umu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Workshop: Learning More from Test Data: New Tools for Test Scoring
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.ORCID iD: 0000-0001-5549-8262
2019 (English)Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

The aim of scoring a test is to give a

s best estimate of an examinee’s ability as possible. The goals of this training session are for the attendees to be able to understand and implement optimal test scoring, and to interpret the results of optimal scoring in a reasonable way. In this training session, we will demonstrate and guide the attendees to use the web-based software TestGardener to implement optimal test scoring on real educational test data. Most of the outputs of this software are in graphical form, and the software is used interactively. The main part of the training session is devoted to practical exercises in how to analyze test data. Optimal scoring will also be compared with the traditional sum scoring, and recent developments in test scoring will be discussed. Expected audience include researchers, graduate students and practitioners. An introductory statistical background is recommended but not required. Please note, programming knowledge is not required.

Place, publisher, year, edition, pages
2019.
Keywords [en]
optimal scoring, educational data, test scores
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-163914OAI: oai:DiVA.org:umu-163914DiVA, id: diva2:1358703
Conference
National Council of Measurement in Education
Available from: 2019-10-08 Created: 2019-10-08 Last updated: 2019-10-08

Open Access in DiVA

No full text in DiVA

Search in DiVA

By author/editor
Wiberg, Marie
By organisation
Statistics
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 8 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf