作者机构：
[Nie, Yanjiao; Luo, Heng] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Peoples R China.;[Sun, Di] Syracuse Univ, Dept Instruct Design Dev & Evaluat, Syracuse, NY USA.
通讯机构：
[Luo, Heng] C;Cent China Normal Univ, Sch Educ Informat Technol, Wuhan, Peoples R China.
关键词：
Massive open online course;diagnostic evaluation;Analytic Hierarchy Process;emotion classification;quality assurance
摘要：
In order to further improve the effect of cooperative learning and promote the discussion and interaction among group members, this paper designs and verifies a grouping strategy. This strategy elicits empathy ability on the basis of homogeneity among groups and heterogeneity within groups. The influence of empathy on cooperative learning is studied. Forty-six fourth grade students who participated in science courses are selected as the research objects. The learner with high empathy ability is chosen as the group leader in the experimental group, while the learner with low empathy ability is chosen as the group leader in the control group. At the same time, statistical analysis and social network analysis method are used to explore the influence of empathy on learning effects and group interaction. It is found that the group of high empathy ability is significantly higher than the group of low empathy ability in group discussion interaction density and learning effect. This also provides a reference to the later development of learners and the future development of cooperative learning.
作者机构：
Authors to whom correspondence should be addressed.;School of Information Technology in Education, South China Normal University, Guangzhou 510631, China;[Qingtang Liu; Linjing Wu] School of Educational Information Technology, Central China Normal University, Wuhan 430079, China;[Yunxiang Zheng; Jingxiu Huang] Authors to whom correspondence should be addressed.<&wdkj&>School of Information Technology in Education, South China Normal University, Guangzhou 510631, China
通讯机构：
[Yunxiang Zheng; Jingxiu Huang] A;Authors to whom correspondence should be addressed.<&wdkj&>School of Information Technology in Education, South China Normal University, Guangzhou 510631, China
关键词：
comma disambiguation;feature engineering;hyperparameter tuning;imbalanced learning;natural language understanding;random forests
摘要：
Natural language understanding technologies play an essential role in automatically solving math word problems. In the process of machine understanding Chinese math word problems, comma disambiguation, which is associated with a class imbalance binary learning problem, is addressed as a valuable instrument to transform the problem statement of math word problems into structured representation. Aiming to resolve this problem, we employed the synthetic minority oversampling technique (SMOTE) and random forests to comma classification after their hyperparameters were jointly optimized. We propose a strict measure to evaluate the performance of deployed comma classification models on comma disambiguation in math word problems. To verify the effectiveness of random forest classifiers with SMOTE on comma disambiguation, we conducted two-stage experiments on two datasets with a collection of evaluation measures. Experimental results showed that random forest classifiers were significantly superior to baseline methods in Chinese comma disambiguation. The SMOTE algorithm with optimized hyperparameter settings based on the categorical distribution of different datasets is preferable, instead of with its default values. For practitioners, we suggest that hyperparameters of a classification models be optimized again after parameter settings of SMOTE have been changed.
作者机构：
[Gang, Zhao; Qing, Xia; Biling, Hu; Jie, Chu; Wenjuan, Zhu; Hui, He] Cent China Normal Univ, Fac Artificial Intelligence Educ, Sch Educ Informat Technol, Wuhan, Peoples R China.
通讯机构：
[Zhu Wenjuan] S;School of Educational Information Technology, Faculty of Artificial Intelligence Education, Central China Normal University, Wuhan, China
关键词：
Teacher behavior;Behavior recognition;Motion region extraction;Key-points tracking;Teacher’s behavior rule
摘要：
The analysis of teacher behavior of massive teaching videos has become a surge of research interest recently. Traditional methods rely on accurate manual analysis, which is extremely complex and time-consuming for analyzing massive teaching videos. However, existing works on action recognition are difficultly transplanted to the teacher behavior recognition, because it is difficult to extract teacher’s behavior from complex teaching scenario, and teacher’s behaviors are given professional educational semantics. These methods are not adequate for the need of the teacher behavior recognition. Thus, a novel and simple recognition method of teacher behavior in the actual teaching scene for massive teaching videos is proposed, which can provide technical assistance for analyzing teacher behavior and fill the blank of automatic recognition of teacher behavior in actual teaching scene. Firstly, we discover the educational pattern which it be named “teacher set”, that is, the spatial region of the video of the whole class where teachers should exist. Based on this, the algorithm of teacher set identification and extraction (Teacher-set IE algorithm) is studied to identify the teacher in the teaching video, and reduce the interference factors of classroom background. Then, an improved behavior recognition network based on 3D bilinear pooling (3D BP-TBR) is presented to enhance fusion representation of three-dimensional features thus identifying the categories of teacher behavior, and experiments show that 3D BP-TBR can achieve better performance on public and self-built dataset (TAD-08). Hence, our whole approach can increase recognition accuracy of teacher behavior in the actual teaching scene to utilize the deep integration of educational characteristics and action recognition technology.
期刊：
International Journal of Human-Computer Interaction,2021年37(9):884-901 ISSN：1044-7318
通讯作者：
Liu, Shan;Zhu, Wen-Juan
作者机构：
[Qi, Yu-Heng; Liu, Shan; Zhu, Wen-Juan; Liu, S; Zhao, Gang] Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.
通讯机构：
[Liu, S; Zhu, WJ] C;Cent China Normal Univ, Sch Educ Informat Technol, Wuhan 430079, Peoples R China.
摘要：
Mobile augmented reality (AR) technology creates realistic learning situations and a strong sense of immersion, which is conducive to enhance learning experience and stimulate learning motivation. However, existing mobile outdoor augmented reality applications generally have a complicated operation process and a mismatch between learning resources and corresponding scenes, which leads to a poor learning experience. Therefore, we propose a lightweight mobile outdoor AR method that combines deep learning and knowledge modeling to perceive learning scenes with a goal to improve learning experience. This method improves the accuracy of scene perception and resources retrieval and provides a convenient mobile AR technology solution for outdoor learning. To evaluate the proposed method, we provide objective criteria to assess the effectiveness of the lightweight object detection model and the learning resources retrieval approach. Simultaneously, we investigate the evaluation of participants majoring in teacher education on the usability of the proposed method by the modified system usability scale questionnaire and net promoter score. Experimental results demonstrate that our method achieves high detection accuracy, good usability, and is of great significance to improve outdoor learning experience.
摘要：
How to improve pre-service teachers’ argumentation skills has been receiving more and more attention from teacher educators. Visual cognitive tool refers to tools which users can learn with and creatively use to construct knowledge online. Current research revealed that it could help to improve learners’ higher-order thinking skills. This experimental study aimed to investigate the effect of two kinds of cognitive tools, the text-based online visual cognitive tool and the visual concept map, on improving the pre-service teachers’ skills on constructing and evaluating arguments. Post-test argumentation measurement scores and attitude questionnaire showed that the text-based cognitive tool was more effective than the concept map on improving pre-service teachers’ argumentation skills. However, the concept map was useful for externalizing the pre-service teachers’ thinking process as well as collaborative learning. This study also found that the pre-service teachers with teaching experience were inferior to the ones without any teaching experience in the ability on constructing arguments.
