Pub Date : 2024-04-02DOI: 10.1177/07356331241240047
Monika Mladenović, Žana Žanko, Goran Zaharija
The use of a pedagogical approach mediated transfer with the bridging method has been successful in facilitating the transitions from block-based to text-based programming languages. Nevertheless, there is a lack of research addressing the impact of this transfer on programming misconceptions during the transition. The way programming concepts are taught to K-12 learners can later result in misconceptions for adult learners. The main objective was to examine the impact of mediated transfer using the bridging method pedagogical approach on the prevalence of programming misconceptions. We conducted a quasi-experimental study in school settings during informatics (computer science) classes among 163 sixth-grade students. The control group received traditional programming lectures using the text-based programming language, Python. Conversely, the experimental group utilized a mediated transfer pedagogical approach by starting with the block-based programming language MakeCode for micro:bit before transitioning to the text-based Python. Our findings indicate that the experimental group significantly reduced programming misconceptions in fundamental programming concepts: variables, sequencing, selection, and loops - compared to the control group. This suggests that the use of block-based programming language as an initial step in programming education, followed by a structured transition to text-based programming language, can effectively mitigate common misconceptions among K-12 learners.
{"title":"From Blocks to Text: Bridging Programming Misconceptions","authors":"Monika Mladenović, Žana Žanko, Goran Zaharija","doi":"10.1177/07356331241240047","DOIUrl":"https://doi.org/10.1177/07356331241240047","url":null,"abstract":"The use of a pedagogical approach mediated transfer with the bridging method has been successful in facilitating the transitions from block-based to text-based programming languages. Nevertheless, there is a lack of research addressing the impact of this transfer on programming misconceptions during the transition. The way programming concepts are taught to K-12 learners can later result in misconceptions for adult learners. The main objective was to examine the impact of mediated transfer using the bridging method pedagogical approach on the prevalence of programming misconceptions. We conducted a quasi-experimental study in school settings during informatics (computer science) classes among 163 sixth-grade students. The control group received traditional programming lectures using the text-based programming language, Python. Conversely, the experimental group utilized a mediated transfer pedagogical approach by starting with the block-based programming language MakeCode for micro:bit before transitioning to the text-based Python. Our findings indicate that the experimental group significantly reduced programming misconceptions in fundamental programming concepts: variables, sequencing, selection, and loops - compared to the control group. This suggests that the use of block-based programming language as an initial step in programming education, followed by a structured transition to text-based programming language, can effectively mitigate common misconceptions among K-12 learners.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"1 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573474","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 : 2024-04-01DOI: 10.1177/07356331241242441
Jiangyue Liu, Siran Li, Qianyan Dong
The emergence of Generative Artificial Intelligence (GAI) has caused significant disruption to the traditional educational teaching ecosystem. GAI possesses remarkable capabilities in generating human-like text and boasts an extensive knowledge repository, thereby paving the way for potential collaboration with humans. However, current research on collaborating with GAI within the educational context remains insufficient and the methods are relatively limited. This study aims to utilize methods such as Lag Sequential Analysis (LSA) and Epistemic Network Analysis (ENA) to unveil the “black box” of the human-machine collaborative process. In this research, 22 students engaged in collaborative tasks with GAI to refine instructional design schemes within an authentic classroom setting. The results show that the participants significantly improved the quality of instructional design. Leveraging the improvement demonstrated in students’ instructional design performance, we categorized them into high- and low-performance groups. Through the analysis of learning behavior, it was observed that the high-performance group adhered to a structured GAI content application framework: “generate → monitor → apply → evaluate.” Moreover, they adeptly employed communication strategies emphasizing exercising cognitive agency and actively cultivating a collaborative environment. The conclusions drawn from this research may serve as a reference for a series of practical applications in human-machine collaboration and provide directions for subsequent studies.
生成式人工智能（GAI）的出现对传统的教育教学生态系统造成了巨大的破坏。GAI 在生成类似人类的文本方面拥有非凡的能力，并拥有广泛的知识库，从而为潜在的人机协作铺平了道路。然而，目前在教育背景下与 GAI 合作的研究仍然不足，方法也相对有限。本研究旨在利用滞后序列分析（LSA）和表观网络分析（ENA）等方法，揭开人机协作过程的 "黑箱"。在这项研究中，22 名学生参与了与 GAI 的协作任务，以便在真实的课堂环境中完善教学设计方案。结果表明，参与者大大提高了教学设计的质量。根据学生在教学设计表现上的进步，我们将他们分为高表现组和低表现组。通过对学习行为的分析，我们发现高绩效组坚持使用结构化的 GAI 内容应用框架："生成→监控→应用→评价"。此外，他们还善于运用交流策略，强调发挥认知能动性，积极营造合作环境。本研究得出的结论可为人机协作的一系列实际应用提供参考，并为后续研究提供方向。
{"title":"Collaboration with Generative Artificial Intelligence: An Exploratory Study Based on Learning Analytics","authors":"Jiangyue Liu, Siran Li, Qianyan Dong","doi":"10.1177/07356331241242441","DOIUrl":"https://doi.org/10.1177/07356331241242441","url":null,"abstract":"The emergence of Generative Artificial Intelligence (GAI) has caused significant disruption to the traditional educational teaching ecosystem. GAI possesses remarkable capabilities in generating human-like text and boasts an extensive knowledge repository, thereby paving the way for potential collaboration with humans. However, current research on collaborating with GAI within the educational context remains insufficient and the methods are relatively limited. This study aims to utilize methods such as Lag Sequential Analysis (LSA) and Epistemic Network Analysis (ENA) to unveil the “black box” of the human-machine collaborative process. In this research, 22 students engaged in collaborative tasks with GAI to refine instructional design schemes within an authentic classroom setting. The results show that the participants significantly improved the quality of instructional design. Leveraging the improvement demonstrated in students’ instructional design performance, we categorized them into high- and low-performance groups. Through the analysis of learning behavior, it was observed that the high-performance group adhered to a structured GAI content application framework: “generate → monitor → apply → evaluate.” Moreover, they adeptly employed communication strategies emphasizing exercising cognitive agency and actively cultivating a collaborative environment. The conclusions drawn from this research may serve as a reference for a series of practical applications in human-machine collaboration and provide directions for subsequent studies.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"18 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140602953","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 : 2024-04-01DOI: 10.1177/07356331241242435
Tongxi Liu
Addressing cognitive disparities has become a paramount concern in computational thinking (CT) education. The intricate and nuanced relationships between CT and cognitive variations emphasize the needs to accommodate diverse cognitive profiles when fostering CT skills, recognizing that these cognitive functions can manifest as either strengths or limitations in different students. Consequently, understanding the connections between students’ cognitive functions and CT skills assumes pivotal importance in the design of personalized instructional strategies for CT. Despite a general consideration of learning variability in CT education, empirical insights exploring the correlation between cognitive skills and CT competencies remain notably scarce. This study endeavors to bridge this research gap by investigating the links between executive functions and CT skills, as well as the associations between their sub-dimensions. The results reveal a statistically significant correlation coefficient of 0.452 between these two domains, underscoring the notable connection between executive functions and CT abilities. Furthermore, the sub-dimensional analysis offers a comprehensive understanding of how specific executive functions uniquely contribute to certain CT skills. In light of these findings, this research offers a promising pathway for the development of tailored CT education programs that can cater to the unique needs of each individual, ultimately facilitating inclusive CT programs and making significant contributions to broaden STEM education and future workforce.
{"title":"Relationships Between Executive Functions and Computational Thinking","authors":"Tongxi Liu","doi":"10.1177/07356331241242435","DOIUrl":"https://doi.org/10.1177/07356331241242435","url":null,"abstract":"Addressing cognitive disparities has become a paramount concern in computational thinking (CT) education. The intricate and nuanced relationships between CT and cognitive variations emphasize the needs to accommodate diverse cognitive profiles when fostering CT skills, recognizing that these cognitive functions can manifest as either strengths or limitations in different students. Consequently, understanding the connections between students’ cognitive functions and CT skills assumes pivotal importance in the design of personalized instructional strategies for CT. Despite a general consideration of learning variability in CT education, empirical insights exploring the correlation between cognitive skills and CT competencies remain notably scarce. This study endeavors to bridge this research gap by investigating the links between executive functions and CT skills, as well as the associations between their sub-dimensions. The results reveal a statistically significant correlation coefficient of 0.452 between these two domains, underscoring the notable connection between executive functions and CT abilities. Furthermore, the sub-dimensional analysis offers a comprehensive understanding of how specific executive functions uniquely contribute to certain CT skills. In light of these findings, this research offers a promising pathway for the development of tailored CT education programs that can cater to the unique needs of each individual, ultimately facilitating inclusive CT programs and making significant contributions to broaden STEM education and future workforce.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"36 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573632","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 : 2024-03-25DOI: 10.1177/07356331241236937
Meital Amzalag, Dorin Kadusi, Shimon Peretz
Abundant research has tried to understand how games can be designed and used effectively to improve the learning process and to examine the correlations between digital learning games and student motivation, engagement, and knowledge retention. The current study examined the correlation between learning through digital game-based learning (DGBL) and students’ achievements, their sense of involvement, and motivation for learning. Using a quantitative approach, data was drawn from questionnaires and exams in two subjects: literature and language. Participants were 320 male and female students aged 12–14 attending a single middle school participated in the study. The students were randomly divided into three groups, each group was given a unique teaching and learning method. Group 1 studied and practiced using the traditional method (a teacher who teaches in the classroom and worksheets for practice), Group 2 studied with the traditional method but practiced with a digital game and Group 3 learned and practiced using a digital game. The findings showed that the students’ attained significantly higher achievements in the group that was taught traditionally but practiced with a digital game. It was also found that when digital learning games are integrated into teaching and learning, the students’ motivation and involvement in the class increased.
{"title":"Enhancing Academic Achievement and Engagement Through Digital Game-Based Learning: An Empirical Study on Middle School Students","authors":"Meital Amzalag, Dorin Kadusi, Shimon Peretz","doi":"10.1177/07356331241236937","DOIUrl":"https://doi.org/10.1177/07356331241236937","url":null,"abstract":"Abundant research has tried to understand how games can be designed and used effectively to improve the learning process and to examine the correlations between digital learning games and student motivation, engagement, and knowledge retention. The current study examined the correlation between learning through digital game-based learning (DGBL) and students’ achievements, their sense of involvement, and motivation for learning. Using a quantitative approach, data was drawn from questionnaires and exams in two subjects: literature and language. Participants were 320 male and female students aged 12–14 attending a single middle school participated in the study. The students were randomly divided into three groups, each group was given a unique teaching and learning method. Group 1 studied and practiced using the traditional method (a teacher who teaches in the classroom and worksheets for practice), Group 2 studied with the traditional method but practiced with a digital game and Group 3 learned and practiced using a digital game. The findings showed that the students’ attained significantly higher achievements in the group that was taught traditionally but practiced with a digital game. It was also found that when digital learning games are integrated into teaching and learning, the students’ motivation and involvement in the class increased.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"17 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140299654","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 : 2024-03-11DOI: 10.1177/07356331241236744
Xiaowen Wang, Kan Kan Chan, Qianru Li, Shing On Leung
The interest in Computational Thinking (CT) development among young learners increases with the number of studies located in literature. In this study, a meta-analysis was conducted to address two main objectives: (a) the effectiveness of empirical interventions on the development of CT in children aged of 3–8 years; and (b) the variables that influence the effectiveness of the interventions. Following PRISMA procedures, we identified 17 empirical studies with 34 effect sizes and 1665 participants meeting the inclusion criteria from Web of Science database. Overall, we found a statistically significant large effect size (d = .83 [95% CI: 730, .890]; p < .001) on the CT development of 3–8 years old children, which provides empirical support for having young children to engage in CT experiences. The effect size was significantly influenced by moderating variables including gender, scaffolding, and education level. Intervention length showed a marginally significant effect. Therefore, educators could refer to the significant moderators when designing tailored interventions for CT development in early childhood education while a call for more empirical studies of CT development in young children is proposed.
{"title":"Do 3–8 Years Old Children Benefit From Computational Thinking Development? A Meta-Analysis","authors":"Xiaowen Wang, Kan Kan Chan, Qianru Li, Shing On Leung","doi":"10.1177/07356331241236744","DOIUrl":"https://doi.org/10.1177/07356331241236744","url":null,"abstract":"The interest in Computational Thinking (CT) development among young learners increases with the number of studies located in literature. In this study, a meta-analysis was conducted to address two main objectives: (a) the effectiveness of empirical interventions on the development of CT in children aged of 3–8 years; and (b) the variables that influence the effectiveness of the interventions. Following PRISMA procedures, we identified 17 empirical studies with 34 effect sizes and 1665 participants meeting the inclusion criteria from Web of Science database. Overall, we found a statistically significant large effect size (d = .83 [95% CI: 730, .890]; p < .001) on the CT development of 3–8 years old children, which provides empirical support for having young children to engage in CT experiences. The effect size was significantly influenced by moderating variables including gender, scaffolding, and education level. Intervention length showed a marginally significant effect. Therefore, educators could refer to the significant moderators when designing tailored interventions for CT development in early childhood education while a call for more empirical studies of CT development in young children is proposed.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"1 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140115811","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 : 2024-02-29DOI: 10.1177/07356331241236882
Chung-Yuan Hsu, Meng-Jung Tsai
This research aimed to investigate the structural relationships among teachers’ computational thinking (CT), design thinking (DT), robotics teaching beliefs, and robotics pedagogical content knowledge (RPCK). A total of 98 in-service and pre-service teachers who participated in a robotics teaching professional development workshop served as the sample of the study. A survey including the Computational Thinking Scale, the Design Thinking Disposition Scale, the Robotics Teaching Beliefs Scale and the Technological Pedagogical Content Knowledge–Robotics Scale was conducted after the workshop. A confirmatory factor analysis was employed to validate the measurement constructs, and Partial Least Squares - Structural Equation Modeling (PLS-SEM) analysis was utilized to examine the relationships among the factors. The results revealed that both CT and DT dispositions could positively predict teachers’ robotics teaching beliefs, which subsequently predicted their RPCK. Moreover, a direct positive relationship between CT and RPCK was identified, while such a relationship was not evident for DT. The model demonstrates the critical role of CT in shaping teachers' beliefs and pedagogical strategies of robotics teaching, and provides insights into the indirect influence of DT. Finally, the Model of Robotics Teaching Professional Development (MRTPD) was proposed to profile how to promote teachers’ pedagogical content knowledge of robotics teaching from their CT and DT dispositions.
{"title":"Predicting Robotics Pedagogical Content Knowledge: The Role of Computational and Design Thinking Dispositions via Teaching Beliefs","authors":"Chung-Yuan Hsu, Meng-Jung Tsai","doi":"10.1177/07356331241236882","DOIUrl":"https://doi.org/10.1177/07356331241236882","url":null,"abstract":"This research aimed to investigate the structural relationships among teachers’ computational thinking (CT), design thinking (DT), robotics teaching beliefs, and robotics pedagogical content knowledge (RPCK). A total of 98 in-service and pre-service teachers who participated in a robotics teaching professional development workshop served as the sample of the study. A survey including the Computational Thinking Scale, the Design Thinking Disposition Scale, the Robotics Teaching Beliefs Scale and the Technological Pedagogical Content Knowledge–Robotics Scale was conducted after the workshop. A confirmatory factor analysis was employed to validate the measurement constructs, and Partial Least Squares - Structural Equation Modeling (PLS-SEM) analysis was utilized to examine the relationships among the factors. The results revealed that both CT and DT dispositions could positively predict teachers’ robotics teaching beliefs, which subsequently predicted their RPCK. Moreover, a direct positive relationship between CT and RPCK was identified, while such a relationship was not evident for DT. The model demonstrates the critical role of CT in shaping teachers' beliefs and pedagogical strategies of robotics teaching, and provides insights into the indirect influence of DT. Finally, the Model of Robotics Teaching Professional Development (MRTPD) was proposed to profile how to promote teachers’ pedagogical content knowledge of robotics teaching from their CT and DT dispositions.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"12 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140019104","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 : 2024-02-27DOI: 10.1177/07356331241236467
Chen-Chung Liu, Wan-Jun Chen, Fang-ying Lo, Chia-Hui Chang, Hung-Ming Lin
Reading requires appropriate strategies to spark initial interest and sustain engagement. One promising strategy is the pedagogical approach of learning-by-teaching, transforming learners into active participants. Integrating this approach into digitalized and individualized reading contexts has the potential to foster the development of young readers. Currently, AI techniques are primarily used in chatbots as tutors, with limited focus on tutee chatbots that employ the learning-by-teaching pedagogy. Therefore, this study adopted a teachable Q&A agent and probed into the effect of chatbot training, employing AI techniques and utilizing student-generated questions and answers, with the aim of enhancing students’ reading interest and engagement. Ninety-five fifth graders participated in a 9-week reading program. A quasi-experimental design was conducted. The results proved that incorporating a learning-by-teaching approach into the chatbot training activity significantly enhanced their reading interest and engagement. However, the quantity of certain question types is negatively correlated with interest and engagement. This implies that asking diverse questions poses a certain level of challenge to young readers, which requires deliberate training and incubation. Additionally, the identification of four distinct student clusters exhibited the affordances and limitations of tutee chatbots for reading.
{"title":"Teachable Q&A Agent: The Effect of Chatbot Training by Students on Reading Interest and Engagement","authors":"Chen-Chung Liu, Wan-Jun Chen, Fang-ying Lo, Chia-Hui Chang, Hung-Ming Lin","doi":"10.1177/07356331241236467","DOIUrl":"https://doi.org/10.1177/07356331241236467","url":null,"abstract":"Reading requires appropriate strategies to spark initial interest and sustain engagement. One promising strategy is the pedagogical approach of learning-by-teaching, transforming learners into active participants. Integrating this approach into digitalized and individualized reading contexts has the potential to foster the development of young readers. Currently, AI techniques are primarily used in chatbots as tutors, with limited focus on tutee chatbots that employ the learning-by-teaching pedagogy. Therefore, this study adopted a teachable Q&A agent and probed into the effect of chatbot training, employing AI techniques and utilizing student-generated questions and answers, with the aim of enhancing students’ reading interest and engagement. Ninety-five fifth graders participated in a 9-week reading program. A quasi-experimental design was conducted. The results proved that incorporating a learning-by-teaching approach into the chatbot training activity significantly enhanced their reading interest and engagement. However, the quantity of certain question types is negatively correlated with interest and engagement. This implies that asking diverse questions poses a certain level of challenge to young readers, which requires deliberate training and incubation. Additionally, the identification of four distinct student clusters exhibited the affordances and limitations of tutee chatbots for reading.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"2011 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140025079","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 : 2024-01-31DOI: 10.1177/07356331241226592
Li Cheng, Ethan Croteau, Sami Baral, Cristina Heffernan, Neil Heffernan
Chatbots represent a promising technology for engaging students in math learning. Guided by Jerome Bruner’s constructivism and Lev Vygotsky’s Zone of Proximal Development, we designed and developed a chatbot that incorporates scaffolding strategies and social-emotional considerations, and we integrated it into ASSISTments, an online math learning platform. We conducted an experimental study to examine the influence of learning math with the chatbot compared to traditional learning with hints. This study involved 85 middle and high school students from three diverse school settings in the United States. The results revealed no significant differences in students' math learning performance and perceived helpfulness and interest between the chatbot and traditional hints conditions. However, students in the chatbot condition displayed significantly lower confidence in solving a similar problem after the intervention, likely due to the removal of the high level of support provided by the chatbot. Despite this, students’ open responses indicated that a significantly higher number of students had positive attitudes towards chatbots. They appreciated the chatting feature, breaking down a problem into steps, and real-time support. The study concludes with a discussion of the findings and implications for chatbot designers and developers and presents avenues for future research and practice in chatbot-assisted learning. In support of Open Science, this study has been preregistered and both the data and the analysis code used in this study are publicly available at https://osf.io/am3p8/ .
{"title":"Facilitating Student Learning With a Chatbot in an Online Math Learning Platform","authors":"Li Cheng, Ethan Croteau, Sami Baral, Cristina Heffernan, Neil Heffernan","doi":"10.1177/07356331241226592","DOIUrl":"https://doi.org/10.1177/07356331241226592","url":null,"abstract":"Chatbots represent a promising technology for engaging students in math learning. Guided by Jerome Bruner’s constructivism and Lev Vygotsky’s Zone of Proximal Development, we designed and developed a chatbot that incorporates scaffolding strategies and social-emotional considerations, and we integrated it into ASSISTments, an online math learning platform. We conducted an experimental study to examine the influence of learning math with the chatbot compared to traditional learning with hints. This study involved 85 middle and high school students from three diverse school settings in the United States. The results revealed no significant differences in students' math learning performance and perceived helpfulness and interest between the chatbot and traditional hints conditions. However, students in the chatbot condition displayed significantly lower confidence in solving a similar problem after the intervention, likely due to the removal of the high level of support provided by the chatbot. Despite this, students’ open responses indicated that a significantly higher number of students had positive attitudes towards chatbots. They appreciated the chatting feature, breaking down a problem into steps, and real-time support. The study concludes with a discussion of the findings and implications for chatbot designers and developers and presents avenues for future research and practice in chatbot-assisted learning. In support of Open Science, this study has been preregistered and both the data and the analysis code used in this study are publicly available at https://osf.io/am3p8/ .","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"24 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139946558","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 : 2024-01-10DOI: 10.1177/07356331241226594
Zhongtian Ji, Kan Guo, Shuang Song
As a significant tool to integrate information technology and education, dynamic mathematical software (DMS) has been widely concerned in recent years. However, how to better apply it to instruction practice deserves further exploration. Thus, we adopted the meta-analysis method to analyze the DMS-based experiments published between 2000 and 2020. A three-level meta-analysis of data from 107 studies involving 10,507 participants and 138 effect sizes revealed a moderate effect size (d = .632, 95% CI = [.550, .713]). Moreover, moderator analyses showed that: (1) cultural background had significant moderating effects; (2) students performed better on near-transfer tests than far-transfer tests; (3) DMS used by students independently had better effects; (4) intervention duration had significant moderating effects; (5) some of the above significant moderating effects were unique after controlling for others. Overall, our findings suggest that DMS has positive effects on students’ performance and teachers should be meticulous in designing their teaching plans.
{"title":"Effects of Dynamic Mathematical Software on Students’ Performance: A Three-Level Meta-Analysis","authors":"Zhongtian Ji, Kan Guo, Shuang Song","doi":"10.1177/07356331241226594","DOIUrl":"https://doi.org/10.1177/07356331241226594","url":null,"abstract":"As a significant tool to integrate information technology and education, dynamic mathematical software (DMS) has been widely concerned in recent years. However, how to better apply it to instruction practice deserves further exploration. Thus, we adopted the meta-analysis method to analyze the DMS-based experiments published between 2000 and 2020. A three-level meta-analysis of data from 107 studies involving 10,507 participants and 138 effect sizes revealed a moderate effect size (d = .632, 95% CI = [.550, .713]). Moreover, moderator analyses showed that: (1) cultural background had significant moderating effects; (2) students performed better on near-transfer tests than far-transfer tests; (3) DMS used by students independently had better effects; (4) intervention duration had significant moderating effects; (5) some of the above significant moderating effects were unique after controlling for others. Overall, our findings suggest that DMS has positive effects on students’ performance and teachers should be meticulous in designing their teaching plans.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"58 13","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139441003","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 : 2024-01-10DOI: 10.1177/07356331231226171
Xueqing Wu, Rui Li
While numerous studies of robot-assisted language learning (RALL) for English-as-a-foreign-language (EFL) learners’ language skill development have been done, a comprehensive and theoretically-driven meta-analysis on its effects is still in paucity. To fill the gap, drawing on Activity Theory (AT), this study reported a meta-analysis from 47 independent studies out of 29 literature samples involving 1791 EFL learners on RALL for language skill development published during 2004–2023. The results indicated that the overall effect size was g = .69, 95% CI [.49, .90], suggesting that RALL outperforms non-RALL conditions. In addition, educational levels and intervention durations were found to be significant moderators. Based on the results, implications for practice were discussed.
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