Computer Based Assessment:an Innovation to Language Assessment

With the integration of technology and education,classroom teaching has experienced a substantial change.In order to improve the efficiency of assessment,computer has been adapted as a promising instrument in assessment area.This paper aims at evaluating the role that computer can play in language assessment.

Science Letters:Crossover randomized controlled trial of the electronic version of the Chinese SF-36

Objectives:to verify the feasibility and reliability of the electronic version of Chinese SF-36 based on the Quality-of-Life-Recorder. Design: A crossover randomized controlled trial, comparing a paper-based and an electronic version of the Chinese SF-36, was conducted. According to generated random numbers, interviewees were asked to fill out either the electronic version or the paper version first. The second version was filled in after a pause of at least 10 min. Settings and participants: One group of 100 medical students at the School of Medicine of Zhejiang University and the other group of 50 outpatients at a clinic for general practice in Hangzhou City (China) were eventually recruited in this study. Results: The acceptance of the electronic version was good (60% of medical students and 84% of outpatients preferred the electronic version). At the level of eight-scale scores, the mean-difference for each scale (except for general health) between the two versions was less than 5%. At the level of 36 questions, the percentage of "exact agreement" ranged within 64%～99%; the percentage of "global agreement" ranged within 72%～99%; 77% of the kappa coefficients demonstrated "good/excellent agreement" and 23% of the kappa coefficients demonstrated "medium agreement". Conclusion: This study, for the first time, can provide empirical basis for the confirmation of the feasibility and reliability of the electronic version of the Chinese SF-36 and may provide an impulse towards widespread deployment of the Quality-of-Life-Recorder in Chinese populations.

Acral melanoma detection using dermoscopic images and convolutional neural networks

Acral melanoma(AM)is a rare and lethal type of skin cancer.It can be diagnosed by expert dermatologists,using dermoscopic imaging.It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers.Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma.However,to date,limited research has been conducted on the classification of melanoma subtypes.The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes,such as,AM.In this study,we present a novel deep learning model,developed to classify skin cancer.We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions.Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection.Our custombuilt model is a seven-layered deep convolutional network that was trained from scratch.Additionally,transfer learning was utilized to compare the performance of our model,where AlexNet and ResNet-18 were modified,fine-tuned,and trained on the same dataset.We achieved improved results from our proposed model with an accuracy of more than 90%for AM and benign nevus,respectively.Additionally,using the transfer learning approach,we achieved an average accuracy of nearly 97%,which is comparable to that of state-of-the-art methods.From our analysis and results,we found that our model performed well and was able to effectively classify skin cancer.Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.