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ERIC Number: ED608001
Record Type: Non-Journal
Publication Date: 2020-Jul
Pages: 7
Abstractor: As Provided
ISBN: N/A
ISSN: N/A
EISSN: N/A
Measuring Ability-to-Learn Using Parametric Learning-Gain Functions
Piech, Chris; Bumbacher, Engin; Davis, Richard
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (13th, Online, Jul 10-13, 2020)
One crucial function of a classroom, and a school more generally, is to prepare students for future learning. Students should have the capacity to learn new information and to acquire new skills. This ability to "learn" is a core competency in our rapidly changing world. But how do we measure ability to learn? And how can we measure how well a school has prepared their students to learn? In this paper we formally pose the problem, and introduce a grounded theory of how to measure ability to learn. Using simulations of students learning we provide initial evidence that this theory provides an elegant solution to this problem. We further validate our ideas using real world data from 70k middle-school students and show that our theory is more accurate and interpretable than current state-of-the-art models of learning gains. We consider our results a modest yet interesting first step for a novel type of test. [For the full proceedings, see ED607784.]
International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Publication Type: Speeches/Meeting Papers; Reports - Evaluative
Education Level: Junior High Schools; Middle Schools; Secondary Education; Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Spain
Grant or Contract Numbers: N/A