Artificial intelligence (AI) technology, such as Chat Generative Pre-trained Transformer (ChatGPT), is evolving quickly and having a significant impact on the higher education sector. Although the impact of ChatGPT on academic integrity processes is a key concern, little is known about whether academics can reliably recognise texts that have been generated by AI. This qualitative study applies Turing’s Imitation Game to investigate 16 education academics’ perceptions of two pairs of texts written by either ChatGPT or a human. Pairs of texts, written in response to the same task, were used as the stimulus for interviews that probed academics’ perceptions of text authorship and the textual features that were important in their decision-making. Results indicated academics were only able to identify AI-generated texts half of the time, highlighting the sophistication of contemporary generative AI technology. Academics perceived the following categories as important for their decision-making: voice, word usage, structure, task achievement and flow. All five categories of decision-making were variously used to rationalise both accurate and inaccurate decisions about text authorship. The implications of these results are discussed with a particular focus on what strategies can be applied to support academics more effectively as they manage the ongoing challenge of AI in higher education. Implications for practice or policy: Experienced academics may be unable to distinguish between texts written by contemporary generative AI technology and humans. Academics are uncertain about the current capabilities of generative AI and need support in redesigning assessments that succeed in providing robust evidence of student achievement of learning outcomes. Institutions must assess the adequacy of their assessment designs, AI use policies, and AI-related procedures to enhance students’ capacity for effective and ethical use of generative AI technology.
{"title":"Academics' perceptions of ChatGPT-generated written outputs: A practical application of Turing’s Imitation Game","authors":"Joshua A Matthews, Catherine Rita Volpe","doi":"10.14742/ajet.8896","DOIUrl":"https://doi.org/10.14742/ajet.8896","url":null,"abstract":"Artificial intelligence (AI) technology, such as Chat Generative Pre-trained Transformer (ChatGPT), is evolving quickly and having a significant impact on the higher education sector. Although the impact of ChatGPT on academic integrity processes is a key concern, little is known about whether academics can reliably recognise texts that have been generated by AI. This qualitative study applies Turing’s Imitation Game to investigate 16 education academics’ perceptions of two pairs of texts written by either ChatGPT or a human. Pairs of texts, written in response to the same task, were used as the stimulus for interviews that probed academics’ perceptions of text authorship and the textual features that were important in their decision-making. Results indicated academics were only able to identify AI-generated texts half of the time, highlighting the sophistication of contemporary generative AI technology. Academics perceived the following categories as important for their decision-making: voice, word usage, structure, task achievement and flow. All five categories of decision-making were variously used to rationalise both accurate and inaccurate decisions about text authorship. The implications of these results are discussed with a particular focus on what strategies can be applied to support academics more effectively as they manage the ongoing challenge of AI in higher education.\u0000Implications for practice or policy:\u0000\u0000Experienced academics may be unable to distinguish between texts written by contemporary generative AI technology and humans.\u0000Academics are uncertain about the current capabilities of generative AI and need support in redesigning assessments that succeed in providing robust evidence of student achievement of learning outcomes.\u0000Institutions must assess the adequacy of their assessment designs, AI use policies, and AI-related procedures to enhance students’ capacity for effective and ethical use of generative AI technology.\u0000","PeriodicalId":47812,"journal":{"name":"Australasian Journal of Educational Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138944194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thanh Pham, Thanh Binh Nguyen, Son Ha, Ngoc Thanh Nguyen Ngoc
This research explored the potential of artificial intelligence (AI)-assisted learning using ChatGPT in an engineering course at a university in South-east Asia. The study investigated the benefits and challenges that students may encounter when utilising ChatGPT-3.5 as a learning tool. This research developed an AI-assisted learning flow that empowers learners and lecturers to integrate ChatGPT into their teaching and learning processes. The flow was subsequently used to validate and assess a variety of exercises, tutorial tasks and assessment-like questions for the course under study. Introducing a self-rating system allowed the study to facilitate users in assessing the generative responses. The findings indicate that ChatGPT has significant potential to assist students; however, there is a necessity for training and offering guidance to students on effective interactions with ChatGPT. The study contributes to the evidence of the potential of AI-assisted learning and identifies areas for future research in refining the use of AI tools to better support students' educational journey. Implications for practice or policy Educators and administrators could review the usage of ChatGPT in an engineering technology course and study the implications of generative AI tools in higher education. Academics could adapt and modify the proposed AI-assisted learning flow in this paper to suit their classroom. Students can review and adopt the proposed AI-assisted learning flow in this paper for their studies. Researchers could follow up on the application of ChatGPT in teaching and learning: teaching quality and student experience, academic integrity and assessment design.
{"title":"Digital transformation in engineering education: Exploring the potential of AI-assisted learning","authors":"Thanh Pham, Thanh Binh Nguyen, Son Ha, Ngoc Thanh Nguyen Ngoc","doi":"10.14742/ajet.8825","DOIUrl":"https://doi.org/10.14742/ajet.8825","url":null,"abstract":"This research explored the potential of artificial intelligence (AI)-assisted learning using ChatGPT in an engineering course at a university in South-east Asia. The study investigated the benefits and challenges that students may encounter when utilising ChatGPT-3.5 as a learning tool. This research developed an AI-assisted learning flow that empowers learners and lecturers to integrate ChatGPT into their teaching and learning processes. The flow was subsequently used to validate and assess a variety of exercises, tutorial tasks and assessment-like questions for the course under study. Introducing a self-rating system allowed the study to facilitate users in assessing the generative responses. The findings indicate that ChatGPT has significant potential to assist students; however, there is a necessity for training and offering guidance to students on effective interactions with ChatGPT. The study contributes to the evidence of the potential of AI-assisted learning and identifies areas for future research in refining the use of AI tools to better support students' educational journey.\u0000Implications for practice or policy\u0000\u0000Educators and administrators could review the usage of ChatGPT in an engineering technology course and study the implications of generative AI tools in higher education.\u0000Academics could adapt and modify the proposed AI-assisted learning flow in this paper to suit their classroom.\u0000Students can review and adopt the proposed AI-assisted learning flow in this paper for their studies.\u0000Researchers could follow up on the application of ChatGPT in teaching and learning: teaching quality and student experience, academic integrity and assessment design.\u0000","PeriodicalId":47812,"journal":{"name":"Australasian Journal of Educational Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138945302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent decades, flipped learning has been adopted by teachers to improve learning achievement. However, it is challenging to provide all students with instant personalised guidance at the same time. To address this gap, based on Chat Generative Pre-trained Transformer (ChatGPT) and the learning scaffolding theory, I developed a ChatGPT-based flipped learning guiding approach (ChatGPT-FLGA) according to the analysis, design, development, implementation and evaluation model. To investigate the effectiveness of ChatGPT-FLGA, a quasi-experiment was conducted in the learning activities of a courseware project. One of two classes was randomly assigned to the experimental group, while the other was assigned to the control group. The students in both classes received flipped classroom instruction and conducted discussions through Tencent QQ applications, but only those in the experimental group learned with ChatGPT-FLGA. The results revealed that the ChatGPT-FLGA significantly improved students’ performance, self-efficacy, learning attitudes, intrinsic motivation and creative thinking. The research findings enrich the literature on ChatGPT in flipped classrooms by addressing the influence of ChatGPT-FLGA on students' performance and perceptions. Implications for practice or policy: Teachers and universities should utilise ChatGPT as a tool for supporting students’ learning and promoting their problem-solving skills. Course designers and academic staff can leverage ChatGPT-FLGA to enact student-centred pedagogical transformation in massive open online courses or flipped learning. Course designers should master how to use ChatGPT-FLGA and its learning system, to foster learners’ self-regulated learning, help them promote online self-efficacy and overcome difficulties in learning motivation and creative thinking ability.
{"title":"Effects of a ChatGPT-based flipped learning guiding approach on learners’ courseware project performances and perceptions","authors":"Haifeng Li","doi":"10.14742/ajet.8923","DOIUrl":"https://doi.org/10.14742/ajet.8923","url":null,"abstract":"In recent decades, flipped learning has been adopted by teachers to improve learning achievement. However, it is challenging to provide all students with instant personalised guidance at the same time. To address this gap, based on Chat Generative Pre-trained Transformer (ChatGPT) and the learning scaffolding theory, I developed a ChatGPT-based flipped learning guiding approach (ChatGPT-FLGA) according to the analysis, design, development, implementation and evaluation model. To investigate the effectiveness of ChatGPT-FLGA, a quasi-experiment was conducted in the learning activities of a courseware project. One of two classes was randomly assigned to the experimental group, while the other was assigned to the control group. The students in both classes received flipped classroom instruction and conducted discussions through Tencent QQ applications, but only those in the experimental group learned with ChatGPT-FLGA. The results revealed that the ChatGPT-FLGA significantly improved students’ performance, self-efficacy, learning attitudes, intrinsic motivation and creative thinking. The research findings enrich the literature on ChatGPT in flipped classrooms by addressing the influence of ChatGPT-FLGA on students' performance and perceptions.\u0000Implications for practice or policy:\u0000\u0000Teachers and universities should utilise ChatGPT as a tool for supporting students’ learning and promoting their problem-solving skills.\u0000Course designers and academic staff can leverage ChatGPT-FLGA to enact student-centred pedagogical transformation in massive open online courses or flipped learning.\u0000Course designers should master how to use ChatGPT-FLGA and its learning system, to foster learners’ self-regulated learning, help them promote online self-efficacy and overcome difficulties in learning motivation and creative thinking ability.\u0000","PeriodicalId":47812,"journal":{"name":"Australasian Journal of Educational Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138944232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ariel Ortiz Beltrán, D. Hernández‐Leo, Ishari Amarasinghe
This paper leverages analytics methods to investigate the impact of changes in teaching modalities shaped by the COVID-19 pandemic on undergraduate students’ satisfaction within a Spanish brick-and-mortar higher education institution. Unlike research that has focused on faculty- or programme-level data, this study offers a comprehensive institutional perspective by analysing large-scale data (N = 83,532) gathered from satisfaction surveys across all undergraduate courses in eight faculties from 2018 to 2021. The longitudinal analysis revealed significant changes (p < 0.05) in satisfaction indicators, particularly overall satisfaction and perceived workload. During the emergency remote teaching period, there was a significant decrease in satisfaction and high levels of variability across courses. However, a year after emergency remote teaching, with increased implementations of technology-supported online and mixed teaching modalities, satisfaction measures not only recovered but exceeded pre-COVID levels in the aforementioned indicators when the teaching modality was fully co-located. The variability of answers also reached historical lows, reflecting more uniform student experiences. These findings highlight the resilience of educators and the current higher education system and suggest a capacity to learn and improve from disruptive pedagogical changes. The study also provides insights into how data analytics can help monitor and inform the evolution of teaching practices. Implications for practice or policy Higher education institution administrators should improve the understanding of the effects derived from changes in their teaching and learning models, for example, in teaching modalities and related technology support. Student satisfaction data analytics offer useful indicators to study the impact of those effects. Higher education institutions should provide support for educators to ensure minimal deviations from expected averages of educational quality indicators regardless of the educators’ capacity to adapt to changes in the teaching models.
{"title":"Surviving and thriving: How changes in teaching modalities influenced student satisfaction before, during and after COVID-19","authors":"Ariel Ortiz Beltrán, D. Hernández‐Leo, Ishari Amarasinghe","doi":"10.14742/ajet.8958","DOIUrl":"https://doi.org/10.14742/ajet.8958","url":null,"abstract":"This paper leverages analytics methods to investigate the impact of changes in teaching modalities shaped by the COVID-19 pandemic on undergraduate students’ satisfaction within a Spanish brick-and-mortar higher education institution. Unlike research that has focused on faculty- or programme-level data, this study offers a comprehensive institutional perspective by analysing large-scale data (N = 83,532) gathered from satisfaction surveys across all undergraduate courses in eight faculties from 2018 to 2021. The longitudinal analysis revealed significant changes (p < 0.05) in satisfaction indicators, particularly overall satisfaction and perceived workload. During the emergency remote teaching period, there was a significant decrease in satisfaction and high levels of variability across courses. However, a year after emergency remote teaching, with increased implementations of technology-supported online and mixed teaching modalities, satisfaction measures not only recovered but exceeded pre-COVID levels in the aforementioned indicators when the teaching modality was fully co-located. The variability of answers also reached historical lows, reflecting more uniform student experiences. These findings highlight the resilience of educators and the current higher education system and suggest a capacity to learn and improve from disruptive pedagogical changes. The study also provides insights into how data analytics can help monitor and inform the evolution of teaching practices.\u0000Implications for practice or policy\u0000\u0000Higher education institution administrators should improve the understanding of the effects derived from changes in their teaching and learning models, for example,\u0000in teaching modalities and related technology support.\u0000Student satisfaction data analytics offer useful indicators to study the impact of those effects.\u0000Higher education institutions should provide support for educators to ensure minimal deviations from expected averages of educational quality indicators regardless of the educators’ capacity to adapt to changes in the teaching models.\u0000","PeriodicalId":47812,"journal":{"name":"Australasian Journal of Educational Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138961597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Under this “new normal” world scenario, online teaching has been essential rather than a choice in continuing learning activities. During the COVID-19 period, virtual teaching platforms played an important role in the success of online teaching in various higher educational institutions. Thus, the current study attempted to predict faculty adoption of online platforms by introducing a set of essential drivers for engaging in online teaching. Following the theory of reasoned action, the study broadened the technology acceptance model variables and security and trust as extrinsic determinants and included resistance to change as moderators to invigorate the research model. Data were collected through an online survey with a sample size of 418 Indian respondents. Our results posit that perceived ease of use, usefulness, security and trust positively influence the faculty's intentions to adopt online platforms. In addition, the study also reported that positive intention leads to the actual use of virtual platforms. Furthermore, the research found the moderating role of the resistance to change dimension in the association of intention and actual use of virtual teaching platforms. The findings provide both theoretical and practical applications of educational technology. Implications for practice or policy The first step for accepting virtual teaching platforms is to help faculty to reduce their resistance for effective online teaching. Higher education institutions should have a policy promising faculty that online teaching using virtual teaching platforms will offer a safer and more trustworthy environment. Higher education institutions should undertake intense organisational renewal and implement bottom-up processes for synchronous learning. Regulators could frame a policy including virtual teaching platforms to provide interactive professional development opportunities.
{"title":"Faculty acceptance of virtual teaching platforms for online teaching: Moderating role of resistance to change","authors":"Harmandeep Singh, Parminder Singh, Dharna Sharma","doi":"10.14742/ajet.7529","DOIUrl":"https://doi.org/10.14742/ajet.7529","url":null,"abstract":"Under this “new normal” world scenario, online teaching has been essential rather than a choice in continuing learning activities. During the COVID-19 period, virtual teaching platforms played an important role in the success of online teaching in various higher educational institutions. Thus, the current study attempted to predict faculty adoption of online platforms by introducing a set of essential drivers for engaging in online teaching. Following the theory of reasoned action, the study broadened the technology acceptance model variables and security and trust as extrinsic determinants and included resistance to change as moderators to invigorate the research model. Data were collected through an online survey with a sample size of 418 Indian respondents. Our results posit that perceived ease of use, usefulness, security and trust positively influence the faculty's intentions to adopt online platforms. In addition, the study also reported that positive intention leads to the actual use of virtual platforms. Furthermore, the research found the moderating role of the resistance to change dimension in the association of intention and actual use of virtual teaching platforms. The findings provide both theoretical and practical applications of educational technology.\u0000Implications for practice or policy\u0000\u0000The first step for accepting virtual teaching platforms is to help faculty to reduce their resistance for effective online teaching.\u0000Higher education institutions should have a policy promising faculty that online teaching using virtual teaching platforms will offer a safer and more trustworthy environment.\u0000Higher education institutions should undertake intense organisational renewal and implement bottom-up processes for synchronous learning.\u0000Regulators could frame a policy including virtual teaching platforms to provide interactive professional development opportunities.\u0000","PeriodicalId":47812,"journal":{"name":"Australasian Journal of Educational Technology","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139004705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
“Educational technology” is a perfectly serviceable name for a field of research and development that is of practical importance and in which interesting intellectual challenges can be found. Stick with it.
{"title":"An education in educational technology","authors":"Peter Goodyear","doi":"10.14742/ajet.9082","DOIUrl":"https://doi.org/10.14742/ajet.9082","url":null,"abstract":"“Educational technology” is a perfectly serviceable name for a field of research and development that is of practical importance and in which interesting intellectual challenges can be found. Stick with it.","PeriodicalId":47812,"journal":{"name":"Australasian Journal of Educational Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135729498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Derek L. Choi-Lundberg, Kerryn Butler-Henderson, Kristyn Harman, Joseph Crawford
In the years prior to the COVID-19 pandemic, there was considerable innovation in designing and implementing teaching and learning with technology in fully online, face-to-face and blended modes. To provide an overview of technology-enhanced learning in higher education, we conducted a systematic literature review following PRISMA guidelines of digital innovations in learning designs between 2014 and 2019, prior to emergency remote teaching responses to the COVID-19 pandemic. From 130 publications, we identified eight overlapping categories of digital technologies being deployed across higher education fields: simulation and augmented or virtual reality; Web 2.0; learning management systems; mobile learning; gamification and serious games; various technologies in classrooms; massive open online courses; and other software, websites, applications and cloud computing. We use these publications, supplemented with findings from selected meta-analyses and systematic reviews of specific technologies, as examples to guide educators designing technology-enhanced learning activities in changing circumstances that may require blended or fully online delivery. As the 130 publications had mixed perceived quality, levels of evidence and details of learning designs and evaluation presented, we suggest educators share their innovations following reporting guidelines relevant to their research methodologies, enabling others to consider transferability to other contexts and to build on their work. Implications for practice or policy: Leaders and administrators should support staff development of technological pedagogical content knowledge and teaching as design for student learning. Educators and instructional designers, in designing learning experiences, should consider adult learning theories, inclusive practices and digital equity and leverage multiple technologies to facilitate students learning their curricula. In educational research or scholarship of teaching and learning, researchers should provide sufficient detail to enable readers to assess transferability to their own contexts.
{"title":"A systematic review of digital innovations in technology-enhanced learning designs in higher education","authors":"Derek L. Choi-Lundberg, Kerryn Butler-Henderson, Kristyn Harman, Joseph Crawford","doi":"10.14742/ajet.7615","DOIUrl":"https://doi.org/10.14742/ajet.7615","url":null,"abstract":"In the years prior to the COVID-19 pandemic, there was considerable innovation in designing and implementing teaching and learning with technology in fully online, face-to-face and blended modes. To provide an overview of technology-enhanced learning in higher education, we conducted a systematic literature review following PRISMA guidelines of digital innovations in learning designs between 2014 and 2019, prior to emergency remote teaching responses to the COVID-19 pandemic. From 130 publications, we identified eight overlapping categories of digital technologies being deployed across higher education fields: simulation and augmented or virtual reality; Web 2.0; learning management systems; mobile learning; gamification and serious games; various technologies in classrooms; massive open online courses; and other software, websites, applications and cloud computing. We use these publications, supplemented with findings from selected meta-analyses and systematic reviews of specific technologies, as examples to guide educators designing technology-enhanced learning activities in changing circumstances that may require blended or fully online delivery. As the 130 publications had mixed perceived quality, levels of evidence and details of learning designs and evaluation presented, we suggest educators share their innovations following reporting guidelines relevant to their research methodologies, enabling others to consider transferability to other contexts and to build on their work. Implications for practice or policy: Leaders and administrators should support staff development of technological pedagogical content knowledge and teaching as design for student learning. Educators and instructional designers, in designing learning experiences, should consider adult learning theories, inclusive practices and digital equity and leverage multiple technologies to facilitate students learning their curricula. In educational research or scholarship of teaching and learning, researchers should provide sufficient detail to enable readers to assess transferability to their own contexts.","PeriodicalId":47812,"journal":{"name":"Australasian Journal of Educational Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136184832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online collaborative learning has been widely used in the field of education. However, unrelated or off-topic information is often included in online collaborative learning. Furthermore, the content of online discussion is often too shallow or narrow. To achieve productive collaborative learning, this study proposed and validated an automated analysis of topic distributions and features (AATDF) approach. In total, 189 college students in China participated in this study and were assigned to one of two experimental groups or a control group. Experimental Group 1 participated in online collaborative learning with the AATDF approach. Experimental Group 2 participated in online collaborative learning with the automated analysis of topic distributions (AATD) approach. The control group participated in traditional online collaborative learning without any specified approach. The results indicate that the AATDF approach can significantly promote group performance, collaborative knowledge building and socially shared regulation compared with the AATD and traditional online collaborative learning approaches. The results and implications are also discussed in depth. The main contribution of this study is that the AATDF approach can improve learning performance and bring online collaborative learning onto new ground. Implications for practice: The AATDF approach is very useful and effective for promoting group performance, collaborative knowledge building and socially shared regulation. Teachers and practitioners can provide personalised interventions and optimise collaborative learning design based on the analysis results of topic distributions and features. Developers can adopt deep neural network models to develop intelligent online
在线协作学习在教育领域得到了广泛的应用。然而，在线协作学习中经常包含不相关或离题的信息。此外，网上讨论的内容往往过于肤浅或狭隘。为了实现高效的协作学习，本研究提出并验证了主题分布和特征的自动分析(AATDF)方法。中国共有189名大学生参与了这项研究，他们被分为两个实验组和对照组。实验组1采用AATDF方式参与在线协作学习。实验组2采用主题分布自动分析(automated analysis of topic distribution, AATD)方法进行在线协同学习。对照组参加传统的在线协作学习，没有任何特定的方法。结果表明，与传统的在线协作学习方式相比，AATDF学习方式可以显著促进团队绩效、协作知识建设和社会共享监管。本文还对研究结果及其意义进行了深入讨论。本研究的主要贡献在于，AATDF方法可以提高学习绩效，并将在线协作学习带入新的领域。对实践的启示:AATDF方法在促进团队绩效、协作知识建设和社会共享监管方面非常有用和有效。教师和实践者可以根据主题分布和特征的分析结果，提供个性化干预，优化协同学习设计。开发者可以采用深度神经网络模型进行智能在线开发
{"title":"An automated analysis of topic distributions and features approach to promoting group performance, collaborative knowledge building and socially shared regulation in online collaborative learning","authors":"Lanqin Zheng, Lu Zhong, Yunchao Fan","doi":"10.14742/ajet.7995","DOIUrl":"https://doi.org/10.14742/ajet.7995","url":null,"abstract":"Online collaborative learning has been widely used in the field of education. However, unrelated or off-topic information is often included in online collaborative learning. Furthermore, the content of online discussion is often too shallow or narrow. To achieve productive collaborative learning, this study proposed and validated an automated analysis of topic distributions and features (AATDF) approach. In total, 189 college students in China participated in this study and were assigned to one of two experimental groups or a control group. Experimental Group 1 participated in online collaborative learning with the AATDF approach. Experimental Group 2 participated in online collaborative learning with the automated analysis of topic distributions (AATD) approach. The control group participated in traditional online collaborative learning without any specified approach. The results indicate that the AATDF approach can significantly promote group performance, collaborative knowledge building and socially shared regulation compared with the AATD and traditional online collaborative learning approaches. The results and implications are also discussed in depth. The main contribution of this study is that the AATDF approach can improve learning performance and bring online collaborative learning onto new ground. Implications for practice: The AATDF approach is very useful and effective for promoting group performance, collaborative knowledge building and socially shared regulation. Teachers and practitioners can provide personalised interventions and optimise collaborative learning design based on the analysis results of topic distributions and features. Developers can adopt deep neural network models to develop intelligent online","PeriodicalId":47812,"journal":{"name":"Australasian Journal of Educational Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135644094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lye Ee (Rebecca) Ng, Sharon Altena, Meredith Hinze
The COVID-19 pandemic disrupted every aspect of life, forcing educational institutions to pivot rapidly to emergency remote learning. Within higher education, learning designers stepped forward and shouldered much of the responsibility of supporting institutional change on an unprecedented scale to ensure continuity of student learning. Although there is a large corpus of literature about the experiences of teachers and students during the pandemic, little is known about the experience of learning designers during this time and how their professional learning was supported. This mixed-methods study provides insights into how Twitter was used by learning designers as part of their professional learning network (PLN) during the pandemic. Using social network analysis and thematic analysis, Twitter provided a level playing field for learning designers within the @TELedvisors community who were highly engaged in global professional and social conversations, with access to continuous learning and social support. We argue that Twitter has undertilised potential for amplifying the voices of underrepresented third space workers within higher education contexts and is an important component to a learning designer’s PLN in the post-pandemic era. This paper will be of interest to learning designers, the @TELedvisors community, professional organisations that support learning designers and other third space professionals. Implications for policy or practice: Twitter can be an effective tool for learning designers and other third space workers as a way to access continuous professional development and to build global, non-hierarchical connections with like-minded professionals outside their institution. Learning designers and other third space workers should include Twitter as an effective and important component of their PLN. Twitter can be used as a tool for amplifying the voices of learning designers and raise the profile of their contributions to higher education by showcasing their skills and expertise to broader audiences.
{"title":"Look who’s talking: Professional conversations of learning designers on Twitter during COVID-19","authors":"Lye Ee (Rebecca) Ng, Sharon Altena, Meredith Hinze","doi":"10.14742/ajet.8022","DOIUrl":"https://doi.org/10.14742/ajet.8022","url":null,"abstract":"The COVID-19 pandemic disrupted every aspect of life, forcing educational institutions to pivot rapidly to emergency remote learning. Within higher education, learning designers stepped forward and shouldered much of the responsibility of supporting institutional change on an unprecedented scale to ensure continuity of student learning. Although there is a large corpus of literature about the experiences of teachers and students during the pandemic, little is known about the experience of learning designers during this time and how their professional learning was supported. This mixed-methods study provides insights into how Twitter was used by learning designers as part of their professional learning network (PLN) during the pandemic. Using social network analysis and thematic analysis, Twitter provided a level playing field for learning designers within the @TELedvisors community who were highly engaged in global professional and social conversations, with access to continuous learning and social support. We argue that Twitter has undertilised potential for amplifying the voices of underrepresented third space workers within higher education contexts and is an important component to a learning designer’s PLN in the post-pandemic era. This paper will be of interest to learning designers, the @TELedvisors community, professional organisations that support learning designers and other third space professionals. Implications for policy or practice: Twitter can be an effective tool for learning designers and other third space workers as a way to access continuous professional development and to build global, non-hierarchical connections with like-minded professionals outside their institution. Learning designers and other third space workers should include Twitter as an effective and important component of their PLN. Twitter can be used as a tool for amplifying the voices of learning designers and raise the profile of their contributions to higher education by showcasing their skills and expertise to broader audiences.","PeriodicalId":47812,"journal":{"name":"Australasian Journal of Educational Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135644092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
During COVID-19, universities are reconfiguring learning environments and increasing flexibility in course offerings. Teachers have found synchronous hybrid teaching challenging with many students preferring online to in-person classroom attendance. Understanding students’ decision-making as to where, when and how they choose to learn will be critical in informing the design of learning spaces and courses. This survey-based study of 369 undergraduates across disciplines explored the relationships between students’ backgrounds and psychological factors (self-efficacy for online learning, conceptions of learning, perceptions of previous online course experiences) and student choices of learning spaces for synchronous online learning. While pre-pandemic studies in Western contexts identified non-traditional student characteristics as major factors associated with students’ choices of learning spaces (i.e., learning online at home), this Hong Kong study found significant associations between undergraduates’ choices, their origin and the disciplines. Logistic regression indicated those who preferred stimulating education and cooperative learning or perceived their previous online course experiences as having clearer goals had greater odds of attending classes synchronously online on campus from locations different from the scheduled teaching spaces. Qualitative analysis suggests personality, self-regulation and the university’s social and organisational structures as factors to consider in future studies of student choices of learning spaces. Implications for practice or policy: Higher education providers may need to diversify course designs to cater to undergraduates’ different hybrid learning preferences and expectations in the post-pandemic return to campus. The first step for online course teachers is to help their students to build a higher level of self-efficacy for online learning. Course teachers can motivate students to take courses online by clarifying their course goals and standards.
{"title":"The effect of conceptions of learning and prior online course experiences on students’ choice of learning spaces for synchronous online learning during COVID-19","authors":"Lily Min Zeng, Susan Margaret Bridges","doi":"10.14742/ajet.8345","DOIUrl":"https://doi.org/10.14742/ajet.8345","url":null,"abstract":"During COVID-19, universities are reconfiguring learning environments and increasing flexibility in course offerings. Teachers have found synchronous hybrid teaching challenging with many students preferring online to in-person classroom attendance. Understanding students’ decision-making as to where, when and how they choose to learn will be critical in informing the design of learning spaces and courses. This survey-based study of 369 undergraduates across disciplines explored the relationships between students’ backgrounds and psychological factors (self-efficacy for online learning, conceptions of learning, perceptions of previous online course experiences) and student choices of learning spaces for synchronous online learning. While pre-pandemic studies in Western contexts identified non-traditional student characteristics as major factors associated with students’ choices of learning spaces (i.e., learning online at home), this Hong Kong study found significant associations between undergraduates’ choices, their origin and the disciplines. Logistic regression indicated those who preferred stimulating education and cooperative learning or perceived their previous online course experiences as having clearer goals had greater odds of attending classes synchronously online on campus from locations different from the scheduled teaching spaces. Qualitative analysis suggests personality, self-regulation and the university’s social and organisational structures as factors to consider in future studies of student choices of learning spaces. Implications for practice or policy: Higher education providers may need to diversify course designs to cater to undergraduates’ different hybrid learning preferences and expectations in the post-pandemic return to campus. The first step for online course teachers is to help their students to build a higher level of self-efficacy for online learning. Course teachers can motivate students to take courses online by clarifying their course goals and standards.","PeriodicalId":47812,"journal":{"name":"Australasian Journal of Educational Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134886247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：