The rapid evolution of AI technologies has reshaped our daily lives. As AI systems become increasingly prevalent, AI literacy, the ability to comprehend and engage with these technologies, becomes paramount in modern society. However, existing research has yet to establish a comprehensive framework for AI literacy. This study aims to fill this gap by developing a holistic AI literacy scale. Three levels of dimensions are considered: individual, interactive, and sociocultural. The scale includes cognitive, behavioral, and normative competencies. After rigorous reliability and validity assessments, the final AI literacy scale comprises six dimensions: AI features, AI processing, algorithm influences, user efficacy, ethical consideration, and threat appraisal. Detailed scale development, validation, and dimension-specific items are thoroughly explained. This comprehensive scale equips individuals with the competencies needed to navigate and critically engage with AI in today’s multifaceted AI landscape.
{"title":"Charting Competence: A Holistic Scale for Measuring Proficiency in Artificial Intelligence Literacy","authors":"Chien Wen (Tina) Yuan, Hsin-yi Sandy Tsai, Yu-Ting Chen","doi":"10.1177/07356331241261206","DOIUrl":"https://doi.org/10.1177/07356331241261206","url":null,"abstract":"The rapid evolution of AI technologies has reshaped our daily lives. As AI systems become increasingly prevalent, AI literacy, the ability to comprehend and engage with these technologies, becomes paramount in modern society. However, existing research has yet to establish a comprehensive framework for AI literacy. This study aims to fill this gap by developing a holistic AI literacy scale. Three levels of dimensions are considered: individual, interactive, and sociocultural. The scale includes cognitive, behavioral, and normative competencies. After rigorous reliability and validity assessments, the final AI literacy scale comprises six dimensions: AI features, AI processing, algorithm influences, user efficacy, ethical consideration, and threat appraisal. Detailed scale development, validation, and dimension-specific items are thoroughly explained. This comprehensive scale equips individuals with the competencies needed to navigate and critically engage with AI in today’s multifaceted AI landscape.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"81 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141738725","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 : 2024-05-30DOI: 10.1177/07356331241249225
Ika Qutsiati Utami, Wu-Yuin Hwang, Uun Hariyanti
Recently, automatic question generation (AQG) has been researched extensively for educational purposes. Existing approaches generally lack relevant information on the authentic context and problem diversity with various difficulty levels, so we proposed a new AQG system for generating contextualized and personalized mathematic word problems (MWP) in authentic contexts using the Generative Pre-trained Transformers (GPT). Our proposed system comprises (1) authentic contextual information acquisition through image recognition by TensorFlow and augmented reality (AR) measurement by AR Core, (2) a personalized mechanism based on instructional prompts to generate three different difficulty levels for learners’ different needs, and (3) MWP generation through GPT with authentic contextual information and personalized needs. We conducted a quasi-experiment with the participation of 52 students to evaluate the effectiveness of the proposed system on geometry learning performance. Further, the learning behaviors were analyzed in the aspects of authentic context, mathematics, and reflective behavior. The findings showed better results in geometry learning performances from students who learned with contextualized and personalized MWPs than those who were taught without contextualization and personalization on MWPs. Moreover, it was found that student’s ability to comprehend the practical situation or scenario presented in a problem (problem context understanding) and students’ ability to recognize relevant information from the problem context (identifying contextual information) significantly improved their learning performance. Moreover, students’ ability to apply math concepts and solve medium-level MWP also contributes to the improvement of learning performance. Meanwhile, learners showed positive perceptions toward the proposed system in facilitating geometry learning. Therefore, it is useful to promote an authentic context setting for mathematical problem-solving.
{"title":"Contextualized and Personalized Math Word Problem Generation in Authentic Contexts Using Generative Pre-trained Transformer and Its Influences on Geometry Learning","authors":"Ika Qutsiati Utami, Wu-Yuin Hwang, Uun Hariyanti","doi":"10.1177/07356331241249225","DOIUrl":"https://doi.org/10.1177/07356331241249225","url":null,"abstract":"Recently, automatic question generation (AQG) has been researched extensively for educational purposes. Existing approaches generally lack relevant information on the authentic context and problem diversity with various difficulty levels, so we proposed a new AQG system for generating contextualized and personalized mathematic word problems (MWP) in authentic contexts using the Generative Pre-trained Transformers (GPT). Our proposed system comprises (1) authentic contextual information acquisition through image recognition by TensorFlow and augmented reality (AR) measurement by AR Core, (2) a personalized mechanism based on instructional prompts to generate three different difficulty levels for learners’ different needs, and (3) MWP generation through GPT with authentic contextual information and personalized needs. We conducted a quasi-experiment with the participation of 52 students to evaluate the effectiveness of the proposed system on geometry learning performance. Further, the learning behaviors were analyzed in the aspects of authentic context, mathematics, and reflective behavior. The findings showed better results in geometry learning performances from students who learned with contextualized and personalized MWPs than those who were taught without contextualization and personalization on MWPs. Moreover, it was found that student’s ability to comprehend the practical situation or scenario presented in a problem (problem context understanding) and students’ ability to recognize relevant information from the problem context (identifying contextual information) significantly improved their learning performance. Moreover, students’ ability to apply math concepts and solve medium-level MWP also contributes to the improvement of learning performance. Meanwhile, learners showed positive perceptions toward the proposed system in facilitating geometry learning. Therefore, it is useful to promote an authentic context setting for mathematical problem-solving.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"49 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188018","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 : 2024-05-30DOI: 10.1177/07356331241248686
Xiaojing Weng, Huiyan Ye, Yun Dai, Oi-lam Ng
A growing body of research is focusing on integrating artificial intelligence (AI) and computational thinking (CT) to enhance student learning outcomes. Many researchers have designed instructional activities to achieve various learning goals within this field. Despite the prevalence of studies focusing on instructional design and student learning outcomes, how instructional efforts result in the integration of AI and CT in students’ learning processes remains unclear. To address this research gap, we conducted a systematic literature review of empirical studies that have integrated AI and CT for student development. We collected 18 papers from four prominent research databases in the fields of education and AI technology: Web of Science, Scopus, IEEE, and ACM. We coded the collected studies, highlighting the students’ learning processes in terms of research methodology and context, learning tools and instructional design, student learning outcomes, and the interaction between AI and CT. The integration of AI and CT occurs in two ways: the integration of disciplinary knowledge and leveraging AI tools to learn CT. Specifically, we discovered that AI- and CT-related tools, projects, and problems facilitated student-centered instructional designs, resulting in productive AI and CT learning outcomes.
越来越多的研究集中于整合人工智能（AI）和计算思维（CT），以提高学生的学习成果。许多研究人员设计了教学活动，以实现这一领域的各种学习目标。尽管关注教学设计和学生学习成果的研究十分普遍，但教学工作如何在学生的学习过程中实现人工智能和计算思维的整合仍不清楚。为了弥补这一研究空白，我们对将人工智能和计算机辅助教学结合起来促进学生发展的实证研究进行了系统的文献综述。我们从教育和人工智能技术领域的四个著名研究数据库中收集了 18 篇论文：Web of Science、Scopus、IEEE 和 ACM。我们对收集到的研究进行了编码，从研究方法和背景、学习工具和教学设计、学生学习成果以及人工智能和计算机辅助学习之间的互动等方面突出了学生的学习过程。人工智能与计算机辅助教学的融合体现在两个方面：学科知识的融合和利用人工智能工具学习计算机辅助教学。具体而言，我们发现人工智能和计算机辅助学习相关的工具、项目和问题促进了以学生为中心的教学设计，从而产生了富有成效的人工智能和计算机辅助学习成果。
{"title":"Integrating Artificial Intelligence and Computational Thinking in Educational Contexts: A Systematic Review of Instructional Design and Student Learning Outcomes","authors":"Xiaojing Weng, Huiyan Ye, Yun Dai, Oi-lam Ng","doi":"10.1177/07356331241248686","DOIUrl":"https://doi.org/10.1177/07356331241248686","url":null,"abstract":"A growing body of research is focusing on integrating artificial intelligence (AI) and computational thinking (CT) to enhance student learning outcomes. Many researchers have designed instructional activities to achieve various learning goals within this field. Despite the prevalence of studies focusing on instructional design and student learning outcomes, how instructional efforts result in the integration of AI and CT in students’ learning processes remains unclear. To address this research gap, we conducted a systematic literature review of empirical studies that have integrated AI and CT for student development. We collected 18 papers from four prominent research databases in the fields of education and AI technology: Web of Science, Scopus, IEEE, and ACM. We coded the collected studies, highlighting the students’ learning processes in terms of research methodology and context, learning tools and instructional design, student learning outcomes, and the interaction between AI and CT. The integration of AI and CT occurs in two ways: the integration of disciplinary knowledge and leveraging AI tools to learn CT. Specifically, we discovered that AI- and CT-related tools, projects, and problems facilitated student-centered instructional designs, resulting in productive AI and CT learning outcomes.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"16 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188178","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 : 2024-05-06DOI: 10.1177/07356331241249956
Stella Xin Yin, Dion Hoe-Lian Goh, Choon Lang Quek
In the past decade, Computational Thinking (CT) education has received growing attention from researchers. Although many reviews have provided synthesized information on CT teaching and learning, few have paid particular attention to collaborative learning (CL) strategies. CL has been widely implemented in CT classes and has become the most popular pedagogy among educators. Therefore, a systematic review of CL in CT classes would provide practical guidance on teaching strategies to enhance CT interventions and improve the quality of teaching and learning, ultimately benefiting students’ CT skills development. To address this gap, this study examined 43 empirical studies that have applied CL strategies, ranging from 2006 to 2022. Several findings were revealed in the analysis. First, a wide range of theories and frameworks were applied to inform research questions, pedagogical design, and research methodologies. Second, despite the acknowledged importance of group composition in effective CL, a large number of studies did not provide details on how the students were grouped. Third, six types of CL activities and instructional designs have been identified in CT classrooms. The synthesized information provides valuable insights that can inform future research directions and guide the design and implementation of CL activities in future CT classes.
{"title":"Collaborative Learning in K-12 Computational Thinking Education: A Systematic Review","authors":"Stella Xin Yin, Dion Hoe-Lian Goh, Choon Lang Quek","doi":"10.1177/07356331241249956","DOIUrl":"https://doi.org/10.1177/07356331241249956","url":null,"abstract":"In the past decade, Computational Thinking (CT) education has received growing attention from researchers. Although many reviews have provided synthesized information on CT teaching and learning, few have paid particular attention to collaborative learning (CL) strategies. CL has been widely implemented in CT classes and has become the most popular pedagogy among educators. Therefore, a systematic review of CL in CT classes would provide practical guidance on teaching strategies to enhance CT interventions and improve the quality of teaching and learning, ultimately benefiting students’ CT skills development. To address this gap, this study examined 43 empirical studies that have applied CL strategies, ranging from 2006 to 2022. Several findings were revealed in the analysis. First, a wide range of theories and frameworks were applied to inform research questions, pedagogical design, and research methodologies. Second, despite the acknowledged importance of group composition in effective CL, a large number of studies did not provide details on how the students were grouped. Third, six types of CL activities and instructional designs have been identified in CT classrooms. The synthesized information provides valuable insights that can inform future research directions and guide the design and implementation of CL activities in future CT classes.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"1 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140888771","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 : 2024-04-30DOI: 10.1177/07356331241251397
Qing Guo, Huan Li, Sha Zhu
Previous research has not adequately explored students’ behavioral processes when addressing computational thinking (CT) problems of varying difficulty, limiting insights into students’ detailed CT development characteristics. This study seeks to fill this gap by employing gamified CT items across multiple difficulty levels to calculate comprehensive behavioral sequence quality indicators. And then, through latent profile analysis, we identified four distinct latent classes of behavioral process. We then examined the in-game performance differences among these classes, uncovering each class’s unique attributes. Class 1 students consistently demonstrated high-quality, efficient behavioral sequences regardless of item difficulty. In contrast, class 2 students applied significant cognitive effort and trial-and-error strategies, achieving acceptable scores despite low behavioral sequence quality. Class 3 students excelled in simpler items but faltered with more complex ones. Class 4 students displayed low motivation for challenging items, often guessing answers quickly. Additionally, we investigated the predictive value of students’ performance in gamified items and their behavioral process classes for their external CT test scores. The study finally elaborated on the theoretical implications for researchers and the practical suggestions for teachers in CT cultivation.
{"title":"Understanding the Characteristics of Students’ Behavioral Processes in Solving Computational Thinking Problems Based on the Behavioral Sequences","authors":"Qing Guo, Huan Li, Sha Zhu","doi":"10.1177/07356331241251397","DOIUrl":"https://doi.org/10.1177/07356331241251397","url":null,"abstract":"Previous research has not adequately explored students’ behavioral processes when addressing computational thinking (CT) problems of varying difficulty, limiting insights into students’ detailed CT development characteristics. This study seeks to fill this gap by employing gamified CT items across multiple difficulty levels to calculate comprehensive behavioral sequence quality indicators. And then, through latent profile analysis, we identified four distinct latent classes of behavioral process. We then examined the in-game performance differences among these classes, uncovering each class’s unique attributes. Class 1 students consistently demonstrated high-quality, efficient behavioral sequences regardless of item difficulty. In contrast, class 2 students applied significant cognitive effort and trial-and-error strategies, achieving acceptable scores despite low behavioral sequence quality. Class 3 students excelled in simpler items but faltered with more complex ones. Class 4 students displayed low motivation for challenging items, often guessing answers quickly. Additionally, we investigated the predictive value of students’ performance in gamified items and their behavioral process classes for their external CT test scores. The study finally elaborated on the theoretical implications for researchers and the practical suggestions for teachers in CT cultivation.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"21 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140838569","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 : 2024-04-17DOI: 10.1177/07356331241247465
Azzeddine Boudouaia, Samia Mouas, Bochra Kouider
The field of computer-assisted language learning has recently brought about a notable change in English as a Foreign Language (EFL) writing. Starting from October 2022, students across different academic fields have increasingly depended on ChatGPT-4 as a helpful resource for addressing particular challenges in EFL writing. This study aimed to investigate the use and acceptance of ChatGPT-4 in students’ EFL writing. To this end, an experiment was conducted with 76 undergraduate students from a private school in Algeria. The participants were randomly allocated into two groups: experimental group (n = 37) and control group (n = 39). Additionally, a questionnaire was administered. The results showed that the experimental group (EG) outperformed the control group (CG). Besides, the findings revealed that students in the EG in post-test outperformed their pre-test scores. The findings also revealed substantial improvements in the EG’s views of perceived usefulness, perceived ease of use, attitudes, and behavioral intention. According to the results, ChatGPT-4 helped boost students' EFL writing skills, which ultimately led to their acceptance. Students appear particularly interested in ChatGPT-4 because of its potential usefulness in putting what they learn about EFL writing into practice. Some suggestions and recommendations were provided.
{"title":"A Study on ChatGPT-4 as an Innovative Approach to Enhancing English as a Foreign Language Writing Learning","authors":"Azzeddine Boudouaia, Samia Mouas, Bochra Kouider","doi":"10.1177/07356331241247465","DOIUrl":"https://doi.org/10.1177/07356331241247465","url":null,"abstract":"The field of computer-assisted language learning has recently brought about a notable change in English as a Foreign Language (EFL) writing. Starting from October 2022, students across different academic fields have increasingly depended on ChatGPT-4 as a helpful resource for addressing particular challenges in EFL writing. This study aimed to investigate the use and acceptance of ChatGPT-4 in students’ EFL writing. To this end, an experiment was conducted with 76 undergraduate students from a private school in Algeria. The participants were randomly allocated into two groups: experimental group (n = 37) and control group (n = 39). Additionally, a questionnaire was administered. The results showed that the experimental group (EG) outperformed the control group (CG). Besides, the findings revealed that students in the EG in post-test outperformed their pre-test scores. The findings also revealed substantial improvements in the EG’s views of perceived usefulness, perceived ease of use, attitudes, and behavioral intention. According to the results, ChatGPT-4 helped boost students' EFL writing skills, which ultimately led to their acceptance. Students appear particularly interested in ChatGPT-4 because of its potential usefulness in putting what they learn about EFL writing into practice. Some suggestions and recommendations were provided.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"2 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140609582","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 : 2024-04-15DOI: 10.1177/07356331241247592
Zhen Wang, Xinrui Pei, Hejie Zhu, Shaoying Gong, Enguo Wang
This research aims to expand our understanding of how to facilitate student feedback engagement processes in a computer-based formative assessment environment. In the present research, we designed a new type of elaborated feedback in terms of combining the correct solution and the erroneous solution, and the erroneous solution matched the student’s initial answer. Furthermore, we analyzed whether this feedback had a stronger positive effect than the other three types of feedback containing different complexities of correct information (i.e., Knowledge of Correct Response, Problem-Solving Cues, or Complete Correct Solutions). As predicted, students who received correct and erroneous solutions experienced more positive feedback perceptions, perceived lower extraneous cognitive load and higher germane cognitive load, and achieved higher transfer performance. This research is one of the first that provides empirical evidence for the positive impact of incorporating students’ errors into the feedback design, and this novel insight can extend current theories on how to optimize feedback design to promote students’ active processing and use of feedback.
{"title":"How to Make Computer-Based Feedback More Productive: The Power of Erroneous Solutions","authors":"Zhen Wang, Xinrui Pei, Hejie Zhu, Shaoying Gong, Enguo Wang","doi":"10.1177/07356331241247592","DOIUrl":"https://doi.org/10.1177/07356331241247592","url":null,"abstract":"This research aims to expand our understanding of how to facilitate student feedback engagement processes in a computer-based formative assessment environment. In the present research, we designed a new type of elaborated feedback in terms of combining the correct solution and the erroneous solution, and the erroneous solution matched the student’s initial answer. Furthermore, we analyzed whether this feedback had a stronger positive effect than the other three types of feedback containing different complexities of correct information (i.e., Knowledge of Correct Response, Problem-Solving Cues, or Complete Correct Solutions). As predicted, students who received correct and erroneous solutions experienced more positive feedback perceptions, perceived lower extraneous cognitive load and higher germane cognitive load, and achieved higher transfer performance. This research is one of the first that provides empirical evidence for the positive impact of incorporating students’ errors into the feedback design, and this novel insight can extend current theories on how to optimize feedback design to promote students’ active processing and use of feedback.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"55 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140602949","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 : 2024-04-04DOI: 10.1177/07356331241240670
Linlin Hu
This study employs meta-analysis to synthesize findings from 30 articles investigating gender differences in computational thinking (CT) among K-12 students. Encompassing 51 independent effect sizes, the meta-analysis involves a participant pool of 9181 individuals from various countries, comprising 4254 males and 4927 females. Results indicate statistically significant gender differences in CT (Hedges’s g = 0.091, p < .05), albeit with a modest effect size, revealing higher CT scores among males compared to females. Further moderation analyses unveil the multifaceted nature of these gender differences. Specifically, while gender differences become significant during high school, recent studies suggest a gradual reduction in CT gender differences with societal progress among K-12 students. Moreover, findings illustrate variations in gender differences across geographical regions. Notably, while the overall gender disparity in CT is non-significant in the “East Asia and Pacific” region, it widens in “North America” and “Europe”, with males scoring higher than females. Conversely, in “Europe and Central Asia”, such gender differences present inconsistent outcomes, with females scoring higher than males. Importantly, assessment tool type does not significantly influence gender differences. Lastly, this study offers recommendations to address CT gender gaps, providing valuable insights for promoting gender equality in education.
{"title":"Exploring Gender Differences in Computational Thinking Among K-12 Students: A Meta-Analysis Investigating Influential Factors","authors":"Linlin Hu","doi":"10.1177/07356331241240670","DOIUrl":"https://doi.org/10.1177/07356331241240670","url":null,"abstract":"This study employs meta-analysis to synthesize findings from 30 articles investigating gender differences in computational thinking (CT) among K-12 students. Encompassing 51 independent effect sizes, the meta-analysis involves a participant pool of 9181 individuals from various countries, comprising 4254 males and 4927 females. Results indicate statistically significant gender differences in CT (Hedges’s g = 0.091, p < .05), albeit with a modest effect size, revealing higher CT scores among males compared to females. Further moderation analyses unveil the multifaceted nature of these gender differences. Specifically, while gender differences become significant during high school, recent studies suggest a gradual reduction in CT gender differences with societal progress among K-12 students. Moreover, findings illustrate variations in gender differences across geographical regions. Notably, while the overall gender disparity in CT is non-significant in the “East Asia and Pacific” region, it widens in “North America” and “Europe”, with males scoring higher than females. Conversely, in “Europe and Central Asia”, such gender differences present inconsistent outcomes, with females scoring higher than males. Importantly, assessment tool type does not significantly influence gender differences. Lastly, this study offers recommendations to address CT gender gaps, providing valuable insights for promoting gender equality in education.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"438 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140602906","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 : 2024-04-04DOI: 10.1177/07356331241240460
Jiangyue Liu, Siran Li
Pair Programming is considered an effective approach to programming education, but the synchronous collaboration of two programmers involves complex coordination, making this method difficult to be widely adopted in educational settings. Artificial Intelligence (AI) code-generation tools have outstanding capabilities in program generation and natural language understanding, creating conducive conditions for pairing with humans in programming. Now some more mature tools are gradually being implemented. This review summarizes the current status of educational applications and research on AI-assisted programming technology. Through thematic coding of literature, existing research focuses on five aspects: underlying technology and tool introduction, performance evaluation, the potential impacts and coping strategies, exploration of behavioral patterns in technological application, and ethical and safety issues. A systematic analysis of current literature provides the following insights for future academic research related to the practice of “human-machine pairing” in programming: (1) Affirming the value of AI code-generation tools while also clearly defining their technical limitations and ethical risks; (2) Developing adaptive teaching ecosystems and educational models, conducting comprehensive empirical research to explore the efficiency mechanisms of AI-human paired programming; (3) Further enriching the application of research methods by integrating speculative research with empirical research, combining traditional methods with emerging technologies.
{"title":"Toward Artificial Intelligence-Human Paired Programming: A Review of the Educational Applications and Research on Artificial Intelligence Code-Generation Tools","authors":"Jiangyue Liu, Siran Li","doi":"10.1177/07356331241240460","DOIUrl":"https://doi.org/10.1177/07356331241240460","url":null,"abstract":"Pair Programming is considered an effective approach to programming education, but the synchronous collaboration of two programmers involves complex coordination, making this method difficult to be widely adopted in educational settings. Artificial Intelligence (AI) code-generation tools have outstanding capabilities in program generation and natural language understanding, creating conducive conditions for pairing with humans in programming. Now some more mature tools are gradually being implemented. This review summarizes the current status of educational applications and research on AI-assisted programming technology. Through thematic coding of literature, existing research focuses on five aspects: underlying technology and tool introduction, performance evaluation, the potential impacts and coping strategies, exploration of behavioral patterns in technological application, and ethical and safety issues. A systematic analysis of current literature provides the following insights for future academic research related to the practice of “human-machine pairing” in programming: (1) Affirming the value of AI code-generation tools while also clearly defining their technical limitations and ethical risks; (2) Developing adaptive teaching ecosystems and educational models, conducting comprehensive empirical research to explore the efficiency mechanisms of AI-human paired programming; (3) Further enriching the application of research methods by integrating speculative research with empirical research, combining traditional methods with emerging technologies.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"24 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573796","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}
Argumentative writing is a fundamental aspect of undergraduate students’ academic and scientific writing related to critical thinking and problem-solving skills. However, previous studies have shown that students face various difficulties with argumentative writing, such as unclear and illogical ideas, less-structured arguments, and unbalanced interpretation of issues, data, and evidence. This study aims to improve the argumentative writing skills of undergraduate students by integrating computer-supported argumentative writing tools, such as annotation, conversational agents (CAs), and collaborative concept maps, into an online learning management system. Since the study was conducted during the COVID-19 pandemic, these tools can support meaningful learning activities and investigation in argumentative writing. The researchers divided sixty participants into the experimental group ( N = 30) and the control group ( N = 30). The results showed that the experimental group’s writing achievements outperformed the control group, and the three tools effectively improved the five elements of argumentative writing, including claims, grounds, warrants, backings, and rebuttal. Furthermore, a deep analysis found that the number of annotations, valid CAs’ responses, and argument nodes on collaborative concept maps can significantly predict students’ argumentative writing development. Moreover, students perceived that the incorporated tools could effectively improve their argumentative writing skills.
{"title":"Crafting Compelling Argumentative Writing for Undergraduates: Exploring the Nexus of Digital Annotations, Conversational Agents, and Collaborative Concept Maps","authors":"Randi Proska Sandra, Wu-Yuin Hwang, Afifah Zafirah, Uun Hariyanti, Engkizar Engkizar, Ahmaddul Hadi, Ahmad Fauzan","doi":"10.1177/07356331241242437","DOIUrl":"https://doi.org/10.1177/07356331241242437","url":null,"abstract":"Argumentative writing is a fundamental aspect of undergraduate students’ academic and scientific writing related to critical thinking and problem-solving skills. However, previous studies have shown that students face various difficulties with argumentative writing, such as unclear and illogical ideas, less-structured arguments, and unbalanced interpretation of issues, data, and evidence. This study aims to improve the argumentative writing skills of undergraduate students by integrating computer-supported argumentative writing tools, such as annotation, conversational agents (CAs), and collaborative concept maps, into an online learning management system. Since the study was conducted during the COVID-19 pandemic, these tools can support meaningful learning activities and investigation in argumentative writing. The researchers divided sixty participants into the experimental group ( N = 30) and the control group ( N = 30). The results showed that the experimental group’s writing achievements outperformed the control group, and the three tools effectively improved the five elements of argumentative writing, including claims, grounds, warrants, backings, and rebuttal. Furthermore, a deep analysis found that the number of annotations, valid CAs’ responses, and argument nodes on collaborative concept maps can significantly predict students’ argumentative writing development. Moreover, students perceived that the incorporated tools could effectively improve their argumentative writing skills.","PeriodicalId":47865,"journal":{"name":"Journal of Educational Computing Research","volume":"3 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140573508","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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