Computerized Adaptive Testing
Computerized adaptive testing (CAT) is a form of Computer-based assessment, computer-based test that adapts to the examinee's ability level. For this reason, it has also been called tailored testing. In other words, it is a form of computer-administered Test (student assessment), test in which the next item or set of items selected to be administered depends on the correctness of the test taker's responses to the most recent items administered. Description CAT successively selects questions (test items) for the purpose of maximizing the precision of the exam based on what is known about the examinee from previous questions. From the examinee's perspective, the difficulty of the exam seems to tailor itself to their level of ability. For example, if an examinee performs well on an item of intermediate difficulty, they will then be presented with a more difficult question. Or, if they performed poorly, they would be presented with a simpler question. Compared to static tests that near ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Computer-based Assessment
Electronic assessment, also known as digital assessment, e-assessment, online assessment or computer-based assessment, is the use of information technology in assessment such as educational assessment, health assessment, psychiatric assessment, and psychological assessment. This covers a wide range of activities ranging from the use of a word processor for assignments to on-screen testing. Specific types of e-assessment include multiple choice, online/electronic submission, computerized adaptive testing such as the Frankfurt Adaptive Concentration Test, and computerized classification testing. Different types of online assessments contain elements of one or more of the following components, depending on the assessment's purpose: formative, summative and diagnostic. Instant and detailed feedback may (or may not) be enabled. In formative assessment, often defined as 'assessment for learning', digital tools are increasingly being adopted by schools, higher education institutio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Statistic
A statistic (singular) or sample statistic is any quantity computed from values in a sample which is considered for a statistical purpose. Statistical purposes include estimating a population parameter, describing a sample, or evaluating a hypothesis. The average (or mean) of sample values is a statistic. The term statistic is used both for the function (e.g., a calculation method of the average) and for the value of the function on a given sample (e.g., the result of the average calculation). When a statistic is being used for a specific purpose, it may be referred to by a name indicating its purpose. When a statistic is used for estimating a population parameter, the statistic is called an '' estimator''. A population parameter is any characteristic of a population under study, but when it is not feasible to directly measure the value of a population parameter, statistical methods are used to infer the likely value of the parameter on the basis of a statistic computed from a s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Collectively Exhaustive Events
In probability theory and logic, a set of events is jointly or collectively exhaustive if at least one of the events must occur. For example, when rolling a six-sided die, the events 1, 2, 3, 4, 5, and 6 are collectively exhaustive, because they encompass the entire range of possible outcomes. Another way to describe collectively exhaustive events is that their union must cover all the events within the entire sample space. For example, events A and B are said to be collectively exhaustive if :A \cup B = S where S is the sample space. Compare this to the concept of a set of mutually exclusive events. In such a set no more than one event can occur at a given time. (In some forms of mutual exclusion only one event can ever occur.) The set of all possible die rolls is both mutually exclusive and collectively exhaustive (i.e., " MECE"). The events 1 and 6 are mutually exclusive but not collectively exhaustive. The events "even" (2,4 or 6) and "not-6" (1,2,3,4, or 5) are als ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Mutually Exclusive
In logic and probability theory, two events (or propositions) are mutually exclusive or disjoint if they cannot both occur at the same time. A clear example is the set of outcomes of a single coin toss, which can result in either heads or tails, but not both. In the coin-tossing example, both outcomes are, in theory, collectively exhaustive, which means that at least one of the outcomes must happen, so these two possibilities together exhaust all the possibilities. However, not all mutually exclusive events are collectively exhaustive. For example, the outcomes 1 and 4 of a single roll of a six-sided die are mutually exclusive (both cannot happen at the same time) but not collectively exhaustive (there are other possible outcomes; 2,3,5,6). Logic In logic, two propositions \phi and \psi are mutually exclusive if it is not logically possible for them to be true at the same time; that is, \lnot (\phi \land \psi) is a tautology. To say that more than two propositions are ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Maximum Likelihood
In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of an assumed probability distribution, given some observed data. This is achieved by maximizing a likelihood function so that, under the assumed statistical model, the observed data is most probable. The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference. If the likelihood function is differentiable, the derivative test for finding maxima can be applied. In some cases, the first-order conditions of the likelihood function can be solved analytically; for instance, the ordinary least squares estimator for a linear regression model maximizes the likelihood when the random errors are assumed to have normal distributions with the same variance. From the perspective of Bayesian inference, ML ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Bayesian Estimation
In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior probability, posterior expected value of a loss function (i.e., the posterior expected loss). Equivalently, it maximizes the posterior expectation of a utility function. An alternative way of formulating an estimator within Bayesian statistics is maximum a posteriori estimation. Definition Suppose an unknown parameter \theta is known to have a prior distribution \pi. Let \widehat = \widehat(x) be an estimator of \theta (based on some measurements ''x''), and let L(\theta,\widehat) be a loss function, such as squared error. The Bayes risk of \widehat is defined as E_\pi(L(\theta, \widehat)), where the Expected value, expectation is taken over the probability distribution of \theta: this defines the risk function as a function of \widehat. An estimator \widehat is said to be a ''Bayes estimator'' if it minimizes the Bayes risk among all estimat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Maximum Likelihood Estimation
In statistics, maximum likelihood estimation (MLE) is a method of estimation theory, estimating the Statistical parameter, parameters of an assumed probability distribution, given some observed data. This is achieved by Mathematical optimization, maximizing a likelihood function so that, under the assumed statistical model, the Realization (probability), observed data is most probable. The point estimate, point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference. If the likelihood function is Differentiable function, differentiable, the derivative test for finding maxima can be applied. In some cases, the first-order conditions of the likelihood function can be solved analytically; for instance, the ordinary least squares estimator for a linear regression model maximizes the likelihood when ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Likelihood Function
A likelihood function (often simply called the likelihood) measures how well a statistical model explains observed data by calculating the probability of seeing that data under different parameter values of the model. It is constructed from the joint probability distribution of the random variable that (presumably) generated the observations. When evaluated on the actual data points, it becomes a function solely of the model parameters. In maximum likelihood estimation, the argument that maximizes the likelihood function serves as a point estimate for the unknown parameter, while the Fisher information (often approximated by the likelihood's Hessian matrix at the maximum) gives an indication of the estimate's precision. In contrast, in Bayesian statistics, the estimate of interest is the ''converse'' of the likelihood, the so-called posterior probability of the parameter given the observed data, which is calculated via Bayes' rule. Definition The likelihood function, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Information
Information is an Abstraction, abstract concept that refers to something which has the power Communication, to inform. At the most fundamental level, it pertains to the Interpretation (philosophy), interpretation (perhaps Interpretation (logic), formally) of that which may be sensed, or their abstractions. Any natural process that is not completely random and any observable pattern in any Media (communication), medium can be said to convey some amount of information. Whereas digital signals and other data use discrete Sign (semiotics), signs to convey information, other phenomena and artifacts such as analog signals, analogue signals, poems, pictures, music or other sounds, and current (fluid), currents convey information in a more continuous form. Information is not knowledge itself, but the meaning (philosophy), meaning that may be derived from a representation (mathematics), representation through interpretation. The concept of ''information'' is relevant or connected t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Theta
Theta (, ) uppercase Θ or ; lowercase θ or ; ''thē̂ta'' ; Modern: ''thī́ta'' ) is the eighth letter of the Greek alphabet, derived from the Phoenician letter Teth 𐤈. In the system of Greek numerals, it has a value of 9. Greek In Ancient Greek, θ represented the aspirated voiceless dental plosive , but in Modern Greek it represents the voiceless dental fricative . Forms In its archaic form, θ was written as a cross within a circle (as in the Etruscan or ), and later, as a line or point in circle ( or ). The cursive form was retained by Unicode as , separate from . (There is also .) For the purpose of writing Greek text, the two can be font variants of a single character, but are also used as distinct symbols in technical and mathematical contexts. Extensive lists of examples follow below at Mathematics and Science. is also common in biblical and theological usage e.g. instead of πρόθεσις (means placing in public or laying out a corpse). ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Automatic Item Generation
Automatic item generation (AIG), or automated item generation, is a process linking psychometrics with computer programming. It uses a computer algorithm to automatically create test items that are the basic building blocks of a psychological test. The method was first described by John R. Bormuth in the 1960s but was not developed until recently. AIG uses a two-step process: first, a test specialist creates a template called an item model; then, a computer algorithm is developed to generate test items. So, instead of a test specialist writing each individual item, computer algorithms generate families of items from a smaller set of parent item models. More recently, neural networks, including Large Language Models, such as the GPT family, have been used successfully for generating items automatically. Context In psychological testing, the responses of the test taker to test items provide objective measurement data for a variety of human characteristics. Some characteristics measu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Summative Assessment
Summative assessment, summative evaluation, or assessment of learning is the assessment of participants in an educational program. Summative assessments are designed both to assess the effectiveness of the program and the learning of the participants. This contrasts with formative assessment which summarizes the participants' development at a particular time to inform instructors of student learning progress. The goal of summative assessment is to evaluate student learning at the end of an instructional unit by comparing it against a standard or benchmark. Summative assessments may be distributed throughout a course or often after a particular unit (or collection of topics) . Summative assessment usually involves students receiving a grade that indicates their level of performance. Grading systems can include a percentage, pass/fail, or some other form of scale grade. Summative assessments are weighed more than formative assessments. Summative assessments are often high stak ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |