Policy Building [electronic resource] : An Extension to User Modeling / Michael V. Yudelson and Emma Brunskill.
In this paper we combine a logistic regression student model with an exercise selection procedure. As opposed to the body of prior work on strategies for selecting practice opportunities, we are working on an assumption of a finite amount of opportunities to teach the student. Our goal is to prescri...
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| Main Authors: | , |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
[S.l.] :
Distributed by ERIC Clearinghouse,
2012.
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| Subjects: |
MARC
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| 003 | CoU | ||
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| 008 | 120601s2012 xx |||| ot ||| | eng d | ||
| 035 | |a (ERIC)ed537226 | ||
| 035 | |a (MvI) 5G000000519307 | ||
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| 099 | |a ED537226 | ||
| 099 | |a ED537226 | ||
| 100 | 1 | |a Yudelson, Michael V. | |
| 245 | 1 | 0 | |a Policy Building |h [electronic resource] : |b An Extension to User Modeling / |c Michael V. Yudelson and Emma Brunskill. |
| 260 | |a [S.l.] : |b Distributed by ERIC Clearinghouse, |c 2012. | ||
| 300 | |a 4 p. | ||
| 500 | |a Availability: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org. |5 ericd. | ||
| 500 | |a Sponsoring Agency: Department of Education (ED). |5 ericd. | ||
| 500 | |a Sponsoring Agency: Pittsburgh Science of Learning Center. |5 ericd. | ||
| 500 | |a Sponsoring Agency: Carnegie Learning, Inc. |5 ericd. | ||
| 500 | |a Abstractor: As Provided. |5 ericd. | ||
| 500 | |a Educational level discussed: Grade 6. | ||
| 500 | |a Educational level discussed: Grade 7. | ||
| 500 | |a Educational level discussed: Middle Schools. | ||
| 516 | |a Text (Reports, Evaluative) | ||
| 516 | |a Text (Speeches/Meeting Papers) | ||
| 520 | |a In this paper we combine a logistic regression student model with an exercise selection procedure. As opposed to the body of prior work on strategies for selecting practice opportunities, we are working on an assumption of a finite amount of opportunities to teach the student. Our goal is to prescribe activities that would maximize the amount learned as evaluated by expected post-test success. We evaluate the proposed approach using an existing dataset where data was collected performing random skill selection. Our results cautiously support the hypothesis that using policies designed to optimize the post-test score associated with higher learning outcomes, but more work is needed. (Contains 2 figures, 3 tables, and 1 footnote.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.] | ||
| 524 | |a International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, Jun 19-21, 2012). |2 ericd. | ||
| 650 | 0 | 7 | |a Scores. |2 ericd. |
| 650 | 0 | 7 | |a Tests. |2 ericd. |
| 650 | 0 | 7 | |a Regression (Statistics) |2 ericd. |
| 650 | 0 | 7 | |a Pretests Posttests. |2 ericd. |
| 650 | 0 | 7 | |a Data. |2 ericd. |
| 650 | 0 | 7 | |a Factor Analysis. |2 ericd. |
| 650 | 0 | 7 | |a Algebra. |2 ericd. |
| 650 | 0 | 7 | |a Mathematics Instruction. |2 ericd. |
| 650 | 0 | 7 | |a Grade 6. |2 ericd. |
| 650 | 0 | 7 | |a Grade 7. |2 ericd. |
| 650 | 0 | 7 | |a Middle School Students. |2 ericd. |
| 650 | 0 | 7 | |a Tutoring. |2 ericd. |
| 650 | 0 | 7 | |a Academic Achievement. |2 ericd. |
| 700 | 1 | |a Brunskill, Emma, |e author. | |
| 710 | 2 | |a International Educational Data Mining Society. | |
| 856 | 4 | 0 | |u http://files.eric.ed.gov/fulltext/ED537226.pdf |z Full Text (via ERIC) |
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