Assessing Knowledge Rather Than Correct Answers: A Cognitive Structure
Analytics Framework for Personalized Learning

Sahasra Ravirala, Matteen Safi; John Leddo

doi:10.46609/IJSSER.2026.v11i09.056

Title: Assessing Knowledge Rather Than Correct Answers: A Cognitive Structure Analytics Framework for Personalized Learning
Authors: Sahasra Ravirala, Matteen Safi and John Leddo
Journal: International Journal of Social Science and Economic Research (IJSSER) ISSN: 2455-8834 Volume: 11 Issue: 9 Publication Date: September 2026 DOI: 10.46609/IJSSER.2026.v11i09.056
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Sahasra Ravirala1, Matteen Safi1 and John Leddo1,2,3 1. MyEdMaster, LLC, Virginia, USA 2. METY Technology, Inc., Virginia, USA 3. METY Foundation, Virginia, USA
MLA 8 Ravirala, Sahasra, et al. "Assessing Knowledge Rather Than Correct Answers: A Cognitive Structure Analytics Framework for Personalized Learning." Int. j. of Social Science and Economic Research, vol. 11, no. 9, Sept. 2026, pp. 5354-5364, doi.org/10.46609/IJSSER.2026.v11i09.056. Accessed Sept. 2026. APA 6 Ravirala, S., Safi, M., & Leddo, J. (2026, September). Assessing Knowledge Rather Than Correct Answers: A Cognitive Structure Analytics Framework for Personalized Learning. Int. j. of Social Science and Economic Research, 11(9), 5354-5364. Retrieved from https://doi.org/10.46609/IJSSER.2026.v11i09.056 Chicago Ravirala, Sahasra, Matteen Safi, and John Leddo. "Assessing Knowledge Rather Than Correct Answers: A Cognitive Structure Analytics Framework for Personalized Learning." Int. j. of Social Science and Economic Research 11, no. 9 (September 2026), 5354-5364. Accessed September, 2026. https://doi.org/10.46609/IJSSER.2026.v11i09.056.
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ABSTRACT: Most educational assessment systems summarize learning as the proportion of questions answered correctly. Although useful for measuring performance, correct-answer scores often provide limited diagnostic information about why a student succeeded or failed and what should be taught next. This paper presents a knowledge-centered analytics framework that shifts the unit of analysis from answers to underlying knowledge. The framework is grounded in the Integrated Knowledge Structure (INKS) model and Cognitive Structure Analysis (CSA), which represent mastery in terms of facts, strategies, procedures, and rationales. Prior CSA studies have found strong relationships between assessed knowledge and problem-solving performance, and experimental studies in high- and middle-school mathematics found larger posttest gains when students' knowledge gaps were assessed and remediated than when incorrect problem-solving steps were assessed and corrected. We describe a software analytics architecture for converting student self-assessment responses into item-, student-, class-, and longitudinal knowledge profiles, and for using those profiles to guide personalized AI remediation. The current software prototype records the four mastery dimensions before and after sessions and contains conventional predictive models, but real-student validation and fuller knowledge-profile reporting remain future work. The framework offers a testable alternative to answer-centered analytics: assessment as diagnosis of the knowledge structures that generate performance.

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