Predicting Student Dropout in Self-Paced MOOC Course Using Random Forest Model

Dass, Sheran; Gary, Kevin; Cunningham, James

doi:10.3390/info12110476

Cited by 33 publications

(26 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some publications regarding data mining (DM) on predicting academic success focus on distance learning platforms and tutoring systems driven by AI [3][4][5]. Queiroga et al [3] developed a solution using only students' interactions with the virtual learning environment and its derivative features for early prediction of at-risk students in a Brazilian distance technical high school course.…”

Section: Background Theory and Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting Students Dropout: A UTAD University Study

et al. 2022

View full text Add to dashboard Cite

In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using the academic grades. Four different machine learning techniques are presented and analyzed. The dataset consists of 331 students who were previously enrolled in the Computer Engineering degree at the Universidade de Trás-os-Montes e Alto Douro (UTAD). The study aims to detect students who may prematurely drop out using existing methods. The most relevant data features were identified using the Permutation Feature Importance technique. In the second phase, several methods to predict the dropouts were applied. Then, each machine learning technique’s results were displayed and compared to select the best approach to predict academic dropout. The methods used achieved good results, reaching an F1-Score of 81% in the final test set, concluding that students’ marks somehow incorporate their living conditions.

show abstract

Section: Background Theory and Literature Reviewmentioning

confidence: 99%

“…The dataset stores activity students' records about which course they are enrolled in. Dass et al [5] presented a model to predict the student dropout in online courses considering features of daily learning progress. They used a Random Forest Model, obtaining 87.5% as the F1-score.…”

Section: Background Theory and Literature Reviewmentioning

confidence: 99%

Forecasting Students Dropout: A UTAD University Study

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Similarly, Gow et al (2012) proposed a user model built from a semi-automatic and unsupervised learning approach combined with multi-class Linear Discriminant Analysis (LDA) (McLachlan, 2004) applied to the logs. Dass et al (2021) have used the random Forest model to predict the dropout of students from a MOOC course given a set of features engineered from student daily learning progress.…”

Section: Machine Learning and Deep Learning/ai Approaches For User Mo...mentioning

confidence: 99%

Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments

Tato

Nkambou

2022

Front. Artif. Intell.

View full text Add to dashboard Cite

Machine learning models are biased toward data seen during the training steps. The models will tend to give good results in classes where there are many examples and poor results in those with few examples. This problem generally occurs when the classes to predict are imbalanced and this is frequent in educational data where for example, there are skills that are very difficult or very easy to master. There will be less data on students that correctly answered questions related to difficult skills and who incorrectly answered those related to skills easy to master. In this paper, we tackled this problem by proposing a hybrid architecture combining Deep Neural Network architectures— especially Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN)—with expert knowledge for user modeling. The proposed solution uses attention mechanism to infuse expert knowledge into the Deep Neural Network. It has been tested in two contexts: knowledge tracing in an intelligent tutoring system (ITS) called Logic-Muse and prediction of socio-moral reasoning in a serious game called MorALERT. The proposed solution is compared to state-of-the-art machine learning solutions and experiments show that the resulting model can accurately predict the current student's knowledge state (in Logic-Muse) and thus enable an accurate personalization of the learning process. Other experiments show that the model can also be used to predict the level of socio-moral reasoning skills (in MorALERT). Our findings suggest the need for hybrid neural networks that integrate prior expert knowledge (especially when it is necessary to compensate for the strong dependency—of deep learning methods—on data size or the possible unbalanced datasets). Many domains can benefit from such an approach to building models that allow generalization even when there are small training data.

show abstract

“…The proposed LSTM outperforms baseline SVM, ANN, and LR, with an accuracy of 93%. To determine student dropout in self-paced MOOC courses, Dass et al [25] proposed an RF-based approach. With an accuracy of 87.5%, the proposed system could predict the student dropout rate in the MOOC course.…”

Section: Related Workmentioning

confidence: 99%

Predicting Students’ Academic Performance with Conditional Generative Adversarial Network and Deep SVM

Sarwat

Ullah

Sadiq

et al. 2022

Sensors

View full text Add to dashboard Cite

The availability of educational data obtained by technology-assisted learning platforms can potentially be used to mine student behavior in order to address their problems and enhance the learning process. Educational data mining provides insights for professionals to make appropriate decisions. Learning platforms complement traditional learning environments and provide an opportunity to analyze students’ performance, thus mitigating the probability of student failures. Predicting students’ academic performance has become an important research area to take timely corrective actions, thereby increasing the efficacy of education systems. This study proposes an improved conditional generative adversarial network (CGAN) in combination with a deep-layer-based support vector machine (SVM) to predict students’ performance through school and home tutoring. Students’ educational datasets are predominantly small in size; to handle this problem, synthetic data samples are generated by an improved CGAN. To prove its effectiveness, results are compared with and without applying CGAN. Results indicate that school and home tutoring combined have a positive impact on students’ performance when the model is trained after applying CGAN. For an extensive evaluation of deep SVM, multiple kernel-based approaches are investigated, including radial, linear, sigmoid, and polynomial functions, and their performance is analyzed. The proposed improved CGAN coupled with deep SVM outperforms in terms of sensitivity, specificity, and area under the curve when compared with solutions from the existing literature.

show abstract

Predicting Student Dropout in Self-Paced MOOC Course Using Random Forest Model

Cited by 33 publications

References 68 publications

Forecasting Students Dropout: A UTAD University Study

Forecasting Students Dropout: A UTAD University Study

Infusing Expert Knowledge Into a Deep Neural Network Using Attention Mechanism for Personalized Learning Environments

Predicting Students’ Academic Performance with Conditional Generative Adversarial Network and Deep SVM

Contact Info

Product

Resources

About