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ERIC Number: ED560885
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 4
Abstractor: As Provided
Reference Count: 25
Semantic Similarity Graphs of Mathematics Word Problems: Can Terminology Detection Help?
John, Rogers Jeffrey Leo; Passonneau, Rebecca J.; McTavish, Thomas S.
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
Curricula often lack metadata to characterize the relatedness of concepts. To investigate automatic methods for generating relatedness metadata for a mathematics curriculum, we first address the task of identifying which terms in the vocabulary from mathematics word problems are associated with the curriculum. High chance-adjusted interannotator agreement on manual identification of math terms was achieved by considering terms in their contexts. These terms represent 13% of the vocabulary in one seventh grade mathematics text. Six classification algorithms were compared to classify math terms for this text. To avoid overfitting to this curriculum, we relied on a small number of features that exploit external knowledge sources. [For complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: Grade 7; Junior High Schools; Middle Schools; Elementary Education; Secondary Education
Audience: N/A
Language: English
Sponsor: Pearson
Authoring Institution: International Educational Data Mining Society