Correlation Analysis and Teaching Optimization of University English Lecture Mode Preferences
The article first discusses the categorization and difficulty of learning resources and proposes a method to quantify the difficulty of teaching resources. Then, the article recommends appropriate learning resources through similarity matching and student knowledge level diagnosis. This includes ana...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns-2024-0584 |
| _version_ | 1797279110651707392 |
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| author | Wang Zhenzhen |
| author_facet | Wang Zhenzhen |
| author_sort | Wang Zhenzhen |
| collection | DOAJ |
| description | The article first discusses the categorization and difficulty of learning resources and proposes a method to quantify the difficulty of teaching resources. Then, the article recommends appropriate learning resources through similarity matching and student knowledge level diagnosis. This includes analyzing students’ test answers and modeling students’ knowledge level based on this. Data analysis and model building were employed to propose four key indicators: knowledge point density, knowledge point depth, evaluation index and correctness rate. In addition, cosine distance was employed to measure the similarity between resources and Hidden Markov Models were used to predict students’ knowledge point mastery. Four key indicators were effectively applied to construct a vector of resource difficulty indicators in quantifying learning resource difficulty. A vector representation of students’ knowledge mastery was successfully built in diagnosing students’ knowledge level. In addition, the article discusses the construction of a personalized learner preference model and a personalized English teaching model based on collaborative filtering algorithms. The effect of college English teaching can be effectively improved through the detailed Analysis of learning resources and the application of personalized recommendation model. |
| first_indexed | 2024-03-07T16:19:32Z |
| format | Article |
| id | doaj.art-3735ff4c210548fbbeddd99dac31fd00 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-07T16:19:32Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-3735ff4c210548fbbeddd99dac31fd002024-03-04T07:30:43ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0584Correlation Analysis and Teaching Optimization of University English Lecture Mode PreferencesWang Zhenzhen01Department of College English II, School of Foreign Studies, Yanshan University, Qinhuangdao, Hebei, 066004, China.The article first discusses the categorization and difficulty of learning resources and proposes a method to quantify the difficulty of teaching resources. Then, the article recommends appropriate learning resources through similarity matching and student knowledge level diagnosis. This includes analyzing students’ test answers and modeling students’ knowledge level based on this. Data analysis and model building were employed to propose four key indicators: knowledge point density, knowledge point depth, evaluation index and correctness rate. In addition, cosine distance was employed to measure the similarity between resources and Hidden Markov Models were used to predict students’ knowledge point mastery. Four key indicators were effectively applied to construct a vector of resource difficulty indicators in quantifying learning resource difficulty. A vector representation of students’ knowledge mastery was successfully built in diagnosing students’ knowledge level. In addition, the article discusses the construction of a personalized learner preference model and a personalized English teaching model based on collaborative filtering algorithms. The effect of college English teaching can be effectively improved through the detailed Analysis of learning resources and the application of personalized recommendation model.https://doi.org/10.2478/amns-2024-0584collaborative filtering algorithmsimilarity matchingknowledge level diagnosisstudent preferenceenglish instruction68q05 |
| spellingShingle | Wang Zhenzhen Correlation Analysis and Teaching Optimization of University English Lecture Mode Preferences Applied Mathematics and Nonlinear Sciences collaborative filtering algorithm similarity matching knowledge level diagnosis student preference english instruction 68q05 |
| title | Correlation Analysis and Teaching Optimization of University English Lecture Mode Preferences |
| title_full | Correlation Analysis and Teaching Optimization of University English Lecture Mode Preferences |
| title_fullStr | Correlation Analysis and Teaching Optimization of University English Lecture Mode Preferences |
| title_full_unstemmed | Correlation Analysis and Teaching Optimization of University English Lecture Mode Preferences |
| title_short | Correlation Analysis and Teaching Optimization of University English Lecture Mode Preferences |
| title_sort | correlation analysis and teaching optimization of university english lecture mode preferences |
| topic | collaborative filtering algorithm similarity matching knowledge level diagnosis student preference english instruction 68q05 |
| url | https://doi.org/10.2478/amns-2024-0584 |
| work_keys_str_mv | AT wangzhenzhen correlationanalysisandteachingoptimizationofuniversityenglishlecturemodepreferences |