Knowledge Spaces
Edited by Jean-Claude Falmagne (),
Dietrich Albert (),
Christopher Doble (),
David Eppstein () and
Xiangen Hu ()
in Springer Books from Springer
Date: 2013
Edition: 2013
ISBN: 978-3-642-35329-1
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Chapters in this book:
- Ch 1 Overview
- Jean-Claude Falmagne and Christopher Doble
- Ch 2 Assessing Mathematical Knowledge in a Learning Space
- Eric Cosyn, Christopher Doble, Jean-Claude Falmagne, Arnaud Lenoble, Nicolas Thiéry and Hasan Uzun
- Ch 3 ALEKS-based Placement at the University of Illinois
- Alison Ahlgren Reddy and Marc Harper
- Ch 4 The Impact of a Mathematical Intelligent Tutoring System on Students’ Performance on Standardized High-Stake Tests
- Jeremiah Sullins, Rob Meister, Scotty D. Craig, William M. Wilson, Anna Bargagliotti and Xiangen Hu
- Ch 5 A Potential Technological Solution for Reducing the Achievement Gap Between White And Black Students
- Xiangen Hu, Yonghong Jade Xu, Charles Hall, Kristin Walker and Theresa Okwumabua
- Ch 6 A Commercial Implementation of Knowledge Space Theory In College General Chemistry
- Christopher J. Grayce
- Ch 7 Using Knowledge Space Theory to Assess Student Understanding of Chemistry
- Mare Taagepera and Ramesh D. Arasasingham
- Ch 8 Learning Spaces: A Mathematical Compendium
- Jean-Paul Doignon, Jean-Claude Falmagne and Eric Cosyn
- Ch 9 Recent Developments in Performance-based Knowledge Space Theory
- Ali Ünlü, Martin Schrepp, Jürgen Heller, Cord Hockemeyer, Gudrun Wesiak and Dietrich Albert
- Ch 10 Heuristics for Generating and Validating Surmise Relations across, between and within Sets/Tests
- Dietrich Albert, Gudrun Wesiak and Ali Ünlü
- Ch 11 Skills, Competencies and Knowledge Structures
- Jürgen Heller, Ali Ünlü and Dietrich Albert
- Ch 12 Recent Developments in Competence-based Knowledge Space Theory
- Jürgen Heller, Thomas Augustin, Cord Hockemeyer, Luca Stefanutti and Dietrich Albert
- Ch 13 Learning Sequences: An Efficient Data Structure for Learning Spaces
- David Eppstein
- Ch 14 Projection, Decomposition, and Adaption of Learning Spaces
- David Eppstein
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sprbok:978-3-642-35329-1
Ordering information: This item can be ordered from
http://www.springer.com/9783642353291
DOI: 10.1007/978-3-642-35329-1
Access Statistics for this book
More books in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().