Pub Date : 2024-03-02DOI: 10.1109/TLT.2024.3396159
Qiuyu Zheng;Zengzhao Chen;Mengke Wang;Yawen Shi;Shaohui Chen;Zhi Liu
The rationality and the effectiveness of classroom teaching behavior directly influence the quality of classroom instruction. Analyzing teaching behavior intelligently can provide robust data support for teacher development and teaching supervision. By observing verbal and nonverbal behaviors of teachers in the classroom, valuable data on teacher–student interaction, classroom atmosphere, and teacher–student rapport can be obtained. However, traditional approaches of teaching behavior analysis primarily focus on student groups in the classroom, neglecting intelligent analysis and intervention of teacher behavior. Moreover, these traditional methods often rely on manual annotation and decision making, which are time consuming and labor intensive, and cannot efficiently facilitate analysis. To address these limitations, this article proposes an innovative automated multimode teaching behavior analysis framework, known as AMTBA. First, a model for segmenting classroom events is introduced, which separates teacher behavior sequences logically. Next, this article utilizes deep learning strategies with optimal performance to conduct multimode analysis and identification of split classroom events, enabling the fine-grained measurement of teacher's behavior in terms of verbal interaction, emotion, gaze, and position. Overall, we establish a uniform description framework. The AMTBA framework is utilized to analyze eight classrooms, and the obtained teacher behavior data are used to analyze differences. The empirical results reveal the differences of teacher behavior in different types of teachers, different teaching modes, and different classes. These findings provide an efficient solution for large-scale and multidisciplinary educational analysis and demonstrate the practical value of AMTBA in educational analytics.
{"title":"Automated Multimode Teaching Behavior Analysis: A Pipeline-Based Event Segmentation and Description","authors":"Qiuyu Zheng;Zengzhao Chen;Mengke Wang;Yawen Shi;Shaohui Chen;Zhi Liu","doi":"10.1109/TLT.2024.3396159","DOIUrl":"10.1109/TLT.2024.3396159","url":null,"abstract":"The rationality and the effectiveness of classroom teaching behavior directly influence the quality of classroom instruction. Analyzing teaching behavior intelligently can provide robust data support for teacher development and teaching supervision. By observing verbal and nonverbal behaviors of teachers in the classroom, valuable data on teacher–student interaction, classroom atmosphere, and teacher–student rapport can be obtained. However, traditional approaches of teaching behavior analysis primarily focus on student groups in the classroom, neglecting intelligent analysis and intervention of teacher behavior. Moreover, these traditional methods often rely on manual annotation and decision making, which are time consuming and labor intensive, and cannot efficiently facilitate analysis. To address these limitations, this article proposes an innovative automated multimode teaching behavior analysis framework, known as AMTBA. First, a model for segmenting classroom events is introduced, which separates teacher behavior sequences logically. Next, this article utilizes deep learning strategies with optimal performance to conduct multimode analysis and identification of split classroom events, enabling the fine-grained measurement of teacher's behavior in terms of verbal interaction, emotion, gaze, and position. Overall, we establish a uniform description framework. The AMTBA framework is utilized to analyze eight classrooms, and the obtained teacher behavior data are used to analyze differences. The empirical results reveal the differences of teacher behavior in different types of teachers, different teaching modes, and different classes. These findings provide an efficient solution for large-scale and multidisciplinary educational analysis and demonstrate the practical value of AMTBA in educational analytics.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1717-1733"},"PeriodicalIF":3.7,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-02DOI: 10.1109/TLT.2024.3396393
Hanall Sung;Martina A. Rau;Barry D. Van Veen
In many science, technology, engineering, and mathematics (STEM) domains, instruction on foundational concepts heavily relies on visuals. Instructors often assume that students can mentally visualize concepts but students often struggle with internal visualization skills—the ability to mentally visualize information. In order to address this issue, we developed a formal, as well as an informal assessment of students’ internal visualization skills in the context of engineering instruction. To validate the assessments, we used data triangulation methods. We drew on data from two separate studies conducted in a small-scale lab experiment and in a larger-scale classroom context. Our studies demonstrate that an intelligent tutoring system with interactive visual representations can serve as an informal assessment of students’ internal visualization skills, predicting their performance on a formal assessment of these skills. Our study enriches methodological and theoretical underpinnings in educational research and practices in multiple ways: it contributes to research methodologies by illustrating how multimodal triangulation can be used for test development, theories of learning by offering pathways to assessing internal visualization skills that are not directly observable, and instructional practices in STEM education by enabling instructors to determine when and where they should provide additional scaffoldings.
{"title":"Development of an Intelligent Tutoring System That Assesses Internal Visualization Skills in Engineering Using Multimodal Triangulation","authors":"Hanall Sung;Martina A. Rau;Barry D. Van Veen","doi":"10.1109/TLT.2024.3396393","DOIUrl":"10.1109/TLT.2024.3396393","url":null,"abstract":"In many science, technology, engineering, and mathematics (STEM) domains, instruction on foundational concepts heavily relies on visuals. Instructors often assume that students can mentally visualize concepts but students often struggle with internal visualization skills—the ability to mentally visualize information. In order to address this issue, we developed a formal, as well as an informal assessment of students’ internal visualization skills in the context of engineering instruction. To validate the assessments, we used data triangulation methods. We drew on data from two separate studies conducted in a small-scale lab experiment and in a larger-scale classroom context. Our studies demonstrate that an intelligent tutoring system with interactive visual representations can serve as an informal assessment of students’ internal visualization skills, predicting their performance on a formal assessment of these skills. Our study enriches methodological and theoretical underpinnings in educational research and practices in multiple ways: it contributes to research methodologies by illustrating how multimodal triangulation can be used for test development, theories of learning by offering pathways to assessing internal visualization skills that are not directly observable, and instructional practices in STEM education by enabling instructors to determine when and where they should provide additional scaffoldings.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1625-1638"},"PeriodicalIF":3.7,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140826860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-26DOI: 10.1109/TLT.2024.3369690
Jijian Lu;Ruxin Zheng;Zikun Gong;Huifen Xu
Generative artificial intelligence (AI) has emerged as a noteworthy milestone and a consequential advancement in the annals of major disciplines within the domains of human science and technology. This study aims to explore the effects of generative AI-assisted preservice teaching skills training on preservice teachers’ self-efficacy and higher order thinking. The participants of this study were 215 preservice mathematics, science, and computer teachers from a university in China. First, a pretest–post-test quasi-experimental design was implemented for an experimental group (teaching skills training by generative AI) and a control group (teaching skills training by traditional methods) by investigating the teacher self-efficacy and higher order thinking of the two groups before and after the experiment. Finally, a semistructured interview comprising open-ended questions was administered to 25 preservice teachers within the experimental group to present their views on generative AI-assisted teaching. The results showed that the scores of preservice teachers in the experimental group, who used generative AI for teachers’ professional development, were considerably higher than those of the control group, both in teacher self-efficacy ( F