Assessing learners' inquiry-based skills is challenging as social, political, and technological dimensions must be considered. The advanced development of artificial intelligence (AI) makes it possible to address these challenges and shape the next generation of science education.
Objectives
The present study evaluated the SSI inquiry skills of students in an AI-enabled scoring environment. An AI model for socioscientific issues that can assess students' inquiry skills was developed. Responses to a learning module were collected from 1250 participants, and the open-ended responses were rated by humans in accordance with a designed rubric. The collected data were then preprocessed and used to train an AI rater that can process natural language. The effects of two hyperparameters, the dropout rate and complexity of the AI neural network, were evaluated.
Results and Conclusion
The results suggested neither of the two hyperparameters was found to strongly affect the accuracy of the AI rater. In general, the human and AI raters exhibited certain levels of agreement; however, agreement varied among rubric categories. Discrepancies were identified and are discussed both quantitatively and qualitatively.
{"title":"AI-Assisted Assessment of Inquiry Skills in Socioscientific Issue Contexts","authors":"Wen Xin Zhang, John J. H. Lin, Ying-Shao Hsu","doi":"10.1111/jcal.13102","DOIUrl":"https://doi.org/10.1111/jcal.13102","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background Study</h3>\u0000 \u0000 <p>Assessing learners' inquiry-based skills is challenging as social, political, and technological dimensions must be considered. The advanced development of artificial intelligence (AI) makes it possible to address these challenges and shape the next generation of science education.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>The present study evaluated the SSI inquiry skills of students in an AI-enabled scoring environment. An AI model for socioscientific issues that can assess students' inquiry skills was developed. Responses to a learning module were collected from 1250 participants, and the open-ended responses were rated by humans in accordance with a designed rubric. The collected data were then preprocessed and used to train an AI rater that can process natural language. The effects of two hyperparameters, the dropout rate and complexity of the AI neural network, were evaluated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusion</h3>\u0000 \u0000 <p>The results suggested neither of the two hyperparameters was found to strongly affect the accuracy of the AI rater. In general, the human and AI raters exhibited certain levels of agreement; however, agreement varied among rubric categories. Discrepancies were identified and are discussed both quantitatively and qualitatively.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dominic Lohr, Marc Berges, Abhishek Chugh, Michael Kohlhase, Dennis Müller
Background
Over the past few decades, the process and methodology of automatic question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content.
Objectives
This paper explores the potential of large language models (LLMs) for generating computer science questions that are sufficiently annotated for automatic learner model updates, are fully situated in the context of a particular course and address the cognitive dimension understand.
Methods
Unlike previous attempts that might use basic methods such as ChatGPT, our approach involves more targeted strategies such as retrieval-augmented generation (RAG) to produce contextually relevant and pedagogically meaningful learning objects.
Results and Conclusions
Our results show that generating structural, semantic annotations works well. However, this success was not reflected in the case of relational annotations. The quality of the generated questions often did not meet educational standards, highlighting that although LLMs can contribute to the pool of learning materials, their current level of performance requires significant human intervention to refine and validate the generated content.
{"title":"Leveraging Large Language Models to Generate Course-Specific Semantically Annotated Learning Objects","authors":"Dominic Lohr, Marc Berges, Abhishek Chugh, Michael Kohlhase, Dennis Müller","doi":"10.1111/jcal.13101","DOIUrl":"https://doi.org/10.1111/jcal.13101","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Over the past few decades, the process and methodology of automatic question generation (AQG) have undergone significant transformations. Recent progress in generative natural language models has opened up new potential in the generation of educational content.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This paper explores the potential of large language models (LLMs) for generating computer science questions that are sufficiently annotated for automatic learner model updates, are fully situated in the context of a particular course and address the cognitive dimension <span>understand</span>.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Unlike previous attempts that might use basic methods such as ChatGPT, our approach involves more targeted strategies such as retrieval-augmented generation (RAG) to produce contextually relevant and pedagogically meaningful learning objects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>Our results show that generating structural, semantic annotations works well. However, this success was not reflected in the case of relational annotations. The quality of the generated questions often did not meet educational standards, highlighting that although LLMs can contribute to the pool of learning materials, their current level of performance requires significant human intervention to refine and validate the generated content.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jcal.13101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multimedia learning, encapsulating both visual and verbal information, has become pivotal in educational settings, with extensive research underscoring its influence. However, a notable research gap exists concerning the application and effectiveness of multimedia learning principles, precisely segmenting, pre-training and modality, within emerging technologies like augmented reality (AR) and virtual reality (VR). This deficit, coupled with inconsistent findings in computer-based learning studies and a lack of focus on intrinsic cognitive load and motivation in multimedia learning, motivates the current research.
Objectives
This research aims to provide insights by comparing the efficacy of these principles across different learning environments, including traditional, AR and VR.
Methods
Employing a 3 × 4 factorial design, this study involved 383 university students, randomly assigned to 12 treatment groups, to investigate multimedia learning principles in different environments. Participants engaged with learning materials on lightning formation and cell structure in various settings, including AR and VR, with data collected through multiple instruments, such as retention and transfer tests, and cognitive load and motivation questionnaires.
Results and Conclusions
Results reveal that AR environments significantly improved retention scores in cell structure compared to traditional methods but had no notable impact on lightning formation. The effectiveness of educational strategies, such as modality and segmenting, depends on the subject's complexity and the learning environment's specifics. Importantly, the study underscores the need to tailor educational techniques to the subject matter, learning environment and individual learner nuances to enhance learning efficacy.
{"title":"Examination of Multimedia Learning Principles in Augmented Reality and Virtual Reality Learning Environments","authors":"Burç Çeken, Nazım Taşkın","doi":"10.1111/jcal.13097","DOIUrl":"https://doi.org/10.1111/jcal.13097","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Multimedia learning, encapsulating both visual and verbal information, has become pivotal in educational settings, with extensive research underscoring its influence. However, a notable research gap exists concerning the application and effectiveness of multimedia learning principles, precisely segmenting, pre-training and modality, within emerging technologies like augmented reality (AR) and virtual reality (VR). This deficit, coupled with inconsistent findings in computer-based learning studies and a lack of focus on intrinsic cognitive load and motivation in multimedia learning, motivates the current research.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This research aims to provide insights by comparing the efficacy of these principles across different learning environments, including traditional, AR and VR.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Employing a 3 × 4 factorial design, this study involved 383 university students, randomly assigned to 12 treatment groups, to investigate multimedia learning principles in different environments. Participants engaged with learning materials on lightning formation and cell structure in various settings, including AR and VR, with data collected through multiple instruments, such as retention and transfer tests, and cognitive load and motivation questionnaires.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>Results reveal that AR environments significantly improved retention scores in cell structure compared to traditional methods but had no notable impact on lightning formation. The effectiveness of educational strategies, such as modality and segmenting, depends on the subject's complexity and the learning environment's specifics. Importantly, the study underscores the need to tailor educational techniques to the subject matter, learning environment and individual learner nuances to enhance learning efficacy.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, immersive virtual reality in education has garnered attention, however, there have been mixed findings on the efficacy of IVR in education. Thus, exploring which strategies are effective in transferring learning from IVR to real-world applications is imperative.
Objective
This study aims to investigate the efficacy of the pretraining principle for acquiring procedural knowledge and skills in an IVR setting that will transfer to real-world environments.
Methods
Ninety-three participants were randomly assigned to either a pretraining or no-pretraining group. The pretraining group watched a video before the IVR lesson, providing the names and characteristics of the physical objects and actions of a micropipette, while the no-pretraining group did not receive this video. During the IVR lesson, participants completed a training phase, followed by a four-step serial dilution test. Afterwards, all participants completed a modified serial dilution test in a real-life setting, along with a knowledge test and assessment on cognitive load, presence, self-efficacy and demographic information.
Results and Conclusions
Analyses demonstrated the pretraining group scored significantly higher on the knowledge test and committed fewer errors in the real-life serial dilution task compared to the no-pretraining group. The pretraining group also reported lower cognitive load, with no observable differences in presence, self-efficacy ratings or errors during the virtual serial dilution task between groups. Theoretical and practical implications are discussed.
{"title":"Implementing Pretraining to Optimise Learning in Immersive Virtual Reality","authors":"Cynthia Y. Delgado, Richard E. Mayer","doi":"10.1111/jcal.13099","DOIUrl":"https://doi.org/10.1111/jcal.13099","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>In recent years, immersive virtual reality in education has garnered attention, however, there have been mixed findings on the efficacy of IVR in education. Thus, exploring which strategies are effective in transferring learning from IVR to real-world applications is imperative.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study aims to investigate the efficacy of the pretraining principle for acquiring procedural knowledge and skills in an IVR setting that will transfer to real-world environments.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Ninety-three participants were randomly assigned to either a pretraining or no-pretraining group. The pretraining group watched a video before the IVR lesson, providing the names and characteristics of the physical objects and actions of a micropipette, while the no-pretraining group did not receive this video. During the IVR lesson, participants completed a training phase, followed by a four-step serial dilution test. Afterwards, all participants completed a modified serial dilution test in a real-life setting, along with a knowledge test and assessment on cognitive load, presence, self-efficacy and demographic information.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>Analyses demonstrated the pretraining group scored significantly higher on the knowledge test and committed fewer errors in the real-life serial dilution task compared to the no-pretraining group. The pretraining group also reported lower cognitive load, with no observable differences in presence, self-efficacy ratings or errors during the virtual serial dilution task between groups. Theoretical and practical implications are discussed.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jcal.13099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Miao Chao, Weiyi Sun, Jie Liu, Jiahui Ding, Ye Zhu
Background
The use of social media among students has become debatable concern due to both positive and negative effects on academic performance. Yet, understanding of the diverse patterns of social media use and their influence on actual and perceived academic performance remains limited.
Objectives
This study distinguishes between academic and excessive social media usage that predicts academic performance while considering academic motivation as a predictive antecedent variable.
Methods
A sample of 887 high school students participated in this study through an online questionnaire. The research model was evaluated using the structural equation modelling approach.
Results and Conclusions
The results revealed that academic motivation prompts academic social media usage and reduces excessive use. Additionally, academic usage positively impacts perceived academic performance but has no impact on actual performance. Paradoxically, although excessive use doesn't affect perceived academic performance, there is an observed negative impact on actual academic performance. These findings provide valuable insights for students and educators, illuminating the limitations of academic social media usage and highlighting the detrimental effects of excessive social media use.
{"title":"Exploring the Bright and Dark Sides of Social Media Use on Academic Performance: Contrasting Effects on Actual vs. Perceived Performance","authors":"Miao Chao, Weiyi Sun, Jie Liu, Jiahui Ding, Ye Zhu","doi":"10.1111/jcal.13111","DOIUrl":"https://doi.org/10.1111/jcal.13111","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The use of social media among students has become debatable concern due to both positive and negative effects on academic performance. Yet, understanding of the diverse patterns of social media use and their influence on actual and perceived academic performance remains limited.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study distinguishes between academic and excessive social media usage that predicts academic performance while considering academic motivation as a predictive antecedent variable.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A sample of 887 high school students participated in this study through an online questionnaire. The research model was evaluated using the structural equation modelling approach.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>The results revealed that academic motivation prompts academic social media usage and reduces excessive use. Additionally, academic usage positively impacts perceived academic performance but has no impact on actual performance. Paradoxically, although excessive use doesn't affect perceived academic performance, there is an observed negative impact on actual academic performance. These findings provide valuable insights for students and educators, illuminating the limitations of academic social media usage and highlighting the detrimental effects of excessive social media use.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Angelica Ronconi, Lucia Mason, Lucia Manzione, Anne Schüler
Background
During digital reading on internet-connected devices, students may be exposed to a variety of on-screen distractions. Learning by reading can therefore become a fragmented experience with potentially negative consequences for reading processes and outcomes.
Objectives
This study investigated the effects of on-screen distractions, as advertisements and social media notifications, during reading on text processing, perception of cognitive load and text comprehension.
Methods
University students (N = 54) participated in a within-participant design. They read two digital science expository texts, one with and the other without distractions. Participants' eye movements were recorded during reading. Process variables were the first-pass fixation time on text areas and the fixation time on distractions. Working memory was taken into account as possible moderator of outcome variables, while controlling for prior knowledge and text topic.
Results
Participants spent very short time fixating the distractions. From linear mixed models the main effect of distractions did not emerge for the immediate text processing. Perception of cognitive load and text comprehension were not affected by distractions either. Among individual differences, prior knowledge contributed to text comprehension. Text topic contributed to the perception of cognitive load.
Takeaways
The study suggests that simple, static and very usual on-screen distractions during reading do not seem particularly harmful for university students' processing and comprehension of expository texts. Findings indicate the importance of students' top-down attentional control over on-screen distractions not to impair their own comprehension of complex content.
{"title":"Effects of Digital Reading With On-Screen Distractions: An Eye-Tracking Study","authors":"Angelica Ronconi, Lucia Mason, Lucia Manzione, Anne Schüler","doi":"10.1111/jcal.13106","DOIUrl":"https://doi.org/10.1111/jcal.13106","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>During digital reading on internet-connected devices, students may be exposed to a variety of on-screen distractions. Learning by reading can therefore become a fragmented experience with potentially negative consequences for reading processes and outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study investigated the effects of on-screen distractions, as advertisements and social media notifications, during reading on text processing, perception of cognitive load and text comprehension.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>University students (<i>N</i> = 54) participated in a within-participant design. They read two digital science expository texts, one with and the other without distractions. Participants' eye movements were recorded during reading. Process variables were the first-pass fixation time on text areas and the fixation time on distractions. Working memory was taken into account as possible moderator of outcome variables, while controlling for prior knowledge and text topic.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Participants spent very short time fixating the distractions. From linear mixed models the main effect of distractions did not emerge for the immediate text processing. Perception of cognitive load and text comprehension were not affected by distractions either. Among individual differences, prior knowledge contributed to text comprehension. Text topic contributed to the perception of cognitive load.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Takeaways</h3>\u0000 \u0000 <p>The study suggests that simple, static and very usual on-screen distractions during reading do not seem particularly harmful for university students' processing and comprehension of expository texts. Findings indicate the importance of students' top-down attentional control over on-screen distractions not to impair their own comprehension of complex content.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jcal.13106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In today's high-tech society, the relationship between social networks and the formation of political orientation and socio-political activity within the student environment has become a key subject of research.
Objectives
The aim of this article is to investigate the correlations between the influence of various social media platforms, the level of political orientation and the degree of socio-political engagement among university students.
Methods
A cross-sectional design was employed to understand the influence of social networks on political orientation and socio-political activity and socio-political activity. Political orientation and socio-political activity tests were applied to provide objective data for further analysis. The research findings indicate an equally positive correlation between Facebook and Instagram with students' levels of political orientation and socio-political activity, whereas a weaker correlation was observed with TikTok.
Results and Conclusions
Prospects for future research may include a more profound analysis of the relationships between types of content and political orientation and socio-political activity, as well as the study of the dynamics of changes in the influence of social networks at different stages of education and personality development.
{"title":"The Relationship Between University Students' Use of Social Networks and Their Political Knowledge and Activity","authors":"Jian Li, Zhaojie Wang, Shubin Zhao","doi":"10.1111/jcal.13104","DOIUrl":"https://doi.org/10.1111/jcal.13104","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>In today's high-tech society, the relationship between social networks and the formation of political orientation and socio-political activity within the student environment has become a key subject of research.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>The aim of this article is to investigate the correlations between the influence of various social media platforms, the level of political orientation and the degree of socio-political engagement among university students.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A cross-sectional design was employed to understand the influence of social networks on political orientation and socio-political activity and socio-political activity. Political orientation and socio-political activity tests were applied to provide objective data for further analysis. The research findings indicate an equally positive correlation between Facebook and Instagram with students' levels of political orientation and socio-political activity, whereas a weaker correlation was observed with TikTok.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>Prospects for future research may include a more profound analysis of the relationships between types of content and political orientation and socio-political activity, as well as the study of the dynamics of changes in the influence of social networks at different stages of education and personality development.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div> <section> <h3> Background</h3> <p>Generative artificial intelligence (AI) represents a significant technological leap, with platforms like OpenAI's ChatGPT and Baidu's Ernie Bot at the forefront of innovation. This technology has seen widespread adoption across various sectors of society and is anticipated to revolutionise the educational landscape, especially in the domain of tertiary education. However, there is a gap in understanding factors influencing university students' behavioural intention to use generative AI, leading to hesitation in its adoption.</p> </section> <section> <h3> Objectives</h3> <p>The primary objective of this study was to investigate the factors that influence university students' behavioural intention to engage with and utilise generative AI. The study sought to delve into the fundamental reasons and obstacles that university students encounter when contemplating the adoption of this technology for their academic endeavours.</p> </section> <section> <h3> Methods</h3> <p>The study used a quantitative research design, utilising a revised version of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. Data were collected from a sample of 380 university students in Changsha, the capital city of Hunan in China. Partial least squares structural equation modelling (PLS-SEM) was used to analyse the relationships between the variables of the model, which included performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), learning value, habit and behavioural intention.</p> </section> <section> <h3> Results</h3> <p>The analysis revealed that PE and EE have a direct impact on learning value. Additionally, SI and FC were found to directly affect the formation of habit. Among these factors, learning value emerged as the most potent predictor of university students' behavioural intention to use generative AI. Habit also demonstrated a significant, albeit smaller, effect on behavioural intention.</p> </section> <section> <h3> Conclusions</h3> <p>The study's findings underscore the importance of learning value in driving the adoption of generative AI among university students. Efforts to enhance the learning value of generative AI could significantly increase its uptake in higher education. Furthermore, the role of habit, while less pronounced, suggests that consistent exposure and use can foster a greater inclination towards generative AI. These insights provide a foundation for targeted interventions aimed at improving the integration and application of generative A
{"title":"Factors Influencing University Students' Behavioural Intention to Use Generative Artificial Intelligence for Educational Purposes Based on a Revised UTAUT2 Model","authors":"Xin Tang, Zhiqiang Yuan, Shaojun Qu","doi":"10.1111/jcal.13105","DOIUrl":"https://doi.org/10.1111/jcal.13105","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Generative artificial intelligence (AI) represents a significant technological leap, with platforms like OpenAI's ChatGPT and Baidu's Ernie Bot at the forefront of innovation. This technology has seen widespread adoption across various sectors of society and is anticipated to revolutionise the educational landscape, especially in the domain of tertiary education. However, there is a gap in understanding factors influencing university students' behavioural intention to use generative AI, leading to hesitation in its adoption.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>The primary objective of this study was to investigate the factors that influence university students' behavioural intention to engage with and utilise generative AI. The study sought to delve into the fundamental reasons and obstacles that university students encounter when contemplating the adoption of this technology for their academic endeavours.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The study used a quantitative research design, utilising a revised version of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. Data were collected from a sample of 380 university students in Changsha, the capital city of Hunan in China. Partial least squares structural equation modelling (PLS-SEM) was used to analyse the relationships between the variables of the model, which included performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), learning value, habit and behavioural intention.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The analysis revealed that PE and EE have a direct impact on learning value. Additionally, SI and FC were found to directly affect the formation of habit. Among these factors, learning value emerged as the most potent predictor of university students' behavioural intention to use generative AI. Habit also demonstrated a significant, albeit smaller, effect on behavioural intention.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The study's findings underscore the importance of learning value in driving the adoption of generative AI among university students. Efforts to enhance the learning value of generative AI could significantly increase its uptake in higher education. Furthermore, the role of habit, while less pronounced, suggests that consistent exposure and use can foster a greater inclination towards generative AI. These insights provide a foundation for targeted interventions aimed at improving the integration and application of generative A","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142861620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, the integration of cutting-edge technology into professional sports training (ST) has revolutionized the way athletes prepare for competition. The study aims to quantitatively analyse the impact of cutting-edge technology on enhancing performance, efficiency, and outcomes in professional ST programs.
Purpose
The purpose of this study is to investigate how advanced technologies influence training effectiveness in professional sports, with a focus on performance enhancement, injury prevention, and overall athlete development.
Methods
This study utilized a quantitative descriptive research design to assess technology's impact on ST. A sample of 450 sports professionals, selected through purposive sampling, completed structured questionnaires. Data were analysed using SPSS to identify correlations between technology use and training outcomes.
Findings
The research findings indicate that athletes utilizing virtual reality (VR), AR, and wearable technology (WT) in their training exhibit significant improvements in performance metrics (PM) and a reduction in injury risks compared to those relying on traditional training methods.
Result
The study results demonstrate that technology-assisted training improves cognitive skills (CS), decision-making, and psychological aspects like motivation and focus. Additionally, advanced analytics and artificial intelligence (AI) enable more effective, personalized coaching strategies, enhancing overall training outcomes.
Originality
This study's contribution lies in its novel approach, demonstrating virtual and augmented reality, wearable tech, and advanced analytics enhance ST by improving performance, CS, and resilience, while also reducing injuries and speeding up recovery.
Conclusion
This research significantly advances the sports science domain by offering empirical evidence supporting technology integration, filling prevalent gaps in the current literature.
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Augmented reality (AR) emerges as a technology with considerable promise and substantial potential for pedagogical integration within language education contexts. However, there remains a scarcity of review studies exploring the best practices and principles for oral communication facilitation based on robust theoretical models or research-proven theories.
Objectives
This paper aims to explore the major constituents for achieving the successful outcomes of AR-assisted oral communication, grounded in the theory-based design model, and formulate theoretical guidelines to enhance the best practice facilitation process.
Methods
Starting from 255 sources, 25 studies from the ISI Web of Science were selected through rigorous screening processes based on the guidelines of the PRISMA statement. Furthermore, the researchers adopted qualitative content analysis and complementary quantitative descriptive analysis for data synthesis in accordance with four key variables of the established model.
Results and Conclusions
The results showed that the guided learner-directed learning approach supported by constructionists and reflective learning mechanisms was identified as an effective pedagogical design. Additionally, educational practitioners could employ hybrid learning strategies, including situated, collaborative, self-regulated and game-based learning strategies, to facilitate effective verbal communication. Finally, besides augmenting location-based content and facilitating contextual information and discussion, AR designers were advised to integrate personalised features such as self-monitoring and evaluating and learner manipulation of virtual objects into system development to promote learners' locus of control and embodied cognition.
增强现实（AR）作为一项具有相当前景和巨大潜力的技术出现在语言教育背景下的教学整合中。然而，基于强大的理论模型或研究证明的理论，探索促进口头交流的最佳实践和原则的综述研究仍然很少。本文旨在以基于理论的设计模型为基础，探讨实现ar辅助口头交流成功的主要因素，并制定理论指导方针，以增强最佳实践促进过程。方法根据PRISMA声明的指导原则，通过严格的筛选程序，从ISI Web of Science的255个来源中选出25项研究。根据所建立模型的四个关键变量，采用定性内容分析和补充性定量描述性分析进行数据综合。结果与结论结果表明，建构主义和反思性学习机制支持的学习者导向学习方法是一种有效的教学设计。此外，教育从业者可以采用混合学习策略，包括情境学习策略、协作学习策略、自我调节学习策略和基于游戏的学习策略，以促进有效的语言交流。最后，除了增强基于位置的内容和促进上下文信息和讨论外，AR设计师还被建议将个性化功能（如自我监控和评估以及学习者对虚拟物体的操作）集成到系统开发中，以促进学习者的控制点和具身认知。
{"title":"Towards a Design of the Best Practices for AR-Guided Oral Communication Development: A Systematic Review of Selected Research Published From 2000 to 2023","authors":"Keng-Chih Hsu, Gi-Zen Liu","doi":"10.1111/jcal.13103","DOIUrl":"https://doi.org/10.1111/jcal.13103","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Augmented reality (AR) emerges as a technology with considerable promise and substantial potential for pedagogical integration within language education contexts. However, there remains a scarcity of review studies exploring the best practices and principles for oral communication facilitation based on robust theoretical models or research-proven theories.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This paper aims to explore the major constituents for achieving the successful outcomes of AR-assisted oral communication, grounded in the theory-based design model, and formulate theoretical guidelines to enhance the best practice facilitation process.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Starting from 255 sources, 25 studies from the ISI Web of Science were selected through rigorous screening processes based on the guidelines of the PRISMA statement. Furthermore, the researchers adopted qualitative content analysis and complementary quantitative descriptive analysis for data synthesis in accordance with four key variables of the established model.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results and Conclusions</h3>\u0000 \u0000 <p>The results showed that the guided learner-directed learning approach supported by constructionists and reflective learning mechanisms was identified as an effective pedagogical design. Additionally, educational practitioners could employ hybrid learning strategies, including situated, collaborative, self-regulated and game-based learning strategies, to facilitate effective verbal communication. Finally, besides augmenting location-based content and facilitating contextual information and discussion, AR designers were advised to integrate personalised features such as self-monitoring and evaluating and learner manipulation of virtual objects into system development to promote learners' locus of control and embodied cognition.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48071,"journal":{"name":"Journal of Computer Assisted Learning","volume":"41 1","pages":""},"PeriodicalIF":5.1,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142868359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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