Pub Date : 2023-10-17DOI: 10.1177/00472395231208751
Thomas T. Liao
{"title":"Editorial Overview for Vol. 52 No. 2","authors":"Thomas T. Liao","doi":"10.1177/00472395231208751","DOIUrl":"https://doi.org/10.1177/00472395231208751","url":null,"abstract":"","PeriodicalId":300288,"journal":{"name":"Journal of Educational Technology Systems","volume":"220 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135994112","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 : 2023-10-09DOI: 10.1177/00472395231204852
Sarah Portway
This case study assesses a course re-designed to scaffold a semester-long networking opportunity and experiential learning project. The course occurred online twice using Microsoft Teams in the spring and fall of 2021 and later in person once in the spring of 2023. I present quantitative student course evaluation data from all three semesters and compare them to the department, school, and university. I also address open comments from students, pedagogy, media coverage, and our fashion industry partner's perspective. Findings suggest that the course design was successful across modalities. Students rated the course highly compared to others on campus, even while online, and ratings improved over time. Student comments praised the course for offering valuable career preparation. Our industry partner also derived professional and personal value by interacting with and mentoring students. The case study concludes by encouraging educators to implement experiential learning in any modality, offering guidance, and addressing obstacles.
{"title":"Networking With Industry Partners, Experiential Learning, and Teamwork: Engaging and Preparing Students Across Modalities","authors":"Sarah Portway","doi":"10.1177/00472395231204852","DOIUrl":"https://doi.org/10.1177/00472395231204852","url":null,"abstract":"This case study assesses a course re-designed to scaffold a semester-long networking opportunity and experiential learning project. The course occurred online twice using Microsoft Teams in the spring and fall of 2021 and later in person once in the spring of 2023. I present quantitative student course evaluation data from all three semesters and compare them to the department, school, and university. I also address open comments from students, pedagogy, media coverage, and our fashion industry partner's perspective. Findings suggest that the course design was successful across modalities. Students rated the course highly compared to others on campus, even while online, and ratings improved over time. Student comments praised the course for offering valuable career preparation. Our industry partner also derived professional and personal value by interacting with and mentoring students. The case study concludes by encouraging educators to implement experiential learning in any modality, offering guidance, and addressing obstacles.","PeriodicalId":300288,"journal":{"name":"Journal of Educational Technology Systems","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135142694","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 : 2023-10-04DOI: 10.1177/00472395231205212
Mark Savignano, John Holbrook
This experience paper recounts the research and outcomes of applying a modified professional learning community (PLC) to a pre-service teacher education course. The purpose of applying the PLC to the pre-service teacher education course was to increase community amongst students participating in a HyFlex classroom. In a HyFlex classroom, students can participate in person or online synchronously. This causes issues with students feeling a lack of community and connectedness to their peers. The result was that students felt more connected to their peers during the course and appreciated the PLC as part of the class.
{"title":"Increasing Community in a HyFlex Class During COVID-19","authors":"Mark Savignano, John Holbrook","doi":"10.1177/00472395231205212","DOIUrl":"https://doi.org/10.1177/00472395231205212","url":null,"abstract":"This experience paper recounts the research and outcomes of applying a modified professional learning community (PLC) to a pre-service teacher education course. The purpose of applying the PLC to the pre-service teacher education course was to increase community amongst students participating in a HyFlex classroom. In a HyFlex classroom, students can participate in person or online synchronously. This causes issues with students feeling a lack of community and connectedness to their peers. The result was that students felt more connected to their peers during the course and appreciated the PLC as part of the class.","PeriodicalId":300288,"journal":{"name":"Journal of Educational Technology Systems","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135644874","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}
The increase in usage of Online Learning systems has provided a challenge to deliver e-contents based on user needs. This study uses learning style data to determine the learning profile and provide the content based on the user's needs, whereas gamified elements are used to increase user engagement. The combination of these two approaches would eventually increase the overall user motivation and interest in the platform. The model developed is used to predict the Learning Style (LS) preference by analyzing the activity data obtained from the student interaction in the course. Further, clustering is done to determine the LS preference of the user in the system. Content is designed based on user's learning styles and preferences and uploaded to the platform. Data were collected through surveys after the end of the course to measure students’ perception of the three variables: motivation, learning satisfaction, and learning outcomes. The findings from the study suggest that the students showed high motivation, positive learning outcomes, and satisfaction with the developed contents.
{"title":"Integration of Gamified Elements and Learning Style Data in Online Learning System","authors":"Sushil Shrestha, Manish Joshi, Aakash Bashyal, Arun Timilsina, Sushant Subedi","doi":"10.1177/00472395231202004","DOIUrl":"https://doi.org/10.1177/00472395231202004","url":null,"abstract":"The increase in usage of Online Learning systems has provided a challenge to deliver e-contents based on user needs. This study uses learning style data to determine the learning profile and provide the content based on the user's needs, whereas gamified elements are used to increase user engagement. The combination of these two approaches would eventually increase the overall user motivation and interest in the platform. The model developed is used to predict the Learning Style (LS) preference by analyzing the activity data obtained from the student interaction in the course. Further, clustering is done to determine the LS preference of the user in the system. Content is designed based on user's learning styles and preferences and uploaded to the platform. Data were collected through surveys after the end of the course to measure students’ perception of the three variables: motivation, learning satisfaction, and learning outcomes. The findings from the study suggest that the students showed high motivation, positive learning outcomes, and satisfaction with the developed contents.","PeriodicalId":300288,"journal":{"name":"Journal of Educational Technology Systems","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193617","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 : 2023-09-28DOI: 10.1177/00472395231197675
Kimberly Coy, Libbi R Miller
While virtual and online schools for students in grades K-12 gained in popularity before the coronavirus disease 2019 (COVID-19) pandemic and served a wide variety of student populations, including students with disabilities during the pandemic virtual teaching became the reluctant norm. As online school sites have begun to welcome students with disabilities, inclusive classroom practices such as co-teaching as a model for instruction became an interesting step. The purpose of this year-long, before COVID-19, case study was to better understand the implementation of the co-teaching model at an online K-8 school. Data analysis revealed salient themes which informed the following findings: A need for preplanning before implementation and sustained professional development during implementation, universal benefits for all students with accessibility within the least restrictive environment, and redefining the roles of general education teachers for successful collaboration. Implications and scholarly significance are shared.
{"title":"Co-Teaching in a Digital World: It's Not Teaching by Title, It's Teaching by Talent","authors":"Kimberly Coy, Libbi R Miller","doi":"10.1177/00472395231197675","DOIUrl":"https://doi.org/10.1177/00472395231197675","url":null,"abstract":"While virtual and online schools for students in grades K-12 gained in popularity before the coronavirus disease 2019 (COVID-19) pandemic and served a wide variety of student populations, including students with disabilities during the pandemic virtual teaching became the reluctant norm. As online school sites have begun to welcome students with disabilities, inclusive classroom practices such as co-teaching as a model for instruction became an interesting step. The purpose of this year-long, before COVID-19, case study was to better understand the implementation of the co-teaching model at an online K-8 school. Data analysis revealed salient themes which informed the following findings: A need for preplanning before implementation and sustained professional development during implementation, universal benefits for all students with accessibility within the least restrictive environment, and redefining the roles of general education teachers for successful collaboration. Implications and scholarly significance are shared.","PeriodicalId":300288,"journal":{"name":"Journal of Educational Technology Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135385689","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 : 2023-09-06DOI: 10.1177/00472395231196532
S. Onal, Derya Kulavuz-Onal
This paper examines the potential uses of ChatGPT in generating assessment tasks that can be used across different disciplines in higher education. To illustrate this, we provide examples from three courses in the disciplines of industrial engineering and applied linguistics: Project Analysis and Control, Manufacturing Processes, and Introduction to Linguistics. Our examination of ChatGPT focuses on how some of the common, but time-consuming, tasks can be generated by using ChatGPT as a supportive instructional tool. We observed in our analysis that ChatGPT demonstrates a high level of performance in generating assessment questions and tasks that are accurate and on-topic, and a level of creativity and flexibility in its question generation capabilities. However, it is important to note that ChatGPT is not designed to replace human expertise or judgment. It is crucial that instructors carefully evaluate the reliability and accuracy of the assessments or information generated by ChatGPT.
{"title":"A Cross-Disciplinary Examination of the Instructional Uses of ChatGPT in Higher Education","authors":"S. Onal, Derya Kulavuz-Onal","doi":"10.1177/00472395231196532","DOIUrl":"https://doi.org/10.1177/00472395231196532","url":null,"abstract":"This paper examines the potential uses of ChatGPT in generating assessment tasks that can be used across different disciplines in higher education. To illustrate this, we provide examples from three courses in the disciplines of industrial engineering and applied linguistics: Project Analysis and Control, Manufacturing Processes, and Introduction to Linguistics. Our examination of ChatGPT focuses on how some of the common, but time-consuming, tasks can be generated by using ChatGPT as a supportive instructional tool. We observed in our analysis that ChatGPT demonstrates a high level of performance in generating assessment questions and tasks that are accurate and on-topic, and a level of creativity and flexibility in its question generation capabilities. However, it is important to note that ChatGPT is not designed to replace human expertise or judgment. It is crucial that instructors carefully evaluate the reliability and accuracy of the assessments or information generated by ChatGPT.","PeriodicalId":300288,"journal":{"name":"Journal of Educational Technology Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129554009","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 : 2023-08-22DOI: 10.1177/00472395231185843
Shabnam Ara S.J, R. Tanuja, S. Manjula, K. Venugopal
Learning analytics (LA) is considered a promising field of study as it's helping to improve learning and the context in which it occurs. A learner's performance can be defined as how well students are learning in terms of knowledge and skills development and can be analyzed based on students’ outcomes and engagement in the course. We have consolidated the work carried out from 2011 to 2022 to improve learners’ performance using LA, describe criteria that define learners’ performance, discuss parameters that impact learners’ performance, and how predictive models can be created to forecast learners’ performance using these parameters. Results showed that the data collected from log files of the Learning Management System (LMS) had been used to get insights into the learner's performance in online platforms and LA could bring incredible benefits in the field of the education sector, such as improvement of learners’ involvement with learning activities as well as learning outcomes, identification of students at risk, providing real-time feedback, and personalization of learning. Hence, we can say usage of LA significantly helps learners’ performance improvement in learning portals. But we can get better results if we augment data from log files of LMS with the learner's personal data from his birth to the current moment, which is a bit challenging with respect to data collection i.e., huge and from multiple sources.
{"title":"A Comprehensive Survey on Usage of Learning Analytics for Enhancing Learner's Performance in Learning Portals","authors":"Shabnam Ara S.J, R. Tanuja, S. Manjula, K. Venugopal","doi":"10.1177/00472395231185843","DOIUrl":"https://doi.org/10.1177/00472395231185843","url":null,"abstract":"Learning analytics (LA) is considered a promising field of study as it's helping to improve learning and the context in which it occurs. A learner's performance can be defined as how well students are learning in terms of knowledge and skills development and can be analyzed based on students’ outcomes and engagement in the course. We have consolidated the work carried out from 2011 to 2022 to improve learners’ performance using LA, describe criteria that define learners’ performance, discuss parameters that impact learners’ performance, and how predictive models can be created to forecast learners’ performance using these parameters. Results showed that the data collected from log files of the Learning Management System (LMS) had been used to get insights into the learner's performance in online platforms and LA could bring incredible benefits in the field of the education sector, such as improvement of learners’ involvement with learning activities as well as learning outcomes, identification of students at risk, providing real-time feedback, and personalization of learning. Hence, we can say usage of LA significantly helps learners’ performance improvement in learning portals. But we can get better results if we augment data from log files of LMS with the learner's personal data from his birth to the current moment, which is a bit challenging with respect to data collection i.e., huge and from multiple sources.","PeriodicalId":300288,"journal":{"name":"Journal of Educational Technology Systems","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131702301","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 : 2023-08-09DOI: 10.1177/00472395231191139
F. Orji, Julita Vassileva
This research presents a proposed approach that could be applied in modeling students’ study strategies and performance in higher education. The research used key learning attributes, including intrinsic motivation, extrinsic motivation, autonomy, relatedness, competence, and self-esteem in the modeling. Five machine learning models were implemented, trained, evaluated, and tested with data from 924 university students. The comparative analysis reveals that tree-based models, particularly random forest and decision trees, outperform other models, achieving a prediction accuracy of 94.9%. The models built in this research can be used in predicting student study strategies and performance and this can be applied in implementing targeted interventions for improving learning progress. The research findings emphasize the importance of incorporating strategies that address diverse motivation dimensions in online educational systems, as it increases student engagement and promotes continuous learning. The findings also highlight the potential for modeling these attributes collectively to personalize and adapt learning process.
{"title":"Modeling the Impact of Motivation Factors on Students’ Study Strategies and Performance Using Machine Learning","authors":"F. Orji, Julita Vassileva","doi":"10.1177/00472395231191139","DOIUrl":"https://doi.org/10.1177/00472395231191139","url":null,"abstract":"This research presents a proposed approach that could be applied in modeling students’ study strategies and performance in higher education. The research used key learning attributes, including intrinsic motivation, extrinsic motivation, autonomy, relatedness, competence, and self-esteem in the modeling. Five machine learning models were implemented, trained, evaluated, and tested with data from 924 university students. The comparative analysis reveals that tree-based models, particularly random forest and decision trees, outperform other models, achieving a prediction accuracy of 94.9%. The models built in this research can be used in predicting student study strategies and performance and this can be applied in implementing targeted interventions for improving learning progress. The research findings emphasize the importance of incorporating strategies that address diverse motivation dimensions in online educational systems, as it increases student engagement and promotes continuous learning. The findings also highlight the potential for modeling these attributes collectively to personalize and adapt learning process.","PeriodicalId":300288,"journal":{"name":"Journal of Educational Technology Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121762687","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 : 2023-07-18DOI: 10.1177/00472395231187022
T. Liao
In order to design and manage more learner-centered Instructional systems, the needs and active involvement of students must be addressed. Sub-systems for promoting active learning need to be properly designed to create learning communities that integrate educational technology with quality pedagogy. In this issue, we feature a set of six papers that report on findings of research that focus on innovations such as student feedback, artificial intelligence (AI)-based conversation-based assessment (CBA), discussion groups, collaborative learning, and other aspects of effective and synergistic learning communities. This issue concludes with two papers that discuss programs that deal with the instruction of languages. In this case, the learning communities are undergraduate students from Ecuador and third-grade students from Qatar. The first two papers focus on two innovative ways of increasing student participation in learning communities. The lead paper deals with a peer critique program for graduate students. The main objective of the research is to determine the quality of the feedback that is provided in the peer review. Both the benefits and problems associated with the feedback are observed and analyzed. The paper also provides lessons learned for the re-design of peer critique programs. The second paper discusses a digital version of the CBA program for undergraduate students. In this study, a CBA system was deployed with Google Chat to support formative assessment. For faculty involved in AI-based tools such as Rasa, this paper is must reading. The next paper describes a high school learning community that tested an online experimental instructional intervention that is designed to foster intrinsic motivation. This experiment integrated active learning elements such as collaborative teamwork engagement and a student response system that addressed student questions. The fourth paper provides a report about an experiment designed to compare various ways of using group discussion to help pre-service non-traditional special education elementary school teachers to study and explore metacognition as a learning tool. The group discussion among the career-oriented learning community members was achieved via virtual simulation. The next two papers address similar types of instructional content, namely learning languages (in this case, English and Arabic). However, the members of the learning Editorial
{"title":"Editorial Overview","authors":"T. Liao","doi":"10.1177/00472395231187022","DOIUrl":"https://doi.org/10.1177/00472395231187022","url":null,"abstract":"In order to design and manage more learner-centered Instructional systems, the needs and active involvement of students must be addressed. Sub-systems for promoting active learning need to be properly designed to create learning communities that integrate educational technology with quality pedagogy. In this issue, we feature a set of six papers that report on findings of research that focus on innovations such as student feedback, artificial intelligence (AI)-based conversation-based assessment (CBA), discussion groups, collaborative learning, and other aspects of effective and synergistic learning communities. This issue concludes with two papers that discuss programs that deal with the instruction of languages. In this case, the learning communities are undergraduate students from Ecuador and third-grade students from Qatar. The first two papers focus on two innovative ways of increasing student participation in learning communities. The lead paper deals with a peer critique program for graduate students. The main objective of the research is to determine the quality of the feedback that is provided in the peer review. Both the benefits and problems associated with the feedback are observed and analyzed. The paper also provides lessons learned for the re-design of peer critique programs. The second paper discusses a digital version of the CBA program for undergraduate students. In this study, a CBA system was deployed with Google Chat to support formative assessment. For faculty involved in AI-based tools such as Rasa, this paper is must reading. The next paper describes a high school learning community that tested an online experimental instructional intervention that is designed to foster intrinsic motivation. This experiment integrated active learning elements such as collaborative teamwork engagement and a student response system that addressed student questions. The fourth paper provides a report about an experiment designed to compare various ways of using group discussion to help pre-service non-traditional special education elementary school teachers to study and explore metacognition as a learning tool. The group discussion among the career-oriented learning community members was achieved via virtual simulation. The next two papers address similar types of instructional content, namely learning languages (in this case, English and Arabic). However, the members of the learning Editorial","PeriodicalId":300288,"journal":{"name":"Journal of Educational Technology Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114718468","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 : 2023-06-26DOI: 10.1177/00472395231184566
Deana J. Ford, S. Luke, S. M. Vaughn, Angelica Fulchini-Scruggs
The purpose of this study was to investigate the impact explicit teaching of metacognition had on preservice teachers’ metacognitive awareness and accuracy of whole group discussions using virtual simulations. One class of preservice teachers was randomly assigned to the explicit metacognitive teaching group, while the other class was assigned to the information-only group. A total of 40 nontraditional preservice teachers participated in the research study. A pre-, mid-, and post-assessment quasi-experimental design was employed. The independent variables were the two training groups (explicit metacognitive teaching group and information-only group). The dependent variables were preservice teachers’ self-reported metacognitive awareness, the researchers’ metacognitive awareness rubric scores, and metacognitive accuracy. The results revealed that engaging in the virtual simulation experiences improved preservice teachers’ metacognitive awareness of leading a whole group discussion and that preservice teachers were overconfident in assessing their metacognitive awareness.
{"title":"Virtual Simulations to Practice Whole Group Discussions: Preservice Teachers’ Metacognitive Awareness","authors":"Deana J. Ford, S. Luke, S. M. Vaughn, Angelica Fulchini-Scruggs","doi":"10.1177/00472395231184566","DOIUrl":"https://doi.org/10.1177/00472395231184566","url":null,"abstract":"The purpose of this study was to investigate the impact explicit teaching of metacognition had on preservice teachers’ metacognitive awareness and accuracy of whole group discussions using virtual simulations. One class of preservice teachers was randomly assigned to the explicit metacognitive teaching group, while the other class was assigned to the information-only group. A total of 40 nontraditional preservice teachers participated in the research study. A pre-, mid-, and post-assessment quasi-experimental design was employed. The independent variables were the two training groups (explicit metacognitive teaching group and information-only group). The dependent variables were preservice teachers’ self-reported metacognitive awareness, the researchers’ metacognitive awareness rubric scores, and metacognitive accuracy. The results revealed that engaging in the virtual simulation experiences improved preservice teachers’ metacognitive awareness of leading a whole group discussion and that preservice teachers were overconfident in assessing their metacognitive awareness.","PeriodicalId":300288,"journal":{"name":"Journal of Educational Technology Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130589058","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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