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Imanudin Kudus; Heru Nurasa; Ida Widianingsih; Nina Karlina; Jayum Anak Jawan – Cogent Education, 2024
Currently, Indonesia has 122 State Universities (PTN) under the Ministry of Education and Culture and other ministries. Improving the quality of the selection process for new student admissions at PTN is critical for Indonesia's human resources development. Then in 2019, there was a transformation with the implementation of the exam becoming a…
Descriptors: Foreign Countries, Higher Education, Public Colleges, Organizational Climate
Gorgun, Guher; Bulut, Okan – Large-scale Assessments in Education, 2023
In low-stakes assessment settings, students' performance is not only influenced by students' ability level but also their test-taking engagement. In computerized adaptive tests (CATs), disengaged responses (e.g., rapid guesses) that fail to reflect students' true ability levels may lead to the selection of less informative items and thereby…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Algorithms
Chen, Chia-Wen; Wang, Wen-Chung; Chiu, Ming Ming; Ro, Sage – Journal of Educational Measurement, 2020
The use of computerized adaptive testing algorithms for ranking items (e.g., college preferences, career choices) involves two major challenges: unacceptably high computation times (selecting from a large item pool with many dimensions) and biased results (enhanced preferences or intensified examinee responses because of repeated statements across…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Selection
Bao, Yu; Bradshaw, Laine – Measurement: Interdisciplinary Research and Perspectives, 2018
Diagnostic classification models (DCMs) can provide multidimensional diagnostic feedback about students' mastery levels of knowledge components or attributes. One advantage of using DCMs is the ability to accurately and reliably classify students into mastery levels with a relatively small number of items per attribute. Combining DCMs with…
Descriptors: Test Items, Selection, Adaptive Testing, Computer Assisted Testing
Carroll, Ian A. – ProQuest LLC, 2017
Item exposure control is, relative to adaptive testing, a nascent concept that has emerged only in the last two to three decades on an academic basis as a practical issue in high-stakes computerized adaptive tests. This study aims to implement a new strategy in item exposure control by incorporating the standard error of the ability estimate into…
Descriptors: Test Items, Computer Assisted Testing, Selection, Adaptive Testing
Sahin, Alper; Ozbasi, Durmus – Eurasian Journal of Educational Research, 2017
Purpose: This study aims to reveal effects of content balancing and item selection method on ability estimation in computerized adaptive tests by comparing Fisher's maximum information (FMI) and likelihood weighted information (LWI) methods. Research Methods: Four groups of examinees (250, 500, 750, 1000) and a bank of 500 items with 10 different…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Test Content
Choosing versus Receiving Feedback: The Impact of Feedback Valence on Learning in an Assessment Game
Cutumisu, Maria; Schwartz, Daniel L. – International Educational Data Mining Society, 2016
Studies examining feedback in educational settings have largely focused on feedback that is received, rather than chosen, by students. This study investigates whether adult participants learn more from choosing rather than receiving feedback from virtual characters in a digital poster design task. We employed a yoked study design and two versions…
Descriptors: Feedback (Response), Educational Games, Computer Assisted Testing, Selection
He, Wei; Diao, Qi; Hauser, Carl – Educational and Psychological Measurement, 2014
This study compared four item-selection procedures developed for use with severely constrained computerized adaptive tests (CATs). Severely constrained CATs refer to those adaptive tests that seek to meet a complex set of constraints that are often not conclusive to each other (i.e., an item may contribute to the satisfaction of several…
Descriptors: Comparative Analysis, Test Items, Selection, Computer Assisted Testing
Cheng, Ying; Patton, Jeffrey M.; Shao, Can – Educational and Psychological Measurement, 2015
a-Stratified computerized adaptive testing with b-blocking (AST), as an alternative to the widely used maximum Fisher information (MFI) item selection method, can effectively balance item pool usage while providing accurate latent trait estimates in computerized adaptive testing (CAT). However, previous comparisons of these methods have treated…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Item Banks
Sanchez-Gordon, Sandra; Luján-Mora, Sergio – Journal of Educational Computing Research, 2018
There are millions of people worldwide--of all ages, conditions, backgrounds, and motivations--with significant learning needs. Unfortunately, traditional education is not efficient enough to meet these needs. That is, the available educational resources are not fully exploited to help cover the demand. There is an increasing need for large-scale…
Descriptors: Technological Advancement, Large Group Instruction, Online Courses, Technology Uses in Education
Yao, Lihua – Journal of Educational Measurement, 2014
The intent of this research was to find an item selection procedure in the multidimensional computer adaptive testing (CAT) framework that yielded higher precision for both the domain and composite abilities, had a higher usage of the item pool, and controlled the exposure rate. Five multidimensional CAT item selection procedures (minimum angle;…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Selection
Han, Kyung T. – Journal of Educational Measurement, 2012
Successful administration of computerized adaptive testing (CAT) programs in educational settings requires that test security and item exposure control issues be taken seriously. Developing an item selection algorithm that strikes the right balance between test precision and level of item pool utilization is the key to successful implementation…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Selection
He, Wei; Diao, Qi; Hauser, Carl – Online Submission, 2013
This study compares the four existing procedures handling the item selection in severely constrained computerized adaptive tests (CAT). These procedures include weighted deviation model (WDM), weighted penalty model (WPM), maximum priority index (MPI), and shadow test approach (STA). Severely constrained CAT refer to those adaptive tests seeking…
Descriptors: Computer Assisted Testing, Adaptive Testing, Test Items, Item Banks
Wang, Chun – Journal of Educational and Behavioral Statistics, 2014
Many latent traits in social sciences display a hierarchical structure, such as intelligence, cognitive ability, or personality. Usually a second-order factor is linearly related to a group of first-order factors (also called domain abilities in cognitive ability measures), and the first-order factors directly govern the actual item responses.…
Descriptors: Measurement, Accuracy, Item Response Theory, Adaptive Testing
Leroux, Audrey J.; Lopez, Myriam; Hembry, Ian; Dodd, Barbara G. – Educational and Psychological Measurement, 2013
This study compares the progressive-restricted standard error (PR-SE) exposure control procedure to three commonly used procedures in computerized adaptive testing, the randomesque, Sympson-Hetter (SH), and no exposure control methods. The performance of these four procedures is evaluated using the three-parameter logistic model under the…
Descriptors: Computer Assisted Testing, Adaptive Testing, Comparative Analysis, Statistical Analysis