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ERIC Number: ED560588
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 7
Abstractor: As Provided
Reference Count: 17
Using Partial Credit and Response History to Model User Knowledge
Van Inwegen, Eric G.; Adjei, Seth A.; Wang, Yan; Heffernan, Neil T.
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
User modelling algorithms such as Performance Factors Analysis and Knowledge Tracing seek to determine a student's knowledge state by analyzing (among other features) right and wrong answers. Anyone who has ever graded an assignment by hand knows that some answers are "more wrong" than others; i.e. they display less of an understanding of the skill(s) involved. This investigation seeks to understand the effects of progression through wrong answers to right answers in a way to determine how the "level" of wrongness affects future performance. The key findings are that: A) where in a series of opportunities a student reaches the goal impacts future performance, as does B) the "level" of previous wrongness, even two questions before the current opportunity. [For complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: N/A
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF); Department of Education (ED); Office of Naval Research (ONR); Institute of Education Sciences (ED)
Authoring Institution: International Educational Data Mining Society
IES Funded: Yes
Grant or Contract Numbers: NSF 1316736; NSF 1252297; NSF 1109483; NSF 1031398; NSF 0742503; NSF 1440753; GAANN P200A120238; R305A120125; R305C100024