D. Joshi, K. Adhikari, J. Khanal, Shashidhar Belbase
The purpose of this study was to measure the effect of classroom practices of using communication tools, collaboration skills, digital skills, and software skills of teachers on the communication behaviors of students during mathematics instruction. A cross-sectional online survey was conducted among 466 mathematics teachers in Nepal. The primary statistical techniques applied in the data analysis were mean, standard deviation, one-sample t-test, and structural equation modeling (SEM). The results showed that the level of skill transformations of mathematics teachers in digital skills was found to be significantly low. Moreover, practices of using communication tools, collaborative skills of teachers, digital skills enhancement of teachers, and software skills enhancement of teachers were found to be significant predictors of the communication behavior of students. The results of this study suggested that teachers’ technological empowerment is essential for developing digitally competent teachers who can transform the traditional mathematics classrooms into an online mode that is more constructive, collaborative, engaging, and supportive to the learners in a flexible and joyful learning environment. The study contributes to providing the knowledge of digital instructional skills of mathematics teachers to the communication behavior of the students. Moreover, the study gives an insight into using multi-group SEM in studying teachers’ technological skills on students’ learning of soft skills, such as communication behavior.
{"title":"Impact of digital skills of mathematics teachers to promote students’ communication behavior in the classroom","authors":"D. Joshi, K. Adhikari, J. Khanal, Shashidhar Belbase","doi":"10.30935/cedtech/13495","DOIUrl":"https://doi.org/10.30935/cedtech/13495","url":null,"abstract":"The purpose of this study was to measure the effect of classroom practices of using communication tools, collaboration skills, digital skills, and software skills of teachers on the communication behaviors of students during mathematics instruction. A cross-sectional online survey was conducted among 466 mathematics teachers in Nepal. The primary statistical techniques applied in the data analysis were mean, standard deviation, one-sample t-test, and structural equation modeling (SEM). The results showed that the level of skill transformations of mathematics teachers in digital skills was found to be significantly low. Moreover, practices of using communication tools, collaborative skills of teachers, digital skills enhancement of teachers, and software skills enhancement of teachers were found to be significant predictors of the communication behavior of students. The results of this study suggested that teachers’ technological empowerment is essential for developing digitally competent teachers who can transform the traditional mathematics classrooms into an online mode that is more constructive, collaborative, engaging, and supportive to the learners in a flexible and joyful learning environment. The study contributes to providing the knowledge of digital instructional skills of mathematics teachers to the communication behavior of the students. Moreover, the study gives an insight into using multi-group SEM in studying teachers’ technological skills on students’ learning of soft skills, such as communication behavior.","PeriodicalId":37088,"journal":{"name":"Contemporary Educational Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41519328","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}
Chamil Arkhasa Nikko Mazlan, Mohd Hassan Abdullah, Mohd Azam Sulong, Ashardi Abas, M. Ramdan, Abdul Rahman Safian, Dayang Rafidah Syariff M. Fuad
This scoping review investigates the potential of bite-sized learning approach in music education. The review identified articles from Scopus and ERIC databases, revealing that bite-sized learning is widely discussed in the field of ICT, mathematics, and medicine. Bite-sized learning is pedagogical and pragmatic, providing easy access, convenience, and reducing cognitive load. The study suggests that music educators can incorporate bite-sized learning by refining music content into manageable small units, utilizing flexible platforms such as TikTok, and tailoring the approach according to learner interests. Bite-sized learning can improve the quality of learning by creating an enjoyable, useful, and understandable learning session, reducing time to mastery, and improving mental health. Moreover, bite-sized learning can align with the 21st century learning traits such as personalization. This review highlights the potential of bite-sized learning in music education and recommends further research to examine its effectiveness in various instruments and related subjects. The study concludes that bite-sized learning can be recognized as a pragmatic, flexible, brevity and personalized learning approach that aligns with the needs of modern learners for the 21st century.
{"title":"Exploring the integration of bite-sized learning: A scoping review of research in education and related disciplines","authors":"Chamil Arkhasa Nikko Mazlan, Mohd Hassan Abdullah, Mohd Azam Sulong, Ashardi Abas, M. Ramdan, Abdul Rahman Safian, Dayang Rafidah Syariff M. Fuad","doi":"10.30935/cedtech/13622","DOIUrl":"https://doi.org/10.30935/cedtech/13622","url":null,"abstract":"This scoping review investigates the potential of bite-sized learning approach in music education. The review identified articles from Scopus and ERIC databases, revealing that bite-sized learning is widely discussed in the field of ICT, mathematics, and medicine. Bite-sized learning is pedagogical and pragmatic, providing easy access, convenience, and reducing cognitive load. The study suggests that music educators can incorporate bite-sized learning by refining music content into manageable small units, utilizing flexible platforms such as TikTok, and tailoring the approach according to learner interests. Bite-sized learning can improve the quality of learning by creating an enjoyable, useful, and understandable learning session, reducing time to mastery, and improving mental health. Moreover, bite-sized learning can align with the 21st century learning traits such as personalization. This review highlights the potential of bite-sized learning in music education and recommends further research to examine its effectiveness in various instruments and related subjects. The study concludes that bite-sized learning can be recognized as a pragmatic, flexible, brevity and personalized learning approach that aligns with the needs of modern learners for the 21st century.","PeriodicalId":37088,"journal":{"name":"Contemporary Educational Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45059370","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}
L. Chikileva, A. Chistyakov, M. Busygina, A. Prokopyev, E. V. Grib, D. N. Tsvetkov
The purpose of this research is to review peer-reviewed articles on the effects of e-learning on the academic performance of university students. The SCOPUS database was searched for peer-reviewed articles. The data obtained were analyzed using the content analysis method. Twenty-seven articles were found in journals indexed in the SCOPUS database and considered suitable for this study. Two researchers used the content analysis method to determine the effects of the articles reviewed. The results showed that studies in this area have increased in intensity in recent years. These studies were generally conducted over five years. It was found that quantitative methods were predominantly chosen. Researchers published most articles in 2021 and 2022. Most of the studies reviewed used a quantitative design, and only seven articles chose an experimental research design. Most studies were conducted in Pakistan, Saudi Arabia, Spain, India, Iran, and Turkey. The results show that different measurement instruments or tools were used to measure students' academic achievement. The impact of the peer-reviewed articles on the impact of e-learning on college students' academic achievement was examined in four categories. These categories are detailed in the results. Finally, pedagogical conclusions are drawn in light of the results obtained.
{"title":"A review of empirical studies examining the effects of e-learning on university students' academic achievement","authors":"L. Chikileva, A. Chistyakov, M. Busygina, A. Prokopyev, E. V. Grib, D. N. Tsvetkov","doi":"10.30935/cedtech/13418","DOIUrl":"https://doi.org/10.30935/cedtech/13418","url":null,"abstract":"The purpose of this research is to review peer-reviewed articles on the effects of e-learning on the academic performance of university students. The SCOPUS database was searched for peer-reviewed articles. The data obtained were analyzed using the content analysis method. Twenty-seven articles were found in journals indexed in the SCOPUS database and considered suitable for this study. Two researchers used the content analysis method to determine the effects of the articles reviewed. The results showed that studies in this area have increased in intensity in recent years. These studies were generally conducted over five years. It was found that quantitative methods were predominantly chosen. Researchers published most articles in 2021 and 2022. Most of the studies reviewed used a quantitative design, and only seven articles chose an experimental research design. Most studies were conducted in Pakistan, Saudi Arabia, Spain, India, Iran, and Turkey. The results show that different measurement instruments or tools were used to measure students' academic achievement. The impact of the peer-reviewed articles on the impact of e-learning on college students' academic achievement was examined in four categories. These categories are detailed in the results. Finally, pedagogical conclusions are drawn in light of the results obtained.","PeriodicalId":37088,"journal":{"name":"Contemporary Educational Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48951368","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}
Automated essay scoring (AES) has become a valuable tool in educational settings, providing efficient and objective evaluations of student essays. However, the majority of AES systems have primarily focused on native English speakers, leaving a critical gap in the evaluation of non-native speakers’ writing skills. This research addresses this gap by exploring the effectiveness of automated essay-scoring methods specifically designed for non-native speakers. The study acknowledges the unique challenges posed by variations in language proficiency, cultural differences, and linguistic complexities when assessing non-native speakers’ writing abilities. This work focuses on the automated student assessment prize and Khon Kaen University academic English language test dataset and presents an approach that leverages variants of the long short-term memory network model to learn features and compare results with the Kappa coefficient. The findings demonstrate that the proposed framework and approach, which involve joint learning of different essay representations, yield significant benefits and achieve results comparable to state-of-the-art deep learning models. These results suggest that the novel text representation proposed in this paper holds promise as a new and effective choice for assessing the writing tasks of non-native speakers. The result of this study can apply to advance educational assessment practices and promote equitable opportunities for language learners worldwide by enhancing the evaluation process for non-native speakers
{"title":"Exploring effective methods for automated essay scoring of non-native speakers","authors":"Kornwipa Poonpon, Paiboon Manorom, Wirapong Chansanam","doi":"10.30935/cedtech/13740","DOIUrl":"https://doi.org/10.30935/cedtech/13740","url":null,"abstract":"Automated essay scoring (AES) has become a valuable tool in educational settings, providing efficient and objective evaluations of student essays. However, the majority of AES systems have primarily focused on native English speakers, leaving a critical gap in the evaluation of non-native speakers’ writing skills. This research addresses this gap by exploring the effectiveness of automated essay-scoring methods specifically designed for non-native speakers. The study acknowledges the unique challenges posed by variations in language proficiency, cultural differences, and linguistic complexities when assessing non-native speakers’ writing abilities. This work focuses on the automated student assessment prize and Khon Kaen University academic English language test dataset and presents an approach that leverages variants of the long short-term memory network model to learn features and compare results with the Kappa coefficient. The findings demonstrate that the proposed framework and approach, which involve joint learning of different essay representations, yield significant benefits and achieve results comparable to state-of-the-art deep learning models. These results suggest that the novel text representation proposed in this paper holds promise as a new and effective choice for assessing the writing tasks of non-native speakers. The result of this study can apply to advance educational assessment practices and promote equitable opportunities for language learners worldwide by enhancing the evaluation process for non-native speakers","PeriodicalId":37088,"journal":{"name":"Contemporary Educational Technology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135373000","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}
Galiya A. Abayeva, Gulzhan S. Orazayeva, Saltanat J. Omirbek, G. Ibatova, V. Zakirova, Vera K. Vlasova
The concept of ubiquitous learning has emerged as a pedagogical approach in response to the advancements made in mobile, wireless communication, and sensing technologies. The domain of ubiquitous learning is distinguished by swift progression, thereby presenting a difficulty in maintaining current knowledge of its developments. The implementation of bibliometric analysis would enable the tracking of its development and current status. The objective of the present investigation is to perform a thorough bibliometric examination of the domain of ubiquitous learning. This research aims to discern significant attributes, patterns, and influencers within the discipline by analyzing scholarly works. The primary objective of this study is to provide a comprehensive depiction of the salient characteristics and patterns exhibited by the datasets employed in ubiquitous learning research, namely Scopus, Web of Science (WoS), and merged datasets. Additionally, the study seeks to trace the historical development of publications in this domain and to ascertain the most noteworthy publications and authors that have exerted a significant impact on this field. This study provides an extensive bibliometric analysis of ubiquitous learning, examining output from Scopus, WoS, and a merged dataset. It highlights the field’s growth and the rising use of diverse data sources, with Scopus and the merged dataset revealing broader insights. The analysis reveals an interest peak in 2016 and a subsequent decline likely due to incomplete recent data. Documents, predominantly articles, differ across databases, underscoring the unique contributions of each. The study identifies “Lecture Notes in Computer Science” and “Ubiquitous Learning” as major research sources. It recognizes Hwang, G.-J. as a highly influential author, with Asian institutions leading in research output. However, Western institutions also show strong representation in WoS and merged databases. Despite variations in total citation counts, countries like China, Switzerland, and Ireland contribute significantly to the field. Terms like “mobile learning” and “life log” have vital roles in bridging research clusters, while thematic maps reveal evolving trends like mobile learning and learning analytics. The collaborative structure and key figures in ubiquitous learning are illuminated through network analysis, emphasizing the importance of cross-database analysis for a comprehensive view of the field.
泛在学习的概念已经作为一种教学方法出现，以应对移动、无线通信和传感技术的进步。泛在学习领域的特点是进展迅速，因此难以保持其发展的当前知识。文献计量分析的实施将有助于跟踪其发展和现状。本研究的目的是对泛在学习领域进行彻底的文献计量学检查。本研究旨在通过分析学术著作来辨别学科中的重要属性、模式和影响者。本研究的主要目的是全面描述泛在学习研究中使用的数据集所表现出的显著特征和模式，即Scopus、Web of Science（WoS）和合并数据集。此外，该研究试图追踪该领域出版物的历史发展，并确定对该领域产生重大影响的最值得注意的出版物和作者。本研究对泛在学习进行了广泛的文献计量分析，检查了Scopus、WoS和合并数据集的输出。它强调了该领域的发展和对不同数据源的日益使用，Scopus和合并后的数据集揭示了更广泛的见解。分析显示，兴趣在2016年达到峰值，随后可能由于近期数据不完整而下降。文档（主要是文章）因数据库而异，突出了每个数据库的独特贡献。该研究确定“计算机科学讲义”和“普遍学习”是主要的研究来源。它承认黄是一位极具影响力的作家，亚洲机构在研究成果方面处于领先地位。然而，西方机构在WoS和合并数据库中也表现出强大的代表性。尽管引用总数各不相同，但中国、瑞士和爱尔兰等国在这一领域做出了重大贡献。“移动学习”和“生活日志”等术语在连接研究集群方面发挥着至关重要的作用，而主题地图则揭示了移动学习和学习分析等不断发展的趋势。通过网络分析阐明了泛在学习中的协作结构和关键人物，强调了跨数据库分析对全面了解该领域的重要性。
{"title":"A cross-database bibliometric analysis of ubiquitous learning: Trends, influences, and future directions","authors":"Galiya A. Abayeva, Gulzhan S. Orazayeva, Saltanat J. Omirbek, G. Ibatova, V. Zakirova, Vera K. Vlasova","doi":"10.30935/cedtech/13648","DOIUrl":"https://doi.org/10.30935/cedtech/13648","url":null,"abstract":"The concept of ubiquitous learning has emerged as a pedagogical approach in response to the advancements made in mobile, wireless communication, and sensing technologies. The domain of ubiquitous learning is distinguished by swift progression, thereby presenting a difficulty in maintaining current knowledge of its developments. The implementation of bibliometric analysis would enable the tracking of its development and current status. The objective of the present investigation is to perform a thorough bibliometric examination of the domain of ubiquitous learning. This research aims to discern significant attributes, patterns, and influencers within the discipline by analyzing scholarly works. The primary objective of this study is to provide a comprehensive depiction of the salient characteristics and patterns exhibited by the datasets employed in ubiquitous learning research, namely Scopus, Web of Science (WoS), and merged datasets. Additionally, the study seeks to trace the historical development of publications in this domain and to ascertain the most noteworthy publications and authors that have exerted a significant impact on this field. This study provides an extensive bibliometric analysis of ubiquitous learning, examining output from Scopus, WoS, and a merged dataset. It highlights the field’s growth and the rising use of diverse data sources, with Scopus and the merged dataset revealing broader insights. The analysis reveals an interest peak in 2016 and a subsequent decline likely due to incomplete recent data. Documents, predominantly articles, differ across databases, underscoring the unique contributions of each. The study identifies “Lecture Notes in Computer Science” and “Ubiquitous Learning” as major research sources. It recognizes Hwang, G.-J. as a highly influential author, with Asian institutions leading in research output. However, Western institutions also show strong representation in WoS and merged databases. Despite variations in total citation counts, countries like China, Switzerland, and Ireland contribute significantly to the field. Terms like “mobile learning” and “life log” have vital roles in bridging research clusters, while thematic maps reveal evolving trends like mobile learning and learning analytics. The collaborative structure and key figures in ubiquitous learning are illuminated through network analysis, emphasizing the importance of cross-database analysis for a comprehensive view of the field.","PeriodicalId":37088,"journal":{"name":"Contemporary Educational Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42278841","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}
R. S. Akhmadieva, N. Udina, Y. Kosheleva, Sergei P. Zhdanov, Maria O. Timofeeva, Roza L. Budkevich
A descriptive bibliometric analysis of works on artificial intelligence (AI) in science education is provided in this article to help readers understand the state of the field’s research at the time. This study’s main objective is to give bibliometric data on publications regarding AI in science education printed in periodicals listed in the Scopus database between 2002 and 2023 end of May. The data gathered from publications scanned and published within the study’s parameters was subjected to descriptive bibliometric analysis based on seven categories: number of articles and citations per year, countries with the most publications, most productive author, most significant affiliation, funding institutions, publication source and subject areas. Most of the papers were published between 2016 and 2022. The United States of America, United Kingdom, and China were the top-3 most productive nations, with the United States of America producing the most publications. The number of citations to the publications indexed in Scopus database increased in a progressive way and reached to maximum number in 2022 with 178 citations. Most productive author on this topic was Salles, P. with four publications. Moreover, Carnegie Mellon University, University of Memphis, and University of Southern California have the maximum number of publications as affiliations. The National Science Foundation was the leader funding institution in terms of number of publications produced. In addition, “Proceedings Frontiers in Education Conference Fie” have the highest number of publications by year as a publication source. Distribution of the publications by subject area was analyzed. The subject areas of the publications were computer sciences, social sciences, science education, technology and engineering education respectively. This study presents a vision for future research and provides a global perspective on AI in science education.
本文提供了对科学教育中人工智能(AI)工作的描述性文献计量分析，以帮助读者了解该领域当时的研究状况。本研究的主要目的是给出2002年至2023年5月底在Scopus数据库中列出的期刊上发表的关于科学教育中人工智能的出版物的文献计量数据。从在研究参数范围内扫描和出版的出版物中收集的数据进行了描述性文献计量学分析，基于七个类别:每年的文章数量和引用次数、出版物最多的国家、最多产的作者、最重要的合作关系、资助机构、出版来源和主题领域。大部分论文发表于2016年至2022年之间。美国、英国和中国是生产率最高的三个国家，其中美国出版的出版物最多。被Scopus数据库收录的出版物被引次数呈递增趋势，在2022年达到最大，被引次数为178次。在这个主题上最多产的作者是萨勒斯，P.，发表了四篇文章。此外，卡内基梅隆大学、孟菲斯大学和南加州大学的出版物数量最多。美国国家科学基金会是发表论文数量最多的资助机构。此外，“Proceedings Frontiers In Education Conference 5”作为出版物来源，按年计算的出版物数量最多。按学科领域分析了出版物的分布情况。这些刊物的主题分别为电脑科学、社会科学、科学教育、科技及工程教育。本研究提出了未来研究的愿景，并提供了人工智能在科学教育中的全球视角。
{"title":"Artificial intelligence in science education: A bibliometric review","authors":"R. S. Akhmadieva, N. Udina, Y. Kosheleva, Sergei P. Zhdanov, Maria O. Timofeeva, Roza L. Budkevich","doi":"10.30935/cedtech/13587","DOIUrl":"https://doi.org/10.30935/cedtech/13587","url":null,"abstract":"A descriptive bibliometric analysis of works on artificial intelligence (AI) in science education is provided in this article to help readers understand the state of the field’s research at the time. This study’s main objective is to give bibliometric data on publications regarding AI in science education printed in periodicals listed in the Scopus database between 2002 and 2023 end of May. The data gathered from publications scanned and published within the study’s parameters was subjected to descriptive bibliometric analysis based on seven categories: number of articles and citations per year, countries with the most publications, most productive author, most significant affiliation, funding institutions, publication source and subject areas. Most of the papers were published between 2016 and 2022. The United States of America, United Kingdom, and China were the top-3 most productive nations, with the United States of America producing the most publications. The number of citations to the publications indexed in Scopus database increased in a progressive way and reached to maximum number in 2022 with 178 citations. Most productive author on this topic was Salles, P. with four publications. Moreover, Carnegie Mellon University, University of Memphis, and University of Southern California have the maximum number of publications as affiliations. The National Science Foundation was the leader funding institution in terms of number of publications produced. In addition, “Proceedings Frontiers in Education Conference Fie” have the highest number of publications by year as a publication source. Distribution of the publications by subject area was analyzed. The subject areas of the publications were computer sciences, social sciences, science education, technology and engineering education respectively. This study presents a vision for future research and provides a global perspective on AI in science education.","PeriodicalId":37088,"journal":{"name":"Contemporary Educational Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49009045","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}
Niurka Guevara-Otero, Elena Cuevas-Molano, E. Vázquez-Cano, Eloy López-Meneses
The objective of this study was to identify university student profiles with different levels of predisposition and usage of digital competences in social communication and collaborative learning (CSCCL) as well as technology use in information search and treatment (CSTI). The sample comprised 383 students from three state universities in Spain. The study employed a questionnaire called “basic digital competences 2.0 in university students” (COBADI). Chi-squared automatic interaction detection (CHAID) algorithm was used for data analysis due to its capability to handle both quantitative and qualitative variables, enabling profiling and the generation of predictive models with easily interpretable graphical representations (decision trees). The results revealed a high level of digital competence in socialization and execution of tasks online, managing digital tools for planning study time, and using resources for information searching and browsing. These findings align with previous works on collaborative writing on the Internet and digital competence. However, students demonstrated low digital competence in data analysis processes and image production using social software apps, which has been linked to task complexity and heavy workload in other studies. Interestingly, the students’ sociodemographic characteristics (age, sex, and university attended) did not influence their predisposition towards the analyzed digital competences. In conclusion, enhancing effective digital teaching in higher education can be achieved by incorporating the teaching of critical analysis of information, addressing information overload, providing instruction on social software apps, and emphasizing collaborative learning. These strategies aim to help students acquire and apply knowledge relevant to the current job market.
{"title":"Analysis of predisposition in levels of individual digital competence among Spanish university students","authors":"Niurka Guevara-Otero, Elena Cuevas-Molano, E. Vázquez-Cano, Eloy López-Meneses","doi":"10.30935/cedtech/13420","DOIUrl":"https://doi.org/10.30935/cedtech/13420","url":null,"abstract":"The objective of this study was to identify university student profiles with different levels of predisposition and usage of digital competences in social communication and collaborative learning (CSCCL) as well as technology use in information search and treatment (CSTI). The sample comprised 383 students from three state universities in Spain. The study employed a questionnaire called “basic digital competences 2.0 in university students” (COBADI). Chi-squared automatic interaction detection (CHAID) algorithm was used for data analysis due to its capability to handle both quantitative and qualitative variables, enabling profiling and the generation of predictive models with easily interpretable graphical representations (decision trees). The results revealed a high level of digital competence in socialization and execution of tasks online, managing digital tools for planning study time, and using resources for information searching and browsing. These findings align with previous works on collaborative writing on the Internet and digital competence. However, students demonstrated low digital competence in data analysis processes and image production using social software apps, which has been linked to task complexity and heavy workload in other studies. Interestingly, the students’ sociodemographic characteristics (age, sex, and university attended) did not influence their predisposition towards the analyzed digital competences. In conclusion, enhancing effective digital teaching in higher education can be achieved by incorporating the teaching of critical analysis of information, addressing information overload, providing instruction on social software apps, and emphasizing collaborative learning. These strategies aim to help students acquire and apply knowledge relevant to the current job market.","PeriodicalId":37088,"journal":{"name":"Contemporary Educational Technology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41382925","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}
Learning experience design (LXD) is a new wave in educational technology and learning design. This study was conducted to clarify conceptual change to practice by applying a systematic literature review to a combination text mining and bibliometric analysis technique to visualization network. Based on the study selection articles from SCOPUS. Our research questions focused on the changing concept, the elements of dimensionality, and the process or practice of LXD. The findings showed that 152 articles were finally selected to be analyzed. Conceptualizing LXD is currently underway in design thinking and user research methods with emphasis on the empathize process. Moreover, three dimensions to consider including (1) design dimension focus on user experience design in a technology context, (2) learning dimension focus on instructional design and learning theory, and (3) standard dimension focus on assessment and evaluation in learning goal and project management. In addition, five steps cycle for practice follows: research learners as users and learning goals, design with ideate, develop prototyping, validity testing, and launch and follow-up. These factors enhance learning engagement and aesthetics for a great learner experience and learning efficacy.
{"title":"A systematic review of changing conceptual to practice in learning experience design: Text mining and bibliometric analysis","authors":"Warakon Phommanee, Boonrat Plangsorn, Sutithep Siripipattanakul","doi":"10.30935/cedtech/13480","DOIUrl":"https://doi.org/10.30935/cedtech/13480","url":null,"abstract":"Learning experience design (LXD) is a new wave in educational technology and learning design. This study was conducted to clarify conceptual change to practice by applying a systematic literature review to a combination text mining and bibliometric analysis technique to visualization network. Based on the study selection articles from SCOPUS. Our research questions focused on the changing concept, the elements of dimensionality, and the process or practice of LXD. The findings showed that 152 articles were finally selected to be analyzed. Conceptualizing LXD is currently underway in design thinking and user research methods with emphasis on the empathize process. Moreover, three dimensions to consider including (1) design dimension focus on user experience design in a technology context, (2) learning dimension focus on instructional design and learning theory, and (3) standard dimension focus on assessment and evaluation in learning goal and project management. In addition, five steps cycle for practice follows: research learners as users and learning goals, design with ideate, develop prototyping, validity testing, and launch and follow-up. These factors enhance learning engagement and aesthetics for a great learner experience and learning efficacy.","PeriodicalId":37088,"journal":{"name":"Contemporary Educational Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46783977","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}
S. Utami, A. Andayani, R. Winarni, Sumarwati Sumarwati
Research shows that artificial intelligence (AI) technology positively influences students’ writing skills, but this area has yet to be touched by Indonesian researchers. This study aims to map perception, obstacles, and recommendations for optimizing use of AI in teaching academic writing in Indonesian. This article focuses on a case study of three senior high schools in Central Java, Indonesia. It employs quantitative and qualitative data. The researcher collected the data using questionnaires presented with Likert scale, followed by an in-depth interview through mobile instant messaging interview. Findings show that (1) AI-based learning tools help students to do academic research, especially in the planning step, to identify and develop the topics, as well as in the drafting step, to develop a paper draft, (2) AI-based learning tools are deemed flexible in accessibility despite not being able to cover all necessities required by students in writing process, (3) students are interested in using AI technology in academic writing class so that learning process will not be boring. Although AI has been used in academic writing classes, tools have not positively impacted quality of students’ academic papers in all indicators. There are several obstacles to using AI, namely (1) need for more available feature, especially in editing Indonesian text, and (2) in contrast, the features still need to be optimized. These are the recommendations for the optimization of AI-based learning tools, which are (1) adding features to edit Indonesian text, including spelling, diction, and sentence structure, and (2) enhancing AI literacy to be able to explore and leverage the existing features optimally. This research has yet to accommodate the possible coverage in checking the originality and accuracy of the written product assisted by AI-based learning tools, which could become a focus for future researchers.
{"title":"Utilization of artificial intelligence technology in an academic writing class: How do Indonesian students perceive?","authors":"S. Utami, A. Andayani, R. Winarni, Sumarwati Sumarwati","doi":"10.30935/cedtech/13419","DOIUrl":"https://doi.org/10.30935/cedtech/13419","url":null,"abstract":"Research shows that artificial intelligence (AI) technology positively influences students’ writing skills, but this area has yet to be touched by Indonesian researchers. This study aims to map perception, obstacles, and recommendations for optimizing use of AI in teaching academic writing in Indonesian. This article focuses on a case study of three senior high schools in Central Java, Indonesia. It employs quantitative and qualitative data. The researcher collected the data using questionnaires presented with Likert scale, followed by an in-depth interview through mobile instant messaging interview. Findings show that (1) AI-based learning tools help students to do academic research, especially in the planning step, to identify and develop the topics, as well as in the drafting step, to develop a paper draft, (2) AI-based learning tools are deemed flexible in accessibility despite not being able to cover all necessities required by students in writing process, (3) students are interested in using AI technology in academic writing class so that learning process will not be boring. Although AI has been used in academic writing classes, tools have not positively impacted quality of students’ academic papers in all indicators. There are several obstacles to using AI, namely (1) need for more available feature, especially in editing Indonesian text, and (2) in contrast, the features still need to be optimized. These are the recommendations for the optimization of AI-based learning tools, which are (1) adding features to edit Indonesian text, including spelling, diction, and sentence structure, and (2) enhancing AI literacy to be able to explore and leverage the existing features optimally. This research has yet to accommodate the possible coverage in checking the originality and accuracy of the written product assisted by AI-based learning tools, which could become a focus for future researchers.","PeriodicalId":37088,"journal":{"name":"Contemporary Educational Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46554145","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}
M. Zheltukhina, N. Kislitsyna, O. V. Sergeeva, Roza M. Ignateva, Y. Kosheleva, L. Lutskovskaia
Communication style refers to the distinct ways individuals exhibit verbal, paraverbal, and nonverbal communication patterns in social interactions. It involves receiving, interpreting, and delivering feedback and messages. Factors like culture and personality affect communication styles, and tools like communication styles inventory (CSI) help evaluate and improve individuals’ communication skills. Cultural differences significantly impact communication styles, so it’s important to adapt and validate measurement instruments for diverse cultural settings, such as adapting CSI for the Russian context. This study aims to adapt CSI for use in the Russian context. The research follows a quantitative approach, collecting data from 407 undergraduate and graduate students across different universities. CSI is a questionnaire assessing six distinct communication patterns with 96 items. The researchers conducted exploratory and confirmatory factor analyses to examine CSI’s validity and reliability in the Russian context. The analyses yielded an eight-factor model explaining 59.5% of the total variance. Although two factors from the original scale were preserved, other factors were newly named. The confirmatory factor analysis tested the relationship between the original sub-dimensions and the new dimensions, resulting in a better-adapted model with significant relationships between items and factors. The findings indicate the scale’s suitability for different cultures and sample groups, supporting its validity and reliability. Further research should adapt the scale to other cultures and utilize it in studies in the Russian context.
{"title":"Adaptation of communication styles inventory to Russian context","authors":"M. Zheltukhina, N. Kislitsyna, O. V. Sergeeva, Roza M. Ignateva, Y. Kosheleva, L. Lutskovskaia","doi":"10.30935/cedtech/13512","DOIUrl":"https://doi.org/10.30935/cedtech/13512","url":null,"abstract":"Communication style refers to the distinct ways individuals exhibit verbal, paraverbal, and nonverbal communication patterns in social interactions. It involves receiving, interpreting, and delivering feedback and messages. Factors like culture and personality affect communication styles, and tools like communication styles inventory (CSI) help evaluate and improve individuals’ communication skills. Cultural differences significantly impact communication styles, so it’s important to adapt and validate measurement instruments for diverse cultural settings, such as adapting CSI for the Russian context. This study aims to adapt CSI for use in the Russian context. The research follows a quantitative approach, collecting data from 407 undergraduate and graduate students across different universities. CSI is a questionnaire assessing six distinct communication patterns with 96 items. The researchers conducted exploratory and confirmatory factor analyses to examine CSI’s validity and reliability in the Russian context. The analyses yielded an eight-factor model explaining 59.5% of the total variance. Although two factors from the original scale were preserved, other factors were newly named. The confirmatory factor analysis tested the relationship between the original sub-dimensions and the new dimensions, resulting in a better-adapted model with significant relationships between items and factors. The findings indicate the scale’s suitability for different cultures and sample groups, supporting its validity and reliability. Further research should adapt the scale to other cultures and utilize it in studies in the Russian context.","PeriodicalId":37088,"journal":{"name":"Contemporary Educational Technology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49389468","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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