Probabilistic Linguistic Decision-Making Intelligent Model for College Digital Literacy Education Quality Evaluation
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.