Transfer Learning from Deep Neural Networks for Predicting Student Performance

Tsiakmaki, Maria; Kostopoulos, Georgios; Kotsiantis, Sotiris; Ragos, Omiros

doi:10.3390/app10062145

Cited by 95 publications

(57 citation statements)

References 20 publications

(24 reference statements)

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“…TL has been a useful ML method in which a pre-trained model of CNN is reused to take advantage of its weights to take them into account as initialization for a new CNN model for a different purpose [24]. There exist two primary ways to use TL from a model: reuse a model as a feature extractor and use a new totally different classifier; or reuse the model to perform Fine-Tuning (FT).…”

Section: Transfer Learning and Chest Diseasesmentioning

confidence: 99%

A Transfer Learning Method for Pneumonia Classification and Visualization

et al. 2020

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Pneumonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists.

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Section: Transfer Learning and Chest Diseasesmentioning

confidence: 99%

A Transfer Learning Method for Pneumonia Classification and Visualization

et al. 2020

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“…The authors in [10] investigated the effectiveness of transfer learning from deep neural networks for the task of students' performance prediction in higher education. Experiments were conducted based on data originating from five compulsory courses of two undergraduate programs.…”

Section: A Literature Review On Student Performance Predictionmentioning

confidence: 99%

“…[16] also presented a review on machine learning based approaches for predicting student performance. Other approaches can be found in [17]- [20].…”

Section: A Literature Review On Student Performance Predictionmentioning

confidence: 99%

Deep Learning with Data Transformation and Factor Analysis for Student Performance Prediction

Điện¹,

Hoai²,

Thanh-Hai³

et al. 2020

IJACSA

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Student performance prediction is one of the most concerning issues in the field of education and training, especially educational data mining. The prediction supports students to select courses and design appropriate study plans for themselves. Moreover, student performance prediction enables lecturers as well as educational managers to indicate what students should be monitored and supported to complete their programs with the best results. These supports can reduce formal warnings and expulsions from universities due to students' poor performance. This study proposes a method to predict student performance using various deep learning techniques. Also, we analyze and present several techniques for data pre-processing (e.g., Quantile Transforms and MinMax Scaler) before fetching them into well-known deep learning models such as Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) to do prediction tasks. Experiments are built on 16 datasets related to numerous different majors with appropriately four million samples collected from the student information system of a Vietnamese multidisciplinary university. Results show that the proposed method provides good prediction results, especially when using data transformation. The results are feasible for applying to practical cases.

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“…Other transfer learning techniques use larger datasets as source task datasets [16]. Our study is similar to this approach, to evaluate the effectiveness of transfer learning methods in the repurposed heterogeneous domain [17].…”

Section: Introductionmentioning

confidence: 99%

Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle

Magotra

Kim

2020

Applied Sciences

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Transfer learning algorithms have been widely studied for machine learning in recent times. In particular, in image recognition and classification tasks, transfer learning has shown significant benefits, and is getting plenty of attention in the research community. While performing a transfer of knowledge among source and target tasks, homogeneous dataset is not always available, and heterogeneous dataset can be chosen in certain circumstances. In this article, we propose a way of improving transfer learning efficiency, in case of a heterogeneous source and target, by using the Hebbian learning principle, called Hebbian transfer learning (HTL). In computer vision, biologically motivated approaches such as Hebbian learning represent associative learning, where simultaneous activation of brain cells positively affect the increase in synaptic connection strength between the individual cells. The discriminative nature of learning for the search of features in the task of image classification fits well to the techniques, such as the Hebbian learning rule—neurons that fire together wire together. The deep learning models, such as convolutional neural networks (CNN), are widely used for image classification. In transfer learning, for such models, the connection weights of the learned model should adapt to new target dataset with minimum effort. The discriminative learning rule, such as Hebbian learning, can improve performance of learning by quickly adapting to discriminate between different classes defined by target task. We apply the Hebbian principle as synaptic plasticity in transfer learning for classification of images using a heterogeneous source-target dataset, and compare results with the standard transfer learning case. Experimental results using CIFAR-10 (Canadian Institute for Advanced Research) and CIFAR-100 datasets with various combinations show that the proposed HTL algorithm can improve the performance of transfer learning, especially in the case of a heterogeneous source and target dataset.

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Transfer Learning from Deep Neural Networks for Predicting Student Performance

Cited by 95 publications

References 20 publications

A Transfer Learning Method for Pneumonia Classification and Visualization

A Transfer Learning Method for Pneumonia Classification and Visualization

Deep Learning with Data Transformation and Factor Analysis for Student Performance Prediction

Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle

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