arXiv:2606. 01982v1 Announce Type: new Abstract: Schema-constrained information extraction from diverse educational and labor-market corpora remains an open challenge in natural language processing because existing pipelines rely primarily on lexical-surface methods that cannot recover implicit competencies, lack grounding in shared taxonomies, and provide no formal measures of extraction reliability or document-level completeness.
By Sherzod Turaev, Mary John, Mamoun Awad, Nazar Zaki, Khaled Shuaib
arXiv:2608. 12356v1 Announce Type: cross Abstract: A college offering several overlapping computing degrees implicitly assumes that its programs are differentiated in line with how the labor market segments computing work and that, together, they prepare graduates for that market.
By Sherzod Turaev, Saja Aldabet, Mary John, Namya Musthafa, Mamoun Awad, Nazar Zaki, Khaled Shuaib
arXiv:2606. 15349v1 Announce Type: cross Abstract: Standardized examinations are typically treated as uniform syllabus coverage problems.
By Joy Bose, Om Thomas
CaSKG introduces a counterfactual‑causal skill graph framework that calibrates procedural relations before retrieval, building a high‑recall directed candidate graph from semantic, lexical, input/output, and structural evidence and refining it with repair evidence and optional LLM judgment. The framework applies direction‑conditioned textual counterfactual probes—removing, substituting, and reordering skill pairs—to aggregate evidence with Bayesian smoothing, producing a state‑filtered weighted graph for task‑conditioned expansion. Evaluated across six LLM backbones on ALFWorld and ScienceWorld, CaSKG outperforms existing Graph‑of‑Skills methods, improving macro‑average scores and reducing mean environment steps while preserving essential skill dependencies.
By Zhiyuan Li, Linyuan Gao, Xuechun Ding, Hongwei Chen, Yuan Wu, Yi Chang
arXiv:2607. 02432v1 Announce Type: new Abstract: Scalable and reliable grading of command-line examinations remains a challenge in computing education, where rising enrolments make manual marking difficult and rule-based autograders cannot handle partial credit, equivalent solutions, or syntactic variation.
By Manuel Alonso-Carracedo, Ruben Fernandez-Boullon, Pedro Celard, Francisco J. Rodriguez-Martinez, Lorena Otero-Cerdeira
The paper introduces Intelligent Target Locator (ITL), a method that measures how well a document aligns with concepts in a Structured Reference Document (SRD) by creating concept‑specific term profiles and computing a textual‑unit–concept affinity matrix. ITL assigns importance weights to terms based on concept membership, term specificity, and discriminability, enabling traceable, quantitative alignment scores at multiple granularity levels. An internal consistency test on the 17 Sustainable Development Goals showed that each goal statement achieved its highest affinity with its corresponding concept, demonstrating ITL’s ability to distinguish conceptual profiles.
By Ra\'ul Gir\'aldez, Dayrelis Mena, Jes\'us S. Aguilar--Ruiz