Corrigendum to “Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization” [Computers in Human Behavior 58 (2016) 119–129]

Xing, Wanli; Chen, Xin; Stein, Jared; Marcinkowski, Michael

doi:10.1016/j.chb.2016.08.051

Cited by 5 publications

(4 citation statements)

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“…However, they all generally refer to the students' interactions with the course content and the traces of their activities on the VLE platform. They represent the natural behavioral features that the LMS Moodle platform keeps track of [31,32].…”

Section: Data Cleaning and Feature Selectionmentioning

confidence: 99%

“…A decision tree (DT) is defined by recursively partitioning the input space and defining a local model in each resulting region of input space. This algorithm divides this complex classification task into a set of simple classification tasks [31].…”

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confidence: 99%

“…Random Forest (RF) is a technique that uses multiple decision trees designed to perform the estimate using bootstrapping, resampling, and then applying majority voting or averaging [2,31]. 6.…”

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confidence: 99%

“…A neural network (NN) is a parallel processing system that takes advantage of the brain's structure and functions to maximize predictive accuracy. Deep learning NN takes data as input and then processes the data to compute the output across various layers [31,33].…”

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confidence: 99%

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Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques

Kabathova

Drlík

2021

Applied Sciences

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Early and precisely predicting the students’ dropout based on available educational data belongs to the widespread research topic of the learning analytics research field. Despite the amount of already realized research, the progress is not significant and persists on all educational data levels. Even though various features have already been researched, there is still an open question, which features can be considered appropriate for different machine learning classifiers applied to the typical scarce set of educational data at the e-learning course level. Therefore, the main goal of the research is to emphasize the importance of the data understanding, data gathering phase, stress the limitations of the available datasets of educational data, compare the performance of several machine learning classifiers, and show that also a limited set of features, which are available for teachers in the e-learning course, can predict student’s dropout with sufficient accuracy if the performance metrics are thoroughly considered. The data collected from four academic years were analyzed. The features selected in this study proved to be applicable in predicting course completers and non-completers. The prediction accuracy varied between 77 and 93% on unseen data from the next academic year. In addition to the frequently used performance metrics, the comparison of machine learning classifiers homogeneity was analyzed to overcome the impact of the limited size of the dataset on obtained high values of performance metrics. The results showed that several machine learning algorithms could be successfully applied to a scarce dataset of educational data. Simultaneously, classification performance metrics should be thoroughly considered before deciding to deploy the best performance classification model to predict potential dropout cases and design beneficial intervention mechanisms.

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Section: Data Cleaning and Feature Selectionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques

Kabathova

Drlík

2021

Applied Sciences

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A machine learning-based methodology to predict learners’ dropout, success or failure in MOOCs

Mourdi

Sadgal

Kabtane

et al. 2019

IJWIS

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Purpose Even if MOOCs (massive open online courses) are becoming a trend in distance learning, they suffer from a very high rate of learners’ dropout, and as a result, on average, only 10 per cent of enrolled learners manage to obtain their certificates of achievement. This paper aims to give tutors a clearer vision for an effective and personalized intervention as a solution to “retain” each type of learner at risk of dropping out. Design/methodology/approach This paper presents a methodology to provide predictions on learners’ behaviors. This work, which uses a Stanford data set, was divided into several phases, namely, a data extraction, an exploratory study and then a multivariate analysis to reduce dimensionality and to extract the most relevant features. The second step was the comparison between five machine learning algorithms. Finally, the authors used the principle of association rules to extract similarities between the behaviors of learners who dropped out from the MOOC. Findings The results of this work have given that deep learning ensures the best predictions in terms of accuracy, which is an average of 95.8 per cent, and is comparable to other measures such as precision, AUC, Recall and F1 score. Originality/value Many research studies have tried to tackle the MOOC dropout problem by proposing different dropout predictive models. In the same context, comes the present proposal with which the authors have tried to predict not only learners at a risk of dropping out of the MOOCs but also those who will succeed or fail.

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MOOC dropout prediction using machine learning techniques: Review and research challenges

Dalipi

Imran

Kastrati

2018

2018 IEEE Global Engineering Education Conference (EDUCON)

155

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MOOC represents an ultimate way to deliver educational content in higher education settings by providing high-quality educational material to the students throughout the world. Considering the differences between traditional learning paradigm and MOOCs, a new research agenda focusing on predicting and explaining dropout of students and low completion rates in MOOCs has emerged. However, due to different problem specifications and evaluation metrics, performing a comparative analysis of state-of-the-art machine learning architectures is a challenging task. In this paper, we provide an overview of the MOOC student dropout prediction phenomenon where machine learning techniques have been utilized. Furthermore, we highlight some solutions being used to tackle with dropout problem, provide an analysis about the challenges of prediction models, and propose some valuable insights and recommendations that might lead to developing useful and effective machine learning solutions to solve the MOOC dropout problem.

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Corrigendum to “Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization” [Computers in Human Behavior 58 (2016) 119–129]

Cited by 5 publications

References 0 publications

Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques

Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques

A machine learning-based methodology to predict learners’ dropout, success or failure in MOOCs

MOOC dropout prediction using machine learning techniques: Review and research challenges

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