芯(光纤)
协同过滤
读写能力
相似性(几何)
背景(考古学)
数学教育
信息素养
图形
价值(数学)
计算机科学
核心竞争力
路径分析(统计学)
数学
心理学
教育学
人工智能
机器学习
理论计算机科学
推荐系统
地理
万维网
电信
考古
营销
业务
图像(数学)
作者
Wang Zhan-feng,Yunfei Liu
标识
DOI:10.2478/amns.2023.2.01672
摘要
Abstract In this paper, Bayesian estimation and Coleman filtering are used to compose a multidimensional data fusion algorithm to study the cultivation of students’ personalized core literacy. A Trans E model is constructed based on the calculation of students’ specific course ratings using collaborative filtering algorithms. Combining the knowledge graph and collaborative filtering methods, the similarities between the course and user ratings are calculated respectively. The user’s rating situation can be predicted by combining similarity. Through the empirical analysis method, the overall and specific competency dimensions of students’ core literacy and the effect of core literacy cultivation, as well as students’ satisfaction, were analyzed, and the corresponding practice paths were proposed based on the analysis results. The mean value of students’ core literacy test is 46.25, and the number of students scoring between 50-60 points is the largest, but the number of excellent students is 0, which is poor in the cultivation of core literacy. The analysis of the effect of core literacy cultivation shows that in the correlation analysis of teaching effect and teaching support, the p-value is less than 0.01, but most of the correlation coefficients are less than 0.35, and there exists a weak relationship between the two, and the school authorities should strengthen the teaching support of core literacy.
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