使用模糊逻辑系统的大数据辅助学生英语学习能力评价模型

Lin Fan, Wenli Wang
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摘要

不同水平的学生的能力、兴趣和先前的成就都会影响他们学习英语的方式。准确的验证对于促进高效的评价和培训模式至关重要。该研究的创新意义在于将个人属性、渐进评价和基于模糊逻辑的评价纳入英语学习。PA2M 模型解决了现有模型的不足,提供了全面、准确的评估,可为不同技能水平的学生提供个性化建议和强化教学策略。本研究提出了基于模糊逻辑系统(FLS)的持久性评价评估模型(PA2M)。该模型基于学生不断变化的表现和积累的数据,评估学生的英语学习能力。该模型采用模糊化方法评估学生的能力,通过将个人属性与成绩联系起来,减少鉴定验证中的差异。Mamdani FIS 在评价方法的框架内对学生的英语学习能力进行了清晰而全面的评估。利用成绩和积累的能力数据对输入进行更新,以提高验证的一致性并减少收敛误差。在模糊化过程中,消除了不可用的评估序列产生的预收敛。PA2M 方法通过合并先前和当前数据,根据学生能力确定精确的改进和评价。经过多次评估验证,最终提出了明确的新建议。根据实验数据,所建议的模型在一定输入范围内提高了 9.79% 的推荐率、8.79% 的评价验证率、8.25% 的收敛因子、12.56% 的错误率和 8.77% 的验证时间。PA2M 模型为评估英语学习潜力提供了一种新的有用方法,填补了知识和实践方面的一些空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Big data-assisted student’s English learning ability appraisal model using fuzzy logic system
The ability, interest, and prior accomplishments of students with varying proficiency levels all impact how they learn English. Exact validation is essential for facilitating efficient evaluation and training models. The research’s innovative significance resides in incorporating personal attributes, progressive appraisal, and Fuzzy Logic-based appraisal in English language learning. The PA2M model, which addresses the shortcomings of existing models, offers a thorough and accurate assessment, enabling personalized recommendations and enhanced teaching tactics for students with varied skill levels. This research proposes the Fuzzy Logic System (FLS)-based Persistent Appraisal Assessment Model (PA2M). Based on the students’ evolving performance and accumulated data, this model evaluates the students’ English learning capabilities. The model assesses the student’s ability using fuzzification approaches to reduce variations in appraisal verification by linking personal attributes with performance. Mamdani FIS offers a clear and thorough evaluation of student’s English learning capacity within the framework of the appraisal methodology. The inputs are updated utilizing performance and accumulated ability data to improve validation consistently and reduce converge errors. During the fuzzification process, pre-convergence from unavailable appraisal sequences is eliminated. The PA2M approach determines precise improvements and evaluations depending on student ability by merging prior and current data. Several appraisal validations and verifications result in clear fresh suggestions. According to experimental data, the suggested model enhances 9.79% of recommendation rates, 8.79% of appraisal verification, 8.25% of convergence factor, 12.56% error ratio, and verification time with 8.77% over a range of inputs. The PA2M model provides a fresh and useful way to evaluate English learning potential, filling in some gaps in the body of knowledge and practice.
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