Spelling suggestions: "subject:"computeradaptiven"" "subject:"computerizedadaptive""
21 |
Computerized adaptive test item response times for correct and incorrect pretest and operational items testing fairness and test-taking strategies /Chang, Shu-Ren. January 1900 (has links)
Thesis (Ph.D.)--University of Nebraska-Lincoln, 2006. / Title from title screen (site viewed June 8, 2007). PDF text: 141 p. : col. ill. ; 0.80Mb. UMI publication number: AAT 3239362. Includes bibliographical references. Also available in paper, microfilm and microfiche formats.
|
22 |
Model-adaptive tests for regressionsZhu, Xuehu 26 August 2015 (has links)
In this thesis, we firstly develop a model-adaptive checking method for partially parametric single-index models, which combines the advantages of both dimension reduction technique and global smoothing tests. Besides, we propose a dimension reduction-based model adaptive test of heteroscedasticity checks for nonparametric and semi-parametric regression models. Finally, to extend our testing approaches to nonparametric regressions with some restrictions, we consider significance testing under a nonparametric framework. In Chapter 2, “Model Checking for Partially Parametric Single-index Models: A Model-adaptive Approach", we consider the model checking problems for more general parametric models which include generalized linear models and generalized nonlinear models. We develop a model-adaptive dimension reduction test procedure by extending an existing directional test. Compared with traditional smoothing model checking methodologies, the procedure of this test not only avoids the curse of dimensionality but also is an omnibus test. The resulting test is omnibus adapting the null and alternative models to fully utilize the dimension-reduction structure under the null hypothesis and can detect fully nonparametric global alternatives, and local alternatives distinct from the null model at a convergence rate as close to square root of the sample size as possible. Finally, both Monte Carlo simulation studies and real data analysis are conducted to compare with existing tests and illustrate the finite sample performance of the new test. In Chapter 3,Heteroscedasticity Checks for Nonparametric and Semi-parametric Regression Model: A Dimension Reduction Approach", we consider heteroscedasticity checks for nonparametric and semi-parametric regression models. Existing local smoothing tests suffer severely from the curse of dimensionality even when the number of covariates is moderate because of use of nonparametric estimation. In this chapter, we propose a dimension reduction-based model adaptive test that behaves like a local smoothing test as if the number of covariates is equal to the number of their linear combinations in the mean regression function, in particular, equal to 1 when the mean function contains a single index. The test statistic is asymptotically normal under the null hypothesis such that critical values are easily determined. The finite sample performances of the test are examined by simulations and a real data analysis. In Chapter 4,Dimension Reduction-based Significance Testing in Nonparametric Regression", as nonparametric techniques need much less restrictive conditions than those required for parametric approaches, we consider to check nonparametric regressions with some restrictions under sufficient dimension reduction structure. A dimension-reduction-based model-adaptive test is proposed for significance of a subset of covariates in the context of a nonparametric regression model. Unlike existing local smoothing significance tests, the new test behaves like a local smoothing test as if the number of covariates is just that under the null hypothesis and it can detect local alternative hypotheses distinct from the null hypothesis at the rate that is only related to the number of covariates under the null hypothesis. Thus, the curse of dimensionality is largely alleviated when nonparametric estimation is inevitably required. In the cases where there are many insignificant covariates, the improvement of the new test is very significant over existing local smoothing tests on the significance level maintenance and power enhancement. Simulation studies and a real data analysis are conducted to examine the finite sample performance of the proposed test. Finally, we conclude the main results and discuss future research directions in Chapter 5. Keywords: Model checking; Partially parametric single-index models; Central mean subspace; Central subspace; Partial central subspace; Dimension reduction; Ridge-type eigenvalue ratio estimate; Model-adaption; Heteroscedasticity checks; Significance testing.
|
23 |
Optimizing Classification Decisions for Paper-and-Pencil and Computer Adaptive TestsThomas, Leslie A. 05 December 1997 (has links)
Throughout the social sciences, tests have been used for two primary - and different - purposes: a) to estimate where an examinee is located on an ability/trait continuum (e.g., intelligence tests), or b) to classify an examinee as either above or below a particular point on the ability continuum (e.g., criterion-referenced tests). From a psychometric perspective, the scoring procedure of the test should reflect the purpose for which the test is being used. From a practical perspective, the administration procedure should be as efficient as possible.
The Myers-Briggs Type Indicator (MBTI; Briggs & Myers, 1976) is a personality inventory designed to classify examinees according to four bipolar dimensions. Although the MBTI is quite popular within corporate America, critics have threatened the validity of the MBTI with two seemingly contradictory faults: the test is too unreliable and too long.
The purpose of this study was to examine the degree to which using an item response theory (IRT) / Ph. D.
|
24 |
Stability and sensitivity of a model-based person-fit index in detecting item pre-knowledge in computerized adaptive test. / 特定模型個人擬合指數在探測預見題目時的穩定性及靈敏度 / CUHK electronic theses & dissertations collection / Te ding mo xing ge ren ni he zhi shu zai tan ce yu jian ti mu shi de wen ding xing ji ling min duJanuary 2008 (has links)
After the stability and sensitivity of FLOR were investigated, the application of it in the CAT environment had become the main concern. The present studies found that both the test length and the number of exposed items affect the final value of FLOR. In the fixed length CAT, the FLOR has a much stronger sensitivity than lz and CUSUM in detecting item pre-knowledge. The sensitivity of FLOR in the fixed length CAT was the same as that in the fixed length fixed items test. If the test length could vary, the sensitivity of FLOR in CAT would be slightly weakened. The Adjusted FLOR index could increase the sensitivity. Concerning about the effect of ability on the sensitivity of FLOR in CAT, it was found that the abilities of the test takers in CAT did not affect the sensitivity of FLOR and Adjusted FLOR. / Item response theory is a modern test theory. It focuses on the performance of each item. Under this framework, the performance of test takers on a test item can be predicted by a set of abilities. The relationship between the test takers' item performances and the set of abilities underlying item performances can be described by a monotonically increasing function called an item characteristic curve. Due to various personal reasons, the performances of the test takers may depart from the response patterns predicted by the underlying test model. In order to calculate the extent of departure of these aberrant response patterns, a number of methods have been developed under the theme "person-fit statistics". The degree of aberration is calculated as an index called person-fit index. Inside the computerized adaptive testing (CAT), test takers with different abilities will answer different numbers of questions and the difficulties of the items administered to them are usually clustered at the abilities of the test takers. Due to this reason, the application of person-fit indices in the computerized adaptive testing environment to measure misfit is difficult. / The present study also found that FLOR has a much superior sensitivity over other indices in detecting item pre-knowledge. Concerning about the sensitivity over different abilities of test takers, it was found that the sensitivity of FLOR was the highest among low ability test takers and the weakest among strong ability test takers in the fixed length and fixed items tests. However, the sensitivities of FLOR became the same among different abilities of test takers if items with difficulties matching their abilities were used in the tests. The number of beneficiaries among the test takers did not affect the sensitivity of FLOR. Moreover, in a simulation to test the differentiating power of FLOR, it was found that FLOR could differentiate item pre-knowledge from other reasons of personal misfits (test anxiety, player, random response and challenger) effectively. / The present study assessed the stability of FLOR over other variables, which were unrelated to item pre-knowledge. It found that FLOR was stable over the discrimination and difficulty parameters of test items. It was also stable over positions of the exposed items in the test and the initial assignment of prior probability of item pre-knowledge. However, the asymptotes (guessing factor) and the probabilities of item exposure did affect the final values of FLOR seriously. / The present study used the hf plot to access the sensitivity of the person-fit indices. hf plot is a plot of hit rate against false alarm rate. For a higher hit rate, usually a higher false alarm rate is followed. hf plot provides a good tools for comparison between indices by inspection of the speed of rise of the curves. A sensitive index should give a faster rise of the curve. In this study, sensitivity of an index was defined as the speed of rise of the hf plot, which is represented by a parameter hftau estimated from the data obtained from hf plot. / When the frequent accesses to the item bank has become feasible, test takers may memorize blocks of test items and share these items with future test takers. Individuals with prior knowledge of some items may use that information to get high scores, in the sense that their test scores have been artificially inflated. FLOR is an index of posterior log-odds ratio used for detecting the use of item pre-knowledge. It can be applied both in the fixed item, fixed length test and the CAT environment. It is a model-based index in which aberrant models are defined in the situation of item pre-knowledge. FLOR describes the likelihood that a response pattern arises from the aberrant models. / Hui Hing-fai. / Adviser: Kit-tai Hau. / Source: Dissertation Abstracts International, Volume: 70-09, Section: A, page: . / Thesis (Ed.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 108-111). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.
|
25 |
Strategies for controlling item exposure in computerized adaptive testing with polytomously scored itemsDavis, Laurie Laughlin 28 August 2008 (has links)
Not available / text
|
26 |
Monitoring growth in early reading skills validation of a computer adaptive test /DeGraff, Amanda J. Torgesen, Joseph K. January 2005 (has links)
Thesis (Ph. D.)--Florida State University, 2005. / Advisor: Dr. Joseph K. Torgesen, Florida State University, College of Arts and Sciences, Dept. of Psychology. Title and description from dissertation home page (viewed June 9, 2005). Document formatted into pages; contains vii, 56 pages. Includes bibliographical references.
|
27 |
The application of cognitive diagnosis and computerized adaptive testing to a large-scale assessmentMcGlohen, Meghan Kathleen, Chang, Hua-Hua, January 2004 (has links) (PDF)
Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: Hua-Hua Chang. Vita. Includes bibliographical references.
|
28 |
Measurement of Korean EFL college students' foreign language classroom speaking anxiety evidence of psychometric properties and accuracy of a computerized adaptive test (CAT) with dichotomously scored items using a CAT simulation /Yang, Tae-kyoung, Chang, Hua-Hua, January 2005 (has links) (PDF)
Thesis (Ph. D.)--University of Texas at Austin, 2005. / Supervisor: Hua-Hua Chang. Vita. Includes bibliographical references.
|
29 |
Projected adaptive-to-model tests for regression modelsTan, Falong 21 August 2017 (has links)
This thesis investigates Goodness-of-Fit tests for parametric regression models. With the help of sufficient dimension reduction techniques, we develop adaptive-to-model tests using projection in both the fixed dimension settings and the diverging dimension settings. The first part of the thesis develops a globally smoothing test in the fixed dimension settings for a parametric single index model. When the dimension p of covariates is larger than 1, existing empirical process-based tests either have non-tractable limiting null distributions or are not omnibus. To attack this problem, we propose a projected adaptive-to-model approach. If the null hypothesis is a parametric single index model, our method can fully utilize the dimension reduction structure under the null as if the regressors were one-dimensional. Then a martingale transformation proposed by Stute, Thies, and Zhu (1998) leads our test to be asymptotically distribution-free. Moreover, our test can automatically adapt to the underlying alternative models such that it can be omnibus and thus detect all alternative models departing from the null at the fastest possible convergence rate in hypothesis testing. A comparative simulation is conducted to check the performance of our test. We also apply our test to a self-noise mechanisms data set for illustration. The second part of the thesis proposes a globally smoothing test for parametric single-index models in the diverging dimension settings. In high dimensional data analysis, the dimension p of covariates is often large even though it may be still small compared with the sample size n. Thus we should regard p as a diverging number as n goes to infinity. With this in mind, we develop an adaptive-to-model empirical process as the basis of our test statistic, when the dimension p of covariates diverges to infinity as the sample size n tends to infinity. We also show that the martingale transformation proposed by Stute, Thies, and Zhu (1998) still work in the diverging dimension settings. The limiting distributions of the adaptive-to-model empirical process under both the null and the alternative are discussed in this new situation. Simulation examples are conducted to show the performance of this test when p grows with the sample size n. The last Chapter of the thesis considers the same problem as in the second part. Bierens's (1982) first constructed tests based on projection pursuit techniques and obtained an integrated conditional moment (ICM) test. We notice that Bierens's (1982) test performs very badly for large p, although it may be viewed as a globally smoothing test. With the help of sufficient dimension techniques, we propose an adaptive-to-model integrated conditional moment test for regression models in the diverging dimension setting. We also give the asymptotic properties of the new tests under both the null and alternative hypotheses in this new situation. When p grows with the sample size n, simulation studies show that our new tests perform much better than Bierens's (1982) original test.
|
30 |
The Impact of Computer-Adaptive Benchmark Data and Assessment Literacy on Student Achievement and Motivation in MathematicsRushton, Sheryl J. 01 May 2014 (has links)
Over the past several decades there has been an emphasis in educational research on student assessment and achievement in mathematics. Formative assessments are designed to inform the instructional decision making process and require assessment literacy to interpret and use data provided by these assessments. Many teachers and students were lacking assessment literacy; therefore, they were unable to adjust their instruction and study habits to increase student performance on summative assessments. This study investigated the impact Scholastic Math Inventory (SMI) benchmarks and assessment literacy training had on summative assessments and student motivation in mathematics. The researcher analyzed unit posttest scores and results from the Instructional Materials Motivation Survey (IMMS) for seventh- and eighth-grade mathematics control and treatment groups. The study took place in a public International Baccalaureate (IB) charter school that served families from suburban communities in northern Utah. An analysis of covariance (ANCOVA), using previous CRT math scores as the covariate, was conducted to determine whether there was a difference in the scores of the students who received the SMI benchmarks and assessment literacy training and the students who did not receive this treatment. An analysis of variance (ANOVA) tested whether there was a difference between the IMMS scores for the students who received the SMI benchmarks and assessment literacy training versus students who did not receive this treatment. The study results indicated that summative scores for seventh- and eighth-grade students who received instruction for the unit along with SMI benchmark and assessment literacy training were not statistically different from students in the control group. The results also showed that the student mathematical motivation overall mean scores were not statistically significant. However, the subscale of satisfaction did show a significant difference in the means. The researcher recommended that use of SMI and assessment literacy training be examined carefully, as these strategies may not improve summative assessment scores in all cases.
|
Page generated in 0.0763 seconds