A New Learning Resource Recommendation Method for Improving the Efficiency of  Students' Online Independent Learning

Shi, Lei; Fan, Cunjia

doi:10.3991/ijet.v18i05.38503

Cited by 2 publications

(2 citation statements)

References 27 publications

(27 reference statements)

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“…Before students can receive a final course certificate, MOOCs like Coursera and EdX frequently require them to pass online exams [6], [7]. The subject of course evaluation has received the most attention in online education studies [8], [9]. The lack of direct student-teacher interaction is problematic for online course evaluation [10] Online exams require proctoring, like those taken in lecture halls and colleges [11].…”

Section: Introductionmentioning

confidence: 99%

Abnormal Behavior Detection in Online Exams Using Deep Learning and Data Augmentation Techniques

Alkhalisy,

Abid

2023

Int. J. Onl. Eng.

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Massive open online courses (MOOCs) and other forms of distance learning have gained popularity in recent years. The success of remote online exam proctoring determines the integrity of the exam. Deep-learning-powered proctoring services have also grown in popularity. A large number of samples are needed for deep-learning training. The network’s generalization ability is poor due to insufficient training data or an uneven lack of variation. This study illustrates how to analyze students’ anomalous behavior by utilizing a YOLOv5 deep model trained using newly produced dataset. To overcome insufficient training data for deep-learning-related issues, this paper proposes a data-augmentation method based on semantic segmentation. The MobileNetV3 model was used to get an image semantic segmentation mask, which was used to get a binary mask, which in turn was used to replace the image background by using conditional subtraction with randomly selected background images. Finally, randomly pixel-based color augmentation was added to the resulting image. The behavioral detection model used in this study achieved 0.98 mean average precision (mAP) on the produced dataset, showing acceptable detection precision. The experimental findings indicate that the suggested augmentation method improves behavioral detection precision by more than 0.3%.

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Section: Introductionmentioning

confidence: 99%

Abnormal Behavior Detection in Online Exams Using Deep Learning and Data Augmentation Techniques

Alkhalisy,

Abid

2023

Int. J. Onl. Eng.

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“…However, due to factors such as traditional education models and exam oriented systems, students' lack of comprehensive understanding of their majors and unreasonable school curriculum, there are many problems in online autonomous teaching in universities that need to be addressed. This article aims to provide effective suggestions to help improve the quality and efficiency of online classroom learning and enhance its application value, which can help solve the current difficulties faced by college students in online autonomous development and thus promote the healthy and sustainable development of higher education [1][2].…”

Section: Introductionmentioning

confidence: 99%

Current Situation Dissection and Ability Cultivation Strategies of Online Autonomous Learning for College Students

2023

aetp

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With the progress of society, college students have a strong demand for new media technologies such as the internet, digitization and information resources, which has led to the formation of autonomous learning models in the internet environment. This article aimed to study the cultivation strategies of online autonomous learning ability for college students. Through the investigation of the online learning platform, the problems existing in the current situation and their influencing factors could be understood. Then the hypothesis was verified based on the theoretical model, constructing the model for practical application effectiveness testing. Finally, based on the data results, a preliminary summary was made that the participation rate of college students in online extracurricular expansion classes was not very high, basically maintaining between 73% and 78%. However, due to the rich and diverse online courses, most students were able to actively communicate and exchange with teachers. What they need to do was how to access the internet and use online platforms to acquire and internalize knowledge. This indicated that most students have mastered the ways to acquire knowledge on online platforms and had a high level of understanding of them.

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A New Learning Resource Recommendation Method for Improving the Efficiency of Students' Online Independent Learning

Cited by 2 publications

References 27 publications

Abnormal Behavior Detection in Online Exams Using Deep Learning and Data Augmentation Techniques

Abnormal Behavior Detection in Online Exams Using Deep Learning and Data Augmentation Techniques

Current Situation Dissection and Ability Cultivation Strategies of Online Autonomous Learning for College Students

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