Automatic short answer grading (ASAG) techniques are designed to automatically assess short answers written in natural language having a length of a few words to a few sentences. In this paper, we report an intriguing finding that the set of short answers to a question, collectively, share significant lexical commonalities. Based on this finding, we propose an unsupervised ASAG technique that o...

We offer a fluctuation smoothing computational approach for unsupervised automatic short answer grading (ASAG) techniques in the educational ecosystem. A major drawback of the existing techniques is the significant effect that variations in model answers could have on their performances. The proposed fluctuation smoothing approach, based on classical sequential pattern mining, exploits lexical ...

Grading students’ answers has always been a daunting task which takes lot of teachers’ time. The aim this study is to grade automatically in high school’s e-learning system. grading process must be fast, and the result as close possible teacher assigned grades. We collected total 840 from 40 students for study, each already graded by their teachers. used Python library sentence-transformers thr...

Developing an effective and impartial grading system for short answers is a challenging problem in educational measurement and assessment, due to the diversity of answers and the subjectivity of graders. In this paper, we design an automatic grading approach for short answers, based on the non-negative semi-supervised document clustering method. After assigning several answer keys, our approach...

We introduce a new approach to the machine-assisted grading of short answer questions. We follow past work in automated grading by first training a similarity metric between student responses, but then go on to use this metric to group responses into clusters and subclusters. The resulting groupings allow teachers to grade multiple responses with a single action, provide rich feedback to groups...

Question difficulty estimates guide test creation, but are too costly for small-scale testing. We empirically verify that Bloom’s Taxonomy, a standard tool for difficulty estimation during question creation, reliably predicts question difficulty observed after testing in a short-answer corpus. We also find that difficulty can be approximated by the amount of variation in student answers, which ...

This paper presents a system which uses a combination of multiple text similarity measures of varying complexities to classify Quora question pairs as duplicate or different. The solution uses a support vector classifier model trained using the precomputed features ranging from longest common sub-string and sub sequences to word similarity based on lexical and semantic resources. The scope of t...

In this paper we investigate the potential of answer clustering for semi-automatic scoring of short answer questions for German as a foreign language. We use surface features like word and character n-grams to cluster answers to listening comprehension exercises per question and simulate having human graders only label one answer per cluster and then propagating this label to all other members ...