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Theoretical and Practical Advances in Computer-Based Educational Measurement.

Yazar:Veldkamp, Bernard P
Katkıda bulunan(lar):Sluijter, Cor
Materyal türü: KonuKonuSeri kaydı: Yayıncı: Cham : Springer International Publishing AG, 2019Telif hakkı tarihi: �2019Tanım: 1 online resource (394 pages)İçerik türü:text Ortam türü:computer Taşıyıcı türü: online resourceISBN: 9783030184803Tür/Form:Electronic books.Ek fiziksel biçimler:Print version:: Theoretical and Practical Advances in Computer-Based Educational MeasurementLOC classification: LB3050-3060.87Çevrimiçi kaynaklar: Click to View
İçindekiler:
Intro -- Preface -- Introduction -- Contents -- Improving Test Quality -- 1 The Validity of Technology Enhanced Assessments-Threats and Opportunities -- 1.1 Introduction -- 1.2 Innovations in Technology-Enhanced Assessments -- 1.2.1 Innovations in Items and Tasks -- 1.2.2 Innovations in Test Construction, Assembly and Delivery -- 1.2.3 Innovations Regarding Personal Needs and Preferences -- 1.3 Validity and Validation -- 1.4 Validity of Innovative Technology-Enhanced Assessments -- 1.4.1 Inferences Within the IUA -- 1.4.2 Validity Argument of Technology-Enhanced Assessments -- 1.5 Concluding Remarks -- References -- 2 A Framework for Improving the Accessibility of Assessment Tasks -- 2.1 Accessibility of Assessments -- 2.2 Principles that Underlie Accessible Assessment Design -- 2.2.1 Principles from Universal Design -- 2.2.2 Principles from Cognitive Load Theory -- 2.3 Evaluating and Improving Accessibility of Assessment Tasks from a Test Takers' Perspective -- 2.3.1 Supporting Orientation by a Clear Assignment -- 2.3.2 Supporting Information Processing and Devising Solutions -- 2.3.3 Facilitating Responding -- 2.3.4 Facilitating Monitoring and Adjusting -- 2.4 An Application of the Test Accessibility Framework: The Dutch Driving Theory Exam -- 2.4.1 Innovations in the Dutch Traffic Theory Exam for Car Drivers -- 2.4.2 Applied Modifications in the Response Mode of Theory Items -- 2.4.3 Psychometric Indications of Accessibility Improvement? -- 2.4.4 Item Selection -- 2.4.5 Data Collection -- 2.4.6 Data Analyses -- 2.4.7 Results -- 2.5 Discussion -- References -- 3 The Design and Validation of the Renewed Systems-Oriented Talent Management Model -- 3.1 Introduction -- 3.1.1 Problem Situation and Purpose of the Study -- 3.2 Theoretical Framework -- 3.2.1 The Management Building Blocks Framework -- 3.2.2 Systems Theory.
3.2.3 Evidence-Based Systems-Oriented Talent Management -- 3.3 Renewed STM Diagrams -- 3.3.1 Renewed STM Diagram 1: Aligning Organisational Structure and Human Talent -- 3.3.2 Renewed STM Diagram 2: Aligning Organisational Culture and Human Talent -- 3.3.3 Renewed STM Diagram 3: Aligning Business Strategy and Human Talent -- 3.4 Implications of the STM for Educational Measurement -- 3.5 Conclusion, Limitations, and Recommendations -- 3.5.1 Conclusion -- 3.5.2 Application to Educational Measurement -- 3.5.3 Limitations -- 3.5.4 Recommendations and Implications -- References -- 4 Assessing Computer-Based Assessments -- 4.1 Introduction -- 4.2 The RCEC Review System for the Evaluation of Computer-Based Tests -- 4.2.1 Purpose and Use of the Educational Test or Exam -- 4.2.2 Quality of Test Material -- 4.2.3 Representativeness -- 4.2.4 Reliability -- 4.2.5 Standard Setting and Standard Maintenance -- 4.2.6 Test Administration and Security -- 4.3 Reviewing a Computer Based Test -- 4.3.1 Purpose and Use of the Test -- 4.3.2 Quality of Test Material -- 4.3.3 Representativeness -- 4.3.4 Reliability (Measurement Precision) -- 4.3.5 Standard Setting and Standard Maintenance -- 4.3.6 Test Administration and Security -- 4.3.7 Review Conclusion -- 4.4 Discussion -- References -- Psychometrics -- 5 Network Psychometrics in Educational Practice -- 5.1 Introduction -- 5.2 The Curie-Weiss Model -- 5.2.1 Some Statistical Properties of the Curie-Weiss Model -- 5.2.2 The Curie-Weiss to Rasch Connection -- 5.3 Maximum Likelihood Estimation of the Curie-Weiss Model -- 5.3.1 Maximum Likelihood in the Complete Data Case -- 5.3.2 Maximum Likelihood Estimation in the Incomplete Data Case -- 5.3.3 The M-Step -- 5.4 Numerical Illustrations -- 5.4.1 Simulated Example -- 5.4.2 The Cito Eindtoets 2012 -- 5.5 Discussion -- References.
6 On the Number of Items in Testing Mastery of Learning Objectives -- 6.1 Introduction -- 6.2 Method -- 6.2.1 Simulation Study with Homogeneous Item Characteristics -- 6.2.2 Empirical Example -- 6.2.3 Simulation Study Based on Empirical Data and Heterogeneous Item Characteristics -- 6.2.4 Estimating and Validating a Predictive Model for Bayes Factors -- 6.3 Results -- 6.3.1 Simulation Study with Homogeneous Item Characteristics -- 6.3.2 Empirical Example -- 6.3.3 Simulation Based on the Empirical Data and with Heterogeneous Item Characteristics -- 6.3.4 Prediction Model -- 6.4 Discussion and Conclusions -- References -- 7 Exponential Family Models for Continuous Responses -- 7.1 Introduction -- 7.2 A Rasch Model for Continuous Responses -- 7.2.1 The Model -- 7.2.2 Parameter Estimation -- 7.3 An Extension of the M�uller Model -- 7.3.1 The Model -- 7.3.2 Parameter Estimation -- 7.4 Comparison of Information Functions Across Models -- 7.4.1 The Unit of the Latent Variable -- 7.4.2 An Example -- 7.5 Discussion -- Appendix -- References -- 8 Tracking Ability: Defining Trackers for Measuring Educational Progress -- 8.1 Introduction -- 8.2 Methods -- 8.2.1 Formalizing a Tracker -- 8.2.2 Example of a Tracker -- 8.2.3 Convergence in Kullback-Leibler Divergence -- 8.2.4 Simulating Surveys -- 8.3 Discussion -- References -- 9 Finding Equivalent Standards in Small Samples -- 9.1 Introduction -- 9.2 Method -- 9.3 Results -- 9.4 Conclusion and Discussion -- References -- Large Scale Assessments -- 10 Clustering Behavioral Patterns Using Process Data in PIAAC Problem-Solving Items -- 10.1 Introduction -- 10.1.1 Problem-Solving Items in PIAAC -- 10.1.2 Employability and PSTRE Skills -- 10.2 Method -- 10.2.1 Sample -- 10.2.2 Instrumentation -- 10.2.3 Features Extracted from Process Data -- 10.2.4 Clustering Sequence Data -- 10.2.5 K-Means Clustering -- 10.3 Results.
10.3.1 Cluster Determination -- 10.3.2 Cluster Membership and Proficiency Level -- 10.3.3 Cluster Membership and Employment-Based Background Variables -- 10.4 Discussion -- References -- 11 Reliability Issues in High-Stakes Educational Tests -- 11.1 Outline of the Problem -- 11.2 Preliminaries -- 11.3 MAP Proficiency Estimates Based on Number-Correct Scores -- 11.4 Equating Error -- 11.5 Simulation Study of Equating Errors -- 11.6 Conclusion -- References -- 12 Differential Item Functioning in PISA Due to Mode Effects -- 12.1 Introduction -- 12.2 Changes in PISA 2015 -- 12.3 Data -- 12.4 Differential Item Functioning -- 12.5 Results -- 12.5.1 DIF Between Modes -- 12.5.2 Trend Effects in the Netherlands -- 12.6 Conclusions and Discussion -- References -- 13 Investigating Rater Effects in International Large-Scale Assessments -- 13.1 Introduction -- 13.2 Scoring Human-Coded Items in PISA 2015 -- 13.2.1 Categorization of Items by Item Formats -- 13.2.2 Coding Design and Procedures -- 13.3 Construct Equivalence of Different Scoring Types in PISA -- 13.3.1 Methods -- 13.3.2 Findings -- 13.4 Rater Effects that Are Comparable Across Countries -- 13.4.1 Methods -- 13.4.2 Findings -- 13.5 Conclusion -- References -- Computerized Adaptive Testing in Educational Measurement -- 14 Multidimensional Computerized Adaptive Testing for Classifying Examinees -- 14.1 Introduction -- 14.2 Multidimensional Item Response Theory -- 14.3 Classification Methods -- 14.3.1 The SPRT for Between-Item Multidimensionality -- 14.3.2 The Confidence Interval Method for Between-Item Multidimensionality -- 14.3.3 The SPRT for Within-Item Multidimensionality -- 14.3.4 The Confidence Interval Method for Within-Item Multidimensionality -- 14.4 Item Selection Methods -- 14.4.1 Item Selection Methods for Between-Item Multidimensionality.
14.4.2 Item Selection Methods for Within-Item Multidimensionality -- 14.5 Examples -- 14.5.1 Example 1: Between-Item Multidimensionality -- 14.5.2 Example 2: Within-Item Multidimensionality -- 14.6 Conclusions and Discussion -- References -- 15 Robust Computerized Adaptive Testing -- 15.1 Introduction -- 15.2 Robust Test Assembly -- 15.3 Robust CAT Assembly -- 15.3.1 Constructing a Robust Item Pool -- 15.3.2 Numerical Example to Illustrate the Concept of Robust Item Pools -- 15.3.3 Towards an Algorithm for Robust CAT -- 15.4 Simulation Studies -- 15.4.1 Study 1 -- 15.4.2 Study 2 -- 15.4.3 Study 3 -- 15.4.4 Study Setup -- 15.5 Results -- 15.6 Conclusion -- References -- 16 On-the-Fly Calibration in Computerized Adaptive Testing -- 16.1 Introduction -- 16.1.1 Replenishment Strategies and On-the-Fly Calibration -- 16.1.2 On-the-Fly Calibration Methods -- 16.1.3 The Use of Reference Items in Modelling Bias -- 16.1.4 The Need for Underexposure Control -- 16.1.5 A Combination of Calibration Methods -- 16.2 Research Questions -- 16.3 Simulation Studies -- 16.3.1 Use of Reference Items in Elimination of Bias -- 16.3.2 Comparison of the Methods -- 16.4 Discussion -- References -- 17 Reinforcement Learning Applied to Adaptive Classification Testing -- 17.1 Introduction -- 17.2 Method -- 17.3 Framework -- 17.3.1 General Idea -- 17.3.2 Sequential Classification -- 17.3.3 Item Selection -- 17.3.4 Algorithm -- 17.4 Experiments -- 17.5 Discussion -- References -- Technological Developments in Educational Measurement -- 18 Feasibility and Value of Using a GoPro Camera and iPad to Study Teacher-Student Assessment Feedback Interactions -- 18.1 Introduction -- 18.1.1 The Value of Video Feedback -- 18.2 Method -- 18.2.1 Participants and Context -- 18.2.2 Data Collection Instruments and Procedures -- 18.2.3 Analysis -- 18.3 Results -- 18.3.1 Technical Results.
18.3.2 Teacher and Student Experiences.
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Intro -- Preface -- Introduction -- Contents -- Improving Test Quality -- 1 The Validity of Technology Enhanced Assessments-Threats and Opportunities -- 1.1 Introduction -- 1.2 Innovations in Technology-Enhanced Assessments -- 1.2.1 Innovations in Items and Tasks -- 1.2.2 Innovations in Test Construction, Assembly and Delivery -- 1.2.3 Innovations Regarding Personal Needs and Preferences -- 1.3 Validity and Validation -- 1.4 Validity of Innovative Technology-Enhanced Assessments -- 1.4.1 Inferences Within the IUA -- 1.4.2 Validity Argument of Technology-Enhanced Assessments -- 1.5 Concluding Remarks -- References -- 2 A Framework for Improving the Accessibility of Assessment Tasks -- 2.1 Accessibility of Assessments -- 2.2 Principles that Underlie Accessible Assessment Design -- 2.2.1 Principles from Universal Design -- 2.2.2 Principles from Cognitive Load Theory -- 2.3 Evaluating and Improving Accessibility of Assessment Tasks from a Test Takers' Perspective -- 2.3.1 Supporting Orientation by a Clear Assignment -- 2.3.2 Supporting Information Processing and Devising Solutions -- 2.3.3 Facilitating Responding -- 2.3.4 Facilitating Monitoring and Adjusting -- 2.4 An Application of the Test Accessibility Framework: The Dutch Driving Theory Exam -- 2.4.1 Innovations in the Dutch Traffic Theory Exam for Car Drivers -- 2.4.2 Applied Modifications in the Response Mode of Theory Items -- 2.4.3 Psychometric Indications of Accessibility Improvement? -- 2.4.4 Item Selection -- 2.4.5 Data Collection -- 2.4.6 Data Analyses -- 2.4.7 Results -- 2.5 Discussion -- References -- 3 The Design and Validation of the Renewed Systems-Oriented Talent Management Model -- 3.1 Introduction -- 3.1.1 Problem Situation and Purpose of the Study -- 3.2 Theoretical Framework -- 3.2.1 The Management Building Blocks Framework -- 3.2.2 Systems Theory.

3.2.3 Evidence-Based Systems-Oriented Talent Management -- 3.3 Renewed STM Diagrams -- 3.3.1 Renewed STM Diagram 1: Aligning Organisational Structure and Human Talent -- 3.3.2 Renewed STM Diagram 2: Aligning Organisational Culture and Human Talent -- 3.3.3 Renewed STM Diagram 3: Aligning Business Strategy and Human Talent -- 3.4 Implications of the STM for Educational Measurement -- 3.5 Conclusion, Limitations, and Recommendations -- 3.5.1 Conclusion -- 3.5.2 Application to Educational Measurement -- 3.5.3 Limitations -- 3.5.4 Recommendations and Implications -- References -- 4 Assessing Computer-Based Assessments -- 4.1 Introduction -- 4.2 The RCEC Review System for the Evaluation of Computer-Based Tests -- 4.2.1 Purpose and Use of the Educational Test or Exam -- 4.2.2 Quality of Test Material -- 4.2.3 Representativeness -- 4.2.4 Reliability -- 4.2.5 Standard Setting and Standard Maintenance -- 4.2.6 Test Administration and Security -- 4.3 Reviewing a Computer Based Test -- 4.3.1 Purpose and Use of the Test -- 4.3.2 Quality of Test Material -- 4.3.3 Representativeness -- 4.3.4 Reliability (Measurement Precision) -- 4.3.5 Standard Setting and Standard Maintenance -- 4.3.6 Test Administration and Security -- 4.3.7 Review Conclusion -- 4.4 Discussion -- References -- Psychometrics -- 5 Network Psychometrics in Educational Practice -- 5.1 Introduction -- 5.2 The Curie-Weiss Model -- 5.2.1 Some Statistical Properties of the Curie-Weiss Model -- 5.2.2 The Curie-Weiss to Rasch Connection -- 5.3 Maximum Likelihood Estimation of the Curie-Weiss Model -- 5.3.1 Maximum Likelihood in the Complete Data Case -- 5.3.2 Maximum Likelihood Estimation in the Incomplete Data Case -- 5.3.3 The M-Step -- 5.4 Numerical Illustrations -- 5.4.1 Simulated Example -- 5.4.2 The Cito Eindtoets 2012 -- 5.5 Discussion -- References.

6 On the Number of Items in Testing Mastery of Learning Objectives -- 6.1 Introduction -- 6.2 Method -- 6.2.1 Simulation Study with Homogeneous Item Characteristics -- 6.2.2 Empirical Example -- 6.2.3 Simulation Study Based on Empirical Data and Heterogeneous Item Characteristics -- 6.2.4 Estimating and Validating a Predictive Model for Bayes Factors -- 6.3 Results -- 6.3.1 Simulation Study with Homogeneous Item Characteristics -- 6.3.2 Empirical Example -- 6.3.3 Simulation Based on the Empirical Data and with Heterogeneous Item Characteristics -- 6.3.4 Prediction Model -- 6.4 Discussion and Conclusions -- References -- 7 Exponential Family Models for Continuous Responses -- 7.1 Introduction -- 7.2 A Rasch Model for Continuous Responses -- 7.2.1 The Model -- 7.2.2 Parameter Estimation -- 7.3 An Extension of the M�uller Model -- 7.3.1 The Model -- 7.3.2 Parameter Estimation -- 7.4 Comparison of Information Functions Across Models -- 7.4.1 The Unit of the Latent Variable -- 7.4.2 An Example -- 7.5 Discussion -- Appendix -- References -- 8 Tracking Ability: Defining Trackers for Measuring Educational Progress -- 8.1 Introduction -- 8.2 Methods -- 8.2.1 Formalizing a Tracker -- 8.2.2 Example of a Tracker -- 8.2.3 Convergence in Kullback-Leibler Divergence -- 8.2.4 Simulating Surveys -- 8.3 Discussion -- References -- 9 Finding Equivalent Standards in Small Samples -- 9.1 Introduction -- 9.2 Method -- 9.3 Results -- 9.4 Conclusion and Discussion -- References -- Large Scale Assessments -- 10 Clustering Behavioral Patterns Using Process Data in PIAAC Problem-Solving Items -- 10.1 Introduction -- 10.1.1 Problem-Solving Items in PIAAC -- 10.1.2 Employability and PSTRE Skills -- 10.2 Method -- 10.2.1 Sample -- 10.2.2 Instrumentation -- 10.2.3 Features Extracted from Process Data -- 10.2.4 Clustering Sequence Data -- 10.2.5 K-Means Clustering -- 10.3 Results.

10.3.1 Cluster Determination -- 10.3.2 Cluster Membership and Proficiency Level -- 10.3.3 Cluster Membership and Employment-Based Background Variables -- 10.4 Discussion -- References -- 11 Reliability Issues in High-Stakes Educational Tests -- 11.1 Outline of the Problem -- 11.2 Preliminaries -- 11.3 MAP Proficiency Estimates Based on Number-Correct Scores -- 11.4 Equating Error -- 11.5 Simulation Study of Equating Errors -- 11.6 Conclusion -- References -- 12 Differential Item Functioning in PISA Due to Mode Effects -- 12.1 Introduction -- 12.2 Changes in PISA 2015 -- 12.3 Data -- 12.4 Differential Item Functioning -- 12.5 Results -- 12.5.1 DIF Between Modes -- 12.5.2 Trend Effects in the Netherlands -- 12.6 Conclusions and Discussion -- References -- 13 Investigating Rater Effects in International Large-Scale Assessments -- 13.1 Introduction -- 13.2 Scoring Human-Coded Items in PISA 2015 -- 13.2.1 Categorization of Items by Item Formats -- 13.2.2 Coding Design and Procedures -- 13.3 Construct Equivalence of Different Scoring Types in PISA -- 13.3.1 Methods -- 13.3.2 Findings -- 13.4 Rater Effects that Are Comparable Across Countries -- 13.4.1 Methods -- 13.4.2 Findings -- 13.5 Conclusion -- References -- Computerized Adaptive Testing in Educational Measurement -- 14 Multidimensional Computerized Adaptive Testing for Classifying Examinees -- 14.1 Introduction -- 14.2 Multidimensional Item Response Theory -- 14.3 Classification Methods -- 14.3.1 The SPRT for Between-Item Multidimensionality -- 14.3.2 The Confidence Interval Method for Between-Item Multidimensionality -- 14.3.3 The SPRT for Within-Item Multidimensionality -- 14.3.4 The Confidence Interval Method for Within-Item Multidimensionality -- 14.4 Item Selection Methods -- 14.4.1 Item Selection Methods for Between-Item Multidimensionality.

14.4.2 Item Selection Methods for Within-Item Multidimensionality -- 14.5 Examples -- 14.5.1 Example 1: Between-Item Multidimensionality -- 14.5.2 Example 2: Within-Item Multidimensionality -- 14.6 Conclusions and Discussion -- References -- 15 Robust Computerized Adaptive Testing -- 15.1 Introduction -- 15.2 Robust Test Assembly -- 15.3 Robust CAT Assembly -- 15.3.1 Constructing a Robust Item Pool -- 15.3.2 Numerical Example to Illustrate the Concept of Robust Item Pools -- 15.3.3 Towards an Algorithm for Robust CAT -- 15.4 Simulation Studies -- 15.4.1 Study 1 -- 15.4.2 Study 2 -- 15.4.3 Study 3 -- 15.4.4 Study Setup -- 15.5 Results -- 15.6 Conclusion -- References -- 16 On-the-Fly Calibration in Computerized Adaptive Testing -- 16.1 Introduction -- 16.1.1 Replenishment Strategies and On-the-Fly Calibration -- 16.1.2 On-the-Fly Calibration Methods -- 16.1.3 The Use of Reference Items in Modelling Bias -- 16.1.4 The Need for Underexposure Control -- 16.1.5 A Combination of Calibration Methods -- 16.2 Research Questions -- 16.3 Simulation Studies -- 16.3.1 Use of Reference Items in Elimination of Bias -- 16.3.2 Comparison of the Methods -- 16.4 Discussion -- References -- 17 Reinforcement Learning Applied to Adaptive Classification Testing -- 17.1 Introduction -- 17.2 Method -- 17.3 Framework -- 17.3.1 General Idea -- 17.3.2 Sequential Classification -- 17.3.3 Item Selection -- 17.3.4 Algorithm -- 17.4 Experiments -- 17.5 Discussion -- References -- Technological Developments in Educational Measurement -- 18 Feasibility and Value of Using a GoPro Camera and iPad to Study Teacher-Student Assessment Feedback Interactions -- 18.1 Introduction -- 18.1.1 The Value of Video Feedback -- 18.2 Method -- 18.2.1 Participants and Context -- 18.2.2 Data Collection Instruments and Procedures -- 18.2.3 Analysis -- 18.3 Results -- 18.3.1 Technical Results.

18.3.2 Teacher and Student Experiences.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2022. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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