Jul 2026· International Journal of Cognitive Informatics and Natural Intelligence· Vol 20, pp. 1-21· 0 citations
TL;DR
The present contribution is the modeling of multi-attribute group decision-making problems in a probabilistic linguistic environment through an integrated probabilistic linguistic exponential interactive multi-criteria decision making and multi-attributive border approximation area comparison method approach.
Abstract
This research outlines the logic of dynamic evolution: digital competence, innovative teaching ability, and the symbiotic mutual uplift of university educators spiraling upwards, all towards the strategic goal of establishing an educational powerhouse. Indeed, this process involves developing qualitative assessments of higher education digital literacy, which can be framed as complex multi-attribute group decision-making problems. The present contribution in this respect is the modeling of multi-attribute group decision-making problems in a probabilistic linguistic environment through an integrated probabilistic linguistic exponential interactive multi-criteria decision making and multi-attributive border approximation area comparison method approach. Attribute weights are considered to be objective and expressed in terms of probabilistic linguistic term set data using the method based on the removal effects of criteria method. A case study on the assessment of the quality of digital literacy education confirms the feasibility and effectiveness of the proposed method.
This article presents an intelligent system designed for objective and effective assessment of student assignments in the educational process. The proposed approach has the function of automatic processing and text extraction of documents in PDF and DOCX formats, and evaluates the semantic similarity between assignment conditions and student responses through deep learning models based on the Sentence Transformer architecture. In addition to semantic proximity metrics, the system also analyzes multidimensional linguistic features such as content coverage, internal consistency, and structural connectivity. A probabilistic classification mechanism is implemented to determine the probability of content generated by artificial intelligence. A decision-making system based on fuzzy logic theory is used to combine all assessment components into a single final score, which ensures interpretability and transparency of assessments. The result of this research makes a practical contribution to the field of automated educational technologies and creates a reliable basis for supporting decisions in large-scale educational environments.
Jasur Davletov, G. Toirova, Mukhriddin Fayziyev et al.· International Conference on...· 0 citations
A hierarchical student evaluation enhanced decision-making model which is based on a zero-order fuzzy classifier as the construction unit and achieves the collaborative optimization of evaluation efficiency and interpretability is proposed.
Aiqin Wang, Wenliang Li· Journal of Advanced Computat...· 0 citations
The college English teaching quality evaluation is a prerequisite to improving teaching efficiency and promoting students' academic performance. Systematic teaching method inspection, syllabus design, and in-class interaction provide constructive suggestions for teachers to improve the syllabus framework and teaching method through such a mechanism. Thus, assessing college English teaching quality is essentially an example of multi-attribute decision-making. To solve the corresponding decision problem, this paper proposes an induced interval-valued intuitionistic fuzzy Hamacher interactive ordered weighted geometric operator. Moreover, the essential mathematical properties of the novel operator are investigated. A numerical example of college English teaching quality evaluation is provided to show its operability, indicating the proposed operator's credible practical applicability under interval-valued intuitionistic fuzzy uncertainty.
Huimin Tian· International Journal of Inf...· 0 citations
Digital literacy in higher education is essential for lifelong learning, as technology has swiftly transformed communication, work, and study practices. Schools and colleges are having a hard time keeping up with the rapid growth of employment skills and the need for new ones, especially since that society is going through big changes in technology and social norms. This study compiles educational data from many sources and use data preprocessing to cleanse, arrange, and transform raw data into significant digital literacy indicators for predicting levels of digital literacy in HE. This work uses Latent Dirichlet Allocation (LDA) feature extraction to identify noteworthy patterns and enhance the dataset. Using past training data, a classification method based on Support Vector Machine (SVM) from statistical learning theory predicts digital literacy, which solves difficult prediction problems. The proposed model performs effectively, with the SVM method achieving an optimal accuracy of 95.15%, representing a substantial enhancement above the majority baseline accuracy. These results suggest that LDA and SVM may be used to measure and enhance Digital Literacy in Higher Education, which will help with online learning and learning new skills for life.
R. Jesudas, Zakiuddin Mohammed, Mandeep Kour et al.· International Conference on...· 0 citations
Efficient data acquisition, intelligent analysis, and adaptive decision-making are fundamental capabilities for modern digital engineering systems. To address the limitations of homogeneous teacher training under digital transformation, this study proposes a hierarchical diagnosis and progressive cultivation framework for teacher data literacy based on multimodal assessment and latent profile analysis. A context-embedded evaluation tool integrating situational judgment, behavioral retrospection, and ethical dilemma tasks was developed and applied to 1,263 teachers to identify four distinct competency levels. Based on the diagnosed developmental characteristics, a four-stage cultivation mechanism incorporating intelligent recommendation, competency dashboards, and micro-certification was established and iteratively optimized through design-based research. Experimental results demonstrate that the proposed framework achieves an upward competency migration rate of 60.7%, significantly outperforming conventional training approaches, while the competency dashboard provides the greatest independent contribution to performance improvement. The hierarchical and evidence-driven strategy effectively enhances personalized learning trajectories and supports scalable digital capability development. Beyond educational applications, the proposed multimodal profiling and adaptive optimization framework provides a practical reference for intelligent information processing, data fusion, human-centered decision support, and adaptive sensing architectures, offering potential guidance for data-driven optimization and intelligent management in electromagnetic wave analysis, antenna-enabled sensing systems, and propagation-oriented engineering applications.
In response to the problems such as complex feature dimensions, large errors in manual weighting, and ambiguous classification boundaries in the evaluation of digital literacy of college foreign language teachers, a digital literacy evaluation model based on machine learning algorithms is constructed. In this paper, the identification of teachers' digital literacy is described as a supervised classification task driven by multi-source heterogeneous data. A feature space is established around teaching platform behaviors, digital resource calls, online interaction records, and evaluation feedback information. After data cleaning, normalization, feature selection, and vectorization encoding, the model input is formed. The model layer introduces support vector machines, random forests, and deep neural networks for hierarchical identification, and combines the cross-entropy loss function and parameter optimization mechanism to complete training. Experimental results show that the model has good performance in terms of accuracy, recall rate, and F1 value, and can provide a computable technical path for intelligent evaluation of teachers' digital literacy.
Cuiping Ma· International Conference on...· 0 citations