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Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Thesis (Ph. D.)--Rutgers University, 2010. / "Graduate Program in Education." Includes bibliographical references (p. 104-108).

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Broadly defined, group testing is the study of finding defective items in a large set. In the medical infection setting, that implies classifying each member of a population as infected or uninfected, while minimizing the total number of tests.

The research reported in this thesis investigated issues relating to the use of computer-assisted assessment in Higher Education through the design, implementation and evaluation of a computer-adaptive test (CAT) for the assessment of and provision of feedback to Computer Science undergraduates. The CAT developed for this research unobtrusively monitors the performance of students during a test, and then employs this information to adapt the sequence and level of difficulty of the questions to individual students. The information about each student performance obtained through the CAT is subsequently employed for the automated generation of feedback that is tailored to each individual student. In the first phase of the research, a total of twelve empirical studies were carried out in order to investigate issues related to the adaptive algorithm, stakeholders’ attitude, and validity and reliability of the approach. The CAT approach was found to be valid and reliable, and also effective at tailoring the level of difficulty of the test to the ability of individual students. The two main groups of stakeholders, students and academic staff, both exhibited a positive attitude towards the CAT approach and the user interface. The second phase of the research was concerned with the design, implementation and evaluation of an automated feedback prototype based on the CAT approach. Five empirical studies were conducted in order to assess stakeholders’ attitude towards the automated feedback, and its effectiveness at providing feedback on performance. It was found that both groups of stakeholders exhibited a positive attitude towards the feedback approach. Furthermore, it was found that the approach was effective at identifying the strengths and weaknesses of individual students, and at supporting the adaptive selection of learning resources that meet their educational needs. This work discusses the implications of the use of the CAT approach in Higher Education assessment. In addition, it demonstrates the ways in which the adaptive test generated by the CAT approach can be used to provide students with tailored feedback that is timely and useful.

The Armed Services Vocational Aptitude Battery (ASVAB) is a test that approximately 700,000 students in 12,000 high schools take each year to determine military occupation placement. Form Assembly for the ASVAB refers to the selection of 20-35 questions, known as items, from an item pool of approximately 300 items to create a paper and pencil test in one of its ten topics. Previous research formulates form assembly as an Integer Linear Program (ILP). The current ASVAB mostly uses a Computer Adaptive Test (CAT), which estimates an examinee's ability after the examinee answers each item and selects the next item based on prior performance. The current CAT-ASVAB implementation does not control the number of items selected from each subject (taxonomy group) for a test. This thesis introduces ILPs, previously used for form assembly, that impose taxonomy restrictions and applies them to the CAT-ASVAB. We create four ILP variations and test them against the current method of item selection, by simulating 3,500 examinees (500 examinees each for seven given ability levels). The results show that all of the ILPs have acceptable solution times for CAT use, and taxonomy restrictions can be imposed while also having more even exposure rates (the number of times an item is administered divided by the number of examinees) than the current implementation of the CAT-ASVAB. A variation that relaxes most of the binary variables and constrains the difficulty of each item to be within a predetermined magnitude of the current ability estimate, performs the best in terms of item exposure (for both under and over-utilized items) and error between an examinee's estimated ability level and actual ability level. / Defense Manpower Data Center author (civilian).

Chi-Keung Leung. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (p. 138-146). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

An efficient pool is critical for CAT administrations. Two approaches have been developed to design an optimal CAT pool: the linear programming method (LP; Veldkamp & van der Linden, 2000, 2010) and the bin-and-union method (BU; Reckase, 2003, 2010). This study manipulated different content balancing approaches and exposure conditions to investigate their impacts on the pool performances of the LP and BU methods under practical testing situations. The optimal pools were constructed in terms of the specification of an operational fixed-length CAT program and the IRT model employed. This study considered the one-parameter logistic (1PL) model to simulate adaptive test item responses using optimal and operational pools. Several psychometric properties were compared between the pools designed under the LP and BU methods. This research attempted to answer the following question: Under the consideration of content balancing and exposure control, what were the benefits and limitations of the LP and BU methods with respect to the optimal pool design? The results were evaluated in terms of pool characteristics, content constraint management, item exposure control, pool utilization, test reliability, and measurement precision. Similar pool characteristics were found between the LP and BU methods. With respect to the evaluation criteria, the LP and BU pools exhibited consistent performance. However, compared to the LP pools, the BU pools demonstrated slight superiority under the condition with strict content balancing and exposure control. Given two bin widths (.35 and .70), the pools with a bin-width of .35 exhibited better performance than those with a bin-width of .70 with respect to various evaluation criteria. Especially under the condition with the strict content balancing and exposure control, a bin-width of .35 might be a better option to generate an optimal pool than a bin-width of .70 in order to maintain a higher test on-target rate.

Computerized adaptive testing attempts to provide the most suitable question for an examinee depending on the examinee¡¦s ability to achieve the best result. Although Maximum Likelihood Estimation (MLE) and Bayesian Likelihood Estimation (BLE) have been provided to solve ability estimation and have good results in the literature, little attention has been paid to the situation when the answer of an item does not conform with the examinee¡¦s ability as expected nor standard derivation changes of the ability estimation. We hypothesized that the Adaptive-Network-Based Fuzzy Inference System (ANFIS) can be used to infer flexible examinee¡¦s ability estimation automically by analyzing the relevant data of the examinee in a test. Consequently, the study presents a novel learning ability model based on ANFIS, which can adaptively choose questions by Item Response Theory. Taking the item discrimination, difficulty, guessing, and the examinee¡¦s ability before he/she answers a question as parameters, the proposed method can infer the adjustment of the examinee¡¦s ability to update its value after he/she answers the question. The ANFIS model of the experiments were developed using MATLAB. The examinees were simulated and the training data were collected under three different situations. Through different combination of ANFIS fuzzy rules, the adjustment of ability is inferred to improve the accuracy of the estimated ability. The error between the true ability and the estimated ability obtained by the proposed model is compared with MLE and BLE. The simulation results show that the estimated ability error of ANFIS is smaller than MLE and BLE when the value of the test information is larger. The proposed method could provide better accuracy of the examinee¡¦s ability and offer more appropriate questions for examinees. Keywords: ANFIS, Item Response Theory, Computerized Adaptive Testing