Early Dropout Prediction in MOOCs through Supervised Learning and Hyperparameter Optimization

Panagiotakopoulos, Theodor; Kotsiantis, Sotiris; Kostopoulos, Georgios; Iatrellis, Omiros; Kameas, Achilles

doi:10.3390/electronics10141701

Cited by 23 publications

(17 citation statements)

References 25 publications

(23 reference statements)

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“…But accuracy is not enhanced. Deep learning algorithm is developed in Reference 19 for building a dropout prediction model. But it failed to perform the more accurate dropout prediction models.…”

Section: Related Workmentioning

confidence: 99%

Rank biserial stochastic feature embed bivariate kernelized regressive bootstrap aggregative classifier for school student dropout prediction

Chinnasamy

Balasubramanian

2022

Concurrency and Computation

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Early and accurately predicting the students' dropout enables schools to recognize the students based on available educational data. The early student dropout prediction is a major concern of education administrators. The existing classification techniques were unable to handle the early stage accurate performance of student dropout prediction with maximum accuracy and minimum time. In order to resolve the issue, a novel technique called rank biserial Otsuka-Ochiai stochastic embedded feature selection based bivariate kernelized regressive bootstrap aggregative classifier (RBOOSEFS-BKBAC) is motivated to perform student dropout prediction. The aim of the designing RBOOSEFS-BKBAC is to improve student dropout accuracy and minimal time consumption. Initially, the data preprocessing is to perform the data normalization, data cleaning, and duplicate data removal. Next, rank biserial correlation is used for discovering the correlated features. Followed by, Otsuka-Ochiai stochastic neighbor embedded feature selection is carried out to select significant features. Finally, bivariate kernelized regressive bootstrap aggregative classification technique is to perform classification with help of weak classifier. By using Bucklin voting scheme, the classification outcomes are obtained for increasing prediction accuracy as well as minimizing error. Experimental evaluation is performed by using Student-Drop-India2016 dataset with different metrics such as prediction accuracy, precision, recall, F-measure, as well as time. The result of proposed RBOOSEFS-BKBAC technique is provided that the higher prediction accuracy by 5% and lesser the prediction time by 15%, as compared to the state-of-the-art methods. K E Y W O R D Sbivariate kernelized regressive bootstrap aggregative classification, Otsuka-Ochiai stochastic neighbor embedded feature selection, preprocessing, rank biserial correlation-based feature extraction, school student dropout prediction INTRODUCTIONStudents' dropout is a difficult issue in the learning process for both the individual and society. Early prediction is a main challenge in the dropout prediction. Predicting the student's at-risk of dropout offers various benefits to educators, students, and the educational institution. Machine learning algorithms were applied in education to address the predicting dropout of students issue from schools. So that potential drop outs are documented

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Section: Related Workmentioning

confidence: 99%

Rank biserial stochastic feature embed bivariate kernelized regressive bootstrap aggregative classifier for school student dropout prediction

Chinnasamy

Balasubramanian

2022

Concurrency and Computation

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“…The student dropout prediction time is estimated using the TCRTFM-TDMBDC technique and existing techniques FWTS-CNN, 1 LLM 2 are estimated using (16). Figure 8 illustrates the prediction time of school student dropout using three methods TCRTFM-TDMBDC technique and existing techniques FWTS-CNN, 1 LLM.…”

Section: Impact Of Prediction Timementioning

confidence: 99%

“…A supervised machine learning technique was designed in Reference 16 to classify the student dropout based on hyperparameter optimization. The designed techniques failed to minimize the time consumption of student dropout prediction.…”

Section: Related Workmentioning

confidence: 99%

Tucker's congruence regressive feature projected Tversky discriminant multiple instance learning boost data classification for school student dropout prediction

Chinnasamy

Balasubramanian

2022

Concurrency and Computation

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Prediction of student dropout in high school is a significant concern in education that affects both a state's education system and its financial system. Early prediction of school student dropout is not an easy issue to resolve since many factors that can influence student retention. The traditional classification techniques were used to solve this problem normally but the higher accuracy was not obtained. In order to improve the accuracy, a novel technique called Tucker's Congruence Regressive Target Feature Matching‐based Tversky Discriminant MIL Boost Data Classification (TCRTFM‐TDMBDC) is introduced. The proposed TCRTFM‐TDMBDC technique consists of four different processes namely data preprocessing, feature extraction, feature selection, and classification. At first, the data preprocessing is carried out for cleaning and altering the raw input data into a valuable and understandable format to minimize the complexity of the classification. After the preprocessing, the feature extraction is carried out by applying Modified Tucker's congruence correlative regression. Thirdly, the feature selection process is performed using Gaussian kernelized target projection feature matching to select the feature subset for accurate classification with minimum time consumption. Finally, the ensemble technique called Tversky Indxive generalized discriminant MIL boost is applied for classifying the given input student data with help of the weak learners. Based on the classification results, the student dropout prediction is accurately performed with minimum time. Experimental results reveal that the proposed technique noticeably predicts student dropout by means of prediction accuracy, precision, recall, F‐measure, and prediction time with respect to the number of student data. The discussed results illustrate that the proposed TCRTFM‐TDMBDC technique achieves higher accuracy with minimum prediction time than the state‐of‐the‐art methods.

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“…The major goal of research by Panagiotakopoulos et al (2021) was to use a variety of well-known, cutting-edge supervised machine learning techniques to predict both student success and early student dropout in a MOOC. Results indicate that random forest (RF) excelled in terms of accuracy, sensitivity, and Cohen's kappa coefficient.…”

Section: Factors That Drive a Learner To Drop Out Of A Moocmentioning

confidence: 99%

Factors Associated with Student Enrollment, Completion, and Dropout of Massive Open Online Courses in the Sultanate of Oman

Sherimon¹,

Sherimon²,

Francis³

et al. 2021

IJLTER

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This research aims to investigate the elements that influence learners' enrollment in, completion of and dropout from Massive Open Online Courses (MOOCs). MOOCs attract a vast number of participants, with enrollments growing at an unprecedented rate; however, only a small number of students who enroll, complete their study. In this research, data were collected from 79 participants using an online survey. Professional growth, flexible time, free of charge, and the opportunity to obtain certification were recognized as the four main factors that directly influenced student enrollment in MOOCs. The motivational variables revealed in this study that led to the effective completion of MOOCs included good-quality course videos, straightforward and clear tutor instructions, good course delivery, and useful instructor feedback, among others. The most prevalent reasons for dropout included failure to fulfil the deadline, the instructor's vocabulary, challenging assignments, and difficulty in grasping the course content. The findings of this study can provide educators and decision-makers with the information they need to better understand the factors that influence MOOC student enrollment, completion, and dropout rates, as well as relevant actions to improve the success rates of MOOC.

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Early Dropout Prediction in MOOCs through Supervised Learning and Hyperparameter Optimization

Cited by 23 publications

References 25 publications

Rank biserial stochastic feature embed bivariate kernelized regressive bootstrap aggregative classifier for school student dropout prediction

Rank biserial stochastic feature embed bivariate kernelized regressive bootstrap aggregative classifier for school student dropout prediction

Tucker's congruence regressive feature projected Tversky discriminant multiple instance learning boost data classification for school student dropout prediction

Factors Associated with Student Enrollment, Completion, and Dropout of Massive Open Online Courses in the Sultanate of Oman

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