Natural language processing (NLP) is an important research direction of artificial intelligence. Text sentiment classification in NLP is a compromising method to exploit the constructive feedback to improve teaching quality. This paper captures the course reviews from online learning platform China University MOOC as the dataset, and uses an aspect-level sentiment classification method to analyze the course evaluation, via a graph convolution network (GCN) to characterize the syntactic dependency between context words and various aspects of sentences, and decide the emotions described by multiple non-adjacent Chinese words. As for the 1837 comments of online courses, there is obvious aggregation in the aspect of extraction. Most of the comments mainly focus on the two aspects of course and teacher, and a few comments describe other aspects related to the course. The results demonstrate that the accuracy of the model is more than 80%. Additionally, a visual interface is designed to provide the sentiment analysis results no matter what data set of course reviews is given, and make the graph learning based sentiment analysis tool user-friendly.
{"title":"Graph Learning Based Sentiment Analysis System for Chinese Course Evaluation","authors":"Jiajia Jiao, Dongjue Chen, Bo Chen","doi":"10.1145/3498765.3498770","DOIUrl":"https://doi.org/10.1145/3498765.3498770","url":null,"abstract":"Natural language processing (NLP) is an important research direction of artificial intelligence. Text sentiment classification in NLP is a compromising method to exploit the constructive feedback to improve teaching quality. This paper captures the course reviews from online learning platform China University MOOC as the dataset, and uses an aspect-level sentiment classification method to analyze the course evaluation, via a graph convolution network (GCN) to characterize the syntactic dependency between context words and various aspects of sentences, and decide the emotions described by multiple non-adjacent Chinese words. As for the 1837 comments of online courses, there is obvious aggregation in the aspect of extraction. Most of the comments mainly focus on the two aspects of course and teacher, and a few comments describe other aspects related to the course. The results demonstrate that the accuracy of the model is more than 80%. Additionally, a visual interface is designed to provide the sentiment analysis results no matter what data set of course reviews is given, and make the graph learning based sentiment analysis tool user-friendly.","PeriodicalId":273698,"journal":{"name":"Proceedings of the 13th International Conference on Education Technology and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125535839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Probing the relationship between teacher-student interaction and student engagement during online learning is of paramount importance for the improvement of the quality of online education. Based on the questionnaire concerning online education conducted by Xiamen University during the pandemic, this essay introduces relational embedding theory, and uses descriptive statistics, factor analysis and correlation analysis methods to analyze the influence of teacher-student interaction on online learning engagement. According to the results, teacher-student interaction exerts a significantly positive influence on learning engagement, and it has a stronger influence on behavioral engagement. Different interaction modes exert great influence on learning engagement. Synchronous interaction has a stronger influence on engagement, especially on emotional engagement than asynchronous interaction. The connectivity between teacher-student interaction and learning engagement is in relation to the forms and methods of online teaching. Therefore, engagement can be enhanced and improved mainly from three dimensions: teachers and students, platform-technology, and teacher-student interaction.
{"title":"A Research on the Influence of Teacher-student Interaction on College Student Engagement in Online Learning","authors":"Lichun Liu, Wenli Ma, Guifan Han","doi":"10.1145/3498765.3498799","DOIUrl":"https://doi.org/10.1145/3498765.3498799","url":null,"abstract":"Probing the relationship between teacher-student interaction and student engagement during online learning is of paramount importance for the improvement of the quality of online education. Based on the questionnaire concerning online education conducted by Xiamen University during the pandemic, this essay introduces relational embedding theory, and uses descriptive statistics, factor analysis and correlation analysis methods to analyze the influence of teacher-student interaction on online learning engagement. According to the results, teacher-student interaction exerts a significantly positive influence on learning engagement, and it has a stronger influence on behavioral engagement. Different interaction modes exert great influence on learning engagement. Synchronous interaction has a stronger influence on engagement, especially on emotional engagement than asynchronous interaction. The connectivity between teacher-student interaction and learning engagement is in relation to the forms and methods of online teaching. Therefore, engagement can be enhanced and improved mainly from three dimensions: teachers and students, platform-technology, and teacher-student interaction.","PeriodicalId":273698,"journal":{"name":"Proceedings of the 13th International Conference on Education Technology and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131666229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to the rapid development of Internet technology and the popularity of smart phones, the combination of information technology and traditional teaching practice has also become more and more important, as a result, blended learning has been booming and popular in China, particularly during and post COVID-19 pandemic. With gradual decrease of class length and credits of College English course, college English teaching needs to be extended beyond lectures in class with more teaching contents to promote after-class teaching and learning. Hopefully, blended learning model based on intelligent learning platform has been a new channel to promote English learning. Based on WELearn learning platform, this study explores the blended teaching model that uses online platform and offline entity classroom, in which 4187 students are involved. The results show that relying on the massive online and offline teaching resources and various teaching task activities, it realizes the blended teaching successfully, enabling students to spend more time in their self-directed learning with higher efficiency. Meanwhile, learning process is ensured and students are much more motivated for English learning which leads to better marks in their test results. Furthermore, it is beneficial for personalized learning and teaching and for achieving objectives of college English course as well.
{"title":"An Empirical Study on Blended Learning Design and Practice of College English Based on WELearn Learning Platform","authors":"Yuanbing Duan, Jingzheng Wang","doi":"10.1145/3498765.3498783","DOIUrl":"https://doi.org/10.1145/3498765.3498783","url":null,"abstract":"Due to the rapid development of Internet technology and the popularity of smart phones, the combination of information technology and traditional teaching practice has also become more and more important, as a result, blended learning has been booming and popular in China, particularly during and post COVID-19 pandemic. With gradual decrease of class length and credits of College English course, college English teaching needs to be extended beyond lectures in class with more teaching contents to promote after-class teaching and learning. Hopefully, blended learning model based on intelligent learning platform has been a new channel to promote English learning. Based on WELearn learning platform, this study explores the blended teaching model that uses online platform and offline entity classroom, in which 4187 students are involved. The results show that relying on the massive online and offline teaching resources and various teaching task activities, it realizes the blended teaching successfully, enabling students to spend more time in their self-directed learning with higher efficiency. Meanwhile, learning process is ensured and students are much more motivated for English learning which leads to better marks in their test results. Furthermore, it is beneficial for personalized learning and teaching and for achieving objectives of college English course as well.","PeriodicalId":273698,"journal":{"name":"Proceedings of the 13th International Conference on Education Technology and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132706302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A central tendency in these innovations is to base corpus more on the educational needs of students in European universities. Corpus-involved education and learning attempt to optimize students’ learning process, stimulation, creation and active learning environment. A logical step in placing students at the center of their education is involving them in the quality control, organization and development of curricula based on corpus learning. Opportunities for student participation in curriculum planning and organization are given, including advantages and possible disadvantages of corpus involvement teaching and learning. Implications for European university wishing to incorporate students in their corpus organization are discussed to improve students’ input.
{"title":"Corpus-involved Education and Learning in European Universities","authors":"Shuo Zhao","doi":"10.1145/3498765.3498820","DOIUrl":"https://doi.org/10.1145/3498765.3498820","url":null,"abstract":"A central tendency in these innovations is to base corpus more on the educational needs of students in European universities. Corpus-involved education and learning attempt to optimize students’ learning process, stimulation, creation and active learning environment. A logical step in placing students at the center of their education is involving them in the quality control, organization and development of curricula based on corpus learning. Opportunities for student participation in curriculum planning and organization are given, including advantages and possible disadvantages of corpus involvement teaching and learning. Implications for European university wishing to incorporate students in their corpus organization are discussed to improve students’ input.","PeriodicalId":273698,"journal":{"name":"Proceedings of the 13th International Conference on Education Technology and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114592617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study reports on a survey of experiences in online Chinese learning among 128 Chinese as a second language (CSL) learners in China. The data were collected by means of a self-adapted questionnaire. Factor analysis of the collected data reveals that the experiences of CSL learners’ in online Chinese courses include their experiences in online learning environment (Course Organization, function and technology environment, and content and resources), online learning activities (learning support and service, learning freedom and evaluation), and perception of online course efficiency (course knowledge and competence, informationized learning skills and attitudes).The findings are implicative for increasing learning interest, improving learning methods and effect to learners in CSL, besides, helping teachers better understand students' learning situation. According to these gives the suggestions to international learners of Chinese in China and other similar contexts about online Chinese learning.
{"title":"Understanding Online Chinese Course Learning Experience among Chinese as a Second Language Learners in China","authors":"Chili Li, C. Ma","doi":"10.1145/3498765.3498794","DOIUrl":"https://doi.org/10.1145/3498765.3498794","url":null,"abstract":"This study reports on a survey of experiences in online Chinese learning among 128 Chinese as a second language (CSL) learners in China. The data were collected by means of a self-adapted questionnaire. Factor analysis of the collected data reveals that the experiences of CSL learners’ in online Chinese courses include their experiences in online learning environment (Course Organization, function and technology environment, and content and resources), online learning activities (learning support and service, learning freedom and evaluation), and perception of online course efficiency (course knowledge and competence, informationized learning skills and attitudes).The findings are implicative for increasing learning interest, improving learning methods and effect to learners in CSL, besides, helping teachers better understand students' learning situation. According to these gives the suggestions to international learners of Chinese in China and other similar contexts about online Chinese learning.","PeriodicalId":273698,"journal":{"name":"Proceedings of the 13th International Conference on Education Technology and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126975423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
More and more teachers tend to use online learning platforms to assist teaching. Meanwhile, the instant messaging has generated increasing awareness of its educational value, due to its flexibility, convenience and widespread use. Many researches are to model and analyze the learning behavior based on data collected from online learning platforms or instant messaging (IM) platforms separately. But relatively little is known about how the students behave when combining these two kinds of platforms with traditional classroom learning. In this study, we investigate and analyze learning behaviors of first-year university students in a project-based course by exploring data collected from an online learning platform and an IM platform which are used in the course. Firstly, an interactive social network is constructed based on the IM messages, from which students’ engagement in the course and their preferred collaborative learning styles can be observed. Then learning behavior features extracted from both platforms are analyzed to get insights of the learning behaviors of students when combining these two kinds of platforms in the course. Our findings reveal that instant messaging platform can help students adapt to new fields, and their learning patterns on the IM platform are different, which ultimately leads to different learning performance. Finally, a learning performance prediction model called Student Predict Based On BI-LSTM (SPBI-LSTM) is proposed, which fuses student behavior sequences from two platforms to predict students who under-performing. We experimentally verify the improvement in accuracy when integrating instant messaging data.
{"title":"Learning Behavior Analysis Based on Instant Message and Online Learning Platform","authors":"Ting-Ting Yang, Xinning Zhu, Yang Ji","doi":"10.1145/3498765.3498795","DOIUrl":"https://doi.org/10.1145/3498765.3498795","url":null,"abstract":"More and more teachers tend to use online learning platforms to assist teaching. Meanwhile, the instant messaging has generated increasing awareness of its educational value, due to its flexibility, convenience and widespread use. Many researches are to model and analyze the learning behavior based on data collected from online learning platforms or instant messaging (IM) platforms separately. But relatively little is known about how the students behave when combining these two kinds of platforms with traditional classroom learning. In this study, we investigate and analyze learning behaviors of first-year university students in a project-based course by exploring data collected from an online learning platform and an IM platform which are used in the course. Firstly, an interactive social network is constructed based on the IM messages, from which students’ engagement in the course and their preferred collaborative learning styles can be observed. Then learning behavior features extracted from both platforms are analyzed to get insights of the learning behaviors of students when combining these two kinds of platforms in the course. Our findings reveal that instant messaging platform can help students adapt to new fields, and their learning patterns on the IM platform are different, which ultimately leads to different learning performance. Finally, a learning performance prediction model called Student Predict Based On BI-LSTM (SPBI-LSTM) is proposed, which fuses student behavior sequences from two platforms to predict students who under-performing. We experimentally verify the improvement in accuracy when integrating instant messaging data.","PeriodicalId":273698,"journal":{"name":"Proceedings of the 13th International Conference on Education Technology and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121888583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper the author critiques the term ‘Quality of Research’ and reviews the different smart eResearch tools and technologies that enhance the quality of research through eResearch engagement. Development of digital tools and eResearch skills for brainstorming, conceptual and critical thinking, research planning and execution, using social networking platforms for data collection and improving collaboration are examined. This is followed by a brief review of some of the online survey tools and social networking platforms for research data collection and its analysis. The author also appraises specific considerations of technology related research ethics and academic integrity. Finally, attributes have been reviewed for development of ‘Digital Skills for eResearch’ as a catalyst to initiate wider eResearch engagement.
{"title":"Enhancing the Quality of Research using Smart eResearch Tools and Technologies","authors":"M. Bhattacharya","doi":"10.1145/3498765.3498807","DOIUrl":"https://doi.org/10.1145/3498765.3498807","url":null,"abstract":"In this paper the author critiques the term ‘Quality of Research’ and reviews the different smart eResearch tools and technologies that enhance the quality of research through eResearch engagement. Development of digital tools and eResearch skills for brainstorming, conceptual and critical thinking, research planning and execution, using social networking platforms for data collection and improving collaboration are examined. This is followed by a brief review of some of the online survey tools and social networking platforms for research data collection and its analysis. The author also appraises specific considerations of technology related research ethics and academic integrity. Finally, attributes have been reviewed for development of ‘Digital Skills for eResearch’ as a catalyst to initiate wider eResearch engagement.","PeriodicalId":273698,"journal":{"name":"Proceedings of the 13th International Conference on Education Technology and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125180427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Benjamín Maraza-Quispe, Enrique Damian Valderrama-Chauca, Lenin Henry Cari-Mogrovejo, Jorge Milton Apaza-Huanca
The present research aims to implement a predictive model in the KNIME platform to analyze and compare the prediction of academic performance using data from a Learning Management System (LMS), identifying students at academic risk in order to generate timely and timely interventions. The CRISP-DM methodology was used, structured in six phases: Problem analysis, data analysis, data understanding, data preparation, modeling, evaluation and implementation. Based on the analysis of online learning behavior through 22 behavioral indicators observed in the LMS of the Faculty of Educational Sciences of the National University of San Agustin. These indicators are distributed in five dimensions: Academic Performance, Access, Homework, Social Aspects and Quizzes. The model has been implemented in the KNIME platform using the Simple Regression Tree Learner training algorithm. The total population consists of 30,000 student records from which a sample of 1,000 records has been taken by simple random sampling. The accuracy of the model for early prediction of students' academic performance is evaluated, the 22 observed behavioral indicators are compared with the means of academic performance in three courses. The prediction results of the implemented model are satisfactory where the mean absolute error compared to the mean of the first course was 3. 813 and with an accuracy of 89.7%, the mean absolute error compared to the mean of the second course was 2.809 with an accuracy of 94.2% and the mean absolute error compared to the mean of the third course was 2.779 with an accuracy of 93.8%. These results demonstrate that the proposed model can be used to predict students' future academic performance from an LMS data set.
{"title":"Predictive Model of Student Academic Performance from LMS data based on Learning Analytics","authors":"Benjamín Maraza-Quispe, Enrique Damian Valderrama-Chauca, Lenin Henry Cari-Mogrovejo, Jorge Milton Apaza-Huanca","doi":"10.1145/3498765.3498768","DOIUrl":"https://doi.org/10.1145/3498765.3498768","url":null,"abstract":"The present research aims to implement a predictive model in the KNIME platform to analyze and compare the prediction of academic performance using data from a Learning Management System (LMS), identifying students at academic risk in order to generate timely and timely interventions. The CRISP-DM methodology was used, structured in six phases: Problem analysis, data analysis, data understanding, data preparation, modeling, evaluation and implementation. Based on the analysis of online learning behavior through 22 behavioral indicators observed in the LMS of the Faculty of Educational Sciences of the National University of San Agustin. These indicators are distributed in five dimensions: Academic Performance, Access, Homework, Social Aspects and Quizzes. The model has been implemented in the KNIME platform using the Simple Regression Tree Learner training algorithm. The total population consists of 30,000 student records from which a sample of 1,000 records has been taken by simple random sampling. The accuracy of the model for early prediction of students' academic performance is evaluated, the 22 observed behavioral indicators are compared with the means of academic performance in three courses. The prediction results of the implemented model are satisfactory where the mean absolute error compared to the mean of the first course was 3. 813 and with an accuracy of 89.7%, the mean absolute error compared to the mean of the second course was 2.809 with an accuracy of 94.2% and the mean absolute error compared to the mean of the third course was 2.779 with an accuracy of 93.8%. These results demonstrate that the proposed model can be used to predict students' future academic performance from an LMS data set.","PeriodicalId":273698,"journal":{"name":"Proceedings of the 13th International Conference on Education Technology and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127657203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Blockchain technology is considered to be the most important invention of human society since the birth of the Internet, which is deeply influencing the operation concept, organization and business mode of institutions or services such as global governance, economic development, finance and education, especially in the banking, securities, insurance, notarization, music, distributed storage, Internet of things and other industries and fields. At the same time, blockchain technology has also been initially developed in the field of education, and its impact on education and teaching is increasingly reflected. At present, educational institutions, institutions concerned about education and future social development, as well as people of insight, have taken action to guide and utilize blockchain technology in a forward-looking manner by providing blockchain technology teaching and developing a teaching management platform based on blockchain technology, so as to meet the new education and teaching reform. In this paper, the essence, characteristics and development process of blockchain technology are discussed, and the application prospect of blockchain technology in higher education is given. At the same time, thoughts on the development of "blockchain+education" are also put forward, in order to provide reference for exploring the deep integration of modern information technology and education, and leading the innovation of education concept and education mode with informatization.
{"title":"Blockchain Technology and Its Application Prospect in Higher Education","authors":"Shuguang Liu, Lin Ba","doi":"10.1145/3498765.3498802","DOIUrl":"https://doi.org/10.1145/3498765.3498802","url":null,"abstract":"Blockchain technology is considered to be the most important invention of human society since the birth of the Internet, which is deeply influencing the operation concept, organization and business mode of institutions or services such as global governance, economic development, finance and education, especially in the banking, securities, insurance, notarization, music, distributed storage, Internet of things and other industries and fields. At the same time, blockchain technology has also been initially developed in the field of education, and its impact on education and teaching is increasingly reflected. At present, educational institutions, institutions concerned about education and future social development, as well as people of insight, have taken action to guide and utilize blockchain technology in a forward-looking manner by providing blockchain technology teaching and developing a teaching management platform based on blockchain technology, so as to meet the new education and teaching reform. In this paper, the essence, characteristics and development process of blockchain technology are discussed, and the application prospect of blockchain technology in higher education is given. At the same time, thoughts on the development of \"blockchain+education\" are also put forward, in order to provide reference for exploring the deep integration of modern information technology and education, and leading the innovation of education concept and education mode with informatization.","PeriodicalId":273698,"journal":{"name":"Proceedings of the 13th International Conference on Education Technology and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127851730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Under the guidance of Overall Plan for Deepening the Reform of Educational Evaluation in the New Era and Implementation Outline for Improving the Quality of Ideological and Political Work in Colleges and Universities, the author has done the following work: analyzing the basic concepts of blended learning evaluation, expounding the present situation and existing problems of blended learning evaluation, exploring the connotation of it, and constructing a blended learning evaluation model and evaluation system.
{"title":"Research on Connotation and System Construction of Blended Learning Evaluation in Colleges and Universities","authors":"Li Lin","doi":"10.1145/3498765.3498787","DOIUrl":"https://doi.org/10.1145/3498765.3498787","url":null,"abstract":"Under the guidance of Overall Plan for Deepening the Reform of Educational Evaluation in the New Era and Implementation Outline for Improving the Quality of Ideological and Political Work in Colleges and Universities, the author has done the following work: analyzing the basic concepts of blended learning evaluation, expounding the present situation and existing problems of blended learning evaluation, exploring the connotation of it, and constructing a blended learning evaluation model and evaluation system.","PeriodicalId":273698,"journal":{"name":"Proceedings of the 13th International Conference on Education Technology and Computers","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121470140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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