Abstract
Many traditional question answering systems depend on an Automatic Short Answer Grading (ASAG) to evaluate misspelled multiple words short answers in an Arabic Language using common edit-based algorithms such as Hamming, Levenshtein, and Jaro_Winkler, but they ignore and hide a big amount of a significant knowledge of the student answer. In this paper, we have implemented a proposed edit-based Hierarchical question answering system (HQAS) using a traversing by Breadth-First Search (BFS) within an m-ary tree to consider the ignored significant knowledge due to the misspelling at the middle of the dual-ordered incomplete answer, the misspelling at middle and the end of the intra-ordered incomplete answer, and the misspelling due to switching in words of the intra-ordered complete answer. It can differentiate among the students based on their significant hidden knowledge and show a distribution of knowledge content on different depths of the topic to determine which of the topic depths the student has the most significant knowledge.
Published Version
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