The Application of Deep Learning in Automated Essay Evaluation The shift from Automated Essay Scoring (AES) to Automated Essay Evaluation (AEE) indicates that natural language processing (NLP) researchers respond positively to the request from language teaching field. Writers and teachers need more feedback about writing content and language use from AEE software beside a precise evaluative score. This requirement can be met by the neural network based deep learning technique. Deep learning has been applied in many NLP fields and great success has been made, such as machine translation, emotional analysis, question answering, and automatic summarization. Neural network based deep learning is suitable for AES research and development since AES requires mainly a precise score of writing quality. This can be accomplished with human accurately scored essays as input and scoring model as output with deep learning technology. However, AEE requires more than a score and deep learning can be used to select linguistically meaningful features for writing quality and apply in the AEE model construction. Related experiments already show the feasibility and further research is worth exploring.

Towards Automated Evaluation of Handwritten Assessments Automated evaluation of handwritten answers has been a challenging problem for scaling the education system for many years. Speeding up the evaluation remains as the major bottleneck for enhancing the throughput of instructors. This paper describes an effective method for automatically evaluating the short descriptive handwritten answers from the digitized images. Our goal is to evaluate a studentu0027s handwritten answer by assigning an evaluation score that is comparable to the humanassigned scores. Existing works in this domain mainly focused on evaluating handwritten essays with handcrafted, nonsemantic features. Our contribution is twofold: 1) we model this problem as a selfsupervised, featurebased classification problem, which can finetune itself for each question without any explicit supervision. 2) We introduce the usage of semantic analysis for autoevaluation in handwritten text space using the combination of Information Retrieval and Extraction (IRE) and, Natural Language Processing (NLP) methods to derive a set of useful features. We tested our method on three datasets created from various domains, using the help of students of different age groups. Experiments show that our method performs comparably to that of human evaluators.