Evaluating pre-trained models for user feedback analysis in software engineering: a study on classification of app-reviews

Hadi, M. A.; Fard, Fatemeh H.

doi:10.1007/s10664-023-10314-x

Cited by 10 publications

(1 citation statement)

References 120 publications

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“…To evaluate the performance of the models, we utilize three key evaluation metrics: accuracy, time, and F1 score [45].…”

Section: Evaluation Metricsmentioning

confidence: 99%

BNMI-DINA: A Bayesian Cognitive Diagnosis Model for Enhanced Personalized Learning

Chen,

Liang

2023

BDCC

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In the field of education, cognitive diagnosis is crucial for achieving personalized learning. The widely adopted DINA (Deterministic Inputs, Noisy And gate) model uncovers students’ mastery of essential skills necessary to answer questions correctly. However, existing DINA-based approaches overlook the dependency between knowledge points, and their model training process is computationally inefficient for large datasets. In this paper, we propose a new cognitive diagnosis model called BNMI-DINA, which stands for Bayesian Network-based Multiprocess Incremental DINA. Our proposed model aims to enhance personalized learning by providing accurate and detailed assessments of students’ cognitive abilities. By incorporating a Bayesian network, BNMI-DINA establishes the dependency relationship between knowledge points, enabling more accurate evaluations of students’ mastery levels. To enhance model convergence speed, key steps of our proposed algorithm are parallelized. We also provide theoretical proof of the convergence of BNMI-DINA. Extensive experiments demonstrate that our approach effectively enhances model accuracy and reduces computational time compared to state-of-the-art cognitive diagnosis models.

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“…To evaluate the performance of the models, we utilize three key evaluation metrics: accuracy, time, and F1 score [45].…”

Section: Evaluation Metricsmentioning

confidence: 99%