Innovations in Assessment: Harnessing Technology for Testing Cognitive Ability, Thinking Skill, and Competency

A special issue of Journal of Intelligence (ISSN 2079-3200).

Deadline for manuscript submissions: 25 December 2024 | Viewed by 1101

Special Issue Editor


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Guest Editor
Institute of Developmental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China
Interests: psychological tests and assessments; higher order thinking; metacognition, complex problem solving

Special Issue Information

Dear Colleagues,

The world is being transformed by the relentless march of technological progress. This evolution has, in turn, significantly influenced the domain of psychological testing and assessment over recent decades. The journey from rudimentary paper-and-pencil tests to mainframe computer-managed assessments, and now to fully computerized evaluations, underscores the profound impact of technology on the measurement of cognitive abilities, thinking skills, and competencies.

This extensive volume compiles insights from preeminent experts, addressing the challenges and opportunities presented by the integration of technology into testing and assessment practices. It delves into various modalities such as game-based assessment, virtual reality evaluation, simulation-based testing, handheld-device-assisted assessment, and artificial intelligence-driven testing and scoring.

Particularly noteworthy is the burgeoning interest in game-based assessment (GBA) within the realms of educational and organizational psychology. This is due to the recognition that traditional assessment techniques may not encapsulate the full spectrum of students’ and employees’ problem-solving skills. Digital games offer the unique advantage of immersing participants in realistic, simulated environments, a feature that sets them apart from conventional testing methods. The wealth of granular interaction data that can be obtained from these digital engagements presents an unparalleled opportunity for crafting GBAs in innovative ways that traditional assessments cannot match. Moreover, digital games often incorporate challenging, engaging, and intricate problems that are crucial for evidencing 21st-century competencies.

In light of these developments, the overarching aim of this Special Issue is to illuminate the design and implementation of cutting-edge measurement tools for cognitive abilities, thinking skills, and competencies.

The Editor of this Special Issue, hailing from the Journal of Intelligence, extends an invitation for submissions that showcase empirical research pertaining to, but not restricted to, the following areas:

  • Game-based assessment;
  • Virtual reality assessment;
  • Testing through simulations;
  • Assessment utilizing handheld devices;
  • AI-driven testing and scoring;
  • Assessment based on big data;
  • Multimodal data-based evaluation.

Additionally, the Editor encourages the submission of innovative theoretical frameworks and systematic reviews, including meta-analyses, on these topics.

Prof. Dr. Jian Li
Guest Editor

Manuscript Submission Information

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Keywords

  • testing and assessment
  • technology
  • cognitive ability
  • thinking skill
  • competency
  • game-based
  • virtual reality
  • simulation-based
  • handheld-device-assisted
  • AI-driven

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Published Papers (1 paper)

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Research

15 pages, 2016 KiB  
Article
Measuring Raven’s Progressive Matrices Combining Eye-Tracking Technology and Machine Learning (ML) Models
by Shumeng Ma and Ning Jia
J. Intell. 2024, 12(11), 116; https://doi.org/10.3390/jintelligence12110116 - 13 Nov 2024
Viewed by 520
Abstract
Extended testing time in Raven’s Progressive Matrices (RPM) can lead to increased fatigue and reduced motivation, which may impair cognitive task performance. This study explores the application of artificial intelligence (AI) in RPM by combining eye-tracking technology with machine learning (ML) models, aiming [...] Read more.
Extended testing time in Raven’s Progressive Matrices (RPM) can lead to increased fatigue and reduced motivation, which may impair cognitive task performance. This study explores the application of artificial intelligence (AI) in RPM by combining eye-tracking technology with machine learning (ML) models, aiming to explore new methods for improving the efficiency of RPM testing and to identify the key metrics involved. Using eye-tracking metrics as features, ten ML models were trained, with the XGBoost model demonstrating superior performance. Notably, we further refined the period of interest and reduced the number of metrics, achieving strong performance, with accuracy, precision, and recall all above 0.8, using only 60% of the response time and nine eye-tracking metrics. This study also examines the role of several key metrics in RPM and offers valuable insights for future research. Full article
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