Pub Date : 2023-11-14DOI: 10.1177/07356331231213932
Fangzheng Zhao, Richard E. Mayer, Nicoletta Adamo-Villani, Christos Mousas, Minsoo Choi, Luchcha Lam, Magzhan Mukanova, Klay Hauser
This study examined how well people can recognize and relate to animated pedagogical agents of varying ethnicities/races and genders. For both Study 1 (realistic-style agents) and Study 2 (cartoon-style agents), participants viewed brief video clips of virtual agents of varying racial/ethnic categories and gender types and then identified their race/ethnicity and gender and rated how human-like and likable the agent appeared. Participants were highly accurate in identifying Black and White agents but were less accurate for Asian, Indian, and Hispanic agents. Participants were accurate in recognizing gender differences. Participants rated all types of agents as moderately human-like, except for White agents. Likability ratings were lowest for White and male agents. The same pattern of results was obtained across two independent studies with different participants and different onscreen agents, which indicates that the results are not solely due to one specific set of agents. Consistent with the Media Equation Hypothesis and the Alliance Hypothesis, this work shows that people are sensitive to the race/ethnicity and gender of onscreen agents and relate to them differently. These findings have implications for how to design animated pedagogical agents for improved multimedia learning environments in the future and serve as a crucial first step in highlighting the possibility and feasibility of incorporating diverse onscreen virtual agents into educational computer software.
{"title":"Recognizing and Relating to the Race/Ethnicity and Gender of Animated Pedagogical Agents","authors":"Fangzheng Zhao, Richard E. Mayer, Nicoletta Adamo-Villani, Christos Mousas, Minsoo Choi, Luchcha Lam, Magzhan Mukanova, Klay Hauser","doi":"10.1177/07356331231213932","DOIUrl":"https://doi.org/10.1177/07356331231213932","url":null,"abstract":"This study examined how well people can recognize and relate to animated pedagogical agents of varying ethnicities/races and genders. For both Study 1 (realistic-style agents) and Study 2 (cartoon-style agents), participants viewed brief video clips of virtual agents of varying racial/ethnic categories and gender types and then identified their race/ethnicity and gender and rated how human-like and likable the agent appeared. Participants were highly accurate in identifying Black and White agents but were less accurate for Asian, Indian, and Hispanic agents. Participants were accurate in recognizing gender differences. Participants rated all types of agents as moderately human-like, except for White agents. Likability ratings were lowest for White and male agents. The same pattern of results was obtained across two independent studies with different participants and different onscreen agents, which indicates that the results are not solely due to one specific set of agents. Consistent with the Media Equation Hypothesis and the Alliance Hypothesis, this work shows that people are sensitive to the race/ethnicity and gender of onscreen agents and relate to them differently. These findings have implications for how to design animated pedagogical agents for improved multimedia learning environments in the future and serve as a crucial first step in highlighting the possibility and feasibility of incorporating diverse onscreen virtual agents into educational computer software.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"58 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134902624","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 : 2023-11-10DOI: 10.1177/07356331231210946
Nikolaos Pellas
Educational technologists and practitioners have made substantial strides in developing affordable digital and tangible resources to support both formal and informal computer science instruction. However, there is a lack of research on practice-based assignments, such as Internet of Things (IoT) projects, that allow undergraduate students to design and demonstrate educational robots using digital or physical assistance, especially when it comes to computational thinking (CT) and programming skills development in association with their psycho-emotional experience. This study compares the impact of Scratch and LEGO ® WeDo robotic kits on students' CT and programming skills development. A quasi-experimental approach was conducted, involving two hundred forty-six participants ( n = 246), who were equally divided between Scratch and LEGO ® WeDo groups. Results indicate that the LEGO ® WeDo group showed greater improvement in CT and programming skills development, while designing and presenting IoT projects. Nevertheless, no significant association between motivation, grit, and CT skills was observed. The findings highlight the potential of tangible robotics in facilitating students’ hands-on learning and enhancing motivation to foster CT and programming skills. This study provides a wide range of implications for instructional designers on how to use tangible robotics to support hands-on IoT projects in computer science courses.
{"title":"Assessing Computational Thinking, Motivation, and Grit of Undergraduate Students Using Educational Robots","authors":"Nikolaos Pellas","doi":"10.1177/07356331231210946","DOIUrl":"https://doi.org/10.1177/07356331231210946","url":null,"abstract":"Educational technologists and practitioners have made substantial strides in developing affordable digital and tangible resources to support both formal and informal computer science instruction. However, there is a lack of research on practice-based assignments, such as Internet of Things (IoT) projects, that allow undergraduate students to design and demonstrate educational robots using digital or physical assistance, especially when it comes to computational thinking (CT) and programming skills development in association with their psycho-emotional experience. This study compares the impact of Scratch and LEGO ® WeDo robotic kits on students' CT and programming skills development. A quasi-experimental approach was conducted, involving two hundred forty-six participants ( n = 246), who were equally divided between Scratch and LEGO ® WeDo groups. Results indicate that the LEGO ® WeDo group showed greater improvement in CT and programming skills development, while designing and presenting IoT projects. Nevertheless, no significant association between motivation, grit, and CT skills was observed. The findings highlight the potential of tangible robotics in facilitating students’ hands-on learning and enhancing motivation to foster CT and programming skills. This study provides a wide range of implications for instructional designers on how to use tangible robotics to support hands-on IoT projects in computer science courses.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"107 50","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135136957","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 : 2023-11-10DOI: 10.1177/07356331231209772
Lusia Maryani Silitonga, Budi Dharmawan, Astrid Tiara Murti, Ting-Ting Wu
Business simulation games (BSG) offer a unique opportunity to provide students with hands-on experience in a simulated business environment. This study aims to explore the effectiveness of BSG in promoting entrepreneurial intentions and competencies among undergraduate students. The study was conducted with 48 students, who participated in BSG as part of their entrepreneurship education (EE). The study used a quasi-experiment to measure changes in students' cognitive and non-cognitive entrepreneurial competencies, as well as their entrepreneurial intentions. The results show that participation in the BSG significantly improved students' cognitive and non-cognitive entrepreneurial competencies, as well as their intention to start a new business. The study concludes that BSG can be an effective teaching strategy for promoting EE and developing entrepreneurial competencies among undergraduate students. However, further research with larger sample sizes and diverse populations is needed to confirm these findings and explore how combining BSG with other teaching methods or interventions can enhance the development of entrepreneurial competencies and intentions.
{"title":"Promoting Entrepreneurial Intentions and Competencies Through Business Simulation Games","authors":"Lusia Maryani Silitonga, Budi Dharmawan, Astrid Tiara Murti, Ting-Ting Wu","doi":"10.1177/07356331231209772","DOIUrl":"https://doi.org/10.1177/07356331231209772","url":null,"abstract":"Business simulation games (BSG) offer a unique opportunity to provide students with hands-on experience in a simulated business environment. This study aims to explore the effectiveness of BSG in promoting entrepreneurial intentions and competencies among undergraduate students. The study was conducted with 48 students, who participated in BSG as part of their entrepreneurship education (EE). The study used a quasi-experiment to measure changes in students' cognitive and non-cognitive entrepreneurial competencies, as well as their entrepreneurial intentions. The results show that participation in the BSG significantly improved students' cognitive and non-cognitive entrepreneurial competencies, as well as their intention to start a new business. The study concludes that BSG can be an effective teaching strategy for promoting EE and developing entrepreneurial competencies among undergraduate students. However, further research with larger sample sizes and diverse populations is needed to confirm these findings and explore how combining BSG with other teaching methods or interventions can enhance the development of entrepreneurial competencies and intentions.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":" 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135141489","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 : 2023-11-10DOI: 10.1177/07356331231191174
Felipe Urrutia, Roberto Araya
Written answers to open-ended questions can have a higher long-term effect on learning than multiple-choice questions. However, it is critical that teachers immediately review the answers, and ask to redo those that are incoherent. This can be a difficult task and can be time-consuming for teachers. A possible solution is to automate the detection of incoherent answers. One option is to automate the review with Large Language Models (LLM). They have a powerful discursive ability that can be used to explain decisions. In this paper, we analyze the responses of fourth graders in mathematics using three LLMs: GPT-3, BLOOM, and YOU. We used them with zero, one, two, three and four shots. We compared their performance with the results of various classifiers trained with Machine Learning (ML). We found that LLMs perform worse than MLs in detecting incoherent answers. The difficulty seems to reside in recursive questions that contain both questions and answers, and in responses from students with typical fourth-grader misspellings. Upon closer examination, we have found that the ChatGPT model faces the same challenges.
{"title":"Who's the Best Detective? Large Language Models vs. Traditional Machine Learning in Detecting Incoherent Fourth Grade Math Answers","authors":"Felipe Urrutia, Roberto Araya","doi":"10.1177/07356331231191174","DOIUrl":"https://doi.org/10.1177/07356331231191174","url":null,"abstract":"Written answers to open-ended questions can have a higher long-term effect on learning than multiple-choice questions. However, it is critical that teachers immediately review the answers, and ask to redo those that are incoherent. This can be a difficult task and can be time-consuming for teachers. A possible solution is to automate the detection of incoherent answers. One option is to automate the review with Large Language Models (LLM). They have a powerful discursive ability that can be used to explain decisions. In this paper, we analyze the responses of fourth graders in mathematics using three LLMs: GPT-3, BLOOM, and YOU. We used them with zero, one, two, three and four shots. We compared their performance with the results of various classifiers trained with Machine Learning (ML). We found that LLMs perform worse than MLs in detecting incoherent answers. The difficulty seems to reside in recursive questions that contain both questions and answers, and in responses from students with typical fourth-grader misspellings. Upon closer examination, we have found that the ChatGPT model faces the same challenges.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"114 19","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135138052","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 : 2023-11-09DOI: 10.1177/07356331231210940
Qi Wang, Shengquan Yu, Xiaofeng Wang
Chinese as a second language (CSL) learning has attracted more attention and supporting learners with adaptive resources becomes difficult. Some online systems recommended pre-designed resources from existing databases while the resources could not match learners’ context. Designing resources dynamically according to learners’ needs could be a solution while it’s time-consuming. Targeting this problem, we proposed a “content-structure” loosely coupled model. Based on the model, we developed an automatic resource generation system and used it in a university. One class was chosen and the students were randomly assigned to the experimental (22 students) and control group (21 students). They participated in the course all the same except the resources generation method. During the learning process, the online behaviors were recorded for behavioral analysis and the learners’ learning outcome and perceptions were measured by tests and questionnaires. Results showed that the system played positive roles in improving learners’ learning outcome and perceptions. Moreover, we found that learners in experimental group participated more actively and there’s evidence that the system could help learners better reflect on their needs. The results revealed the effectiveness of the system in supporting CSL learners’ contextualized learning. This design will provide inspiration for future context-aware CSL learning research.
{"title":"Research on the Role of an Automatic Resource Generation System to Promote Chinese as a Second Language Learners’ Learning in Colleges","authors":"Qi Wang, Shengquan Yu, Xiaofeng Wang","doi":"10.1177/07356331231210940","DOIUrl":"https://doi.org/10.1177/07356331231210940","url":null,"abstract":"Chinese as a second language (CSL) learning has attracted more attention and supporting learners with adaptive resources becomes difficult. Some online systems recommended pre-designed resources from existing databases while the resources could not match learners’ context. Designing resources dynamically according to learners’ needs could be a solution while it’s time-consuming. Targeting this problem, we proposed a “content-structure” loosely coupled model. Based on the model, we developed an automatic resource generation system and used it in a university. One class was chosen and the students were randomly assigned to the experimental (22 students) and control group (21 students). They participated in the course all the same except the resources generation method. During the learning process, the online behaviors were recorded for behavioral analysis and the learners’ learning outcome and perceptions were measured by tests and questionnaires. Results showed that the system played positive roles in improving learners’ learning outcome and perceptions. Moreover, we found that learners in experimental group participated more actively and there’s evidence that the system could help learners better reflect on their needs. The results revealed the effectiveness of the system in supporting CSL learners’ contextualized learning. This design will provide inspiration for future context-aware CSL learning research.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":" 39","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135240826","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}
Science, Technology, Engineering, and Mathematics (STEM) education is essential for developing future-ready learners in both secondary and higher education levels. However, as students transition to higher education, many encounter challenges with independent learning and research. This can negatively impact their Higher-Order Thinking Skills (HOTS), engagement, and practical expertise. This study introduces a solution: Computational Thinking Scaffolding (CTS) in the Jupyter Notebook environment, designed to enhance STEM education at the tertiary level. CTS incorporates five phases: Decomposition, Pattern Recognition, Abstraction, Algorithm Design, and Evaluation. Utilizing a quasi-experimental method, we assessed the impact of CTS on the HOTS, engagement, and practical skills of undergraduate and postgraduate students. Our findings hold substantial relevance for university educators, academic advisors, and curriculum designers aiming to enhance students’ HOTS and hands-on capabilities in STEM disciplines. The results validate the effectiveness of CTS in elevating tertiary STEM learning outcomes, and they spotlight the adaptability of the Jupyter Notebook as a valuable tool in higher education. In conclusion, our research underscores the merits of CTS for improving outcomes in higher STEM education and sets a benchmark for future endeavors in this domain.
{"title":"Integrating Computational Thinking Into Scaffolding Learning: An Innovative Approach to Enhance Science, Technology, Engineering, and Mathematics Hands-On Learning","authors":"Hsin-Yu Lee, Ting-Ting Wu, Chia-Ju Lin, Wei-Sheng Wang, Yueh-Min Huang","doi":"10.1177/07356331231211916","DOIUrl":"https://doi.org/10.1177/07356331231211916","url":null,"abstract":"Science, Technology, Engineering, and Mathematics (STEM) education is essential for developing future-ready learners in both secondary and higher education levels. However, as students transition to higher education, many encounter challenges with independent learning and research. This can negatively impact their Higher-Order Thinking Skills (HOTS), engagement, and practical expertise. This study introduces a solution: Computational Thinking Scaffolding (CTS) in the Jupyter Notebook environment, designed to enhance STEM education at the tertiary level. CTS incorporates five phases: Decomposition, Pattern Recognition, Abstraction, Algorithm Design, and Evaluation. Utilizing a quasi-experimental method, we assessed the impact of CTS on the HOTS, engagement, and practical skills of undergraduate and postgraduate students. Our findings hold substantial relevance for university educators, academic advisors, and curriculum designers aiming to enhance students’ HOTS and hands-on capabilities in STEM disciplines. The results validate the effectiveness of CTS in elevating tertiary STEM learning outcomes, and they spotlight the adaptability of the Jupyter Notebook as a valuable tool in higher education. In conclusion, our research underscores the merits of CTS for improving outcomes in higher STEM education and sets a benchmark for future endeavors in this domain.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135393068","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 : 2023-11-08DOI: 10.1177/07356331231210560
Yu-Sheng Su, Shuwen Wang, Xiaohong Liu
Pair programming (PP) can help improve students’ computational thinking (CT), but the trajectory of CT skills and the differences between high-scoring and low-scoring students in PP are unknown and need further exploration. In this study, a total of 32 fifth graders worked on Scratch tasks in 16 pairs. The group discourse of three learning topics (comprising 9 projects) was collected. After the audio files were transcribed, 1,303 conversations were obtained. They were analyzed via Epistemic Network Analysis (ENA) Webkit, which can reveal the trajectory of students’ CT development via analyzing codes of discourse related to CT in PP. Three Scratch learning topics were assessed based on the Dr. Scratch platform to acquire the level of students’ CT and to determine the low- and high-scoring groups. Results indicated that CT concepts and CT practices were always closely related in PP and CT practices, and CT perspectives could be gradually and closely related after a long period of CT training. A significant difference between the two groups’ CT structures was found. The high-scoring group had more fragments of CT practice and connecting of CT perspectives, while the low-scoring group showed more fragments of CT concepts and expressing of CT perspectives. This research provides insights into cultivating primary school students’ CT using Scratch in the context of PP. The findings can provide suggestions for instructors to design instructional interventions to facilitate students’ CT skills via PP learning. Instructors can improve CT skills by guiding students to constantly ask questions, and specifying the role swap between driver and navigator in PP. Besides, instructors could give more consideration to the development of CT perspectives, and especially the ability to question.
{"title":"Using Epistemic Network Analysis to Explore Primary School Students’ Computational Thinking in Pair Programming Learning","authors":"Yu-Sheng Su, Shuwen Wang, Xiaohong Liu","doi":"10.1177/07356331231210560","DOIUrl":"https://doi.org/10.1177/07356331231210560","url":null,"abstract":"Pair programming (PP) can help improve students’ computational thinking (CT), but the trajectory of CT skills and the differences between high-scoring and low-scoring students in PP are unknown and need further exploration. In this study, a total of 32 fifth graders worked on Scratch tasks in 16 pairs. The group discourse of three learning topics (comprising 9 projects) was collected. After the audio files were transcribed, 1,303 conversations were obtained. They were analyzed via Epistemic Network Analysis (ENA) Webkit, which can reveal the trajectory of students’ CT development via analyzing codes of discourse related to CT in PP. Three Scratch learning topics were assessed based on the Dr. Scratch platform to acquire the level of students’ CT and to determine the low- and high-scoring groups. Results indicated that CT concepts and CT practices were always closely related in PP and CT practices, and CT perspectives could be gradually and closely related after a long period of CT training. A significant difference between the two groups’ CT structures was found. The high-scoring group had more fragments of CT practice and connecting of CT perspectives, while the low-scoring group showed more fragments of CT concepts and expressing of CT perspectives. This research provides insights into cultivating primary school students’ CT using Scratch in the context of PP. The findings can provide suggestions for instructors to design instructional interventions to facilitate students’ CT skills via PP learning. Instructors can improve CT skills by guiding students to constantly ask questions, and specifying the role swap between driver and navigator in PP. Besides, instructors could give more consideration to the development of CT perspectives, and especially the ability to question.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"63 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135430645","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}
Robotics education has received widespread attention in K-12 education. Studies have pointed out that in robotics courses, learners face challenges in learning abstract content, such as constructing a robot with a good structure and writing programs to drive a robot to complete specific learning tasks. The present study proposed the embodied learning-based computer programming approach and applied it to the LEGO Mindstorms EV3 robotics course. To evaluate its effectiveness, a quasi-experiment was conducted in one public primary school to explore its effects on students’ learning achievement, learning motivation, learning attitudes, learning engagement, and cognitive load. The experimental group (40 students) adopted the embodied learning-based computer programming approach, while the control group (40 students) adopted the conventional computer programming approach. The results showed that the experimental group had significantly better learning achievement in robotics than the control group, and that there was no significant difference in the cognitive load of the two groups. In terms of learning motivation, although both groups showed improvement, the experimental group had higher intrinsic learning motivation. In addition, the experimental group outperformed the control group with regard to learning attitudes and learning engagement (including cognitive, behavioral, and emotional engagement). Accordingly, this study could contribute to future research for developing more effective robotics teaching approaches and computer programming activity design.
{"title":"Engaging Young Students in Effective Robotics Education: An Embodied Learning-Based Computer Programming Approach","authors":"Xinli Zhang, Yuchen Chen, Danqing Li, Lailin Hu, Gwo-Jen Hwang, Yun-Fang Tu","doi":"10.1177/07356331231213548","DOIUrl":"https://doi.org/10.1177/07356331231213548","url":null,"abstract":"Robotics education has received widespread attention in K-12 education. Studies have pointed out that in robotics courses, learners face challenges in learning abstract content, such as constructing a robot with a good structure and writing programs to drive a robot to complete specific learning tasks. The present study proposed the embodied learning-based computer programming approach and applied it to the LEGO Mindstorms EV3 robotics course. To evaluate its effectiveness, a quasi-experiment was conducted in one public primary school to explore its effects on students’ learning achievement, learning motivation, learning attitudes, learning engagement, and cognitive load. The experimental group (40 students) adopted the embodied learning-based computer programming approach, while the control group (40 students) adopted the conventional computer programming approach. The results showed that the experimental group had significantly better learning achievement in robotics than the control group, and that there was no significant difference in the cognitive load of the two groups. In terms of learning motivation, although both groups showed improvement, the experimental group had higher intrinsic learning motivation. In addition, the experimental group outperformed the control group with regard to learning attitudes and learning engagement (including cognitive, behavioral, and emotional engagement). Accordingly, this study could contribute to future research for developing more effective robotics teaching approaches and computer programming activity design.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135480439","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 : 2023-10-25DOI: 10.1177/07356331231209773
Huiyan Ye, Oi-Lam Ng, Zhihao Cui
Computational thinking (CT) has received much attention in mathematics education in recent years, and researchers have begun to experiment with the integration of CT into mathematics education to promote students’ CT and mathematical thinking (MT) development. However, there is a lack of empirical evidence and new theoretical perspectives on the mechanisms of interaction between CT and MT. To address this research gap, this study analyses the participants’ thinking processes in solving programming-based mathematical problems from a flexibility perspective, focusing on the interplay between computational and mathematical thinking, that is, how CT and MT work together to influence and determine the problem-solver’s choice of solution strategy. Using data collected from a large design-based study, we summarise two types of flexibility and six subtypes of flexibility demonstrated by participants in the programming-based mathematical problem-solving process using thematic analysis. These different types of flexibility provide researchers and mathematics educators with new theoretical perspectives to examine the interplay of CT and MT. Findings will also contribute toward student learning characteristics in programming-based mathematical problem-solving to sketch the big picture of how CT and MT emerge in complementary or mismatching ways.
{"title":"Conceptualizing Flexibility in Programming-Based Mathematical Problem-Solving","authors":"Huiyan Ye, Oi-Lam Ng, Zhihao Cui","doi":"10.1177/07356331231209773","DOIUrl":"https://doi.org/10.1177/07356331231209773","url":null,"abstract":"Computational thinking (CT) has received much attention in mathematics education in recent years, and researchers have begun to experiment with the integration of CT into mathematics education to promote students’ CT and mathematical thinking (MT) development. However, there is a lack of empirical evidence and new theoretical perspectives on the mechanisms of interaction between CT and MT. To address this research gap, this study analyses the participants’ thinking processes in solving programming-based mathematical problems from a flexibility perspective, focusing on the interplay between computational and mathematical thinking, that is, how CT and MT work together to influence and determine the problem-solver’s choice of solution strategy. Using data collected from a large design-based study, we summarise two types of flexibility and six subtypes of flexibility demonstrated by participants in the programming-based mathematical problem-solving process using thematic analysis. These different types of flexibility provide researchers and mathematics educators with new theoretical perspectives to examine the interplay of CT and MT. Findings will also contribute toward student learning characteristics in programming-based mathematical problem-solving to sketch the big picture of how CT and MT emerge in complementary or mismatching ways.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"24 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135168727","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 : 2023-10-25DOI: 10.1177/07356331231206990
Fengfeng Ke, Chih-Pu Dai, Luke West, Yanjun Pan, Jiabei Xu
Students frequently struggled with the mathematizing process – forging connections between implicit and explicit mathematical thinking – when solving a context-rich applied problem. The current research investigated how students interact with and leverage purposively designed ‘mathematizing’ supports when solving applied math problems in a game-based, inquiry-oriented math learning environment. We conducted a naturalistic observation case study and a mixed-method study to investigate middle school students’ usage of mathematizing supports in relation to their math problem-solving performance. The findings indicated a positive and predictive impact of using mathematizing supports on the logged and observed practice of mathematization as well as the performance of applied math problem solving by the students during and after gaming. However, not all students leverage in-game mathematizing supports or engage in problem mathematizing processes. The grounds of students’ constructive interaction with a mathematizing support include their productive persistence in problem solving, their exercise of agency in gauging the utility of mathematizing, and their engagement with deductive reasoning from concrete to abstract. We also observed an interplay between internal and external mathematizing supports, which is moderated by the modality of learning settings.
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