Automatic assessment of exams is widely preferred by educators than multiple-choice because its efficiency in measuring student performance, lack subjectivity when evaluating response, and faster evaluation time the consuming manual evaluation. In this study, a new approach for Short Answer Grading (ASAG) proposed using MaLSTM sense vectors obtained SemSpace, synset based embedding method built...

We present a fast, simple, and high-accuracy short answer grading system. Given a shortanswer question and its correct answer, key measures of the correctness of a student response can be derived from its semantic similarity with the correct answer. Our supervised model (1) utilizes recent advances in the identification of short-text similarity, and (2) augments text similarity features with ke...

Assessment is a key component of the teaching and learning process. In most Algerian Universities, assessing a student’s answer to an open ended question, even if it is a short answer question, is a difficult and time-consuming activity. In order to enhance the learning process quality and the global student evaluation process and to highly reduce the assessment time and difficulties, most Alge...

In this paper, we explore unsupervised techniques for the task of automatic short answer grading. We compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. Overall...

Automatic short-answer grading promises improved student feedback at reduced teacher effort both during and after instruction. Automated grading is, however, controversial in high-stakes testing and complex systems can be difficult to set up by non-experts, especially for frequently changing questions. We propose a versatile, domain-independent system that assists manual grading by pre-sorting ...

We address the problem of automatic short answer grading, evaluating a collection of approaches inspired by recent advances in distributional text representations. In addition, we propose an unsupervised approach for determining text similarity using one-to-many alignment of word vectors. We evaluate the proposed technique across two datasets from different domains, namely, computer science and...

Many studies in cognitive psychology have shown stronger learning benefits from short-answer questions than multiplechoice questions in student practices. However, multiple-choice is still, by far, the most popular form of assessment questions due to ease of grading. A hybrid method, in which students must first provide a short-answer response and then select from a list of multiplechoice optio...

Most automatic scoring systems use pattern based that requires a lot of hard and tedious work. These systems work in a supervised manner where predefined patterns and scoring rules are generated. This paper presents a different unsupervised approach which deals with students’ answers holistically using text to text similarity. Different String-based and Corpus-based similarity measures were tes...