Browsing by Author "Rajendran, N"

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    item: Conference-Extended-Abstract
    Automatic assessment and error identification of multi-step answers for matrix questions
    (2017) Thirunavukkarasu, N; Selvarasa, A; Rajendran, N; Yogalingam, C; Ranathunga, S; Dias, G
    This paper presents an automatic assessment and error identification system for student answers with matrix expressions, and which may have multiple steps. Teacher’s intervention is needed only during the question set-up stage, to provide the marking rubric. The system currently supports four types of matrix questions: multiplying a matrix by a constant number, matrix addition and subtraction, finding unknown elements within a matrix, and finding the unknown matrix from an equation. A CAS (Computer Algebra System) is used to evaluate each step of the student’s answer. The system is capable of giving full/partial marks according to a marking rubric. Errors commonly made by students were identified and categorized by analyzing sample student answers. Using this categorization, the system is capable of identifying the exact error(s) made by a student.
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    item: Conference-Abstract
    Short Tamil sentence similarity calculation using knowledge-based and corpus-based similarity measures
    Selvarasa, A; Thirunavukkarasu, N; Rajendran, N; Yogalingam, C; Ranathunga, S; Dias, G
    Sentence similarity calculation plays an important role in text processing-related research. Many unsupervised techniques such as knowledge-based techniques, corpus-based techniques, string similarity based techniques, and graph alignment techniques are available to measure sentence similarity. However, none of these techniques have been experimented with Tamil. In this paper, we present the first-ever system to measure semantic similarity for Tamil short phrases using a hybrid approach that makes use of knowledge-based and corpus-based techniques. We tested this system with 2000 general sentence pairs and 100 mathematical sentence pairs. For the dataset of 2000 sentence pairs, this approach achieved a Mean Squared Error of 0.195 and a Pearson Correlation factor of 0.815. For the 100 mathematical sentence pairs, this approach achieved an 85% of accuracy.