Pub Date : 2023-08-30DOI: 10.3991/ijet.v18i16.41353
Yan Zhang, Chang Liu
With the continuous progress in Chinese higher education, the quality of online teaching has become the key to influencing that of the operation and reputation of universities and colleges. Nevertheless, the results of traditional teaching quality evaluation methods are considerably influenced by objectivity due to limitations in single-index and outdated methods. Hence, the construction of a reasonable online teaching quality evaluation model for universities and colleges presents important research significance to optimize the existing evaluation process. An online teaching quality evaluation index system for teachers at 26 observation points was set up from the perspectives of teaching objectives, process, and effect. The Technique for Order Preference by Similarity to Solution (TOPSIS) scores of 215 teachers from six universities in Henan Province, China, were evaluated using the entropy TOPSIS method. In addition, the significance of influencing factors in the ranking results of online teaching quality by teachers was analyzed using a hierarchical regression model. Results demonstrate that the weights of teaching attitude, teaching contents, and cognitive objectives were the highest and occupied the top three positions with weights of 14.94%, 12.99%, and 12.96%. By using three level-1 indexes of teaching objectives, process, and effect as the explanatory variables, students’ scores for teachers are all significant under the 1% level. According to the Chow test, the results are F (4, 207) = 2.725 and p = 0.031 < 0.05, indicating that using the online teaching duration of teachers as a grouping variable brings structural changes. Results can optimize online teaching quality evaluation and provide scientific references to evaluate the teaching quality of teachers.
随着我国高等教育的不断进步，网络教学质量已成为影响高校办学质量和声誉的关键。然而，传统的教学质量评价方法由于指标单一、方法陈旧等因素的限制，结果客观性受到较大影响。因此，构建合理的高校在线教学质量评价模型对优化现有评价流程具有重要的研究意义。从教学目标、教学过程、教学效果三个方面构建了26个观察点教师在线教学质量评价指标体系。采用熵值TOPSIS法对河南省6所高校215名教师的排序偏好相似度(TOPSIS)分数进行了评价。此外，运用层次回归模型分析影响因素对教师在线教学质量排名结果的显著性。结果表明:教学态度、教学内容和认知目标的权重最高，分别以14.94%、12.99%和12.96%的权重占据前三位。以教学目标、过程、效果三个一级指标作为解释变量，学生对教师的得分在1%水平下均显著。根据Chow检验，结果为F (4,207) = 2.725, p = 0.031 < 0.05，说明以教师在线教学时长作为分组变量带来了结构性变化。研究结果可以优化在线教学质量评价，为教师教学质量评价提供科学依据。
{"title":"Online Teaching Quality Evaluation: Entropy TOPSIS and Grouped Regression Model","authors":"Yan Zhang, Chang Liu","doi":"10.3991/ijet.v18i16.41353","DOIUrl":"https://doi.org/10.3991/ijet.v18i16.41353","url":null,"abstract":"With the continuous progress in Chinese higher education, the quality of online teaching has become the key to influencing that of the operation and reputation of universities and colleges. Nevertheless, the results of traditional teaching quality evaluation methods are considerably influenced by objectivity due to limitations in single-index and outdated methods. Hence, the construction of a reasonable online teaching quality evaluation model for universities and colleges presents important research significance to optimize the existing evaluation process. An online teaching quality evaluation index system for teachers at 26 observation points was set up from the perspectives of teaching objectives, process, and effect. The Technique for Order Preference by Similarity to Solution (TOPSIS) scores of 215 teachers from six universities in Henan Province, China, were evaluated using the entropy TOPSIS method. In addition, the significance of influencing factors in the ranking results of online teaching quality by teachers was analyzed using a hierarchical regression model. Results demonstrate that the weights of teaching attitude, teaching contents, and cognitive objectives were the highest and occupied the top three positions with weights of 14.94%, 12.99%, and 12.96%. By using three level-1 indexes of teaching objectives, process, and effect as the explanatory variables, students’ scores for teachers are all significant under the 1% level. According to the Chow test, the results are F (4, 207) = 2.725 and p = 0.031 < 0.05, indicating that using the online teaching duration of teachers as a grouping variable brings structural changes. Results can optimize online teaching quality evaluation and provide scientific references to evaluate the teaching quality of teachers.","PeriodicalId":47933,"journal":{"name":"International Journal of Emerging Technologies in Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41883899","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}
Pub Date : 2023-08-30DOI: 10.3991/ijet.v18i16.36169
A. Drigas, Irene Chaidi, Chara Papoutsi
Schools are cultural curators, along with libraries and museums. The development and use of technologies are a fact and an important tool in the evolution of the educational process, shaping new attitudes in the functioning of the educational community among parents, students, and teachers. At the same time, cultivating and improving the emotional intelligence of all those who make up the school environment will lead to well-being without stress, which is benefitial for the whole world. The teacher of the future is called and must give a resounding presence as he is the connecting link between the school, students, and parents. This article is a bibliographic review of the research results and articles to date about the school and especially the teacher of the future and deals with the existential technological identity of the educational future, its role in shaping the existential identity of the student in educational and social becoming, and the additional important skills a teacher should have for creative, happy, and well-balanced students.
{"title":"Teacher of the Future","authors":"A. Drigas, Irene Chaidi, Chara Papoutsi","doi":"10.3991/ijet.v18i16.36169","DOIUrl":"https://doi.org/10.3991/ijet.v18i16.36169","url":null,"abstract":"Schools are cultural curators, along with libraries and museums. The development and use of technologies are a fact and an important tool in the evolution of the educational process, shaping new attitudes in the functioning of the educational community among parents, students, and teachers. At the same time, cultivating and improving the emotional intelligence of all those who make up the school environment will lead to well-being without stress, which is benefitial for the whole world. The teacher of the future is called and must give a resounding presence as he is the connecting link between the school, students, and parents. This article is a bibliographic review of the research results and articles to date about the school and especially the teacher of the future and deals with the existential technological identity of the educational future, its role in shaping the existential identity of the student in educational and social becoming, and the additional important skills a teacher should have for creative, happy, and well-balanced students.","PeriodicalId":47933,"journal":{"name":"International Journal of Emerging Technologies in Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45331448","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}
Pub Date : 2023-08-30DOI: 10.3991/ijet.v18i16.42707
Yan Zhang, Yue Shi, Fu-yong Bi
With the rapid development of educational technology and the deepening of educational system reform, personalized education has gradually become an important topic in education. However, existing classroom teaching decision-making methods often fail to meet students’ personalized learning needs, resulting in some students being unable to reach their full potential in the classroom. To solve this problem, this study proposed a multi-conditional factor classroom teaching decision optimization method based on the improved particle swarm optimization (IPSO) algorithm, and predicted students’ personalized learning needs by combining with the improved ant colony optimization-support vector regression (IACO-SVR) model. First, the IACO-SVR model was used to collect students’ learning data, such as grades, interests, hobbies and learning progress, to accurately predict their needs in different teaching contexts. Second, the IPSO algorithm was used to optimize the multi-conditional factor classroom teaching decisions, thus meeting the personalized needs of students. The IPSO algorithm had strong global search ability, which effectively found the optimal solution to achieve personalized teaching strategies. It is expected that the teaching quality can be improved by predicting the personalized learning needs of students and optimizing classroom teaching decisions in this study, thus providing better support for their comprehensive development. In addition, the results of this study can provide theoretical basis and reference for administrative departments of education and schools to formulate personalized education policies.
{"title":"Personalizing Students' Learning Needs by a Teaching Decision Optimization Method","authors":"Yan Zhang, Yue Shi, Fu-yong Bi","doi":"10.3991/ijet.v18i16.42707","DOIUrl":"https://doi.org/10.3991/ijet.v18i16.42707","url":null,"abstract":"With the rapid development of educational technology and the deepening of educational system reform, personalized education has gradually become an important topic in education. However, existing classroom teaching decision-making methods often fail to meet students’ personalized learning needs, resulting in some students being unable to reach their full potential in the classroom. To solve this problem, this study proposed a multi-conditional factor classroom teaching decision optimization method based on the improved particle swarm optimization (IPSO) algorithm, and predicted students’ personalized learning needs by combining with the improved ant colony optimization-support vector regression (IACO-SVR) model. First, the IACO-SVR model was used to collect students’ learning data, such as grades, interests, hobbies and learning progress, to accurately predict their needs in different teaching contexts. Second, the IPSO algorithm was used to optimize the multi-conditional factor classroom teaching decisions, thus meeting the personalized needs of students. The IPSO algorithm had strong global search ability, which effectively found the optimal solution to achieve personalized teaching strategies. It is expected that the teaching quality can be improved by predicting the personalized learning needs of students and optimizing classroom teaching decisions in this study, thus providing better support for their comprehensive development. In addition, the results of this study can provide theoretical basis and reference for administrative departments of education and schools to formulate personalized education policies.","PeriodicalId":47933,"journal":{"name":"International Journal of Emerging Technologies in Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42504523","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}
Pub Date : 2023-08-30DOI: 10.3991/ijet.v18i16.42319
Thomas B. Frøsig
While technological innovations are designed with the users in mind, this same design approach—designing technology to enhance the students’ learning experience—often falls short of acceptance with teachers and educators. This paper suggests a theoretical model with which EdTech can be designed to enhance the adoption of technology among teachers. At its core, the Educational Technology Acceptance Model (EdTAM) utilizes a double-user scenario, focusing on both the needs and concerns of teachers as well as the needs of students. As a result, a proof of concept in the form of an educational game designed in strict accordance with the EdTAM Model is presented. Thus showing that the theoretical model can be transferred into the design of a real-world product. A further study testing the effect of the model and the produced games acceptance rate with teachers is planned to support the theoretical model with empirical data.
{"title":"Expanding the Technology Acceptance Model (TAM) to Consider Teachers Needs and Concerns in the Design of Educational Technology (EdTAM)","authors":"Thomas B. Frøsig","doi":"10.3991/ijet.v18i16.42319","DOIUrl":"https://doi.org/10.3991/ijet.v18i16.42319","url":null,"abstract":"While technological innovations are designed with the users in mind, this same design approach—designing technology to enhance the students’ learning experience—often falls short of acceptance with teachers and educators. This paper suggests a theoretical model with which EdTech can be designed to enhance the adoption of technology among teachers. At its core, the Educational Technology Acceptance Model (EdTAM) utilizes a double-user scenario, focusing on both the needs and concerns of teachers as well as the needs of students. As a result, a proof of concept in the form of an educational game designed in strict accordance with the EdTAM Model is presented. Thus showing that the theoretical model can be transferred into the design of a real-world product. A further study testing the effect of the model and the produced games acceptance rate with teachers is planned to support the theoretical model with empirical data.","PeriodicalId":47933,"journal":{"name":"International Journal of Emerging Technologies in Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41672089","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}
Pub Date : 2023-08-30DOI: 10.3991/ijet.v18i16.42705
Jingyao Zhang
Online learning environments have become increasingly popular due to their flexibility and convenience, but they also present new challenges, such as maintaining student motivation and engagement. To address these challenges, it is crucial to understand and predict students’ learning behaviors. This study explores the recognition and management of students’ learning behaviors through cognitive status analysis. By conducting a thorough analysis of students’ cognitive status and applying advanced deep learning models and algorithms, this study demonstrates the effectiveness of recognizing and managing students’ learning behaviors. The proposed model combines convolutional neural networks and long short-term memory networks with attention mechanisms, which incorporate cognitive status evaluation features and use them as filters for text information. The model’s focus on text sentences with distinctive features in cognitive status evaluation leads to more effective recognition and management of students’ learning behaviors. Additionally, by integrating Most Informative Propositions and Semantic Propositional Value into the deep learning model, this study achieved excellent results in cognitive status evaluation recognition tasks. Further experiments show that by mixing different features and using advanced algorithms, the final model achieves high classification accuracy and F1 scores on multiple types of learning behaviors. Continuous assessment of students’ cognitive status and learning behaviors can lead to the development of effective learning strategies and intervention measures, which can enhance students’ mastery of knowledge and overall performance.
{"title":"Cognitive Status Analysis for Recognizing and Managing Students' Learning Behaviors","authors":"Jingyao Zhang","doi":"10.3991/ijet.v18i16.42705","DOIUrl":"https://doi.org/10.3991/ijet.v18i16.42705","url":null,"abstract":"Online learning environments have become increasingly popular due to their flexibility and convenience, but they also present new challenges, such as maintaining student motivation and engagement. To address these challenges, it is crucial to understand and predict students’ learning behaviors. This study explores the recognition and management of students’ learning behaviors through cognitive status analysis. By conducting a thorough analysis of students’ cognitive status and applying advanced deep learning models and algorithms, this study demonstrates the effectiveness of recognizing and managing students’ learning behaviors. The proposed model combines convolutional neural networks and long short-term memory networks with attention mechanisms, which incorporate cognitive status evaluation features and use them as filters for text information. The model’s focus on text sentences with distinctive features in cognitive status evaluation leads to more effective recognition and management of students’ learning behaviors. Additionally, by integrating Most Informative Propositions and Semantic Propositional Value into the deep learning model, this study achieved excellent results in cognitive status evaluation recognition tasks. Further experiments show that by mixing different features and using advanced algorithms, the final model achieves high classification accuracy and F1 scores on multiple types of learning behaviors. Continuous assessment of students’ cognitive status and learning behaviors can lead to the development of effective learning strategies and intervention measures, which can enhance students’ mastery of knowledge and overall performance.","PeriodicalId":47933,"journal":{"name":"International Journal of Emerging Technologies in Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46279437","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}
Pub Date : 2023-08-30DOI: 10.3991/ijet.v18i16.42315
Maira Alejandra Pulgarín Rodríguez
Academic literacy is a crucial aspect of education, yet assessing it remains a challenge for universities in knowledge management. This research focuses on how to support Colombian university students in understanding academic texts in virtual learning environments. To address this issue and fill a gap in the literature, the main objective of this study is to propose a model for assessing academic literacy in virtual learning environments, using the Delphi method as a validation tool. A panel of language experts from various Latin American countries was convened to ensure a diverse and representative perspective of the region for this study. The research involved the participation of 15 experts with doctoral degrees from different countries, which allowed for a broad and heterogeneous perspective on academic literacy in virtual learning contexts in Latin America. Each of the participants contributed their knowledge and experience in the language area. In the results, ten key elements were identified and distributed in different subconstructs that allow for a comprehensive evaluation and improvement of academic competence in virtual learning environments. The identification of these elements provides teachers and university management personnel with a reliable and precise tool for evaluating students’ competence levels in virtual learning environments and designing effective pedagogical strategies to improve their academic performance.
{"title":"Validation of a Didactic Model for the Understanding of Academic Texts in Virtual Higher Education","authors":"Maira Alejandra Pulgarín Rodríguez","doi":"10.3991/ijet.v18i16.42315","DOIUrl":"https://doi.org/10.3991/ijet.v18i16.42315","url":null,"abstract":"Academic literacy is a crucial aspect of education, yet assessing it remains a challenge for universities in knowledge management. This research focuses on how to support Colombian university students in understanding academic texts in virtual learning environments. To address this issue and fill a gap in the literature, the main objective of this study is to propose a model for assessing academic literacy in virtual learning environments, using the Delphi method as a validation tool. A panel of language experts from various Latin American countries was convened to ensure a diverse and representative perspective of the region for this study. The research involved the participation of 15 experts with doctoral degrees from different countries, which allowed for a broad and heterogeneous perspective on academic literacy in virtual learning contexts in Latin America. Each of the participants contributed their knowledge and experience in the language area. In the results, ten key elements were identified and distributed in different subconstructs that allow for a comprehensive evaluation and improvement of academic competence in virtual learning environments. The identification of these elements provides teachers and university management personnel with a reliable and precise tool for evaluating students’ competence levels in virtual learning environments and designing effective pedagogical strategies to improve their academic performance.","PeriodicalId":47933,"journal":{"name":"International Journal of Emerging Technologies in Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48404633","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}
Pub Date : 2023-08-15DOI: 10.3991/ijet.v18i15.37241
Robert Hegestedt, Jalal Nouri, Rebecka Rundquist, U. Fors
Data-driven school improvement has been proposed to improve and support educational practices, and more studies are emerging describing data-driven practices in schools and the effects of data-driven interventions. This paper reports on a study that has taken place within a national program where 15 schools from 6 different municipalities and organizations are working at classroom, school and municipality levels to improve educational practices using data-driven methods. The study aimed at understanding what educational problems teachers, principals and administrative staff in the project aimed to address through the utilization of data-driven methods and the challenges they face in doing so. Using a mixed-methods design, we identified four thematic areas that reflect the focused problem areas of the participants in the project, namely didactics, democracy, assessment and planning, and mental health. All development groups identified problems that can be solved with data-driven methods. Along with this, we also identified five challenges faced by the participants: time and resources, competence, ethics, digital systems and common language. We conclude that the main challenge faced by the participants is data literacy, and that professional development is needed to support effective and successful data-driven practices in schools.
{"title":"Data-Driven School Improvement and Data-Literacy in K-12: Findings from a Swedish National Program","authors":"Robert Hegestedt, Jalal Nouri, Rebecka Rundquist, U. Fors","doi":"10.3991/ijet.v18i15.37241","DOIUrl":"https://doi.org/10.3991/ijet.v18i15.37241","url":null,"abstract":"Data-driven school improvement has been proposed to improve and support educational practices, and more studies are emerging describing data-driven practices in schools and the effects of data-driven interventions. This paper reports on a study that has taken place within a national program where 15 schools from 6 different municipalities and organizations are working at classroom, school and municipality levels to improve educational practices using data-driven methods. The study aimed at understanding what educational problems teachers, principals and administrative staff in the project aimed to address through the utilization of data-driven methods and the challenges they face in doing so. Using a mixed-methods design, we identified four thematic areas that reflect the focused problem areas of the participants in the project, namely didactics, democracy, assessment and planning, and mental health. All development groups identified problems that can be solved with data-driven methods. Along with this, we also identified five challenges faced by the participants: time and resources, competence, ethics, digital systems and common language. We conclude that the main challenge faced by the participants is data literacy, and that professional development is needed to support effective and successful data-driven practices in schools.","PeriodicalId":47933,"journal":{"name":"International Journal of Emerging Technologies in Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43729773","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}
Pub Date : 2023-08-15DOI: 10.3991/ijet.v18i15.40957
Kamal Omari
Serious games are effective educational tools used in higher education to provide practical learning opportunities to students. However, few research works have focused on evaluating serious games as a project for developing a tool dedicated to use in a formative context. This document proposes an intelligent evaluation model that not only allows for the evaluation of serious games but also facilitates their integration into teaching practice. The model is designed around four dimensions, and their measurement criteria are well defined. Fuzzy decision-making methods were used to weight the criteria, and supervised machine-learning algorithms were considered to minimize the evaluator’s bias. The proposed model provides a more objective and consistent solution for evaluating serious games, reducing the impact of evaluators’ biases and subjective preferences on the weightings of the different evaluation dimensions. The multi-output support vector regression (M-SVR) model can be used flexibly and adapted to different contexts and applications, offering a more effective and reliable solution for evaluating serious games.
{"title":"Towards an Intelligent Model for Evaluating Serious Games","authors":"Kamal Omari","doi":"10.3991/ijet.v18i15.40957","DOIUrl":"https://doi.org/10.3991/ijet.v18i15.40957","url":null,"abstract":"Serious games are effective educational tools used in higher education to provide practical learning opportunities to students. However, few research works have focused on evaluating serious games as a project for developing a tool dedicated to use in a formative context. This document proposes an intelligent evaluation model that not only allows for the evaluation of serious games but also facilitates their integration into teaching practice. The model is designed around four dimensions, and their measurement criteria are well defined. Fuzzy decision-making methods were used to weight the criteria, and supervised machine-learning algorithms were considered to minimize the evaluator’s bias. The proposed model provides a more objective and consistent solution for evaluating serious games, reducing the impact of evaluators’ biases and subjective preferences on the weightings of the different evaluation dimensions. The multi-output support vector regression (M-SVR) model can be used flexibly and adapted to different contexts and applications, offering a more effective and reliable solution for evaluating serious games.","PeriodicalId":47933,"journal":{"name":"International Journal of Emerging Technologies in Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46296821","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}
Pub Date : 2023-08-15DOI: 10.3991/ijet.v18i15.40665
Youssef Laaziz, Ghizlane Chemsi, M. Radid
The size of the group is an important subject to discuss, especially in light of the challenges that appear with the trend toward large institutions with large student bodies in higher education, including the quality of student integration and the quality requirements that higher education must meet in light of the high qualifications demanded by the world of employment. In this study, we examine the effect of learning-group size on students’ cognitive and behavioral engagement. In a quantitative approach, a questionnaire survey is used to collect data from 234 students at the Hassan II University in Casablanca. The purpose of this research is to demonstrate the influence of group size on students’ cognitive and behavioral engagement. The participants were separated into 140 students in large groups and 94 small groups. A questionnaire measured the impact of group size on participants’ cognitive and behavioral engagement and had a Cronbach’s alpha value of 0.9. According to the study’s findings, group size should be taken into account because it affects most factors related to cognitive and behavioral engagement. To increase student involvement and boost their academic achievement, it is advised that more empirical research should be done in order to develop pedagogical strategies to control this difference much better.
这个群体的规模是一个需要讨论的重要问题，特别是考虑到高等教育中学生人数众多的大型机构的趋势所带来的挑战，包括学生融合的质量和高等教育必须满足的质量要求，以及就业世界对高资格的要求。在本研究中，我们考察了学习小组规模对学生认知和行为投入的影响。在定量方法中，采用问卷调查的方式收集了卡萨布兰卡哈桑二世大学234名学生的数据。本研究的目的是为了证明群体规模对学生认知和行为投入的影响。参与者被分成140个大组和94个小组。一份调查问卷测量了群体规模对参与者认知和行为投入的影响，Cronbach ' s alpha值为0.9。根据这项研究的发现，团队规模应该被考虑在内，因为它会影响与认知和行为参与相关的大多数因素。为了提高学生的参与度，提高他们的学业成绩，建议进行更多的实证研究，以制定更好地控制这种差异的教学策略。
{"title":"The Effect of Group Size on Cognitive and Behavioural Students' Engagement","authors":"Youssef Laaziz, Ghizlane Chemsi, M. Radid","doi":"10.3991/ijet.v18i15.40665","DOIUrl":"https://doi.org/10.3991/ijet.v18i15.40665","url":null,"abstract":"The size of the group is an important subject to discuss, especially in light of the challenges that appear with the trend toward large institutions with large student bodies in higher education, including the quality of student integration and the quality requirements that higher education must meet in light of the high qualifications demanded by the world of employment. In this study, we examine the effect of learning-group size on students’ cognitive and behavioral engagement. In a quantitative approach, a questionnaire survey is used to collect data from 234 students at the Hassan II University in Casablanca. The purpose of this research is to demonstrate the influence of group size on students’ cognitive and behavioral engagement. The participants were separated into 140 students in large groups and 94 small groups. A questionnaire measured the impact of group size on participants’ cognitive and behavioral engagement and had a Cronbach’s alpha value of 0.9. According to the study’s findings, group size should be taken into account because it affects most factors related to cognitive and behavioral engagement. To increase student involvement and boost their academic achievement, it is advised that more empirical research should be done in order to develop pedagogical strategies to control this difference much better.","PeriodicalId":47933,"journal":{"name":"International Journal of Emerging Technologies in Learning","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44592596","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}
Pub Date : 2023-08-15DOI: 10.3991/ijet.v18i15.41361
Zhanbo Huang, Min Wang, Feng Ling, Bo Chen
The management of learning resources is based on educational information technology, and the interactive learning method can improve teaching efficiency in colleges and universities. However, the diversity of resources leads to differences in teaching levels and attributes. To further improve students’ utilization of learning resources and the level of teaching in colleges and universities, the impact of online teaching using interactive learning methods on the utilization of learning resources was explored, and the application of database teaching in online teaching platform was analyzed. Based on this platform, the structure of an interactive learning-based intelligent Q&A module was established to determine the impact of online teaching on the utilization of learning resources using interactive learning methods. Results indicate that the proposed method can effectively save students learning time while improving learning efficiency. The proportion of students who are not interested in online teaching using interactive learning methods is reduced to 0%. The application of the method described in this study is beneficial for enhancing students’ interest in online teaching using interactive learning methods, improving their learning outcomes, and increasing their academic achievements.
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