Post-Examination Processing System in .NET

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Post-Examination Processing System
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Once a test is completed, the test taker s data are typically transmitted back to a central processing facility In most cases, the same data transmission channel as used to send the test materials and registration data to the test center will serve for transmitting the examination results data When the data arrives at the central processing facility, ve basic functions are needed: (1) reconciliation; (2) archiving; (3) psychometric analyses; (4) nal scoring; and (5) score reporting and communication Data Reconciliation Reconciliation is an essential accounting function that ensures the receipt of an uncorrupted test record for every eligible examinee scheduled to test in every test center The reconciliation subsystem must provide up-to-date counts and details of discrepancies regarding the multitude of examinee records being processed through the system Some examples of reconciliation include matching the test records received from the test centers with test-taker eligibility records, accounting for duplicate records and identifying no shows The reconciliation process also needs to verify the data integrity of each test record (eg identifying incomplete, partial, or corrupted test records for resolution) Data Archiving The archival subsystem places the test records into long-term database storage This process typically stores all the examinee identi cation information, item identi ers, raw responses, scored response values, and cumulative time per item or performance task Ef cient relational database designs for archiving the data are relatively common In principle, a test taker s entire examination sequence should be retrievable from the archive, regardless of how or where the data is actually stored Psychometric Analyses The psychometric analysis subsystem is an omnibus collection of software tools and psychometric expertise that underlies scoring and item banking activities
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Item analysis and key validation are two common types of analysis that must be performed before scoring can take place Other psychometric analyses may include IRT calibrations, equating analyses, differential item functioning analyses, dimensionality analyses, and item banking operations used to generate aggregated item statistics for subsequent scoring or for future test assembly (see the Item development and banking system section) Most of the applications that carry out these types of analysis are separate software utilities An important requirement of the post-examination processing system is therefore the capability to export item response and scored examinee data to the formats needed by various psychometric analysis software tools For example, many item analysis and IRT calibration software programs use xed-column, ASCII (text) response records as inputs The need to exibly select the intersection of a set of items with a set of test takers usually derived by a database query that produces either normalized or rectangular person-by-item at les is a fundamental requirement for subsequently analysing most data, using existing psychometric analysis software tools Final Test Scoring Final test scoring represents a special class of psychometric analyses A separate subsystem for nal scoring provides greater exibility in terms of adding new scoring functions as well as migrating these same software-scoring components from post-examination processing to the test delivery driver, if needed for real-time scoring However, it is important to realize that, for high-stake tests, there are distinct bene ts associated with moving the nal scoring process out of the test delivery system (eg being able to check for cheating problems, miskeys, etc before scores are released) There are three basic levels to nal scoring The rst level of scoring converts the test taker s raw responses to some type of item or unit score For example, a correct answer choice on a multiple-choice question is typically converted to a one; an incorrect choice is converted to a zero A constructed response item might be subjected to a pattern-matching algorithm or some type of semi-intelligent scoring algorithm (see, eg Bejar, 1991; Luecht & Clauser, 2002) The test delivery driver may also need to support this rst level of scoring functionality (ie raw response conversion), especially for adaptive testing However, viewing this conversion function as part of a separate subsystem may lead to a more open-ended design that facilitates adding new scoring protocols (eg intelligent scoring for new item types) as well as providing easy rescore operations The second level of scoring is aggregated scoring over items or test units Number-correct scoring, weighted numbercorrect scoring, and IRT maximum likelihood or Bayesian scoring are common aggregate scoring functions The details of these scoring algorithms are beyond the scope of this chapter Suf ce it to say that a data vector of scored raw responses and a corresponding vector or array of weights or item statistics are the required data for most aggregate scoring functions The nal scoring function is a transformation of the aggregate scoring function These functions
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