http://core.miserver.it.umich.edu/omeka-s/api/items/4714
type: Item
type: ConferencePaper
id: 4714
is public: true
owner: http://core.miserver.it.umich.edu/omeka-s/api/users/4
resource class: http://core.miserver.it.umich.edu/omeka-s/api/resource_classes/834
resource template: http://core.miserver.it.umich.edu/omeka-s/api/resource_templates/24
thumbnail: http://core.miserver.it.umich.edu/omeka-s/api/assets/117
title: Using Data-Driven Discovery of Better Student Models to Improve Student Learning
created: 2022-11-25T15:45:38+00:00
modified: 2022-12-01T23:07:29+00:00
item set: http://core.miserver.it.umich.edu/omeka-s/api/item_sets/4671
site: http://core.miserver.it.umich.edu/omeka-s/api/sites/9
abstract description: Deep analysis of domain content yields novel insights and can be used to produce better courses. Aspects of such analysis can be performed by applying AI and statistical algorithms to student data collected from educational technology and better cognitive models can be discovered and empirically validated in terms of more accurate predictions of student learning. However, can such improved models yield improved student learning? This paper reports positively on progress in closing this loop. We demonstrate that a tutor unit, redesigned based on data-driven cognitive model improvements, helped students reach mastery more efficiently. In particular, it produced better learning on the problem-decomposition planning skills that were the focus of the cognitive model improvements.
author: Koedinger, Kenneth R.
author: Stamper, John C.
author: McLaughlin, Elizabeth A.
author: Nixon, Tristan
date: 2013
in publication: Artificial Intelligence in Education
editor: Lane, H. Chad
editor: Yacef, Kalina
editor: Mostow, Jack
editor: Pavlik, Philip
series: Lecture Notes in Computer Science
pages: 421-430
publisher: Springer
resource type: Research/Scholarly Media
status: Published Text
genre: Empirical
keywords: cognitive modeling
keywords: data mining
keywords: machine learning
language: en
open access: Yes
peer: No
isbn: 978-3-642-39112-5
doi: 978-3-642-39112-5 43
url: https://doi.org/10.1007/978-3-642-39112-5_43
url: http://pact.cs.cmu.edu/pubs/Koedinger%20Stamper%2C%20McLaughlin%20%26%20Nixon.pdf
apa: Koedinger, K. R., Stamper, J. C., McLaughlin, E. A., & Nixon, T. (2013). Using Data-Driven Discovery of Better Student Models to Improve Student Learning. In H. C. Lane, K. Yacef, J. Mostow, & P. Pavlik (Eds.), Artificial Intelligence in Education (pp. 421–430). Springer. https://doi.org/10.1007/978-3-642-39112-5_43
id: QJUS2US8
class: ire:ConferencePaper
year: 2013
date added: 11/24/2022 18:29
date modified: 11/24/2022 18:29
place: Berlin, Heidelberg
catalog: Springer Link
attach file: C:\Users\huynh\Zotero\storage\FHV9RFHP\Koedinger et al. - 2013 - Using Data-Driven Discovery of Better Student Mode.pdf

http://core.miserver.it.umich.edu/omeka-s/api/users/4
id: 4

http://core.miserver.it.umich.edu/omeka-s/api/resource_classes/834
id: 834

http://core.miserver.it.umich.edu/omeka-s/api/resource_templates/24
id: 24

http://core.miserver.it.umich.edu/omeka-s/api/assets/117
id: 117

http://core.miserver.it.umich.edu/omeka-s/api/item_sets/4671
id: 4671

http://core.miserver.it.umich.edu/omeka-s/api/sites/9
id: 9

978-3-642-39112-5 43
type: uri
property id: 1109
property label: DOI
is public: true
label: 10.1007/978-3-642-39112-5_43
property id: 1110
property label: Url
label: Official Publisher's Webpage (Springer Link)

Koedinger Stamper, McLaughlin & Nixon.pdf
type: uri
property id: 1110
property label: Url
is public: true
label: Full-text PDF Shared by Author (Carnegie Mellon University)