Adult higher education and higher education＇ s self-study exam are parts of the continuing education system, and also very important parts of the education system in China. Now, in addition to the ordinary full-time ...Adult higher education and higher education＇ s self-study exam are parts of the continuing education system, and also very important parts of the education system in China. Now, in addition to the ordinary full-time higher education, these two education systems are important for the education system in China, and also are important ways for people to learn knowledge and get a diploma. However, a great number of students raise many questions about the difference between the two systems, and feel highly confused about choosing adult higher education or higher education＇ s self-study exam currently. In this paper, the similarities and differences between adult higher education and higher education ＇ s self-study exam are discussed, so that a good choice is easily made by students before one of them is applied.展开更多
Based on existing problems in the course of development of self-study examination of agricultural science disciplines,this paper analyzes logical conditions for its sustainable development.Major logical conditions inc...Based on existing problems in the course of development of self-study examination of agricultural science disciplines,this paper analyzes logical conditions for its sustainable development.Major logical conditions include requirement for free and comprehensive development of individuals;requirement for social institutional evolution and change;requirement for fairness and justice of education and development;as well as requirement for better-established organization system and higher social reputation.Finally,it presents basic measures for sustainable development of self-study examination of agricultural science disciplines,including carrying on reasonable core elements of self-study examination system,adjusting structure of discipline,improving social assistance system,and expanding service space.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
The rapid shift to online education has introduced significant challenges to maintaining academic integrity in remote assessments,as traditional proctoring methods fall short in preventing cheating.The increase in che...The rapid shift to online education has introduced significant challenges to maintaining academic integrity in remote assessments,as traditional proctoring methods fall short in preventing cheating.The increase in cheating during online exams highlights the need for efficient,adaptable detection models to uphold academic credibility.This paper presents a comprehensive analysis of various deep learning models for cheating detection in online proctoring systems,evaluating their accuracy,efficiency,and adaptability.We benchmark several advanced architectures,including EfficientNet,MobileNetV2,ResNet variants and more,using two specialized datasets(OEP and OP)tailored for online proctoring contexts.Our findings reveal that EfficientNetB1 and YOLOv5 achieve top performance on the OP dataset,with EfficientNetB1 attaining a peak accuracy of 94.59% and YOLOv5 reaching a mean average precision(mAP@0.5)of 98.3%.For the OEP dataset,ResNet50-CBAM,YOLOv5 and EfficientNetB0 stand out,with ResNet50-CBAMachieving an accuracy of 93.61% and EfficientNetB0 showing robust detection performance with balanced accuracy and computational efficiency.These results underscore the importance of selectingmodels that balance accuracy and efficiency,supporting scalable,effective cheating detection in online assessments.展开更多
Cheat sheets are so named because they may be used by students without the instructor's knowledge to cheat on a test.Student learning is greatly enhanced by studying prior to an exam.Allowing students to prepare a...Cheat sheets are so named because they may be used by students without the instructor's knowledge to cheat on a test.Student learning is greatly enhanced by studying prior to an exam.Allowing students to prepare a cheat sheet for the exam helps structure this study time and deepens learning.The crib sheet is well defined:one double-sided page of A4.An award for the best and most creative cheat sheet allows the instructor to appreciate the students' efforts.Using the cheat sheet can not only reduces student anxiety during testing but also incease student learning efficiency.展开更多
文摘Adult higher education and higher education＇ s self-study exam are parts of the continuing education system, and also very important parts of the education system in China. Now, in addition to the ordinary full-time higher education, these two education systems are important for the education system in China, and also are important ways for people to learn knowledge and get a diploma. However, a great number of students raise many questions about the difference between the two systems, and feel highly confused about choosing adult higher education or higher education＇ s self-study exam currently. In this paper, the similarities and differences between adult higher education and higher education ＇ s self-study exam are discussed, so that a good choice is easily made by students before one of them is applied.
文摘Based on existing problems in the course of development of self-study examination of agricultural science disciplines,this paper analyzes logical conditions for its sustainable development.Major logical conditions include requirement for free and comprehensive development of individuals;requirement for social institutional evolution and change;requirement for fairness and justice of education and development;as well as requirement for better-established organization system and higher social reputation.Finally,it presents basic measures for sustainable development of self-study examination of agricultural science disciplines,including carrying on reasonable core elements of self-study examination system,adjusting structure of discipline,improving social assistance system,and expanding service space.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R752),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The rapid shift to online education has introduced significant challenges to maintaining academic integrity in remote assessments,as traditional proctoring methods fall short in preventing cheating.The increase in cheating during online exams highlights the need for efficient,adaptable detection models to uphold academic credibility.This paper presents a comprehensive analysis of various deep learning models for cheating detection in online proctoring systems,evaluating their accuracy,efficiency,and adaptability.We benchmark several advanced architectures,including EfficientNet,MobileNetV2,ResNet variants and more,using two specialized datasets(OEP and OP)tailored for online proctoring contexts.Our findings reveal that EfficientNetB1 and YOLOv5 achieve top performance on the OP dataset,with EfficientNetB1 attaining a peak accuracy of 94.59% and YOLOv5 reaching a mean average precision(mAP@0.5)of 98.3%.For the OEP dataset,ResNet50-CBAM,YOLOv5 and EfficientNetB0 stand out,with ResNet50-CBAMachieving an accuracy of 93.61% and EfficientNetB0 showing robust detection performance with balanced accuracy and computational efficiency.These results underscore the importance of selectingmodels that balance accuracy and efficiency,supporting scalable,effective cheating detection in online assessments.
文摘Cheat sheets are so named because they may be used by students without the instructor's knowledge to cheat on a test.Student learning is greatly enhanced by studying prior to an exam.Allowing students to prepare a cheat sheet for the exam helps structure this study time and deepens learning.The crib sheet is well defined:one double-sided page of A4.An award for the best and most creative cheat sheet allows the instructor to appreciate the students' efforts.Using the cheat sheet can not only reduces student anxiety during testing but also incease student learning efficiency.
