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Yawen Ma

Publications and source records attributed to Yawen Ma.

6 recordsLinked to original sources
arXiv2026

Resilience-Oriented Parametric Insurance Design for Power Systems Under Extreme Weather

Extreme weather leaves power systems exposed to residual outage risk even after physical resilience investments. Parametric insurance can provide pre-agreed contingent liquidity, but its physical value depends on how trigger thresholds and payout levels are designed. This paper proposes a resilience oriented parametric insurance framework that couples a three tier wind-index contract with post-event network restoration. Insurance payout expands the budget available to activate emergency resources, so the contract changes the physical restoration feasible set rather than merely offsetting accounting losses. Trigger thresholds and payout levels are jointly designed to balance actuarial premium, expected post-event system cost, and the conditional value-at-risk (CVaR) of scenario energy not supplied (ENS). A response-library method precomputes the restoration mixed-integer linear program for each scenario-payout pair and then evaluates admissible contracts efficiently. On the IEEE RTS-24 with 80 extreme-wind scenarios, the optimized contract reduces expected EENS and CVaR0.90 of ENS by 21.1% and 21.4%, respectively, relative to no insurance, while requiring 48.8% less premium than a fixed parametric contract with comparable resilience. The results show that insurance design should target the nonlinear liquidity-to-resilience response rather than loss compensation alone.

math.OC↗
arXiv2026

Interpreting Learning Under Competing Models: Joint and Stepwise Approaches for Dynamic Cognitive Diagnosis

Digital learning environments record learners' responses to individual items, making it possible to study the development of specific skills rather than overall scores. Drawing conclusions about learning from these data requires a model that links responses to latent skills and tracks how mastery changes over time. When the skills measured by each item are unknown, the analyst must decide whether to estimate this structure, the Q-matrix, jointly with the learning process, or to establish it first and study learning afterwards. We show that this decision can change substantive conclusions about how learners develop. Using dynamic cognitive diagnostic models, we analyse data from two reading games measuring vocabulary and comprehension from Grade 2 to Grade 3, with item-text embeddings providing prior information for the unknown Q-matrix. A joint analysis and a bias-corrected stepwise analysis agree that most learners move toward mastering both skills, but disagree about how many remain only partially proficient at Grade 3, changing how reading progress would be reported. A simulation study identifies when the two analyses diverge and shows that joint analysis is more reliable when the item-skill structure is uncertain and the item pool changes between grades. We provide R code for both analyses.

cs.LG↗
arXiv2026

A Comparison of Joint and Stepwise Dynamic Cognitive Diagnostic Models

To extend cognitive diagnostic models (CDMs) to longitudinal settings, stepwise approaches that integrate a CDM model with a latent transition model and covariates are widely used due to their flexibility. Previous research has shown that stepwise estimation can yield biased results, motivating classification-error correction as a means of improving inference over uncorrected stepwise procedures. In this study, we evaluate a unified Bayesian dynamic cognitive diagnostic model that jointly estimates measurement (item parameters, latent attribute profiles) and transition components (transition parameters) in longitudinal settings with covariates. We compare this joint approach with the bias-corrected stepwise latent transition CDM through a Monte Carlo study. Results demonstrate that joint modeling provides more accurate recovery of transition parameters, particularly under limited test length and sample size, underscoring its advantages for longitudinal diagnostic analysis and offering practical guidance for applied researchers.

stat.ME↗
arXiv2026

NLP-Informed Dynamic Cognitive Diagnosis Modelling

Digital learning platforms are increasingly used to support reading development while generating rich log files and item-level textual content. Using these data, this study proposes a dynamic cognitive diagnostic modelling (CDM) framework that incorporates text-derived semantic information to inform the estimation of the Q-matrix. We construct item-level semantic representations of question text and response options, and use these representations to define an informative prior on the Q-matrix. This approach treats text-derived signals as proxies for item complexity and cognitive demands, guiding the item-skill mapping in a data-driven manner. The proposed framework jointly estimates latent skill mastery profiles, item parameters, and transition dynamics over time within a Bayesian framework. We apply the model to data from Boost Reading, a digital reading supplement, focusing on students' vocabulary and comprehension skill development. We compare the proposed framework with a baseline model without any text information and show that the text-derived prior can improve Q-matrix recovery, particularly in settings where response data alone provide limited identification, as well as other model parameters for varying scenarios. This study provides a novel integration of natural language processing and dynamic CDMs, offering a data-driven approach to modelling skill acquisition and item-skill relationships in digital learning environments.

stat.ME↗
arXiv2025

A statistical framework for dynamic cognitive diagnosis in digital learning environments

Reading is foundational for educational, employment, and economic outcomes, but a persistent proportion of students globally struggle to develop adequate reading skills. Some countries promote digital tools to support reading development, alongside regular classroom instruction. Such tools generate rich log data capturing students' behaviour and performance. This study proposes a dynamic cognitive diagnostic modeling (CDM) framework based on restricted latent class models to trace students' time-varying skills mastery using log files from digital tools. Unlike traditional CDMs that require expert-defined skill-item mappings (Q-matrix), our approach jointly estimates the Q-matrix and latent skill profiles, integrates log-derived covariates (e.g., reattempts, response times, counts of mastered items) and individual characteristics, and models transitions in mastery using a Bayesian estimation approach. Applied to real-world data, the model demonstrates practical value in educational settings by effectively uncovering individual skill profiles and the skill-item mappings. Simulation studies confirm robust recovery of Q-matrix structures and latent profiles with high accuracy under varied sample sizes, item counts and different sparsity of Q-matrices. The framework offers a data-driven, time-dependent restricted latent class modeling approach to understanding early reading development.

stat.AP↗