Early Prediction of Dropout and Final Exam Performance in an Online Statistics Course

Figueroa-Canas, Josep; Sancho-Vinuesa, Teresa

doi:10.1109/rita.2020.2987727

Cited by 35 publications

(34 citation statements)

References 33 publications

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“…Regarding the early prediction of drop-out in academic examinations using learning analytics, similar studies have been published (see [2,4,7,33,38]). Among them, the review research by Liz-Dominguez et al [4] was versatile and profound.…”

Section: Significance and Effectiveness Of The Researchmentioning

confidence: 99%

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Current Failure Prediction for Final Examination via Nearest Neighbor Method using Past Trends of Weekly Online Testing

Hirose

2021

International Journal of Learning Technologies and Learning Environments

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Section: Significance and Effectiveness Of The Researchmentioning

confidence: 99%

“…The use of similarity in dealing with the nearest neighbor method is also new. In addition, Figueroa-Canas et al [7] mentioned in their paper that Hirose [19] dealt with the ROC curve to find the optimal point for segregating the failed examinees in the final examination.…”

Section: Significance and Effectiveness Of The Researchmentioning

confidence: 99%

Current Failure Prediction for Final Examination via Nearest Neighbor Method using Past Trends of Weekly Online Testing

Hirose

2021

International Journal of Learning Technologies and Learning Environments

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“…The early studies of student performance prediction are mainly based on traditional machine learning methods, such as regression analysis [12,13], decision trees [14,15], Naive Bayes [16], etc. These traditional machine learning algorithms have good interpretability and simple implementation and have achieved good results in the field of student performance prediction.…”

Section: Related Workmentioning

confidence: 99%

Predicting High-Risk Students Using Learning Behavior

et al. 2022

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Over the past few years, the growing popularity of online education has enabled there to be a large amount of students’ learning behavior data stored, which brings great opportunities and challenges to the field of educational data mining. Students’ learning performance can be predicted, based on students’ learning behavior data, so as to identify at-risk students who need timely help to complete their studies and improve students’ learning performance and online teaching quality. In order to make full use of these learning behavior data, a new prediction method was designed based on existing research. This method constructs a hybrid deep learning model, which can simultaneously obtain the temporal behavior information and the overall behavior information from the learning behavior data, so that it can more accurately predict the high-risk students. When compared with existing deep learning methods, the experimental results show that the proposed method offers better predicting performance.

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“…However, the algorithm was not accurately performing the classification with minimum time consumption. Several machine learning techniques were developed in Reference 19 for identifying the student's dropout‐prone. But it failed to improve the performance of accurate prediction with minimum error.…”

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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Early Prediction of Dropout and Final Exam Performance in an Online Statistics Course

Cited by 35 publications

References 33 publications

Current Failure Prediction for Final Examination via Nearest Neighbor Method using Past Trends of Weekly Online Testing

Current Failure Prediction for Final Examination via Nearest Neighbor Method using Past Trends of Weekly Online Testing

Predicting High-Risk Students Using Learning Behavior

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

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