Affiliations: [a] School of Automotive Engineering, Hunan Industry Polytechnic, Changsha, China
| [b] School of Automotive Engineering, Hunan Mechancal Electrical Polytechnic, Changsha, China
| [c] GAC Fiat Chrysler Automobiles Co., Ltd, Changsha, China
Correspondence:
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Corresponding author. Sisi Li, School of Automotive Engineering, Hunan Mechancal Electrical Polytechnic, Changsha 410151, China. E-mail: lisisi188@outlook.com.
Abstract: Educators across different fields disseminate their knowledge and utilize digital technologies to improve student skills for their careers and sustainability. Students’ skills that are improved are verified based on assessment and knowledge application over different circumstances. The article investigates the impact of the effective educator’s knowledge assessment and their role in student skill development. The motivation for the research arises from the realization that teachers’ knowledge and their capacity for transferring skills and information to students successfully play a significant role in the quality of education. The goal of the study is to develop a GA2M that has been verified and can be used by educators to improve their performance, enhance student’s results, and eventually progress educational practices to use Fuzzy methods for reasoning and to include new rules for improving knowledge to bridge the knowledge gap between educators and students’ skill growth. It requires a great effect by the educator to enhance their ability over successive performance improvement. This article analyzes the ability for better improvement using the proposed Guided Ability Assessment Model (GA2M). The proposed model discards the negative impact of the ability on students’ skill deterioration. Besides, the ratio of skill improvement across various new abilities and exposures is analyzed using Fuzzy inference. This analysis frames the interference using knowledge rules required for different circumstances. These rules are framed using existing skill implications and problem-solving ability. This proposed model proposes new rules for development of various abilities of educators. Based on their current ability, further training process for the educator’s skill development is prescribed. Therefore, the inference for fuzzification is performed for the positive impact on students’ skill development. If the inference succeeds, the assessment is leveraged between skill guidance and ability.
Keywords: Digital assessment, educator ability, fuzzy model, student skill
