Pub Date : 2024-09-04DOI: 10.1177/07356331241277967
Gila Apelboim-Dushnitzky, Oren Tova
This study tested the potential of a technological intervention procedure for promoting letter-naming and initial-phoneme detection skills among preschoolers at risk for Specific Learning Disorder. The study rational is based on evidence for paired associated learning of visual-verbal stimuli, integrated with the use of a tangible technological device (educational floor robot) as means for promoting letter knowledge. Two intervention groups ( N = 60) participated. The intervention lasted for eight sessions of phonological training; each dedicated to one target letter. Participants were asked to navigate the robot’s path from a current stopping point to the next according to phonological information processing. Both groups went through the same procedure, but using a different type of mat (one with and one without a visual form of the letter). A control group ( N = 30) of at-risk preschoolers received no intervention except for their preschool educational routine. We found that children who participated in phonological training with linkage to the visual form of the letter scored higher in post-intervention tests. Among other proposed explanations, we mention the advantages of using a tangible robot and seemingly three-dimensional letters, along with the tendency to consider visual features while processing language stimulus among children with dyslexia.
{"title":"Promoting Letter-Naming and Initial-Phoneme Detection Abilities Among Preschoolers at Risk for Specific Learning Disorder Using Technological Intervention With Two Types of Mats: With and Without Target Letter Forms","authors":"Gila Apelboim-Dushnitzky, Oren Tova","doi":"10.1177/07356331241277967","DOIUrl":"https://doi.org/10.1177/07356331241277967","url":null,"abstract":"This study tested the potential of a technological intervention procedure for promoting letter-naming and initial-phoneme detection skills among preschoolers at risk for Specific Learning Disorder. The study rational is based on evidence for paired associated learning of visual-verbal stimuli, integrated with the use of a tangible technological device (educational floor robot) as means for promoting letter knowledge. Two intervention groups ( N = 60) participated. The intervention lasted for eight sessions of phonological training; each dedicated to one target letter. Participants were asked to navigate the robot’s path from a current stopping point to the next according to phonological information processing. Both groups went through the same procedure, but using a different type of mat (one with and one without a visual form of the letter). A control group ( N = 30) of at-risk preschoolers received no intervention except for their preschool educational routine. We found that children who participated in phonological training with linkage to the visual form of the letter scored higher in post-intervention tests. Among other proposed explanations, we mention the advantages of using a tangible robot and seemingly three-dimensional letters, along with the tendency to consider visual features while processing language stimulus among children with dyslexia.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"14 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199103","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}
Pub Date : 2024-09-01DOI: 10.1177/07356331241268349
Lijuan Feng
This study investigates the impact of AI-assisted language learning (AIAL) strategies on cognitive load and learning outcomes in the context of language acquisition. Specifically, the study explores three distinct AIAL strategies: personalized feedback and adaptive learning, interactive exercises with speech recognition, and intelligent tutoring with data-driven insights. The research employs a pretest-posttest random assignment experimental design, utilizing three experimental groups and a control group, with a total of 484 EFL students specializing in teaching English as a foreign language participating in the study. Data collection involves pre- and post-tests, questionnaires, and interviews to assess the influence of AIAL strategies on cognitive load and learning outcomes. Cognitive load is measured using the Cognitive Load Scale, while pretest-posttest assessments evaluate the efficacy of AIAL interventions across various language skills. These results contribute to the existing body of AIAL research by offering empirical evidence for the effectiveness of specific strategies in optimizing language learning experiences. The implications of this study extend to educators, researchers, and developers in the field of AIAL, emphasizing the potential of AIAL to enhance language acquisition processes and inform instructional design practices.
{"title":"Investigating the Effects of Artificial Intelligence-Assisted Language Learning Strategies on Cognitive Load and Learning Outcomes: A Comparative Study","authors":"Lijuan Feng","doi":"10.1177/07356331241268349","DOIUrl":"https://doi.org/10.1177/07356331241268349","url":null,"abstract":"This study investigates the impact of AI-assisted language learning (AIAL) strategies on cognitive load and learning outcomes in the context of language acquisition. Specifically, the study explores three distinct AIAL strategies: personalized feedback and adaptive learning, interactive exercises with speech recognition, and intelligent tutoring with data-driven insights. The research employs a pretest-posttest random assignment experimental design, utilizing three experimental groups and a control group, with a total of 484 EFL students specializing in teaching English as a foreign language participating in the study. Data collection involves pre- and post-tests, questionnaires, and interviews to assess the influence of AIAL strategies on cognitive load and learning outcomes. Cognitive load is measured using the Cognitive Load Scale, while pretest-posttest assessments evaluate the efficacy of AIAL interventions across various language skills. These results contribute to the existing body of AIAL research by offering empirical evidence for the effectiveness of specific strategies in optimizing language learning experiences. The implications of this study extend to educators, researchers, and developers in the field of AIAL, emphasizing the potential of AIAL to enhance language acquisition processes and inform instructional design practices.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"24 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225494","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}
Pub Date : 2024-08-29DOI: 10.1177/07356331241277904
Hsing-Ying Tu, Silvia Wen-Yu Lee
Learning in a virtual environment has been found to foster students’ affective responses, indicating the importance of exploring the factors which affect students’ learning when engaged in a virtual game. This study aimed to explore the relationships among students’ epistemic curiosity, situational interest, and learning engagement in an exploratory 3D virtual game environment. A virtual game was designed for elementary school students to learn concepts of evaporation and condensation in real-life situations. Data were collected using questionnaires from a total of 121 sixth-grade elementary school students, and were analyzed using partial least square structural equation modeling (PLS-SEM). The reliability and validity of the instruments employed in this study were verified. The structural relationships showed that I-type epistemic curiosity showed a stronger influence on the design of the virtual game environment compared to D-type epistemic curiosity. Challenge was found to be negatively predicted by I-type epistemic curiosity, but positively predicted by D-type epistemic curiosity. Instant enjoyment was also found to play a significant mediating role between I-type epistemic curiosity and emotional engagement. It is suggested that future research should consider providing students with interesting contexts and challenging tasks as a critical approach to designing virtual game learning environments.
研究发现，在虚拟环境中学习会促进学生的情感反应，这表明探索影响学生在虚拟游戏中学习的因素非常重要。本研究旨在探索学生在探索性三维虚拟游戏环境中的求知欲、情境兴趣和学习参与度之间的关系。本研究为小学生设计了一款虚拟游戏，让他们在真实情景中学习蒸发和冷凝的概念。通过问卷调查收集了 121 名六年级小学生的数据，并使用偏最小二乘法结构方程模型（PLS-SEM）对数据进行了分析。本研究采用的工具的信度和效度均得到验证。结构关系显示，与 D 型认识好奇心相比，I 型认识好奇心对虚拟游戏环境设计的影响更大。挑战性被 I 型认识好奇心负向预测，但被 D 型认识好奇心正向预测。研究还发现，即时享受在 I 型认识好奇心和情感投入之间起着重要的中介作用。建议未来的研究应考虑为学生提供有趣的情境和具有挑战性的任务，以此作为设计虚拟游戏学习环境的关键方法。
{"title":"Curiosity, Interest, and Engagement: Unpacking Their Roles in Students’ Learning within a Virtual Game Environment","authors":"Hsing-Ying Tu, Silvia Wen-Yu Lee","doi":"10.1177/07356331241277904","DOIUrl":"https://doi.org/10.1177/07356331241277904","url":null,"abstract":"Learning in a virtual environment has been found to foster students’ affective responses, indicating the importance of exploring the factors which affect students’ learning when engaged in a virtual game. This study aimed to explore the relationships among students’ epistemic curiosity, situational interest, and learning engagement in an exploratory 3D virtual game environment. A virtual game was designed for elementary school students to learn concepts of evaporation and condensation in real-life situations. Data were collected using questionnaires from a total of 121 sixth-grade elementary school students, and were analyzed using partial least square structural equation modeling (PLS-SEM). The reliability and validity of the instruments employed in this study were verified. The structural relationships showed that I-type epistemic curiosity showed a stronger influence on the design of the virtual game environment compared to D-type epistemic curiosity. Challenge was found to be negatively predicted by I-type epistemic curiosity, but positively predicted by D-type epistemic curiosity. Instant enjoyment was also found to play a significant mediating role between I-type epistemic curiosity and emotional engagement. It is suggested that future research should consider providing students with interesting contexts and challenging tasks as a critical approach to designing virtual game learning environments.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"135 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199104","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}
Pub Date : 2024-08-28DOI: 10.1177/07356331241270658
Alberto Cannavò, Federico De Lorenzis, Filippo Gabriele Pratticò, Lorenzo Galante, Fabrizio Lamberti
Today, immersive technologies like Virtual Reality (VR) are regarded as disruptive tools in many domains, including education. While the body of literature in the field is growing, studies that present contrasting findings are not uncommon. In fact, although there is evidence of the benefits brought by VR in the educational processes, in some cases the effects of a possible trade-off between learning effectiveness and quality of the learning experience (or QoLE) may be observed. The two dimensions are difficult to disentangle, as besides learning effectiveness, other factors like motivation, technology acceptability, workload, presence, immersion, engagement, and usability come to play. This paper digs into the above scenario by focusing on the QoLE of immersive VR-based learning and comparing it with that of two conventional approaches (a physical prop-based one and a 3D desktop application). Separation of the two dimensions is pursued by imposing equality of the learning performance achieved with the three approaches, aiming at getting rid of possible confounding factors. From the results of the user study performed in the context of a STEM-related laboratory activity, the VR-based approach appeared to be generally superior to the prop-based approach and showed several advantages over the 3D desktop application.
{"title":"On the Quality of the Experience With Virtual Reality-Based Instructional Tools for Science Lab Activities","authors":"Alberto Cannavò, Federico De Lorenzis, Filippo Gabriele Pratticò, Lorenzo Galante, Fabrizio Lamberti","doi":"10.1177/07356331241270658","DOIUrl":"https://doi.org/10.1177/07356331241270658","url":null,"abstract":"Today, immersive technologies like Virtual Reality (VR) are regarded as disruptive tools in many domains, including education. While the body of literature in the field is growing, studies that present contrasting findings are not uncommon. In fact, although there is evidence of the benefits brought by VR in the educational processes, in some cases the effects of a possible trade-off between learning effectiveness and quality of the learning experience (or QoLE) may be observed. The two dimensions are difficult to disentangle, as besides learning effectiveness, other factors like motivation, technology acceptability, workload, presence, immersion, engagement, and usability come to play. This paper digs into the above scenario by focusing on the QoLE of immersive VR-based learning and comparing it with that of two conventional approaches (a physical prop-based one and a 3D desktop application). Separation of the two dimensions is pursued by imposing equality of the learning performance achieved with the three approaches, aiming at getting rid of possible confounding factors. From the results of the user study performed in the context of a STEM-related laboratory activity, the VR-based approach appeared to be generally superior to the prop-based approach and showed several advantages over the 3D desktop application.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"4 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225515","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}
Pub Date : 2024-08-28DOI: 10.1177/07356331241277937
Lihui Sun, Liang Zhou
The use of generative artificial intelligence (Gen-AI) to assist college students in their studies has become a trend. However, there is no academic consensus on whether Gen-AI can enhance the academic achievement of college students. Using a meta-analytic approach, this study aims to investigate the effectiveness of Gen-AI in improving the academic achievement of college students and to explore the effects of different moderating variables. A total of 28 articles (65 independent studies, 1909 participants) met the inclusion criteria for this study. The results showed that Gen-AI significantly improved college students’ academic achievement with a medium effect size (Hedges’s g = 0.533, 95% CI [0.408,0.659], p < .05). There were within-group differences in the three moderator variables, activity categories, sample size, and generated content, when the generated content was text ( g = 0.554, p < .05), and sample size of 21–40 ( g = 0.776, p < .05), the use of independent learning styles ( g = 0.600, p < .05) had the most significant improvement in college student’s academic achievement. The intervention duration, the discipline types, and the assessment tools also had a moderate positive impact on college students’ academic achievement, but there were no significant within-group differences in any of the moderating variables. This study provides a theoretical basis and empirical evidence for the scientific application of Gen-AI and the development of educational technology policy.
{"title":"Does Generative Artificial Intelligence Improve the Academic Achievement of College Students? A Meta-Analysis","authors":"Lihui Sun, Liang Zhou","doi":"10.1177/07356331241277937","DOIUrl":"https://doi.org/10.1177/07356331241277937","url":null,"abstract":"The use of generative artificial intelligence (Gen-AI) to assist college students in their studies has become a trend. However, there is no academic consensus on whether Gen-AI can enhance the academic achievement of college students. Using a meta-analytic approach, this study aims to investigate the effectiveness of Gen-AI in improving the academic achievement of college students and to explore the effects of different moderating variables. A total of 28 articles (65 independent studies, 1909 participants) met the inclusion criteria for this study. The results showed that Gen-AI significantly improved college students’ academic achievement with a medium effect size (Hedges’s g = 0.533, 95% CI [0.408,0.659], p < .05). There were within-group differences in the three moderator variables, activity categories, sample size, and generated content, when the generated content was text ( g = 0.554, p < .05), and sample size of 21–40 ( g = 0.776, p < .05), the use of independent learning styles ( g = 0.600, p < .05) had the most significant improvement in college student’s academic achievement. The intervention duration, the discipline types, and the assessment tools also had a moderate positive impact on college students’ academic achievement, but there were no significant within-group differences in any of the moderating variables. This study provides a theoretical basis and empirical evidence for the scientific application of Gen-AI and the development of educational technology policy.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"31 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225495","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}
Pub Date : 2024-08-28DOI: 10.1177/07356331241278636
Gawon Yun, Kewman M. Lee, Hailey Hyunjin Choi
Scholarly interest in artificial intelligence (AI) has surged as researchers delve into its transformative impact on various aspects of our lives. AI poses both benefits and challenges, particularly in the context of educators' endeavors to comprehend the intricacies of students' learning processes. Although the use of AI to enhance and assist student learning is relatively new, the exponential growth of scholarly attention and publications in AI and student learning in recent years underscores the compelling necessity for further inquiry. Investigating this area is crucial for understanding the emerging trends in this research domain. This study aims to provide insights into the burgeoning research trajectories on AI from a student learning perspective. Using a bibliometric approach, this study examined 663 scholarly articles pertaining to the interface between AI and student learning published between 1961 and 2024. Our findings reveal four major thematic areas including AI in education and educational technology, AI-driven learning environments, essential AI enablers, and human learning and highlight promising avenues at this intersection.
{"title":"Empowering Student Learning Through Artificial Intelligence: A Bibliometric Analysis","authors":"Gawon Yun, Kewman M. Lee, Hailey Hyunjin Choi","doi":"10.1177/07356331241278636","DOIUrl":"https://doi.org/10.1177/07356331241278636","url":null,"abstract":"Scholarly interest in artificial intelligence (AI) has surged as researchers delve into its transformative impact on various aspects of our lives. AI poses both benefits and challenges, particularly in the context of educators' endeavors to comprehend the intricacies of students' learning processes. Although the use of AI to enhance and assist student learning is relatively new, the exponential growth of scholarly attention and publications in AI and student learning in recent years underscores the compelling necessity for further inquiry. Investigating this area is crucial for understanding the emerging trends in this research domain. This study aims to provide insights into the burgeoning research trajectories on AI from a student learning perspective. Using a bibliometric approach, this study examined 663 scholarly articles pertaining to the interface between AI and student learning published between 1961 and 2024. Our findings reveal four major thematic areas including AI in education and educational technology, AI-driven learning environments, essential AI enablers, and human learning and highlight promising avenues at this intersection.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"114 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225514","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}
Pub Date : 2024-08-28DOI: 10.1177/07356331241271059
Keunjae Kim, Kyungbin Kwon
This study presents an inclusive K-12 AI curriculum for elementary schools, focusing on six design principles to address gender disparities. The curriculum, designed by the researchers and an elementary teacher, uses tangible tools, and emphasizes collaboration in solving daily problems. The MANOVA results revealed initial gender differences in AI knowledge. Following MANCOVA analysis showed significant improvements in AI knowledge, as well as perceptions and behavioral intentions toward AI, effectively bridging the observed knowledge gaps without any significant differences attributed to gender or initial knowledge levels. This evidence underscores the success of tangible and collaborative AI interventions in uniformly enhancing AI knowledge and positively changing perceptions and behavioral intentions among all participants, regardless of gender. Both female and non-binary students felt increased engagement and reduced anxiety toward AI, with improved comprehension and a preference for collaborative learning. This study contributes to the design of inclusive AI interventions, highlighting gender differences and including non-binary students as an essential part of the analysis.
{"title":"Designing an Inclusive Artificial Intelligence (AI) Curriculum for Elementary Students to Address Gender Differences With Collaborative and Tangible Approaches","authors":"Keunjae Kim, Kyungbin Kwon","doi":"10.1177/07356331241271059","DOIUrl":"https://doi.org/10.1177/07356331241271059","url":null,"abstract":"This study presents an inclusive K-12 AI curriculum for elementary schools, focusing on six design principles to address gender disparities. The curriculum, designed by the researchers and an elementary teacher, uses tangible tools, and emphasizes collaboration in solving daily problems. The MANOVA results revealed initial gender differences in AI knowledge. Following MANCOVA analysis showed significant improvements in AI knowledge, as well as perceptions and behavioral intentions toward AI, effectively bridging the observed knowledge gaps without any significant differences attributed to gender or initial knowledge levels. This evidence underscores the success of tangible and collaborative AI interventions in uniformly enhancing AI knowledge and positively changing perceptions and behavioral intentions among all participants, regardless of gender. Both female and non-binary students felt increased engagement and reduced anxiety toward AI, with improved comprehension and a preference for collaborative learning. This study contributes to the design of inclusive AI interventions, highlighting gender differences and including non-binary students as an essential part of the analysis.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"21 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225496","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}
Pub Date : 2024-07-31DOI: 10.1177/07356331241270707
Abdullahi Yusuf, Amiru Yusuf Muhammad
The study investigates the potential of anxiety clusters in predicting programming performance in two distinct coding environments. Participants comprised 83 second-year programming students who were randomly assigned to either a block-based or a text-based group. Anxiety-induced behaviors were assessed using physiological measures (Apple Watch and Electrocardiogram machine), behavioral observation, and self-report. Utilizing the Hidden Markov Model and Optimal Matching algorithm, we found three representative clusters in each group. In the block-based group, clusters were designated as follows: “stay calm” (students allocating more of their time to a calm state), “stay hesitant” (students allocating more of their time to a hesitant state), and “to-calm” (those allocating minimal time to a hesitant and anxious state but displaying a pronounced propensity to transition to a calm state). In contrast, clusters in the text-based group were labeled as: “to-hesitant” (exhibiting a higher propensity to transition to a hesitant state), “stay hesitant” (allocating significant time to a hesitant state), and “stay anxious” (remaining persistently anxious in a majority of the coding time). Additionally, our results indicate that novice programmers are more likely to experience anxiety during text-based coding. We discussed the findings and highlighted the policy implications of the study.
{"title":"Exploring Clusters of Novice Programmers’ Anxiety-Induced Behaviors During Block- and Text-Based Coding: A Predictive and Moderation Analysis of Programming Quality and Error Debugging Skills","authors":"Abdullahi Yusuf, Amiru Yusuf Muhammad","doi":"10.1177/07356331241270707","DOIUrl":"https://doi.org/10.1177/07356331241270707","url":null,"abstract":"The study investigates the potential of anxiety clusters in predicting programming performance in two distinct coding environments. Participants comprised 83 second-year programming students who were randomly assigned to either a block-based or a text-based group. Anxiety-induced behaviors were assessed using physiological measures (Apple Watch and Electrocardiogram machine), behavioral observation, and self-report. Utilizing the Hidden Markov Model and Optimal Matching algorithm, we found three representative clusters in each group. In the block-based group, clusters were designated as follows: “stay calm” (students allocating more of their time to a calm state), “stay hesitant” (students allocating more of their time to a hesitant state), and “to-calm” (those allocating minimal time to a hesitant and anxious state but displaying a pronounced propensity to transition to a calm state). In contrast, clusters in the text-based group were labeled as: “to-hesitant” (exhibiting a higher propensity to transition to a hesitant state), “stay hesitant” (allocating significant time to a hesitant state), and “stay anxious” (remaining persistently anxious in a majority of the coding time). Additionally, our results indicate that novice programmers are more likely to experience anxiety during text-based coding. We discussed the findings and highlighted the policy implications of the study.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"50 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141873371","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}
Pub Date : 2024-07-26DOI: 10.1177/07356331241268505
Yu-Chi Chen, Huei-Tse Hou
Technologies like ChatGPT and other AI tools have impacted learning by giving students more chances to ask questions and explore knowledge. The inclusion of Non-Player Characters (NPCs) as scaffolding in game-based situated learning activities can have a positive impact on learning. The application of ChatGPT to role-playing has potential; therefore, this study designed a “ChatGPT-based NPCs scaffolding Workflow Framework” and used it as the basis for designing and evaluating an educational game for employee ethics training with empirical evidence. This study had 61 participants, divided into a document scaffolding group ( n = 32) and a ChatGPT-based NPC group ( n = 29) and examined the learning achievement, flow, motivation, and anxiety in the two groups. The results showed that the designs of both groups benefited learning achievement, and both groups could maintain a certain level of high motivation and engagement. Through qualitatively analyzing the content of students’ discussions with NPCs, it was found that there is potential for NPC-assisted learning through ChatGPT. Overall, this study explored the efficacy and limitations of using ChatGPT-based NPCs as scaffolding in game-based learning and found that it is extensible. We also present a framework for the design of ChatGPT-based NPCs scaffolding mechanism.
{"title":"A Mobile Contextualized Educational Game Framework With ChatGPT Interactive Scaffolding for Employee Ethics Training","authors":"Yu-Chi Chen, Huei-Tse Hou","doi":"10.1177/07356331241268505","DOIUrl":"https://doi.org/10.1177/07356331241268505","url":null,"abstract":"Technologies like ChatGPT and other AI tools have impacted learning by giving students more chances to ask questions and explore knowledge. The inclusion of Non-Player Characters (NPCs) as scaffolding in game-based situated learning activities can have a positive impact on learning. The application of ChatGPT to role-playing has potential; therefore, this study designed a “ChatGPT-based NPCs scaffolding Workflow Framework” and used it as the basis for designing and evaluating an educational game for employee ethics training with empirical evidence. This study had 61 participants, divided into a document scaffolding group ( n = 32) and a ChatGPT-based NPC group ( n = 29) and examined the learning achievement, flow, motivation, and anxiety in the two groups. The results showed that the designs of both groups benefited learning achievement, and both groups could maintain a certain level of high motivation and engagement. Through qualitatively analyzing the content of students’ discussions with NPCs, it was found that there is potential for NPC-assisted learning through ChatGPT. Overall, this study explored the efficacy and limitations of using ChatGPT-based NPCs as scaffolding in game-based learning and found that it is extensible. We also present a framework for the design of ChatGPT-based NPCs scaffolding mechanism.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"80 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769789","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}
The present research conducted two experiments with an intelligent tutoring system to investigate the overall and dynamic impact of emotional support from a pedagogical agent (PA). In Experiment 1, a single factor intergroup design was used to explore the impact of PA’s emotional support (supportive vs. non-supportive) on learners’ emotions, intrinsic motivation, and learning gain. Sixty participants were recruited and randomly assigned to one of the two conditions. Experiment 2 also conducted a single factor between-subjects design to investigated the dynamic patterns between emotions and learning strategies among 30 participants using lag sequential analysis. Results showed that: Compared with the non-supportive pedagogical agent, the supportive pedagogical agent reduced frustration and improved learning gain, but did not increase intrinsic motivation. In addition, learners with the supportive pedagogical agent used more appropriate strategies after frustration and surprise, and use less ineffective strategies after confusion and enjoyment to avoid reaching a wrong answer. If learners did not receive emotional support in such cases, learning strategies following these emotions were more likely lead to negative cognitive results, or negative emotions tended to appear repeatedly. Instructors or PAs should identify learners’ emotions in time and provide the appropriate emotional support according to learners’ emotions.
{"title":"The Roles of Pedagogical Agent’s Emotional Support: Dynamics Between Emotions and Learning Strategies in Multimedia Learning","authors":"Yueru Lang, Shaoying Gong, Xiangen Hu, Boyuan Xiao, Yanqing Wang, Tiantian Jiang","doi":"10.1177/07356331241263598","DOIUrl":"https://doi.org/10.1177/07356331241263598","url":null,"abstract":"The present research conducted two experiments with an intelligent tutoring system to investigate the overall and dynamic impact of emotional support from a pedagogical agent (PA). In Experiment 1, a single factor intergroup design was used to explore the impact of PA’s emotional support (supportive vs. non-supportive) on learners’ emotions, intrinsic motivation, and learning gain. Sixty participants were recruited and randomly assigned to one of the two conditions. Experiment 2 also conducted a single factor between-subjects design to investigated the dynamic patterns between emotions and learning strategies among 30 participants using lag sequential analysis. Results showed that: Compared with the non-supportive pedagogical agent, the supportive pedagogical agent reduced frustration and improved learning gain, but did not increase intrinsic motivation. In addition, learners with the supportive pedagogical agent used more appropriate strategies after frustration and surprise, and use less ineffective strategies after confusion and enjoyment to avoid reaching a wrong answer. If learners did not receive emotional support in such cases, learning strategies following these emotions were more likely lead to negative cognitive results, or negative emotions tended to appear repeatedly. Instructors or PAs should identify learners’ emotions in time and provide the appropriate emotional support according to learners’ emotions.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"11 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141769790","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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