Classification of Learning Sentiments of College Students Based on Topic Discussion Texts of Online Learning Platforms

Niu, Mingzhe

doi:10.3991/ijet.v17i24.35951

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Different from traditional machine learning methods, deep learning can automatically obtain high-level semantic features from input data and has strong expression ability. Some researchers use convolutional neural networks (CNN) for text classification [10]. Yoon Kim proposed a sentence level text classification model based on a convolutional neural network.…”

Section: Text Classification Algorithm Based On Deep Learningmentioning

confidence: 99%

CNN-Bi-LSTM Model for MOOC Forum Post Classification

Zhang,

Sun

2023

Int. J. Emerg. Technol. Learn.

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The discussion forum of the massive open online course (MOOC) is a platform for students to communicate with teachers, teaching assistants, and platform managers. It is one of the important factors related to course quality. A reasonable classification of discussion posts in the forum will help students better communicate and solve problems, so as to improve the quality of teaching. Aiming at the classification of discussion forum posts, this paper proposes a text classification model integrating convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM). Firstly, the user types and behavior characteristics are analyzed to build the taxonomy. The taxonomy includes three categories: course related, teacher related and platform related. Then, a text classification model is constructed based on CNN and Bi-LSTM. In order to verify the effectiveness of the proposed model, it is applied to the classification of 19285 discussion posts from the MOOC platform of icourse163.org. The overall classification accuracy of the proposed model is 93.6%, which is 12%, 10%, and 8% higher than traditional machine learning methods, CNN and Bi-LSTM, respectively. The model is used for automatic text classification in MOOC discussion forum, which can provide effective help and support for learners, teachers and platform managers, and improve the automation level of MOOC platform.

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Section: Text Classification Algorithm Based On Deep Learningmentioning

confidence: 99%

CNN-Bi-LSTM Model for MOOC Forum Post Classification

Zhang,

Sun

2023

Int. J. Emerg. Technol. Learn.

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“…Sentiment analysis applications have already been implemented in a variety of sectors. Nevertheless, one of the domains in which these systems has recently been gaining ground is education [9,10,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Using Sentiment Analysis to Explore Student Feedback: A Lexical Approach

Faizi

2023

Int. J. Emerg. Technol. Learn.

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Given the increasing abundance of online courses over the last couple of years, new forms of student feedback, which are less frequently used by teachers, have been generated in massive amounts. Nonetheless, extracting and processing this student generated content manually is costly and time consuming. In this respect, our objective in this paper is to propose a lexical-based approach that can predict the underlying sentiments of each student review, thus, enabling teachers to assess to what extent are students satisfied with the online learning resources and teaching practices. To enhance the performance of the proposed approach, a new education sentiment lexicon was built and incorporated into the model. After its implementation on a dataset that was extracted from the Web, this sentiment analysis lexical approach has proven to correctly predict the sentiment polarities of the great majority (i.e. 86.45%) of student feedback.

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“…The objective of these architectures is to train the machine to detect the presence of diseases in various types of medical images, such as x-rays, chest radiographs (CXRs), computerized tomography (CT) scans, magnetic resonance imaging (MRI) scans, and so forth. CNNs are used in many fields, including robotics [6], facial expressions [7], education [8] [9], and also in the medical field such as the detection of brain tumors [10] [11] [12], prostate cancer [13] [14], lung nodules [15], COVID-19 [16] [17], and breast cancer [18] [19].…”

Section: Introductionmentioning

confidence: 99%

Discriminative Approach Lung Diseases and COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks: A Promising Approach for Accurate Diagnosis

Benradi,

Bouganssa,

Chater

et al. 2023

Int. J. Onl. Eng.

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Medical imaging treatment is one of the best-known computer science disciplines. It can be used to detect the presence of several diseases such as skin cancer and brain tumors, and since the arrival of the coronavirus (COVID-19), this technique has been used to alleviate the heavy burden placed on all health institutions and personnel, given the high rate of spread of this virus in the population. One of the problems encountered in diagnosing people suspected of having contracted COVID-19 is the difficulty of distinguishing symptoms due to this virus from those of other diseases such as influenza, as they are similar. This paper proposes a new approach to distinguishing between lung diseases and COVID-19 by analyzing chest x-ray images using a convolutional neural network (CNN) architecture. To achieve this, pre-processing was carried out on the dataset using histogram equalization, and then we trained two sub-datasets from the dataset using the Train et Test, the first to be used in the training phase and the second to be used in the model validation phase. Then a CNN architecture composed of several convolution layers and fully connected layers was deployed to train our model. Finally, we evaluated our model using two different metrics: the confusion matrix and the receiver operating characteristic. The simulation results recorded are satisfactory, with an accuracy rate of 96.27%.

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Classification of Learning Sentiments of College Students Based on Topic Discussion Texts of Online Learning Platforms

Cited by 5 publications

References 23 publications

CNN-Bi-LSTM Model for MOOC Forum Post Classification

CNN-Bi-LSTM Model for MOOC Forum Post Classification

Using Sentiment Analysis to Explore Student Feedback: A Lexical Approach

Discriminative Approach Lung Diseases and COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks: A Promising Approach for Accurate Diagnosis

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