Statistical measurement of behavioral effects based on multimodal data
The application of multimodal data is particularly important in accurately assessing behavioral effects and optimizing the decision-making process. This type of data provides more comprehensive and in-depth insights by integrating information from different sources and formats. Comprehensive data su...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-12-01
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| Series: | National Accounting Review |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/NAR.2024027 |
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| author | Suyan Tan Yunyi Zhao Jinjun Wang Jia Fang |
| author_facet | Suyan Tan Yunyi Zhao Jinjun Wang Jia Fang |
| author_sort | Suyan Tan |
| collection | DOAJ |
| description | The application of multimodal data is particularly important in accurately assessing behavioral effects and optimizing the decision-making process. This type of data provides more comprehensive and in-depth insights by integrating information from different sources and formats. Comprehensive data support not only enhances the science and accuracy of decision-making but also significantly improves the quality of behavioral effectiveness assessment. This study first describes the practical significance and theoretical value of multimodal data in behavioral effect assessment. Subsequently, the types of multimodal data involved and the construction methods of data sets are introduced. In order to demonstrate the role of multimodal data in behavioral effect assessment, the teaching effect of English classroom presentations at a comprehensive university in China is taken as a case study, and the effect of the target behavior was statistically measured based on multimodal data such as students' classroom behavioral videos, images, questionnaires, interviews, and assessment data. The results of the case study show that AI+ demonstrates significant advantages in behavioral effect assessment, which is more objective and effectively avoids the limitations of subjectivity in traditional assessment methods. At the same time, multimodal data helps optimize behavioral effects. For example, the presentations made at the beginning of the class show significant advantages in teaching effect compared with the presentation made before the end of the class, which provides data support and optimization direction for the implementation of teaching activities. |
| format | Article |
| id | doaj-art-42557e2accf34e58ad29a59f02c95c7e |
| institution | Kabale University |
| issn | 2689-3010 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | National Accounting Review |
| spelling | doaj-art-42557e2accf34e58ad29a59f02c95c7e2025-01-24T01:06:46ZengAIMS PressNational Accounting Review2689-30102024-12-016457358910.3934/NAR.2024027Statistical measurement of behavioral effects based on multimodal dataSuyan Tan0Yunyi Zhao1Jinjun Wang2Jia Fang3School of Foreign Studies, Guangzhou University, Guangzhou, Guangdong 510006, ChinaSchool of Foreign Studies, Guangzhou University, Guangzhou, Guangdong 510006, ChinaSchool of Foreign Studies, Guangzhou University, Guangzhou, Guangdong 510006, ChinaSchool of Humanities and Economics, Changzhou Open University, Changzhou 213001, ChinaThe application of multimodal data is particularly important in accurately assessing behavioral effects and optimizing the decision-making process. This type of data provides more comprehensive and in-depth insights by integrating information from different sources and formats. Comprehensive data support not only enhances the science and accuracy of decision-making but also significantly improves the quality of behavioral effectiveness assessment. This study first describes the practical significance and theoretical value of multimodal data in behavioral effect assessment. Subsequently, the types of multimodal data involved and the construction methods of data sets are introduced. In order to demonstrate the role of multimodal data in behavioral effect assessment, the teaching effect of English classroom presentations at a comprehensive university in China is taken as a case study, and the effect of the target behavior was statistically measured based on multimodal data such as students' classroom behavioral videos, images, questionnaires, interviews, and assessment data. The results of the case study show that AI+ demonstrates significant advantages in behavioral effect assessment, which is more objective and effectively avoids the limitations of subjectivity in traditional assessment methods. At the same time, multimodal data helps optimize behavioral effects. For example, the presentations made at the beginning of the class show significant advantages in teaching effect compared with the presentation made before the end of the class, which provides data support and optimization direction for the implementation of teaching activities.https://www.aimspress.com/article/doi/10.3934/NAR.2024027multimodal datastatistical measurementbehavioral effectcase studyenglish classroom presentation |
| spellingShingle | Suyan Tan Yunyi Zhao Jinjun Wang Jia Fang Statistical measurement of behavioral effects based on multimodal data National Accounting Review multimodal data statistical measurement behavioral effect case study english classroom presentation |
| title | Statistical measurement of behavioral effects based on multimodal data |
| title_full | Statistical measurement of behavioral effects based on multimodal data |
| title_fullStr | Statistical measurement of behavioral effects based on multimodal data |
| title_full_unstemmed | Statistical measurement of behavioral effects based on multimodal data |
| title_short | Statistical measurement of behavioral effects based on multimodal data |
| title_sort | statistical measurement of behavioral effects based on multimodal data |
| topic | multimodal data statistical measurement behavioral effect case study english classroom presentation |
| url | https://www.aimspress.com/article/doi/10.3934/NAR.2024027 |
| work_keys_str_mv | AT suyantan statisticalmeasurementofbehavioraleffectsbasedonmultimodaldata AT yunyizhao statisticalmeasurementofbehavioraleffectsbasedonmultimodaldata AT jinjunwang statisticalmeasurementofbehavioraleffectsbasedonmultimodaldata AT jiafang statisticalmeasurementofbehavioraleffectsbasedonmultimodaldata |