate for university students is a significant risk to local communities and countries, a dropout prediction model using machine learning is an active research domain to prevent students from dropping out. However, it is challenging to fulfill the needs of consulting institutes and the office of academic affairs. To the consulting institute, the accuracy in the prediction is of the utmost importance; to the offices of academic affairs and other offices, the reason for dropping out is essential. This paper proposes a Student Dropout Prediction (SDP) system, a hybrid model to predict the students who are about to drop out of the university. The model tries to increase the dropout precision and the dropout recall rate in predicting the dropouts. We then analyzed the reason for dropping out by compressing the feature set with PCA and applying K-means clustering to the compressed feature set. The SDP system showed a precision value of 0.963, which is 0.093 higher than the highest-precision model of the existing works. The dropout recall and F1 scores, 0.766 and 0.808, respectively, were also better than those of gradient boosting by 0.117 and 0.011, making them the highest among the existing works; Then, we classified the reasons for dropping out into four categories: “Employed”, “Did Not Register”, “Personal Issue”, and “Admitted to Other University.” The dropout precision of “Admitted to Other University” was the highest, at 0.672. In post-verification, the SDP system increased counseling efficiency by accurately predicting dropouts with high dropout precision in the “High-Risk” group while including more dropouts in total dropouts. In addition, by predicting the reasons for dropouts and presenting guidelines to each department, the students could receive personalized counseling.
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Ameen, Ahmed O., Moshood Alabi Alarape, and Kayode S. Adewole. "STUDENTS’ ACADEMIC PERFORMANCE AND DROPOUT PREDICTION." MALAYSIAN JOURNAL OF COMPUTING 4, no. 2 (2019): 278. http://dx.doi.org/10.24191/mjoc.v4i2.6701.
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Students’ Academic Performance (SAP) is an important metric in determining the status of students in any academic institution. It allows the instructors and other education managers to get an accurate evaluation of the students in different courses in a particular semester and also serve as an indicator to the students to review their strategies for better performance in the subsequent semesters. Predicting SAP is therefore important to help learners in obtaining the best from their studies. 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 dropouts with the goal of preventing the occurrence of the negative final outcome. This paper presents a comprehensive review of related studies that deal with SAP and dropout predictions. To group the studies, this review proposes taxonomy of the methods and features used in the literature for SAP and dropout prediction. The paper identifies some key issues and challenges for SAP and dropout predictions that require substantial research efforts. Limitations of the existing approaches for SAP and dropout prediction are identified. Finally, the paper exposes the current research directions in the area.
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Song, Zihan, Sang-Ha Sung, Do-Myung Park, and Byung-Kwon Park. "All-Year Dropout Prediction Modeling and Analysis for University Students." Applied Sciences 13, no. 2 (2023): 1143. http://dx.doi.org/10.3390/app13021143.
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The core of dropout prediction lies in the selection of predictive models and feature tables. Machine learning models have been shown to predict student dropouts accurately. Because students may drop out of school in any semester, the student history data recorded in the academic management system would have a different length. The different length of student history data poses a challenge for generating feature tables. Most current studies predict student dropouts in the first academic year and therefore avoid discussing this issue. The central assumption of these studies is that more than 50% of dropouts will leave school in the first academic year. However, in our study, we found the distribution of dropouts is evenly distributed in all academic years based on the dataset from a Korean university. This result suggests that Korean students’ data characteristics included in our dataset may differ from those of other developed countries. More specifically, the result that dropouts are evenly distributed throughout the academic years indicates the importance of a dropout prediction for the students in any academic year. Based on this, we explore the universal feature tables applicable to dropout prediction for university students in any academic year. We design several feature tables and compare the performance of six machine learning models on these feature tables. We find that the mean value-based feature table exhibits better generalization, and the model based on the gradient boosting technique performs better than other models. This result reveals the importance of students’ historical information in predicting dropout.
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Amare, Meseret Yihun, and Stanislava Simonova. "Global challenges of students dropout: A prediction model development using machine learning algorithms on higher education datasets." SHS Web of Conferences 129 (2021): 09001. http://dx.doi.org/10.1051/shsconf/202112909001.
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Research background: In this era of globalization, data growth in research and educational communities have shown an increase in analysis accuracy, benefits dropout detection, academic status prediction, and trend analysis. However, the analysis accuracy is low when the quality of educational data is incomplete. Moreover, the current approaches on dropout prediction cannot utilize available sources. Purpose of the article: This article aims to develop a prediction model for students’ dropout prediction using machine learning techniques. Methods: The study used machine learning methods to identify early dropouts of students during their study. The performance of different machine learning methods was evaluated using accuracy, precision, support, and f-score methods. The algorithm that best suits the datasets for these performance measurements was used to create the best prediction model. Findings & value added: This study contributes to tackling the current global challenges of student dropouts from their study. The developed prediction model allows higher education institutions to target students who are likely to dropout and intervene timely to improve retention rates and quality of education. It can also help the institutions to plan resources in advance for the coming academic semester and allocate it appropriately. Generally, the learning analytics prediction model would allow higher education institutions to target students who are likely to dropout and intervene timely to improve retention rates and quality of education.
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Awasthi, Shivani. "Dropout Prediction with Supervised Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44873.
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Student dropout is a critical issue in the education sector, impacting institutional efficiency and student success. This project, Dropout Prediction with Supervised Learning, leverages machine learning models—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbours (KNN), and Naïve Bayes (NB)—to predict student dropouts based on historical academic, demographic, and behavioural data. The study involves data preprocessing, feature selection, and model evaluation to identify key factors influencing dropout rates. Supervised learning techniques are employed to classify students into "at-risk" and "not at-risk" categories. The performance of each model is assessed using accuracy, precision, recall, and F1-score metrics to determine the most effective predictor. The findings aim to provide educational institutions with actionable insights, enabling early intervention strategies such as academic counselling and financial aid support. By implementing predictive analytics, institutions can enhance student retention and improve overall educational outcomes. Keywords – Dropout Prediction, Supervised Learning, Machine Learning Models, Student Retention, Predictive Analytics, Classification Algorithms
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Yujiao, Zhang, Ling Weay Ang, Shi Shaomin, and Sellappan Palaniappan. "Dropout Prediction Model for College Students in MOOCs Based on Weighted Multi-feature and SVM." Journal of Informatics and Web Engineering 2, no. 2 (2023): 29–42. http://dx.doi.org/10.33093/jiwe.2023.2.2.3.
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Due to the COVID -19 pandemic, MOOCs have become a popular form of learning for college students. However, unlike traditional face-to-face courses, MOOCs offer little faculty supervision, which may result in students being insufficiently motivated to continue learning, ultimately leading to a high dropout rate. Consequently, the problem of high dropout rates in MOOCs requires urgent attention in MOOC research. Predicting dropout rates is the first step to address this problem, and MOOCs have a large amount of behavioral data that can be used for such predictions. Most existing models for predicting MOOC dropout based on behavioral data assign equal weights to each behavioral characteristic, despite the fact that each behavioral characteristic has a different effect on predicting dropout. To address this problem, this paper proposes a dropout prediction model based on the fusion of behavioral data and Support Vector Machine (SVM). This innovative model assigns different weights to different behavior features based on Pearson principle and integrates them as data inputs to the model. Dropout prediction is essentially a binary problem, Support Vector Machine Classifier is then trained using the training dataset 1 and dataset 2. Experimental results on both datasets show that this predictive model outperforms previous models that assign the same weights to the behavior features.
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Radovanovic, Sandro, Boris Delibasic, and Milija Suknovic. "Predicting dropout in online learning environments." Computer Science and Information Systems, no. 00 (2020): 53. http://dx.doi.org/10.2298/csis200920053r.
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Online learning environments became popular in recent years. Due to high attrition rates, the problem of student dropouts became of immense importance for course designers, and course makers. In this paper, we utilized lasso and ridge logistic regression to create a prediction model for dropout on the Open University database. We investigated how early dropout can be predicted, and why dropouts occur. To answer the first question, we created models for eight different time frames, ranging from the beginning of the course to the mid-term. There are two results based on two definitions of dropout. Results show that at the beginning AUC of the prediction model is 0.549 and 0.661 and rises to 0.681 and 0.869 at mid-term. By analyzing logistic regression coefficients, we showed that at the beginning of the course demographic features of the student and course description features are the most important variables for dropout prediction, while later student activity gains more importance.
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Arthana, I. Ketut Resika. "Optimizing Dropout Prediction in University Using Oversampling Techniques for Imbalanced Datasets." International Journal of Information and Education Technology 14, no. 8 (2024): 1052–60. http://dx.doi.org/10.18178/ijiet.2024.14.8.2133.
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The phenomenon of student dropout is a significant concern within universities. Institutions must accurately predict the likelihood of student dropout to address this issue effectively. The prediction of student dropout aids universities in identifying early signs of student challenges. Moreover, it enables institutions to implement proactive measures to mitigate dropout rates. This paper presents a novel approach for selecting a classification algorithm to predict student dropout to aid universities in identifying early signs of student dropout. Moreover, it enables institutions to implement proactive measures to mitigate dropout rates. Each university possesses its academic dataset attributes, which can be leveraged for predicting potential dropout cases of student dropout. Our methodology begins with attribute selection, dataset preprocessing, and comparative evaluation of classification algorithms based on priority performance metrics. The research case study is conducted at Universitas Pendidikan Ganesha (Undiksha). The model selection was based on comparing classification algorithm performance, including Naïve Bayesian, Decision Tree (DT), and K-Nearest Neighbors (KNN). The dataset for this research was collected from the Information Academic System of Undiksha, encompassing students who graduated or dropped out between 2013 and 2023. It should be noted that the dataset exhibits class imbalance. Hence, this research utilized the Synthetic Minority Over Sampling Technique (SMOTE) algorithm to address the imbalance in lowsized datasets. The original and oversampled datasets were subjected to each classification algorithm. We chose Recall as the primary evaluation metric to prioritize ensuring that actual dropouts are not incorrectly predicted as graduates. This research demonstrates that the KNN classification algorithm, applied to the oversampled dataset, achieves the highest Recall value of 93.5%, Precision of 94.1%, F1-Score of 93.5%, and AUC value of 97.9%.
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Nicoletti, Maria do Carmo, and Osvaldo Luiz de Oliveira. "A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction." Higher Education Studies 10, no. 4 (2020): 12. http://dx.doi.org/10.5539/hes.v10n4p12.
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In the literature related to higher education, the concept of dropout has been approached from several perspectives and, over the years, its definition has been influenced by the use of diversified semantic interpretations. In a general higher education environment dropout can be broadly characterized as the act of a student engaged in a course leaving the educational institution without finishing the course. This paper describes the proposal of the architecture of a computational system, PDE (Predicting Dropout Events), based on machine learning (ML) algorithms and specifically designed for predicting dropout events in a higher level educational environment. PDE’s main subsystem implements a group of instance-based learning (IBL) algorithms which, taking into account a particular university-course environment, and based on log files containing descriptions of previous dropouts events, is capable to predict when a student already engaged in the course, is prone to dropout, so preventive measures could be quickly implemented.
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Zhou, Fanhao, and Neil Agarwal. "Student Performance Prediction Based on Decision Trees." Journal of Research in Applied Mathematics 10, no. 12 (2024): 114–20. https://doi.org/10.35629/0743-1012114120.
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This study applies a decision tree machine learning model to predict student dropouts and academic performance using a UCI Machine Learning Repository dataset. The dataset includes academic, demographic, and socioeconomic factors to identify key predictors of student outcomes. The goal is to assist educators in developing targeted interventions to reduce dropout rates and improve academic success. The model achieved 71% accuracy in predicting dropout, enrolled, and graduate categories, with a recall of 76% for dropouts, demonstrating its effectiveness in identifying at-risk students. However, the model struggled to differentiate enrolled from graduate students, a challenge heightened by class imbalance. A comparison between a 95/5 and 50/50 train-test split revealed better performance with a more extensive training set, particularly in classifying enrolled students. Key predictors such as second-semester curricular unit performance and tuition payment status were critical to the model’s accuracy. However, additional behavioral and engagement features are emphasized to improve prediction accuracy further. This research provides actionable insights for educational institutions, supporting data-driven interventions to enhance student retention and academic outcomes.
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Chi, Zengxiao, Shuo Zhang, and Lin Shi. "Analysis and Prediction of MOOC Learners’ Dropout Behavior." Applied Sciences 13, no. 2 (2023): 1068. http://dx.doi.org/10.3390/app13021068.
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With the wide spread of massive open online courses ( MOOC ), millions of people have enrolled in many courses, but the dropout rate of most courses is more than 90%. Accurately predicting the dropout rate of MOOC is of great significance to prevent learners’ dropout behavior and reduce the dropout rate of students. Using the PH278x curriculum data on the Harvard X platform in spring 2013, and based on the statistical analysis of the factors that may affect learners’ final completion of the curriculum from two aspects: learners’ own characteristics and learners’ learning behavior, we established the MOOC dropout rate prediction models based on logical regression, K nearest neighbor and random forest, respectively. Experiments with five evaluation metrics (accuracy, precision, recall, F1 and AUC) show that the prediction model based on random forest has the highest accuracy, precision, F1 and AUC, which are 91.726%, 93.0923%, 95.4145%, 0.925341, respectively, its performance is better than that of the prediction model based on logical regression and that of the model based on K-nearest neighbor, whose values of these metrics are 91.395%, 92.8674%, 95.2337%, 0.912316 and 91.726%, 93.0923%, 95.4145% and 0.925341, respectively. As for recall metrics, the value of random forest is higher than that of KNN, but slightly lower than that of logistic regression, which are 0.992476, 0.977239 and 0.978555, respectively. Then, we conclude that random forests perform best in predicting the dropout rate of MOOC learners. This study can help education staff to know the trend of learners’ dropout behavior in advance, so as to put some measures to reduce the dropout rate before it occurs, thus improving the completion rate of the curriculum.
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Brorson, Hanne H., Espen Ajo Arnevik, and Kim Rand. "Predicting Dropout from Inpatient Substance Use Disorder Treatment: A Prospective Validation Study of the OQ-Analyst." Substance Abuse: Research and Treatment 13 (January 2019): 117822181986618. http://dx.doi.org/10.1177/1178221819866181.
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Background and Aims: There is an urgent need for tools allowing therapists to identify patients at risk of dropout. The OQ-Analyst, an increasingly popular computer-based system, is used to track patient progress and predict dropout. However, we have been unable to find empirical documentation regarding the ability of OQ-Analyst to predict dropout. The aim of the present study was to perform the first direct test of the ability of the OQ-Analyst to predict dropout. Design: Patients were consecutively enlisted in a naturalistic, prospective, longitudinal clinical trial. As interventions based on feedback from the OQ-Analyst could alter the outcome and potentially render the prediction wrong, feedback was withheld from patients and therapists. Setting: The study was carried out during 2011–2013 in an inpatient substance use disorder clinic in Oslo, Norway. Participants: Patients aged 18 to 28 years who met criteria for a principal diagnosis of mental or behavioural disorder due to psychoactive substance use (ICD 10; F10.2–F19.2). Measurements: Red signal (predictions of high risk) from the Norwegian version of the OQ-Analyst were compared with dropouts identified using patient medical records as the standard for predictive accuracy. Findings: A total of 40 patients completed 647 OQ assessments resulting in 46 red signals. There were 27 observed dropouts, only one of which followed after a red signal. Patients indicated by the OQ-Analyst as being at high risk of dropping out were no more likely to do so than those indicated as being at low risk. Random intercept logistic regression predicting dropout from a red signal was statistically nonsignificant. Bayes factor supports no association. Conclusions: The study does not support the predictive ability of the OQ-Analyst for the present patient population. In the absence of empirical evidence of predictive ability, it may be better not to assume such ability.
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MUKOOYO, HUMPHREY, and JOHN PAUL KASSE. "Towards Sustainable Education: A Machine Learning Model for Early Student Dropout Prediction in Higher Education Institutions." Uganda Higher Education Review 11, no. 2 (2024): 57–68. http://dx.doi.org/10.58653/nche.v11i2.5.
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Sustaining learners through an education cycle is a challenge for institutions at all levels. For higher education institutions, learners are presumed to be mature enough to complete their study courses. However, the challenge of student dropouts is prevalent. This paper seeks to address the key question of why students continue to drop out of learning institutions despite interventions undertaken by stakeholders. The attrition rates are a major concern that requires immediate attention if sustainable education is to be achieved. Dropping out of school is attributed to both individual factors and external factors. However, both require mitigation to save the future of education. This paper presents an analysis of challenges leading to student dropouts sampled from five institutions within the central region of Uganda (532 respondents). In addition, we leveraged the power of artificial intelligence (AI) to design and present a machine learning model for early student dropout prediction so that early interventions can be undertaken. The study adopted the design science methodology to scientifically support the design and validation of the machine learning student dropout prediction model. The early warning model presents key performance indicators to signal whether a student is predisposed to drop out or on course to completion. This way corrective intervention can be undertaken early enough for likely dropout. The validation experiment was conducted on a sample of 523 from the five institutions predicated a dropout of 10%. This proved the concept and the capacity of the model to predict learner dropout from university.
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Patel, Kinjal K., and Kiran Amin. "Predictive modeling of dropout in MOOCs using machine learning techniques." Scientific Temper 15, no. 02 (2024): 2199–206. http://dx.doi.org/10.58414/scientifictemper.2024.15.2.32.
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The advent of massive open online courses (MOOCs) has revolutionized education, offering unprecedented access to high-quality learning materials globally. However, high dropout rates pose significant challenges to realizing the full potential of MOOCs. This study explores machine learning techniques for predicting student dropout in MOOCs, utilizing the open university learning analytics dataset (OULAD). Seven algorithms, including decision tree, random forest, Gaussian naïve Bayes, AdaBoost classifier, extra tree classifier, XGBoost classifier, and multilayer perceptron (MLP), are employed to predict student outcomes and dropout probabilities. The XGBoost classifier emerges as the top performer, achieving 87% accuracy in pass/fail prediction and 86% accuracy in dropout prediction. Additionally, the study proposes personalized interventions based on individual dropout probabilities to enhance student retention. The findings underscore the potential of machine learning in addressing dropout challenges in MOOCs and offer insights for instructors and educational institutions to proactively support at-risk students.
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Won, Hyun-Sik, Min-Ji Kim, Dohyun Kim, Hee-Soo Kim, and Kang-Min Kim. "University Student Dropout Prediction Using Pretrained Language Models." Applied Sciences 13, no. 12 (2023): 7073. http://dx.doi.org/10.3390/app13127073.
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Predicting student dropout from universities is an imperative but challenging task. Numerous data-driven approaches that utilize both student demographic information (e.g., gender, nationality, and high school graduation year) and academic information (e.g., GPA, participation in activities, and course evaluations) have shown meaningful results. Recently, pretrained language models have achieved very successful results in understanding the tasks associated with structured data as well as textual data. In this paper, we propose a novel student dropout prediction framework based on demographic and academic information, using a pretrained language model to capture the relationship between different forms of information. To this end, we first formulate both types of information in natural language form. We then recast the student dropout prediction task as a natural language inference (NLI) task. Finally, we fine-tune the pretrained language models to predict student dropout. In particular, we further enhance the model using a continuous hypothesis. The experimental results demonstrate that the proposed model is effective for the freshmen dropout prediction task. The proposed method exhibits significant improvements of as much as 9.00% in terms of F1-score compared with state-of-the-art techniques.
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Haryono Setiadi, Indah Paksi Larasati, Esti Suryani, Dewi Wisnu Wardani, Hasan Dwi Cahyono Wardani, and Ardhi Wijayanto. "Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 4 (2024): 542–54. https://doi.org/10.29207/resti.v8i4.5911.
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Predicting student dropout is essential for universities dealing with high attrition rates. This study compares two feature selection (FS) methods—correlation-based feature selection (CFS) and symmetrical uncertainty (SU)—in educational data mining for dropout prediction. We evaluate these methods using three classification algorithms: decision tree (DT), support vector machine (SVM), and naive Bayes (NB). Results show that SU outperforms CFS overall, with SVM achieving the highest accuracy (98.16%) when combined with SU Moreover, this study identifies total credits in the fourth semester, cumulative GPA, gender, and student domicile as key predictors of student dropout. This study shows how using feature selection methods can improve the accuracy of predicting student dropout, helping educational institutions retain students better.
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Yukselturk, Erman, Serhat Ozekes, and Yalın Kılıç Türel. "Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program." European Journal of Open, Distance and E-Learning 17, no. 1 (2014): 118–33. http://dx.doi.org/10.2478/eurodl-2014-0008.
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Abstract This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies Self-Efficacy Scale, Readiness for Online Learning Questionnaire, Locus of Control Scale, and Prior Knowledge Questionnaire). The collected data included 10 variables, which were gender, age, educational level, previous online experience, occupation, self efficacy, readiness, prior knowledge, locus of control, and the dropout status as the class label (dropout/not). In order to classify dropout students, four data mining approaches were applied based on k-Nearest Neighbour (k-NN), Decision Tree (DT), Naive Bayes (NB) and Neural Network (NN). These methods were trained and tested using 10-fold cross validation. The detection sensitivities of 3-NN, DT, NN and NB classifiers were 87%, 79.7%, 76.8% and 73.9% respectively. Also, using Genetic Algorithm (GA) based feature selection method, online technologies self-efficacy, online learning readiness, and previous online experience were found as the most important factors in predicting the dropouts.
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Cai, Ruichu, Xuexin Chen, Yuan Fang, Min Wu, and Yuexing Hao. "Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers." Bioinformatics 36, no. 16 (2020): 4458–65. http://dx.doi.org/10.1093/bioinformatics/btaa211.
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Abstract Motivation Synthetic lethality (SL) is a promising form of gene interaction for cancer therapy, as it is able to identify specific genes to target at cancer cells without disrupting normal cells. As high-throughput wet-lab settings are often costly and face various challenges, computational approaches have become a practical complement. In particular, predicting SLs can be formulated as a link prediction task on a graph of interacting genes. Although matrix factorization techniques have been widely adopted in link prediction, they focus on mapping genes to latent representations in isolation, without aggregating information from neighboring genes. Graph convolutional networks (GCN) can capture such neighborhood dependency in a graph. However, it is still challenging to apply GCN for SL prediction as SL interactions are extremely sparse, which is more likely to cause overfitting. Results In this article, we propose a novel dual-dropout GCN (DDGCN) for learning more robust gene representations for SL prediction. We employ both coarse-grained node dropout and fine-grained edge dropout to address the issue that standard dropout in vanilla GCN is often inadequate in reducing overfitting on sparse graphs. In particular, coarse-grained node dropout can efficiently and systematically enforce dropout at the node (gene) level, while fine-grained edge dropout can further fine-tune the dropout at the interaction (edge) level. We further present a theoretical framework to justify our model architecture. Finally, we conduct extensive experiments on human SL datasets and the results demonstrate the superior performance of our model in comparison with state-of-the-art methods. Availability and implementation DDGCN is implemented in Python 3.7, open-source and freely available at https://github.com/CXX1113/Dual-DropoutGCN. Supplementary information Supplementary data are available at Bioinformatics online.
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Nguyen Thi Cam, Huong, Aliza Sarlan, and Noreen Izza Arshad. "A hybrid model integrating recurrent neural networks and the semi-supervised support vector machine for identification of early student dropout risk." PeerJ Computer Science 10 (November 29, 2024): e2572. http://dx.doi.org/10.7717/peerj-cs.2572.
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Background Student dropout rates are one of the major concerns of educational institutions because they affect the success and efficacy of them. In order to help students continue their learning and achieve a better future, there is a need to identify the risk of student dropout. However, it is challenging to accurately identify the student dropout risk in the preliminary stages considering the complexities associated with it. This research develops an efficient prediction model using machine learning (ML) and deep learning (DL) techniques for identifying student dropouts in both small and big educational datasets. Methods A hybrid prediction model DeepS3VM is designed by integrating a Semi-supervised support vector machine (S3VM) model with a recurrent neural network (RNN) to capture sequential patterns in student dropout prediction. In addition, a personalized recommendation system (PRS) is developed to recommend personalized learning paths for students who are at risk of dropping out. The potential of the DeepS3VM is evaluated with respect to various evaluation metrics and the results are compared with various existing models such as Random Forest (RF), decision tree (DT), XGBoost, artificial neural network (ANN) and convolutional neural network (CNN). Results The DeepS3VM model demonstrates outstanding accuracy at 92.54%, surpassing other current models. This confirms the model’s effectiveness in precisely identifying the risk of student dropout. The dataset used for this analysis was obtained from the student management system of a private university in Vietnam and generated from an initial 243 records to a total of one hundred thousand records.
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Bethell, Daniel, Simos Gerasimou, and Radu Calinescu. "Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 20939–48. http://dx.doi.org/10.1609/aaai.v38i19.30084.
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Deploying deep learning models in safety-critical applications remains a very challenging task, mandating the provision of assurances for the dependable operation of these models. Uncertainty quantification (UQ) methods estimate the model’s confidence per prediction, informing decision-making by considering the effect of randomness and model misspecification. Despite the advances of state-of-the-art UQ methods, they are computationally expensive or produce conservative prediction sets/intervals. We introduce MC-CP, a novel hybrid UQ method that combines a new adaptive Monte Carlo (MC) dropout method with conformal prediction (CP). MC-CP adaptively modulates the traditional MC dropout at runtime to save memory and computation resources, enabling predictions to be consumed by CP, yielding robust prediction sets/intervals. Throughout comprehensive experiments, we show that MC-CP delivers significant improvements over comparable UQ methods, like MC dropout, RAPS and CQR, both in classification and regression benchmarks. MC-CP can be easily added to existing models, making its deployment simple. The MC-CP code and replication package is available at https://github.com/team-daniel/MC-CP.
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Cambruzzi, Wagner, Sandro Rigo, and Jorge Luis Victória Barbosa. "Dropout Prediction and Reduction in Distance Education Courses with the Learning Analytics Multitrail Approach." JUCS - Journal of Universal Computer Science 21, no. (1) (2015): 23–47. https://doi.org/10.3217/jucs-021-01-0023.
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Distance Education courses are present in large number of educational institutions. Virtual Learning Environments development contributes to this wide adoption of Distance Education modality and allows new pedagogical methodologies. However, dropout rates observed in these courses are very expressive, both in public and private educational institutions. This paper presents a Learning Analytics system developed to deal with dropout problem in Distance Education courses on university-level education. Several complementary tools, allowing data visualization, dropout predictions, support to pedagogical actions and textual analysis, among others, are available in the system. The implementation of these tools is feasible due to the adoption of an approach called Multitrail to represent and manipulate data from several sources and formats. The obtained results from experiments carried out with courses in a Brazilian university show the dropout prediction with an average of 87% precision. A set of pedagogical actions concerning students among the higher probabilities of dropout was implemented and we observed average reduction of 11% in dropout rates.
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Mduma, Neema. "Data Balancing Techniques for Predicting Student Dropout Using Machine Learning." Data 8, no. 3 (2023): 49. http://dx.doi.org/10.3390/data8030049.
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Predicting student dropout is a challenging problem in the education sector. This is due to an imbalance in student dropout data, mainly because the number of registered students is always higher than the number of dropout students. Developing a model without taking the data imbalance issue into account may lead to an ungeneralized model. In this study, different data balancing techniques were applied to improve prediction accuracy in the minority class while maintaining a satisfactory overall classification performance. Random Over Sampling, Random Under Sampling, Synthetic Minority Over Sampling, SMOTE with Edited Nearest Neighbor and SMOTE with Tomek links were tested, along with three popular classification models: Logistic Regression, Random Forest, and Multi-Layer Perceptron. Publicly accessible datasets from Tanzania and India were used to evaluate the effectiveness of balancing techniques and prediction models. The results indicate that SMOTE with Edited Nearest Neighbor achieved the best classification performance on the 10-fold holdout sample. Furthermore, Logistic Regression correctly classified the largest number of dropout students (57348 for the Uwezo dataset and 13430 for the India dataset) using the confusion matrix as the evaluation matrix. The applications of these models allow for the precise prediction of at-risk students and the reduction of dropout rates.
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Sultana, Sara, Sharifullah Khan, and Muhammad A. Abbas. "Predicting performance of electrical engineering students using cognitive and non-cognitive features for identification of potential dropouts." International Journal of Electrical Engineering & Education 54, no. 2 (2017): 105–18. http://dx.doi.org/10.1177/0020720916688484.
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The student dropout rate in universities is fascinating, especially among the students of Electrical Engineering. Even the most developed European countries face 40% to 50% dropout rate of engineering students during their first year, and the rate can be as high as 80% for some engineering disciplines. This problem calls attention of educators and university administration to take measures which can help in the reduction of the dropout rate and assist students in successfully completing their degree. Among many other solutions to control the student dropout rate, one is the adoption of a prediction mechanism whereby students can be warned about their potentially poor performance so that they can improve their performance resulting in better grades. Most of the existing prediction mechanisms apply various machine learning techniques on student cognitive features. In addition, non-cognitive features also have significant impact on students’ performance; however, they have been sparsely applied for prediction. This research aims at improving the existing prediction mechanism by exploiting both cognitive and non-cognitive features of students for predicting their results. It has been found in the result analysis that addition of cognitive features increases prediction accuracies of decision tree; however, the addition does not play a significant role in other techniques. The study also identified the individual cognitive features that should be considered by students and universities to cater for drop outs.
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Alghamdi, Saad, Ben Soh, and Alice Li. "A Comprehensive Review of Dropout Prediction Methods Based on Multivariate Analysed Features of MOOC Platforms." Multimodal Technologies and Interaction 9, no. 1 (2025): 3. https://doi.org/10.3390/mti9010003.
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Massive open online courses have revolutionised the learning environment, but their effectiveness is undermined by low completion rates. Traditional dropout prediction models in MOOCs often overlook complex factors like temporal dependencies and context-specific variables. These models are not adaptive enough to manage the dynamic nature of MOOC learning environments, resulting in inaccurate predictions and ineffective interventions. Accordingly, MOOCs dropout prediction models require more sophisticated artificial intelligence models that can address these limitations. Moreover, incorporating feature selection methods and explainable AI techniques can enhance the interpretability of these models, making them more actionable for educators and course designers. This paper provides a comprehensive review of various MOOCs dropout prediction methodologies, focusing on their strategies and research gaps. It highlights the growing MOOC environment and the potential for technology-driven gains in outcome accuracy. This review also discusses the use of advanced models based on machine learning, deep learning, and meta-heuristics approaches to improve course completion rates, optimise learning outcomes, and provide personalised educational experiences.
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Bremer, Vincent, Philip I. Chow, Burkhardt Funk, Frances P. Thorndike, and Lee M. Ritterband. "Developing a Process for the Analysis of User Journeys and the Prediction of Dropout in Digital Health Interventions: Machine Learning Approach." Journal of Medical Internet Research 22, no. 10 (2020): e17738. http://dx.doi.org/10.2196/17738.
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Background User dropout is a widespread concern in the delivery and evaluation of digital (ie, web and mobile apps) health interventions. Researchers have yet to fully realize the potential of the large amount of data generated by these technology-based programs. Of particular interest is the ability to predict who will drop out of an intervention. This may be possible through the analysis of user journey data—self-reported as well as system-generated data—produced by the path (or journey) an individual takes to navigate through a digital health intervention. Objective The purpose of this study is to provide a step-by-step process for the analysis of user journey data and eventually to predict dropout in the context of digital health interventions. The process is applied to data from an internet-based intervention for insomnia as a way to illustrate its use. The completion of the program is contingent upon completing 7 sequential cores, which include an initial tutorial core. Dropout is defined as not completing the seventh core. Methods Steps of user journey analysis, including data transformation, feature engineering, and statistical model analysis and evaluation, are presented. Dropouts were predicted based on data from 151 participants from a fully automated web-based program (Sleep Healthy Using the Internet) that delivers cognitive behavioral therapy for insomnia. Logistic regression with L1 and L2 regularization, support vector machines, and boosted decision trees were used and evaluated based on their predictive performance. Relevant features from the data are reported that predict user dropout. Results Accuracy of predicting dropout (area under the curve [AUC] values) varied depending on the program core and the machine learning technique. After model evaluation, boosted decision trees achieved AUC values ranging between 0.6 and 0.9. Additional handcrafted features, including time to complete certain steps of the intervention, time to get out of bed, and days since the last interaction with the system, contributed to the prediction performance. Conclusions The results support the feasibility and potential of analyzing user journey data to predict dropout. Theory-driven handcrafted features increased the prediction performance. The ability to predict dropout at an individual level could be used to enhance decision making for researchers and clinicians as well as inform dynamic intervention regimens.
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Baek, Eun-Ju, and Seung-Hyung Lee. "Development of a Machine Learning-Based Model for Predicting Dropout Rates in Regional Universities and Exploration of Influencing Factors through Big Data Analysis: Using University Information Disclosure Data from 2017 to 2023." Korean Association For Learner-Centered Curriculum And Instruction 25, no. 1 (2025): 231–56. https://doi.org/10.22251/jlcci.2025.25.1.231.
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Objectives This study aims to analyze the dropout problem in local universities in depth and develop a prediction model for dropout rates. Methods To this end, we developed a predictive model for the dropout rates of 140 regional universities nationwide, utilizing the university information disclosure data from 2017 to 2022. Setting 2023 as the base year for prediction, we applied various machine learning techniques such as linear regression analysis, decision trees, random forests, support vector machines, and gradient boosting machines. Key variables used in the model included the dropout rates from the previous 1-2 years, student enrollment rates, freshmen enrollment rates, full-time faculty ratios, and scholarship recipient rates. The predictive performance of each model was compared and validated using evaluation metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R-squared). Results The analysis results showed that random forest and gradient boosting machine models demonstrated the best predictive performance. Variable importance analysis confirmed that dropout rates from the previous 1-2 years, student enrollment rates, and scholarship rates had significant effects on dropout rates. Predictions for 2024 dropout rates were 3-5% for national universities and 5-12% for private universities. Conclusions This study has methodological significance in approaching the dropout problem using big data and machine learning techniques. Based on the research results, policy recommendations such as expanding financial support for students and strengthening academic adaptation programs were presented.
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Pattanaphanchai, Jarutas, Koranat Leelertpanyakul, and Napa Theppalak. "The Investigation of Student Dropout Prediction Model in Thai Higher Education Using Educational Data Mining: A Case Study of Faculty of Science, Prince of Songkla Uni-versity." JOURNAL OF UNIVERSITY OF BABYLON for Pure and Applied Sciences 27, no. 1 (2019): 356–67. http://dx.doi.org/10.29196/jubpas.v27i1.2191.
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The student’s retention rate is one of the challenging issues that representing the quality of the university. A high dropout rate of students affects not only the reputation of the university but also the students’ career in the future. Therefore, there is a need of student dropout analysis in order to improve the academic plan and management to reduce students drop out from the university as well as to enhance the quality of the higher education system. Data mining technique provides powerful methods for analysis and the prediction the dropout. This paper proposes a model for predicting students’ dropout using the dataset from the representative of the largest public university in the Southen part of Thailand. In this study, data from Faculty of Science, Prince of Songkla University was collected from academic year of 2013 to 2017. The experiment result shows that JRip rule induction is the best technique to generate a prediction model receiving the highest accuracy value of 77.30%. The results highlight the potential prediction model that can be used to detect the early state of dropping out of the student which the university can provide supporting program to improve the student retention rate
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Chen, Zihui. "Exploiting Long Short-term Memory Neural Network for Stock Price Prediction." Applied and Computational Engineering 8, no. 1 (2023): 829–34. http://dx.doi.org/10.54254/2755-2721/8/20230277.
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Stock as a high yield, high risk investment has been favored by the public. In order to increase the return on investing in stocks, investors need to predict stock prices. In the past, investors used traditional mathematical methods to make predictions. Now, neural networks are used by investors to predict stocks, which can improve the accuracy of stock forecasting. To further verify the effectiveness of these methods, this work discusses the effects of different network structures and hyperparameters on stock prediction models using short-term memory (LSTM) neural networks. The results show that deeper network layer can get better training effect, but it needs more training time, resulting in a lot of time waste. In addition, this experiment tests the prediction effect under different dropout parameters. The results show that the dropout function should not be too large or too small. Multiple experiments are needed to find an appropriate dropout value.
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Sobreiro, Pedro, José Garcia-Alonso, Domingos Martinho, and Javier Berrocal. "Hybrid Random Forest Survival Model to Predict Customer Membership Dropout." Electronics 11, no. 20 (2022): 3328. http://dx.doi.org/10.3390/electronics11203328.
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Dropout prediction is a problem that must be addressed in various organizations, as retaining customers is generally more profitable than attracting them. Existing approaches address the problem considering a dependent variable representing dropout or non-dropout, without considering the dynamic perspetive that the dropout risk changes over time. To solve this problem, we explore the use of random survival forests combined with clusters, in order to evaluate whether the prediction performance improves. The model performance was determined using the concordance probability, Brier Score and the error in the prediction considering 5200 customers of a Health Club. Our results show that the prediction performance in the survival models increased substantially in the models using clusters rather than that without clusters, with a statistically significant difference between the models. The model using a hybrid approach improved the accuracy of the survival model, providing support to develop countermeasures considering the period in which dropout is likely to occur.
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Ojoajogu Enemali, Ephraim, Sulaiman Alhaji Dodo Ibrahim, Ibraheem Salaudeen, and Mahmud Abubakar Abdulmalik. "A CONCRETE DROPOUT NEURAL NETWORK FOR SHEAR SONIC LOG PREDICTION." Romanian Journal of Petroleum & Gas Technology 6 (77), no. 1 (2025): 119–36. https://doi.org/10.51865/jpgt.2025.01.08.
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Assessing the risk associated with drilling and wellbore stability studies requires the shear sonic log. These logs apart from distinguishing formation fluid from lithology are needed to obtain geo-mechanical rock parameters required for the safe design of rock fracturing. Although sonic logs are of great importance, they are usually not obtained due to the limiting cost of acquisition. Neural networks have been used to generate these logs to save cost, but these networks are prone to overfitting. The dropout rate has been proposed to tackle this problem, however selecting the optimum dropout probability rate can be challenging manually or expensive computation wise. This research therefore investigated concrete dropout, a dynamic technique for adapting the dropout rate of a neural network to the data. The concrete dropout technique was applied to an artificial neural network (ANN) and a convolutional neural network (CNN) model to predict the shear sonic log with Monte Carlo simulation. Comparison was also made with the deterministic ANN and CNN models which had no dropout layers added and a Bayesian-optimized multilayer perceptron (MLP) model. These models were trained and validated with four (4) wells from the Volve field, using features with the highest correlation. The Concrete dropout ANN was found to outperform both the deterministic versions and the MLP model with R2, RMSE, MSE and MAE scores of 0.9548, 3.6415, 2.4433 and 0.0179 respectively. The neural networks built in this study showed an enhanced predictive performance with concrete dropout addition over the networks with no dropout added, showing that the technique was able to adapt the dropout rate to fit the nature of data and improve performance, which finds great application in real time deployment. The findings of this study also proposed a cost-effective way of sampling and averaging multiple outputs from a single neural network model, leading to enhanced predictive performance as the addition of concrete dropout allowed the network output distributions rather than point predictions.
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Colpo, Miriam Pizzatto, Tiago Thompsen Primo, Marilton Sanchotene de Aguiar, and Cristian Cechinel. "Educational Data Mining for Dropout Prediction: Trends, Opportunities, and Challenges." Revista Brasileira de Informática na Educação 32 (May 20, 2024): 220–56. http://dx.doi.org/10.5753/rbie.2024.3559.
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Today, we face academic, social, and economic losses associated with student dropouts. Several studies have applied data mining techniques to educational datasets to understand dropout profiles and recognize at-risk students. To identify the contextual (academic levels, modalities, and systems), technical (tasks, categories of algorithms, and tools), and data (types, coverage, and volume) characteristics related to these works, we performed a systematic literature review, considering institutional and academic degree dropout. Internationally recognized repositories were searched, and the selected articles demonstrated, among other characteristics, a greater exploration of educational, demographic, and economic data of undergraduate students from classification techniques of decision tree ensembles. In addition to not having identified any study from underdeveloped countries among the selected ones, we found shortcomings in the application of predictive models and in making their predictions available to academic managers, which suggests an underutilization of the efforts and potential of most of these studies in educational practice.
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Xing, Wanli, and Dongping Du. "Dropout Prediction in MOOCs: Using Deep Learning for Personalized Intervention." Journal of Educational Computing Research 57, no. 3 (2018): 547–70. http://dx.doi.org/10.1177/0735633118757015.
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Massive open online courses (MOOCs) show great potential to transform traditional education through the Internet. However, the high attrition rates in MOOCs have often been cited as a scale-efficacy tradeoff. Traditional educational approaches are usually unable to identify such large-scale number of at-risk students in danger of dropping out in time to support effective intervention design. While building dropout prediction models using learning analytics are promising in informing intervention design for these at-risk students, results of the current prediction model construction methods do not enable personalized intervention for these students. In this study, we take an initial step to optimize the dropout prediction model performance toward intervention personalization for at-risk students in MOOCs. Specifically, based on a temporal prediction mechanism, this study proposes to use the deep learning algorithm to construct the dropout prediction model and further produce the predicted individual student dropout probability. By taking advantage of the power of deep learning, this approach not only constructs more accurate dropout prediction models compared with baseline algorithms but also comes up with an approach to personalize and prioritize intervention for at-risk students in MOOCs through using individual drop out probabilities. The findings from this study and implications are then discussed.
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Enemali, Ephraim Ojoajogu, Sulaiman Dodo Alhaji Ibrahim, Ibraheem Salaudeen, and Mahmud Abdulmalik Abubakar. "A concrete dropout neural network for shear sonic log prediction." Romanian Journal of Petroleum and Gas Technology 6, no. 1 (2025): 119–36. https://doi.org/10.51865/JPGT.2025.01.08.
Full textAbstract:
Assessing the risk associated with drilling and wellbore stability studies requires the shear sonic log. These logs apart from distinguishing formation fluid from lithology are needed to obtain geo-mechanical rock parameters required for the safe design of rock fracturing. Although sonic logs are of great importance, they are usually not obtained due to the limiting cost of acquisition. Neural networks have been used to generate these logs to save cost, but these networks are prone to overfitting. The dropout rate has been proposed to tackle this problem, however selecting the optimum dropout probability rate can be challenging manually or expensive computation wise. This research therefore investigated concrete dropout, a dynamic technique for adapting the dropout rate of a neural network to the data. The concrete dropout technique was applied to an artificial neural network (ANN) and a convolutional neural network (CNN) model to predict the shear sonic log with Monte Carlo simulation. Comparison was also made with the deterministic ANN and CNN models which had no dropout layers added and a Bayesian-optimized multilayer perceptron (MLP) model. These models were trained and validated with four (4) wells from the Volve field, using features with the highest correlation. The Concrete dropout ANN was found to outperform both the deterministic versions and the MLP model with R2, RMSE, MSE and MAE scores of 0.9548, 3.6415, 2.4433 and 0.0179 respectively. The neural networks built in this study showed an enhanced predictive performance with concrete dropout addition over the networks with no dropout added, showing that the technique was able to adapt the dropout rate to fit the nature of data and improve performance, which finds great application in real time deployment. The findings of this study also proposed a cost-effective way of sampling and averaging multiple outputs from a single neural network model, leading to enhanced predictive performance as the addition of concrete dropout allowed the network output distributions rather than point predictions.
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Sari, Eka Yulia, Kusrini Kusrini, and Andi Sunyoto. "Analisis Akurasi Jaringan Syaraf Tiruan Dengan Backpropagation Untuk Prediksi Mahasiswa Dropout." Creative Information Technology Journal 6, no. 2 (2021): 85. http://dx.doi.org/10.24076/citec.2019v6i2.235.
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Universitas ABC yogyakarta selalu melakukan evaluasi kinerja mahasiswa guna mengetahui pencapaian pada masing-masing mahasiswa.Mahasiswa yang melampaui masa studi dan tidak melakukan perpanjangan akan dikenakan sanki berupa dropout.Kasus dropout tersebut dapat diminimalisir dengan pendeteksian secara dini terhadap mahasiswa yang beresiko dropout. Pendeteksian dapat dilakukan dengan memanfaatkan tumpukan data untuk memprediksi dropout mahasiswa. Pada penelitian ini bertujuan untuk memprediksi mahasiswa yang berpotensi dropout dengan masa studi maksimal yang harus diselesaikan pada jenjang Sarjana dengan mengimplementasikan Metode Backpropagation. Data yang digunakan dalam penelitian ini adalah data akademik prodi S1 Informatika Universitas ABC pada tahun 2016-2019 denganjumlah dataset sebanyak 129.Tujuan penelitian ini untuk mengukur analisis prediksi dropoutdengan percobaan penggunaan beberapa arsitektur jaringan. Hasil yang diperoleh dari modelyang diusulkan yaitu model arsitektur 12-5-2 merupakan model arsitektur terbaik yangdidapatkan. Learning rate terbaik sebesar 0,4 dengan momentum terbaik sebesar 0,95. Akurasi yang diperoleh dari prediksi mahasiswa dropout dengan arsitektur, learning rate, dan momentum terbaik sebesar 98,2%.ABC University of Yogyakarta always evaluates student performance in order to find out the achievements of each student. Students who have exceeded the study period and not extended would be subject to sanctions in the form of a dropout. The dropout case can be minimized by early detection of students who are at risk of dropout. Detection can be done by utilizing a pile of data to predict student dropouts. In this study aims to predict students who have the potential to drop out with a maximum study period that must be completed at the Undergraduate level by implementing the Backpropagation Method. The data used in this study are academic data of S1 University Informatics Study Program of ABC University in 2016-2019 with the number of datasets as much as 129. The purpose of this study is to measure the dropout prediction analysis with the experiments of using several network architectures. The results obtained from the proposed model, namely architectural models 12-5-2, are the best architectural models obtained. The best learning rate is 0.4 with the best momentum of 0.95. The accuracy obtained from the prediction of dropout students is 98.2%.
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Belleï-Rodriguez, Carmen-Édith, Serge Larivée, and Julien Morizot. "Décrochage scolaire : la relation élève-enseignants peut-elle l'emporter contre le quotient intellectuel?" McGill Journal of Education 55, no. 2 (2021): 439–62. http://dx.doi.org/10.7202/1077976ar.
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Quebec has the highest non graduation rate in Canada. The personal and social consequences are numerous, with long term repercussions. Even if a low intelligence quotient (IQ) is an important risk factor of school dropout, some factors may influence this association. The purpose of this study is to investigate the moderating effect of the student-teacher relationship on the association between the IQ and school dropout using the data of the SIAA (Stratégie d'Intervention Agir Autrement) study. The logistic regression analyzes confirmed that the IQ score contributes in predicting school dropout. However, the results suggest that warm or conflicting student-teacher relationships have no contribution to the prediction model and do not moderate the link between IQ and school dropout.
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Sobreiro, Pedro, Domingos Dos Santos Martinho, Jose G. Alonso, and Javier Berrocal. "A SLR on Customer Dropout Prediction." IEEE Access 10 (2022): 14529–47. http://dx.doi.org/10.1109/access.2022.3146397.
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Kumar, Mukesh, A. J. Singh, and Disha Handa. "Literature Survey on Educational Dropout Prediction." International Journal of Education and Management Engineering 7, no. 2 (2017): 8–19. http://dx.doi.org/10.5815/ijeme.2017.02.02.
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kamal, Md Sarwar, Linkon Chowdhury, and Sonia Farhana Nimmy. "New Dropout Prediction for Intelligent System." International Journal of Computer Applications 42, no. 16 (2012): 26–31. http://dx.doi.org/10.5120/5777-8093.
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Osemwegie, Eric E., Frank I. Amadin, and O. M. Uduehi. "STUDENT DROPOUT PREDICTION USING MACHINE LEARNING." FUDMA JOURNAL OF SCIENCES 7, no. 6 (2023): 347–53. http://dx.doi.org/10.33003/fjs-2023-0706-2103.
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In a higher education environment, we considered the likelihood of probable dropouts from a first-year undergraduate Computer Science program. In order to achieve this, data from five academic sessions were obtained from the Department of Computer Science, University of Benin, Nigeria. Out of nine hundred and forty seven (947) data obtained, only a total of nine hundred and six (906) was usable after cleaning and preprocessing. Six distinct classifiers including Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), and Artificial Neural Networks (ANN) were modeled for the prediction of student success and dropouts. The performance six were stated to have performed on average at 90.4%, 98.9%, 98.5%, 97.4%, 96.0% and 97.3% respectively. Although there wasn't much of a performance difference between the DT, SVM, and LR, the LR model was chosen for deployment since it performs better than the other two models in terms of F1_score and Recall.
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Psathas, Georgios, Theano K. Chatzidaki, and Stavros N. Demetriadis. "Predictive Modeling of Student Dropout in MOOCs and Self-Regulated Learning." Computers 12, no. 10 (2023): 194. http://dx.doi.org/10.3390/computers12100194.
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The primary objective of this study is to examine the factors that contribute to the early prediction of Massive Open Online Courses (MOOCs) dropouts in order to identify and support at-risk students. We utilize MOOC data of specific duration, with a guided study pace. The dataset exhibits class imbalance, and we apply oversampling techniques to ensure data balancing and unbiased prediction. We examine the predictive performance of five classic classification machine learning (ML) algorithms under four different oversampling techniques and various evaluation metrics. Additionally, we explore the influence of self-reported self-regulated learning (SRL) data provided by students and various other prominent features of MOOCs as potential indicators of early stage dropout prediction. The research questions focus on (1) the performance of the classic classification ML models using various evaluation metrics before and after different methods of oversampling, (2) which self-reported data may constitute crucial predictors for dropout propensity, and (3) the effect of the SRL factor on the dropout prediction performance. The main conclusions are: (1) prominent predictors, including employment status, frequency of chat tool usage, prior subject-related experiences, gender, education, and willingness to participate, exhibit remarkable efficacy in achieving high to excellent recall performance, particularly when specific combinations of algorithms and oversampling methods are applied, (2) self-reported SRL factor, combined with easily provided/self-reported features, performed well as a predictor in terms of recall when LR and SVM algorithms were employed, (3) it is crucial to test diverse machine learning algorithms and oversampling methods in predictive modeling.
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Lad, Sakshi S. "Dropout- A Detailed Survey." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1573–78. http://dx.doi.org/10.22214/ijraset.2021.36499.
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Deep Neural Networks are very complex and have large number of parameters. Shortlisting the parameters that influence the model prediction is not possible as each has equal significance. These neural nets have powerful learning skills can model training data well enough. However, in most of these conditions, the models are over-fitting. Combining predictions from large neural nets where neurons are co-dependent alters the performance of the model. Dropout addresses the problem of overfitting and slow convergence in deep neural nets. The core concept of dropout technique is to randomly drop units and their connections from the neural network during training phase. This prevents units from co-adapting and thus improving the performance. The central mechanism behind dropout is to take a large model that overfits easily and repeatedly sample and train smaller sub-models from it. This paper provides an introduction to dropout, the history behind its design and various dropout methods.
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B, Marina, and A. Senthilrajan. "HFIPO-DPNN: A Framework for Predicting the Dropout of Physically Impaired Student from Education." International Journal of Information and Education Technology 13, no. 4 (2023): 696–703. http://dx.doi.org/10.18178/ijiet.2023.13.4.1855.
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Education plays a significant role in individuals’ development and the economic growth of developing countries like India. Dropout of students from their studies is the major concern for any order of education. Some models for predicting the dropout of students are developed with several factors. Many of them lacked consistency as they backed their studies with the academic performance of the students. Especially, for those students who suffered from physical impairment, the dropout depends on several external factors. Hence, this work proposes a novel HFIPO-DPNN to predict the student dropout rooted in the previous semester’s marks. The proposed model enclosed the hybrid firefly and improved particle swarm algorithm to optimize the feature selection that influences the dropout of hearing-impaired students. The optimized feature data are used to predict the dropout with the novel DPNN. The optimized data was split and used for training the DPNN. The testing data is used to evaluate the performance of the proposed framework. The attributes used for predicting the student dropout are Family Size, Subject, Medium of Instruction, and so on. The data must be collected from 250 physically impaired children belonging to ITI institute, Bangalore. The outcome of the proposed framework is evaluated on several metrics. The accuracy of the proposed model is about 99.02%. The HFIPO-DPNN framework can be enhanced for predicting the dropout for students with other disabilities. The optimization showed that factors influencing education other than familial factors are to be considered in the prediction of dropout.
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Zhang, Tiancheng, Hengyu Liu, Jiale Tao, et al. "Enhancing Dropout Prediction in Distributed Educational Data Using Learning Pattern Awareness: A Federated Learning Approach." Mathematics 11, no. 24 (2023): 4977. http://dx.doi.org/10.3390/math11244977.
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Learning patterns are crucial for predicting student dropout in educational settings, providing insights into students’ behaviors and motivations. However, existing mainstream dropout prediction models have limitations in effectively mining these learning patterns and cannot mine these learning patterns in large-scale, distributed educational datasets. In this study, we analyze the representations of mainstream models and identify their inability to capture students’ distinct learning patterns and personalized variations across courses. Addressing these challenges, our study adopts a federated learning approach, tailoring the analysis to leverage distributed data while maintaining privacy and decentralization. We introduce the Federated Learning Pattern Aware Dropout Prediction Model (FLPADPM), which utilizes a one-dimensional convolutional neural network (CNN) and a bidirectional long short-term memory (LSTM) layer within a federated learning framework. This model is designed to effectively capture nuanced learning patterns and adapt to variations across diverse educational settings. To evaluate the performance of LPADPM, we conduct an empirical evaluation using the KDD Cup 2015 and XuetangX datasets. Our results demonstrate that LPADPM outperforms state-of-the-art models in accurately predicting student dropout behavior. Furthermore, we visualize the representations generated by LPADPM, which confirm its ability to effectively mine learning patterns in different courses. Our results showcase the model’s ability to capture and analyze learning patterns across various courses and institutions within a federated learning context.
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Farzana, Walia, Megan A. Witherow, Ahmed Temtam, et al. "24 Key brain region identification in obesity prediction with structural MRI and probabilistic uncertainty aware model." Journal of Clinical and Translational Science 9, s1 (2025): 9. https://doi.org/10.1017/cts.2024.715.
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Objectives/Goals: Predictive performance alone may not determine a model’s clinical utility. Neurobiological changes in obesity alter brain structures, but traditional voxel-based morphometry is limited to group-level analysis. We propose a probabilistic model with uncertainty heatmaps to improve interpretability and personalized prediction. Methods/Study Population: The data for this study are sourced from the Human Connectome Project (HCP), with approval from the Washington University in St. Louis Institutional Review Board. We preprocessed raw T1-weighted structural MRI scans from 525 patients using an automated pipeline. The dataset is divided into training (357 cases), calibration (63 cases), and testing (105 cases). Our probabilistic model is a convolutional neural network (CNN) with dropout regularization. It generates a prediction set containing high-probability correct predictions using conformal prediction techniques, which add an uncertainty layer to the CNN. Additionally, gradient-based localization mapping is employed to identify brain regions associated with low uncertainty cases. Results/Anticipated Results: The performance of the computational conformal model is evaluated using training and testing data with varying dropout rates from 0.1 to 0.5. The best results are achieved with a dropout rate of 0.5, yielding a fivefold cross-validated average precision of 72.19% and an F1-score of 70.66%. Additionally, the model provides probabilistic uncertainty quantification along with gradient-based localization maps that identify key brain regions, including the temporal lobe, putamen, caudate, and occipital lobe, relevant to obesity prediction. Comparisons with standard segmented brain atlases and existing literature highlight that our model’s uncertainty quantification mapping offers complementary evidence linking obesity to structural brain regions. Discussion/Significance of Impact: This research offers two significant advancements. First, it introduces a probabilistic model for predicting obesity from structural magnetic resonance imaging data, focusing on uncertainty quantification for reliable results. Second, it improves interpretability using localization maps to identify key brain regions linked to obesity.
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Goel, Yamini, and Rinkaj Goyal. "On the Effectiveness of Self-Training in MOOC Dropout Prediction." Open Computer Science 10, no. 1 (2020): 246–58. http://dx.doi.org/10.1515/comp-2020-0153.
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AbstractMassive open online courses (MOOCs) have gained enormous popularity in recent years and have attracted learners worldwide. However, MOOCs face a crucial challenge in the high dropout rate, which varies between 91%-93%. An interplay between different learning analytics strategies and MOOCs have emerged as a research area to reduce dropout rate. Most existing studies use click-stream features as engagement patterns to predict at-risk students. However, this study uses a combination of click-stream features and the influence of the learner’s friends based on their demographics to identify potential dropouts. Existing predictive models are based on supervised learning techniques that require the bulk of hand-labelled data to train models. In practice, however, scarcity of massive labelled data makes training difficult. Therefore, this study uses self-training, a semi-supervised learning model, to develop predictive models. Experimental results on a public data set demonstrate that semi-supervised models attain comparable results to state-ofthe-art approaches, while also having the flexibility of utilizing a small quantity of labelled data. This study deploys seven well-known optimizers to train the self-training classifiers, out of which, Stochastic Gradient Descent (SGD) outperformed others with the value of F1 score at 94.29%, affirming the relevance of this exposition.
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Li, Ye, and Xiaohu Shi. "Mine Pressure Prediction Study Based on Fuzzy Cognitive Maps." International Journal of Computational Intelligence and Applications 19, no. 03 (2020): 2050023. http://dx.doi.org/10.1142/s1469026820500236.
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The study on the prediction of mine pressure, while exploiting in coal mine, is a critical and technical guarantee for coal mine safety and production. In this paper, primarily due to the actual demand for the prediction of mine pressure, a practical prediction model Mine Pressure Prediction (MPP) was proposed based on fuzzy cognitive maps (FCMs). The Real Coded Genetic Algorithm (RCGA) was proposed to solve the problem by introducing the weight regularization and dropout regularization. A numerical example involving in-situ monitoring data is studied. Mean Square Error (MSE) and fitness function were used to evaluate the applicability of MPP model which is trained by RCGA, Regularization Genetic Algorithm (RGA) and Weight and Dropout RGA optimization algorithms. The numerical results demonstrate that the proposed Weight and Dropout RGA is better than the other two algorithms, and realizing the requirement for prediction of mine pressure in the coal mine production.
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Bremer, V., P. Chow, B. Funk, F. Thorndike, and L. Ritterband. "1204 Analyzing User Journey Data In Digital Health: Predicting Dropout From A Digital CBT-I Intervention." Sleep 43, Supplement_1 (2020): A460. http://dx.doi.org/10.1093/sleep/zsaa056.1198.
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Abstract Introduction Intervention dropout is an important factor for the evaluation and implementation of digital therapeutics, including in insomnia. Large amounts of individualized data (logins, questionnaires, EMA data) in these interventions can combine to create user journeys - the data generated by the path an individual takes to navigate the digital therapeutic. User journeys can provide insight about how likely users are to drop out of an intervention on an individual level and lead to increased prediction performance. Thus, the goal of this study is to provide a step-by-step guide for the analysis of user journeys and utilize this guide to predict intervention dropout, illustrated with an example from a data in a RCT of digital therapeutic for chronic insomnia, for which outcomes have previously been published. Methods Analysis of user journeys includes data transformation, feature engineering, and statistical model analysis, using machine learning techniques. A framework is established to leverage user journeys to predict various behaviors. For this study, the framework was applied to predict dropouts of 151 participants from a fully automated web-based program (SHUTi) that delivered cognitive behavioral therapy for insomnia. For this task, support vector machines, logistic regression with regularization, and boosted decision trees were applied at different points in 9-week intervention. These techniques were evaluated based on their predictive performance. Results After model evaluation, a decision tree ensemble achieved AUC values ranging between 0.6-0.9 based on application of machine earning techniques. Various handcrafted and theory-driven features (e.g., time to complete certain intervention steps, time to get out of bed after arising, and days since last system interaction contributed to prediction performance. Conclusion Results indicate that utilizing a user journey framework and analysis can predict intervention dropout. Further, handcrafted theory-driven features can increase prediction performance. This prediction of dropout could lead to an enhanced clinical decision-making in digital therapeutics. Support The original study evaluating the efficacy of this intervention has been reported elsewhere and was funded by grant R01 MH86758 from the National Institute of Mental Health.
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Siebra, Clauirton Albuquerque, Ramon N. Santos, and Natasha C. Q. Lino. "A Self-Adjusting Approach for Temporal Dropout Prediction of E-Learning Students." International Journal of Distance Education Technologies 18, no. 2 (2020): 19–33. http://dx.doi.org/10.4018/ijdet.2020040102.
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This work proposes a dropout prediction approach that is able to self-adjust their outcomes at any moment of a degree program timeline. To that end, a rule-based classification technique was used to identify courses, grade thresholds and other attributes that have a high influence on the dropout behavior. This approach, which is generic so that it can be applied to any distance learning degree program, returns different rules that indicate how the predictions are adjusted along with academic terms. Experiments were carried out using four rule-based classification algorithms: JRip, OneR, PART and Ridor. The outcomes show that this approach presents better accuracy according to the progress of students, mainly when the JRip and PART algorithms are used. Furthermore, the use of this method enabled the generation of rules that stress the factors that mainly affect the dropout phenomenon at different degree moments.
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Putra, Lalu Ganda Rady, Didik Dwi Prasetya, and Mayadi Mayadi. "Student Dropout Prediction Using Random Forest and XGBoost Method." INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi 9, no. 1 (2025): 147–57. https://doi.org/10.29407/intensif.v9i1.21191.
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Background: The increasing dropout rate in Indonesia poses significant challenges to the education system, particularly as students advance through higher education levels. Predicting student attrition accurately can help institutions implement timely interventions to improve retention. Objective: This study aims to evaluate the effectiveness of the Random Forest and XGBoost algorithms in predicting student attrition based on demographic, socioeconomic, and academic performance factors. Methods: A quantitative study was conducted using a dataset of 4,424 instances with 34 attributes, categorized into Dropout, Graduate, and Enrolled. The performance of Random Forest and XGBoost was compared based on accuracy, specificity, and sensitivity. Results: Random Forest achieved the highest accuracy at 80.56%, with a specificity of 76.41% and sensitivity of 72.42%, outperforming XGBoost. While XGBoost was slightly less accurate, it remained a competitive approach for student attrition prediction. Conclusion: The findings highlight Random Forest's robustness in handling extensive datasets with diverse attributes, making it a reliable tool for identifying at-risk students. This study underscores the potential of machine learning in addressing educational challenges. Future research should explore advanced ensemble techniques, such as the Ensemble Voting Classifier, or deep learning models to further enhance prediction accuracy and scalability.
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G.Dongre, Prof (Dr) Ganesh. "Predicting Student Dropout Rates in Higher Education: A Comparative Study of Machine Learning Algorithms." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–8. http://dx.doi.org/10.55041/ijsrem38488.
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Recent years have seen a significant increase in study interest in the areas of predicting student performance, avoiding failure, and identifying the variables affecting student dropout. One important indicator in online and open distance learning courses is the student dropout rate. We purpose the naive bayes classification method to construct the student dropout prediction using naive bayes. This work examines the critical topic of forecasting student dropout rates in higher education using machine learning approaches, with a particular emphasis on the random forest algorithm and the naive bayes algorithm. The study's goal is to properly anticipate dropout rates using data mining methods and machine learning algorithms after conducting a thorough evaluation of existing literature and approaches. The systematic method consists of data collection from a Kaggle dataset, data preparation to solve class imbalance via SMOTE oversampling, and algorithm selection. Random forest and naive Bayes approaches outperform other machine learning algorithms in terms of accuracy, sensitivity, specificity, and precision. The study underscores the importance of considering diverse factors such as demographic data, socioeconomic factors, and academic performance in dropout prediction models. The implications of this research extend beyond academia, with the potential to inform proactive interventions and support systems, ultimately leading to improved student outcomes and institutional effectiveness. According to this paper, the paper outputs that for the binary classification on the data set used in this project has best performed with Naive Bayes and Random Forest Algorithm with SMOTE oversampling. Keywords- SMOTE oversampling, machine learning, Random forest, naive bayes.