On the recoverability of cognitive levels in educational text: a multi-task transformer analysis

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dc.contributor.authorAlhajahmad, Bashar
dc.date.accessioned2026-10-23T18:14:12Z
dc.date.available2026-10-23T18:14:12Z
dc.date.issued2026
dc.departmentSiirt Üniversitesi
dc.description.abstractArtificial intelligence systems have demonstrated strong performance in educational reasoning and classification tasks; however, the extent to which transformer-based representations encode structured cognitive abstractions remains insufficiently understood. This study investigates the recoverability of cognitive levels from educational question-answer text using a Cognitive-Conditioned Multi-Task Transformer (CC-MTT) framework. To ensure methodological rigor, a leakage-controlled dataset reconstruction pipeline was developed using question-level partitioning and majority-vote label stabilization. The proposed framework jointly models reasoning and cognitive classification, enabling analysis of predictive performance, representational structure, and stability across multiple random seed initializations. Experimental results show that fine-grained Bloom taxonomy levels remain only weakly recoverable from text-only representations, achieving performance close to random baseline under leakage-controlled evaluation. In contrast, hierarchical abstraction (higher-order vs. lower-order thinking) consistently achieved higher predictive recoverability across multiple runs, although quantitative geometry analysis revealed limited cluster separability in the learned embedding space. Statistical analysis further indicated a consistent trend toward improved binary abstraction performance compared with fine-grained categorization, despite the absence of strong statistical significance under Wilcoxon testing. These findings suggest that coarse-grained cognitive abstraction may be partially encoded in transformer representations, while fine-grained pedagogical categories remain structurally difficult to recover from textual representations alone. The study provides methodological and analytical insights for the development of more reliable AI-driven educational assessment systems.
dc.identifier.doi10.1007/s10339-026-01359-z
dc.identifier.issn1612-4782
dc.identifier.issn1612-4790
dc.identifier.orcid0009-0009-3455-7206
dc.identifier.pmid42257822
dc.identifier.scopus2-s2.0-105040942598
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s10339-026-01359-z
dc.identifier.urihttps://hdl.handle.net/20.500.12604/12325
dc.identifier.wosWOS:001786858700001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorAlhajahmad, Bashar
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofCognitive Processing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20261017
dc.subjectBloom'S Taxonomy
dc.subjectCognitive Abstraction
dc.subjectMulti-Task Learning
dc.subjectTransformer Models
dc.subjectEducational Nlp
dc.subjectRepresentation Learning
dc.titleOn the recoverability of cognitive levels in educational text: a multi-task transformer analysis
dc.typeArticle

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