Literature DB >> 34992310

Evaluating Different Scoring Methods for Multiple Response Items Providing Partial Credit.

Joe Betts1, William Muntean1, Doyoung Kim1, Shu-Chuan Kao1.   

Abstract

The multiple response structure can underlie several different technology-enhanced item types. With the increased use of computer-based testing, multiple response items are becoming more common. This response type holds the potential for being scored polytomously for partial credit. However, there are several possible methods for computing raw scores. This research will evaluate several approaches found in the literature using an approach that evaluates how the inclusion of scoring related to the selection/nonselection of both relevant and irrelevant information is incorporated extending Wilson's approach. Results indicated all methods have potential, but the plus/minus and true/false methods seemed the most promising for items using the "select all that apply" instruction set. Additionally, these methods showed a large increase in information per time unit over the dichotomous method.
© The Author(s) 2021.

Entities:  

Keywords:  multiple response items; polytomous scoring; technology-enhanced items

Year:  2021        PMID: 34992310      PMCID: PMC8725057          DOI: 10.1177/0013164421994636

Source DB:  PubMed          Journal:  Educ Psychol Meas        ISSN: 0013-1644            Impact factor:   2.821


  3 in total

1.  Pick-N multiple choice-exams: a comparison of scoring algorithms.

Authors:  Daniel Bauer; Matthias Holzer; Veronika Kopp; Martin R Fischer
Journal:  Adv Health Sci Educ Theory Pract       Date:  2010-10-31       Impact factor: 3.853

2.  A "new" item format for assessing aspects of clinical competence.

Authors:  D R Ripkey; S M Case; D B Swanson
Journal:  Acad Med       Date:  1996-10       Impact factor: 6.893

3.  Impact of different scoring algorithms applied to multiple-mark survey items on outcome assessment: an in-field study on health-related knowledge.

Authors:  A Domnich; D Panatto; L Arata; I Bevilacqua; L Apprato; R Gasparini; D Amicizia
Journal:  J Prev Med Hyg       Date:  2015
  3 in total
  1 in total

1.  Polytomous Testlet Response Models for Technology-Enhanced Innovative Items: Implications on Model Fit and Trait Inference.

Authors:  Hyeon-Ah Kang; Suhwa Han; Doyoung Kim; Shu-Chuan Kao
Journal:  Educ Psychol Meas       Date:  2021-08-02       Impact factor: 3.088

  1 in total

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