competency mapping

This study addresses the lack of large-scale, formative assessment tools for algorithmic thinking (AT) in Swiss compulsory education. We propose an AT assessment framework integrating a contextualized competency model with dual-modality (unplugged/digital) assessment activities and develop an Intelligent Assessment System (IAS) based on noisy-gate Bayesian networks to enable real-time, probabilistic, multidimensional skill diagnosis. Our contributions are threefold: (1) the first integration of a developmental competency model with human–automated dual-path assessment; (2) the first real-time diagnostic IAS supporting multi-age-group and multi-environmental adaptability, moving beyond traditional summative scoring; and (3) empirical validation showing AT develops progressively, with no significant gender differences but notable influences from teaching materials and school context. Field deployment across multiple Swiss schools confirms the system’s cross-age and cross-context applicability, as well as its reliability and validity.

This study investigates the extent to which higher education curricula embed 21st-century core competencies and align with societal demands. To this end, we construct a dataset comprising 7,600 human-annotated samples and introduce a "Curricular Chain-of-Thought" (Curricular CoT) prompting strategy to enhance large language models’ reasoning capabilities in educational contexts, mitigate keyword-matching biases, and improve detection of subtle pedagogical evidence in lengthy texts. Experimental results demonstrate that detailed descriptions of teaching activities are the most informative; open-source models perform comparably to closed-source counterparts on coarse-grained competency mapping tasks; and while Curricular CoT yields a modest yet statistically significant performance gain, models still fall substantially short of human-level proficiency in fine-grained educational reasoning.

This study addresses the challenges of assessing students’ comprehensive competencies in algorithm courses and the disconnect between academic instruction and industry needs. Grounded in the CC2020 competency model, it proposes a multidimensional assessment framework that integrates knowledge, skills, and professional dispositions. Behavioral data from programming assignments and written coursework of 169 students were collected using the xAPI specification. Learning behavior sequences were modeled via Markov processes, and cluster analysis was employed to identify distinct competency profiles. Additionally, a timeliness metric for submissions was introduced to quantify task difficulty. The framework not only enables computable representations of student competencies but also provides empirical support for personalized instructional interventions and curriculum refinement, thereby effectively bridging the gap between academic training and industry requirements.

Contemporary computer science education suffers from theory-heavy curricula that lack explicit modeling of skill dependencies. Method: This paper proposes a skill-centered dual-tree curriculum structuring framework comprising Skill Trees (explicitly encoding skill hierarchies and dependencies) and Concept Trees (representing foundational conceptual mechanisms underpinning skills). We formally define a computable dual-tree model—overcoming traditional theory-oriented limitations—to enable skill dependency modeling and automated pedagogical path planning. Our methodology includes skill graph construction, concept–skill mapping design, structured curriculum planning, and empirical educational evaluation. Contribution/Results: Applied in an undergraduate database course, the framework significantly reduced students’ cognitive confusion and perceived learning pressure, while decreasing average time-to-mastery for target skills.

This study investigates the convergence and complementarity between gamified assessments and self-report questionnaires in measuring problem-solving ability. Employing a method-comparison design, 78 participants completed both a five-minute gamified task and the Problem-Solving Inventory–Brief (PSI-B). Behavioral performance was systematically analyzed through behavioral coding and a four-level proficiency classification, enabling a detailed examination of the relationship between observed behaviors and self-perceived competence. Results revealed no significant correlation between self-reported scores and behavioral indicators, providing the first empirical evidence that these two approaches yield complementary—rather than interchangeable—information. These findings underscore the necessity of multimodal assessment strategies in talent selection and challenge the limitations inherent in relying solely on a single assessment modality.

This study addresses the challenge of delayed and incomplete evaluation in team tabletop exercises (TTX), which often arises due to the open-ended and complex nature of such tasks, hindering effective assessment of team learning outcomes. To overcome this limitation, the authors propose a novel approach that integrates clustering algorithms with large language models (GPT-4o and GPT-5.2) to enable automated, scalable evaluation of team performance. Leveraging action logs and communication transcripts from 81 multinational participants alongside standardized scoring rubrics, the method demonstrates that clustering is computationally efficient and reliable, while GPT-5.2 significantly outperforms GPT-4o in evaluating team communication with lower error rates. All data, tools, and the complete TTX scenario have been open-sourced and integrated into the INJECT platform to support educational applications.

Current educational systems lack effective assessments of students’ ability to co-reason with generative AI, particularly in diagnosing performance across critical subprocesses such as task framing, output evaluation, and model steering. To address this gap, this work proposes the CoRe-3 (Co-Reasoning) competency framework, which decomposes human–AI collaborative reasoning into three independently assessable dimensions: Framing, Judging, and Steering. The authors implement an open platform, CoReasoningLab, that empirically measures these competencies by simulating learner interactions using multiple AI models and leveraging cross-vendor large language models as scoring backends for fine-grained diagnostic feedback. Empirical validation demonstrates that the three competencies exhibit strong discriminant and convergent validity, confirming both the theoretical coherence and practical utility of the proposed model.

This study addresses the challenge of accurately tracking students’ dynamic mastery of specific skills when the Q-matrix is unknown. Building upon dynamic cognitive diagnosis models, it compares a joint estimation approach—simultaneously inferring the Q-matrix and learning trajectories—with a two-step strategy that first estimates the Q-matrix and then analyzes skill development. Leveraging reading game data and item text embeddings, the research investigates vocabulary and comprehension growth among second- to third-grade students. The authors propose a bias-corrected two-step method and use simulation studies to delineate the conditions under which each approach performs best: joint modeling proves more reliable when the Q-matrix is uncertain and items vary across grade levels. Empirical results indicate that both methods identify a general trend toward mastering both skills, yet they diverge in estimating the proportion of partial mastery in third grade, underscoring the substantive impact of modeling choices on diagnostic conclusions.

This study addresses the gap between existing Bodies of Knowledge (BoKs) in computing and their effective translation into assessable, competency-oriented curricula. The authors propose an innovative competency-mapping methodology that systematically aligns BoKs with a structured competency framework, resulting in a five-year engineering curriculum encompassing 23 core competencies, organized into five thematic modules and three specialization tracks, and integrating mandatory work-integrated learning projects. To support explicit linkage, collaborative maintenance, and continuous evolution of knowledge-to-competency mappings, the authors developed ISANUMpedia—a semantic web–based collaborative platform. Implemented in the ISANUM engineering degree program, this approach successfully mapped 494 knowledge topics to the 23 competencies, significantly enhancing the curriculum’s professional relevance and assessability.

This study addresses the lack of a standardized framework for assessing computational thinking by introducing the Common European Framework of Reference for Languages (CEFR) into the evaluation of Scratch programming proficiency. Leveraging Dr.Scratch, the authors applied fuzzy C-means clustering to over two million projects and mapped the results ordinally onto the CEFR’s A1–C2 scale, establishing a reproducible and transparent diagnostic and progression-tracking system. The work innovatively proposes mechanisms for identifying transitional proficiency stages, quantifying classification confidence, and triggering human-in-the-loop interventions. It reveals a prevalent “B2 bottleneck” in educational settings—only 13.3% of learners attain this level—attributed to difficulties in integrating logical synchronization and data representation, thereby providing empirical grounding for personalized instruction and automated feedback systems.