Development of an Answer Verification System for Helping Visually Impaired People to Learn a Spreadsheet Software We have developed an answer verification system for helping visually impaired people who are learning how to use a spreadsheet software by answering problemsolving exercises. Visually impaired learners have difficulty in verifying their answers on their own with conventional software tools. Hence, the answer verification system is designed to provide feedback to the learners with respect to the numbers of errors in their answers, the error locations, and the reasons behind the errors. The messages output from our system can be read out by a screen reader, and our system can treat various kinds of problemsolving exercises of a spreadsheet software. We conducted an evaluation study of our system both with the teachers and the students to assess its usability and functionality. Based on the results of this study, we generated an improved version of our system, which is also described here.

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.