Essay writing is widely used for student performance assessment. This paper presents a hybrid automatic essay scoring system (AES) for Arabic essays. The system attempts at saving the time teachers spend on reading and scoring Arabic essays. It utilizes latent semantic analysis (LSA) and three linguistic features (i.e., word stemming, number of words and number of spelling mistakes). This paper also describes an algorithm to determine the optimal reduced dimensionality used in LSA. To evaluate the performance of this system, an Arabic dataset was developed based on essays collected from college students. The experimental results show the effectiveness of using LSA for scoring Arabic essays, especially when combined with other linguistic features. The system shows that 96.72% of the test data are correctly scored and the correlation between automatic and manual scores is 0.78, which is close to the interhuman correlation of 0.7.
In this research, we provide an overview of the cloud computing model and discuss its applications for collaboration between academic institutions. Cloud computing is considered a typical paradigm that provides suitable, efficient network login to an appropriate pool of computing resources which can be provided and released with just nominal assiduity and service providers reciprocity. However, many organizations understand cloud computing in different ways. We briefly analyse the cloud computing applications in this research, and describe some current and accomplished educational and research products. Then, we go on to evaluate the successful applications of cloud computing models at educational institutions, and the different ways to implement cloud computing. Finally, we present different education applications for education infrastructures which are implemented for academic use.
Sentiment is currently one of the most emerging areas of research due to the large amount of web content coming from social networking websites. Sentiment analysis is a crucial process for recommending systems for most people. Generally, the purpose of sentiment analysis is to determine an author’s attitude toward a subject or the overall tone of a document. There is a huge collection of studies that make an effort to predict how useful online reviews will be and have produced conflicting results on the efficacy of different methodologies. Furthermore, many of the current solutions employ manual feature generation and conventional shallow learning methods, which restrict generalization. As a result, the goal of this research is to develop a general approach using transfer learning by applying the “BERT (Bidirectional Encoder Representations from Transformers)”-based model. The efficiency of BERT classification is then evaluated by comparing it with similar machine learning techniques. In the experimental evaluation, the proposed model demonstrated superior performance in terms of outstanding prediction and high accuracy compared to earlier research. Comparative tests conducted on positive and negative Yelp reviews reveal that fine-tuned BERT classification performs better than other approaches. In addition, it is observed that BERT classifiers using batch size and sequence length significantly affect classification performance.
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