Field Test of a Computer‐based Gre General Test

Schaeffer, Gary A.; Reese, Clyde M.; Steffen, Manfred; McKinley, Robert L.; Mills, Craig N.

doi:10.1002/j.2333-8504.1993.tb01518.x

Cited by 35 publications

(21 citation statements)

References 5 publications

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“…Although some studies comparing pencil-paper and computer test formats have shown one testing condition superior to the other (e.g., DeAngelis 2000 ;Federico 1989;Hargreaves et al 2004;Mazzeo et al 1991), other studies have found no differences between testing conditions (Andersson et al 2003;Clariana and Wallace 2002;Mason et al 2001;Schaeffer et al 1993). Each achievement group scored significantly higher on the posttest than they did on the pretest, an indication that students, regardless of ability level, profited from EAI.…”

Section: Discussionmentioning

confidence: 87%

Assessing and tracking students’ problem solving performances in anchored learning environments

Bottge

Rueda

Kwon

et al. 2007

Education Tech Research Dev

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The purpose of this randomized experiment was to compare the performance of high-, average-, and low-achieving middle school students who were assessed with parallel versions of a computer-based test (CBT) or a paper-pencil test (PPT). Tests delivered in interactive, immersive environments like the CBT may have the advantage of providing teachers with diagnostic tools that can lead to instruction tailored to the needs of students at different achievement levels. To test the feasibility of CBT, students were randomly assigned to the CBT or PPT test conditions to measure what they had learned from an instructional method called enhanced anchored math instruction. Both assessment methods showed that students benefited from instruction and differentiated students by achievement status. The navigation maps generated from the CBT revealed that the low-achieving students were able to navigate the test, spent about the same amount of time solving the subproblems as the more advanced students, and made use of the learning scaffolds.

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Section: Discussionmentioning

confidence: 87%

Assessing and tracking students’ problem solving performances in anchored learning environments

Bottge

Rueda

Kwon

et al. 2007

Education Tech Research Dev

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“…While computer-based test scores were greater than paper-based test scores for a dental hygiene course unit midterm examination (DeAngelis, 2000); and some studies, in contrast, have reported non-significant difference between computer and paper-based tests (Schaeffer et al, 1993;Mason, et al, 2001). In regard to such different results in comparability studies of PPT and CBT, Yurdabakan (2012) believes that even though computer accession opportunities increase students' computer competencies and CBT achievements (Bennett, et al 2008), it is possible to evaluate that such approaches could be the reason of students' limited accession opportunities.…”

Section: International Letters Of Social and Humanistic Sciences Vol 11mentioning

confidence: 99%

The Investigation of Difference between PPT and CBT Results of EFL Learners in Iran: Computer Familiarity and Test Performance in CBT

Hosseini

Abidin

Kamarzarrin

et al. 2013

ILSHS

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The purpose of this study is to examine the score comparability of institutional English reading tests in two testing methods, i.e. paper-based and computer-based tests taken by Iranian EFL learners in four language institutes and their branches in Iran. In the present study, the researcher tried to examine whether there is any difference between computer-based test results (henceforth CBT) and paper-based test (PPT) results of a reading comprehension test as well as exploring the relationship between students' prior computer experience and their test performance in CBT. Two equivalent tests were administered to one group of EFL learners in two different occasions, one in paper-based format and the other in computer-based test. Utilizing t-test, the means of two modes have been compared and the results showed the priority of PPT over CBT with .01 degree of difference at p < 05. Using ANOVA, the findings revealed that computer experience had no significant influence on the students' performance in computerized test.

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“…This means that the model does not consider the possibility that time allocation (as opposed to total amount of time allocated) may also be informative about ability. However, there have been strong empirical findings supporting the notion that there are relevant differences in the time allocation of high-versus low-ability students, with high-ability students spending more or less time on particular items (Gitomer, Curtis, Glaser, & Lensky, 1987;Schaeffer, Reese, Steffen, McKinley, & Mills, 1993). This can, for example, be explained by their higher level of insight helping them to realize which items benefit (the most) from spending additional time on them, such as the more difficult items on a test.…”

Section: Introductionmentioning

confidence: 99%

Improving precision of ability estimation: Getting more from response times

Bolsinova

Tijmstra

2017

Brit J Math & Statis

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By considering information about response time (RT) in addition to response accuracy (RA), joint models for RA and RT such as the hierarchical model can improve the precision with which ability is estimated over models that only consider RA. The hierarchical model, however, assumes that only the person's speed is informative of ability. This assumption of conditional independence between RT and ability given speed may be violated in practice, and ignores collateral information about ability that may be present in the residual RTs. We propose a posterior predictive check for evaluating the assumption of conditional independence between RT and ability given speed. Furthermore, we propose an extension of the hierarchical model that contains cross-loadings between ability and RT, which enables one to take additional collateral information about ability into account beyond what is possible in the standard hierarchical model. A Bayesian estimation procedure is proposed for the model. Using simulation studies, the performance of the model is evaluated in terms of parameter recovery, and the possible gain in precision over the standard hierarchical model and an RA-only model is considered. The model is applied to data from a high-stakes educational test.

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Field Test of a Computer‐based Gre General Test

Cited by 35 publications

References 5 publications

Assessing and tracking students’ problem solving performances in anchored learning environments

Assessing and tracking students’ problem solving performances in anchored learning environments

The Investigation of Difference between PPT and CBT Results of EFL Learners in Iran: Computer Familiarity and Test Performance in CBT

Improving precision of ability estimation: Getting more from response times

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