MOOC Dropouts: A Multi-system Classifier

Vitiello, Massimo; Walk, Simon; Chang, Vanessa; Hernández, Rocael; Helić, Denis; Guetl, Christian

doi:10.1007/978-3-319-66610-5_22

Cited by 15 publications

(15 citation statements)

References 16 publications

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“…The proposed transfer learning methods proved to be quite effective for improving the dropout prediction, in terms of Area Under Curve (AUC) scores, compared to the baseline method. In a similar study, Vitiello et al [25] examined how models trained on a MOOC system could be transferred to another. Therefore, they built a unified model allowing the early prediction of dropout students across two different systems.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning from Deep Neural Networks for Predicting Student Performance

et al. 2020

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Transferring knowledge from one domain to another has gained a lot of attention among scientists in recent years. Transfer learning is a machine learning approach aiming to exploit the knowledge retrieved from one problem for improving the predictive performance of a learning model for a different but related problem. This is particularly the case when there is a lack of data regarding a problem, but there is plenty of data about another related one. To this end, the present study intends to investigate the effectiveness of transfer learning from deep neural networks for the task of students’ performance prediction in higher education. Since building predictive models in the Educational Data Mining field through transfer learning methods has been poorly studied so far, we consider this study as an important step in this direction. Therefore, a plethora of experiments were conducted based on data originating from five compulsory courses of two undergraduate programs. The experimental results demonstrate that the prognosis of students at risk of failure can be achieved with satisfactory accuracy in most cases, provided that datasets of students who have attended other related courses are available.

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Section: Related Workmentioning

confidence: 99%

Transfer Learning from Deep Neural Networks for Predicting Student Performance

et al. 2020

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“…In order to predict, detect and intervene in dropout patterns, contemporary researchers resorted to machine learning, data mining and learning analytics (Kloft, Stiehler, Zheng & Pinkwart, 2014). For instance, these methods have been used to build a dropout prediction model (Xing, Chen, Stein & Marcinkowski, 2016), to identify and classify at-risk dropout learners (Vitiello et al, 2017) and to examine learner behavior patterns (Hong, Wei & Yang, 2017). In short, existing literature demonstrates an increasing interest in and awareness of dropout rates which have been analyzed also through cutting-edge analytical solutions.…”

Section: Literature Reviewmentioning

confidence: 99%

Dropout Patterns and Cultural Context in Online Networked Learning Spaces

Bozkurt

Akbulut

2019

Open Praxis

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Dropout is a major concern in networked learning practices, however, little is known about the issue within the perspective of cultural contexts. On this basis, cultural context and dropout patterns were examined through a mixed-methods approach in which social network analysis and two-way between-group comparisons (culture vs. dropout) were conducted. The sample comprised 179 MOOC learners who were active in a networked extension of the Introduction to Open Education MOOC (#openEDMOOC). The dependent variables of interest were centrality metrics, whereas the independent variables were dropout (i.e., yes-no) and cultural contexts (i.e., high-low). The findings of the social network analysis suggested that non-dropout learners hold central positions in the network. Furthermore, learners from high cultural contexts tend to drop out, whereas those from low contexts tend not to drop out.

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“…MOOCs emerged as the natural solution to offer distance education with online learning enormously changing over the past years. MOOCs are widely used because of a potentially unlimited enrollment, nongeographical limitation, free accessibility for majority of courses, and structure resemblance with traditional lectures [1,2]. Simply, they allow learners to learn anytime and anywhere at their own pace.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies applied traditional machine learning algorithms to it. These algorithms include logistic regression [14][15][16][17][18], support vector machine [19], decision tree [20], boosted decision trees [2,21,22], and hidden Markov model [23]. However, there exists the problem of low accuracy leading to misidentification of at-risk learners, those who may quit courses.…”

Section: Introductionmentioning

confidence: 99%

MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine

Jing

Feng

Sun

et al. 2019

Mathematical Problems in Engineering

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Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. High dropout rate is a universal but unsolved problem in MOOCs. Dropout prediction has received much attention recently. A previous study reported the problem of learning behavior discrepancy leading to a wide range of fluctuation of prediction results. Besides, previous methods require iterative training which is time intensive. To address these problems, we propose DT-ELM, a novel hybrid algorithm combining decision tree and extreme learning machine (ELM), which requires no iterative training. The decision tree selects features with good classification ability. Further, it determines enhanced weights of the selected features to strengthen their classification ability. To achieve accurate prediction results, we optimize ELM structure by mapping the decision tree to ELM based on the entropy theory. Experimental results on the benchmark KDD 2015 dataset demonstrate the effectiveness of DT-ELM, which is 12.78%, 22.19%, and 6.87% higher than baseline algorithms in terms of accuracy, AUC, and F1-score, respectively.

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MOOC Dropouts: A Multi-system Classifier

Cited by 15 publications

References 16 publications

Transfer Learning from Deep Neural Networks for Predicting Student Performance

Transfer Learning from Deep Neural Networks for Predicting Student Performance

Dropout Patterns and Cultural Context in Online Networked Learning Spaces

MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine

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