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  • Computerized adaptive testing - Wikipedia;
  • Introduction.

As the test taker does less well, the questions become less difficult see fig. Computerized adaptive tests require the following components: a pool of questions to draw from, calibrated to a common measurement scale; a mechanism to select questions on the basis of the student's responses; a process to score the student's responses; a process to terminate the test; and reports that relate scores to the student's instructional needs. Since adaptive testing was first developed, its use has expanded exponentially. It has been adopted by the armed forces, and by the licensure and certification bodies for a variety of professions.

In most studies, adaptive tests have been found to be as accurate as fixed-form tests that are twice as long. This enables the assessment to have fewer questions and to take less time while still providing good information about student achievement. Studies also reveal that adaptive tests drawing from large item pools can provide much more information, and more precise information, than fixed-form tests do about both students who are struggling and students who are excelling.

Because they are administered on a computer, adaptive tests provide immediate feedback to students and teachers. These instantaneous results help ensure that test data can be used to adjust instruction.

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Even with the increased sophistication of today's adaptive testing programs, there are some limitations. The primary one is ensuring that schools have the network infrastructure to successfully implement a web-based adaptive testing model.

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A related issue is providing the devices to take an adaptive test, such as laptops or tablets. It is the responsibility of both schools and test publishers to confirm that adaptive tests function comparably on multiple delivery devices. It often feels as though our schools are designed as factories in which the goal is to create a consistent product the successful graduate with the same attributes time after time. And our accountability and assessment policies appear to be built to reinforce this factory-style model. But the purpose of education isn't to create a single model of an adult.

It is to help create the next generation of lawyers, mathematicians, firefighters, college professors, bricklayers, political activists, doctors, architects, writers, bakers, and sculptors. It is to foster adults who will make positive contributions to a world that doesn't exist today.


Imagine a school in the s trying to teach a student to be a webpage designer! If we envision education as meeting each student's academic needs, why wouldn't we devise an assessment system that adjusts to these same individual needs? That is exactly what adaptive testing does. The advantage of an adaptive test is that it is customized, providing a better measure of achievement by offering questions that are specifically targeted to each student's ability level.

High-performing students are not bored by breezing through items that are too easy for them, and lower-performing students are not discouraged by slogging their way through a large set of items that are too difficult. Student engagement increases because students at all ability levels encounter questions that are challenging, yet not insurmountable. Educators know that, even when students enter kindergarten, they have a wide range of achievement levels.

Some of them know how to read sentences, others can recognize a few words, some know their alphabet, and some are unfamiliar with books. We need to strengthen all students' reading skills regardless of his or her starting point. As students move up through the grades, they continue to progress at different rates and to learn some skills more quickly than they learn others. Treating students as if they don't have differences in achievement is not a rational way to help all students succeed.

By providing assessment information tailored to each student, adaptive testing enables teachers to better target instructional materials and programs. Adaptive tests are becoming more prevalent in school districts across the United States. It is estimated that approximately 7, school districts representing 30, schools are currently using some form of computerized adaptive testing.

The number of school districts using these tests will likely continue to increase as providers develop adaptive assessments that reflect the Common Core State Standards and that can be used to help identify students who are likely to have difficulty in demonstrating proficiency on the new Common Core assessments. Mountain View teachers use a team-based, collaborative approach to instruction.

Each instructional team includes four core subject teachers and one special education instructor, and computerized adaptive assessment data, along with information from other assessments, play an integral role in their decision making. Mountain View's principal, Julie Arnold, and assistant principal, Veronica Sanders, have built time into the school's schedule to enable teachers to examine student data and collaboratively build responsive instructional strategies. The teachers group students in similar or intentionally mixed-level groupings for appropriate interventions.

We look at the strands within the test to see where students are performing the best and the worst comparatively. That helps us design instruction. Over the last couple of years, many students were struggling with the critical-thinking strand. So we as a team made a decision to focus on open-ended questioning and work on student responses to those questions. As a result, student performance on critical-thinking tasks has improved in class and on the test. In fall , 82 percent of Weaks's students were proficient in critical thinking tasks as measured by district quarterly assessments and classroom final exams.

Following the educators' intensive focus on open-ended questioning, the percentage of students demonstrating proficiency rose to 91 percent by spring and to 94 percent by spring We typically find that higher-performing kids are scoring at levels indicating that they are ready for content they've never encountered outside of the test. This has driven us to do special projects that we wouldn't have otherwise considered and that helped those high-performing students grow further. Weaks' instructional team gave these high-performing students the opportunity, in small groups, to self-select reading and writing projects that interested them.

One group took on the challenge of creating and self-publishing a book together, pushing the boundaries of their individual abilities and practicing successful collaboration. Similarly, 6th grade mathematics teacher Rebecca Spaeth shifts students from individual desks to groups and back to individual desks on the basis of their performance in particular areas of mathematics.

Recently, Spaeth and other math educators used the adaptive tool to identify students who needed additional help with measurement tasks. They found online resources, changed classroom practices, and provided targeted instruction to boost individual and overall student achievement in that area. Mountain View educators have also used the data from adaptive assessments to encourage students to take greater ownership of their learning.

An understanding of the test data is part of the vocabulary of students, teachers, and even parents. Students know their scores—and they also quickly grasp the mechanics of an adaptive test. Although adaptive tests have exposure control algorithms to prevent overuse of a few items, [2] the exposure conditioned upon ability is often not controlled and can easily become close to 1.

That is, it is common for some items to become very common on tests for people of the same ability. This is a serious security concern because groups sharing items may well have a similar functional ability level. In fact, a completely randomized exam is the most secure but also least efficient. Review of past items is generally disallowed.

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Adaptive tests tend to administer easier items after a person answers incorrectly. Supposedly, an astute test-taker could use such clues to detect incorrect answers and correct them. Or, test-takers could be coached to deliberately pick wrong answers, leading to an increasingly easier test. After tricking the adaptive test into building a maximally easy exam, they could then review the items and answer them correctly--possibly achieving a very high score.

Computerized adaptive testing: A primer (second edition) | SpringerLink

Test-takers frequently complain about the inability to review. This list does not include practical issues, such as item pretesting or live field release. A pool of items must be available for the CAT to choose from. Typically, item response theory is employed as the psychometric model. In CAT, items are selected based on the examinee's performance up to a given point in the test.

However, the CAT is obviously not able to make any specific estimate of examinee ability when no items have been administered. So some other initial estimate of examinee ability is necessary. If some previous information regarding the examinee is known, it can be used, [1] but often the CAT just assumes that the examinee is of average ability - hence the first item often being of medium difficulty.

As mentioned previously, item response theory places examinees and items on the same metric. Therefore, if the CAT has an estimate of examinee ability, it is able to select an item that is most appropriate for that estimate. After an item is administered, the CAT updates its estimate of the examinee's ability level.

If the examinee answered the item correctly, the CAT will likely estimate their ability to be somewhat higher, and vice versa. This is done by using the item response function from item response theory to obtain a likelihood function of the examinee's ability. Two methods for this are called maximum likelihood estimation and Bayesian estimation. The latter assumes an a priori distribution of examinee ability, and has two commonly used estimators: expectation a posteriori and maximum a posteriori.

The CAT algorithm is designed to repeatedly administer items and update the estimate of examinee ability. This will continue until the item pool is exhausted unless a termination criterion is incorporated into the CAT. Often, the test is terminated when the examinee's standard error of measurement falls below a certain user-specified value, hence the statement above that an advantage is that examinee scores will be uniformly precise or "equiprecise. In many situations, the purpose of the test is to classify examinees into two or more mutually exclusive and exhaustive categories.

This includes the common "mastery test" where the two classifications are "Pass" and "Fail," but also includes situations where there are three or more classifications, such as "Insufficient," "Basic," and "Advanced" levels of knowledge or competency. For example, a new termination criterion and scoring algorithm must be applied that classifies the examinee into a category rather than providing a point estimate of ability.

There are two primary methodologies available for this. The more prominent of the two is the sequential probability ratio test SPRT. Note that this is a point hypthesis formulation rather than a composite hypothesis formulation [8] that is more conceptually appropriate. A composite hypothesis formulation would be that the examinee's ability is in the region above the cutscore or the region below the cutscore. A confidence interval approach is also used, where after each item is administered, the algorithm determines the probability that the examinee's true-score is above or below the passing score [9] [10].

As a practical matter, the algorithm is generally programmed to have a minimum and a maximum test length or a minimum and maximum administration time. This approach was originally called "adaptive mastery testing" [9] but it can be applied to non-adaptive item selection and classification situations of two or more cutscores the typical mastery test has a single cutscore.

The item selection algorithm utilized depends on the termination criterion.

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Maximizing information at the cutscore is more appropriate for the SPRT because it maximizes the difference in the probabilities used in the likelihood ratio.