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Education Sciences
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Educational Assessment Theories and Methodologies: Trends in Standardized Testing
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Educational Assessment Theories and Methodologies: Trends in Standardized Testing

A special issue of Education Sciences (ISSN 2227-7102).

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 10451

Special Issue Editors

Prof. Dr. Rachel Vannatta

E-Mail Website
Guest Editor
College of Education and Human Development, Bowling Green State University, Bowling Green, OH 43403, USA
Interests: the instruction of statistics with the integration of technology; assessment and evaluation
Dr. Audrey Conway Roberts

E-Mail Website
Guest Editor
College of Education and Human Development, Bowling Green State University, Bowling Green, OH 43403, USA
Interests: learning environments; quantitative methodologies; survey validation

Special Issue Information

Dear Colleagues,

Standardized testing has long played a pivotal role in shaping educational systems globally, influencing policy decisions, curriculum development, and student outcomes. Due to recent advances in technology and artificial intelligence as well as changes in assessment policy, the theory and methodologies of standardized testing continue to evolve.

This Special Issue, titled Educational Assessment Theories and Methodologies: Trends in Standardized Testing, aims to foster a deeper understanding of these complex dynamics and invites scholars to explore the theories, methodologies, policies, and research shaping standardized testing today. By bringing together diverse and international perspectives, this issue seeks to offer fresh insights into the role of standardized testing in an era of rapid educational and technological change.

We invite original research papers that address one or more of the following themes:

  • Theoretical Foundations in Educational Assessment: Frameworks and paradigms guiding testing practices
  • National, State, and Provincial Testing Policies: Impacts of policy changes on the design and implementation of assessments and/or on student outcomes.
  • Artificial Intelligence (AI) in Standardized Testing: Innovations in AI-driven design, scoring, feedback systems, and ethical considerations.
  • Advances in Adaptive Testing: Novel trends and adaptations, and implications for personalized learning and assessment.
  • Bias, Equity, and Inclusivity: Addressing bias in test design, accessibility, and fairness.
  • Global Perspectives: Comparative analyses of testing trends across different regions of the world.

Articles may include empirical research, theoretical or conceptual analyses, case studies, and/or reviews of current trends and practices. We look forward to engaging with work that critically reflects on the complexities of standardized testing and its future in education.

Prof. Dr. Rachel Vannatta
Dr. Audrey Conway Roberts
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • standardized testing
  • PK-16 assessment
  • educational policy
  • artificial intelligence
  • educational technology

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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28 pages, 1707 KB  
Article
Validation Is a Methodology! Guideposts for Assessment Development and Validation
by Jonathan David Bostic
Educ. Sci. 2026, 16(4), 565; https://doi.org/10.3390/educsci16040565 - 2 Apr 2026
Viewed by 495
Abstract
Measurement and assessment in Science, Technology, Engineering, and Mathematics (STEM) education is one central topic within STEM education scholarship. While there has been an increase in validation-related scholarship within STEM education, there are few guides for users to conduct validation work. Providing guidance [...] Read more.
Measurement and assessment in Science, Technology, Engineering, and Mathematics (STEM) education is one central topic within STEM education scholarship. While there has been an increase in validation-related scholarship within STEM education, there are few guides for users to conduct validation work. Providing guidance for a broad readership, not just methodologists, offers potential for scholars from more backgrounds to engage in validation. To that end, the purpose of this paper is to build upon past scholarship and both articulate and situate validation as a methodology. Guideposts are provided to support readers as they engage in validation scholarship. A strategy is also provided to give readers support as they engage in validation scholarship. One key outcome from this paper is foundational work that scholars can leverage and extend, challenge, and generate new validation-related work, which in turn moves assessment practice and scholarship forward. Full article
(This article belongs to the Special Issue Educational Assessment Theories and Methodologies: Trends in Standardized Testing)
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15 pages, 496 KB  
Article
Predictors of Early College Success in the U.S.: An Initial Examination of Test-Optional Policies
by Kaylani Rae Othman, Rachel A. Vannatta and Audrey Conway Roberts
Educ. Sci. 2025, 15(9), 1089; https://doi.org/10.3390/educsci15091089 - 22 Aug 2025
Viewed by 2715
Abstract
For decades, the U.S. college admissions process has utilized standardized exams as critical indicators of college readiness. With the onset of the COVID pandemic, the majority of 4-year universities implemented the Test-Optional policy to improve college access and enrollment. The Test-Optional policy allows [...] Read more.
For decades, the U.S. college admissions process has utilized standardized exams as critical indicators of college readiness. With the onset of the COVID pandemic, the majority of 4-year universities implemented the Test-Optional policy to improve college access and enrollment. The Test-Optional policy allows prospective high school students to apply to institutions that have implemented this policy without a SAT or ACT score. This study examined the use of the Test-Optional policy and its relationship with early college success. Forward multiple regression examined which variables of High School GPA, Students of Color, First-Generation Status, Test-Optional, Pell Eligible, and Pre-College Credits best predict undergraduate first-year GPA. The results generated a five-variable model that accounted for 31% of the variability in first-year college GPA. High School GPA was the strongest predictor, while Test-Optional was not entered into the model. Binary logistic regression examined predictors of first-year college completion. Our results revealed the model including High School GPA, which tripled the odds of first-year completion. Again, Test-Optional was not included in the model. Although Students of Color and Pell Eligibility utilized Test-Optional significantly more than their peers, Test-Optional was not a significant predictor of first-year College GPA or first-year completion. Full article
(This article belongs to the Special Issue Educational Assessment Theories and Methodologies: Trends in Standardized Testing)
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22 pages, 1165 KB  
Article
AI-Assisted Exam Variant Generation: A Human-in-the-Loop Framework for Automatic Item Creation
by Charles MacDonald Burke
Educ. Sci. 2025, 15(8), 1029; https://doi.org/10.3390/educsci15081029 - 11 Aug 2025
Cited by 3 | Viewed by 5660
Abstract
Educational assessment relies on well-constructed test items to measure student learning accurately, yet traditional item development is time-consuming and demands specialized psychometric expertise. Automatic item generation (AIG) offers template-based scalability, and recent large language model (LLM) advances promise to democratize item creation. However, [...] Read more.
Educational assessment relies on well-constructed test items to measure student learning accurately, yet traditional item development is time-consuming and demands specialized psychometric expertise. Automatic item generation (AIG) offers template-based scalability, and recent large language model (LLM) advances promise to democratize item creation. However, fully automated approaches risk introducing factual errors, bias, and uneven difficulty. To address these challenges, we propose and evaluate a hybrid human-in-the-loop (HITL) framework for AIG that combines psychometric rigor with the linguistic flexibility of LLMs. In a Spring 2025 case study at Franklin University Switzerland, the instructor collaborated with ChatGPT (o4-mini-high) to generate parallel exam variants for two undergraduate business courses: Quantitative Reasoning and Data Mining. The instructor began by defining “radical” and “incidental” parameters to guide the model. Through iterative cycles of prompt, review, and refinement, the instructor validated content accuracy, calibrated difficulty, and mitigated bias. All interactions (including prompt templates, AI outputs, and human edits) were systematically documented, creating a transparent audit trail. Our findings demonstrate that a HITL approach to AIG can produce diverse, psychometrically equivalent exam forms with reduced development time, while preserving item validity and fairness, and potentially reducing cheating. This offers a replicable pathway for harnessing LLMs in educational measurement without sacrificing quality, equity, or accountability. Full article
(This article belongs to the Special Issue Educational Assessment Theories and Methodologies: Trends in Standardized Testing)
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Review

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11 pages, 251 KB  
Review
The Difficulty with Setting a Standard Based on Difficulty: The Role of Validity in Determining Assessment Standards
by Steven Ashley Burr, Daniel Zahra and Iain Martin Robinson
Educ. Sci. 2026, 16(3), 488; https://doi.org/10.3390/educsci16030488 - 21 Mar 2026
Viewed by 199
Abstract
When setting standards for high-stakes assessments, it is necessary to be clear whether the primary criterion should be importance or difficulty. While standard setting practices frequently rely on the difficulty to determine passing or failing thresholds, this can undermine the intended inferences [...] Read more.
When setting standards for high-stakes assessments, it is necessary to be clear whether the primary criterion should be importance or difficulty. While standard setting practices frequently rely on the difficulty to determine passing or failing thresholds, this can undermine the intended inferences and uses of assessments. Standard setting based on difficulty conflates importance with subjective perceptions of difficulty, misguiding decisions about what candidates need to demonstrate for safe and effective practice. We explore several reasons why difficulty does not align with the aims of assessment and how prioritising importance can better reflect the intended uses and inferences of a test. Our analysis incorporates: (1) the relevance of validity to setting standards, (2) potential misunderstandings of norm- and criterion referencing, (3) critical differentiation between importance and difficulty, (4) reasons for prioritising validity, (5) the variability of candidates’ difficulty perceptions, (6) challenges of assessor judgments, (7) the need for clear definitions of competence, and (8) the appropriate use of ‘would’ vs. ‘should’ in establishing standards. Ultimately, a valid standard reflects the ability to demonstrate performance of what is important, not difficult, in the workplace. Full article
(This article belongs to the Special Issue Educational Assessment Theories and Methodologies: Trends in Standardized Testing)
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