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Charting Competence: A Holistic Scale for Measuring Proficiency in Artificial Intelligence Literacy 能力图表:衡量人工智能素养能力的整体量表
IF 4.8 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2024-07-18 DOI: 10.1177/07356331241261206
Chien Wen (Tina) Yuan, Hsin-yi Sandy Tsai, Yu-Ting Chen
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.
人工智能技术的快速发展重塑了我们的日常生活。随着人工智能系统的日益普及,人工智能素养,即理解和使用这些技术的能力,在现代社会中变得至关重要。然而,现有的研究还没有为人工智能素养建立一个全面的框架。本研究旨在通过制定全面的人工智能素养量表来填补这一空白。研究考虑了三个层面:个人、互动和社会文化。量表包括认知、行为和规范能力。经过严格的信度和效度评估,最终的人工智能素养量表包括六个维度:人工智能特征、人工智能处理、算法影响、用户效能、道德考量和威胁评估。详细的量表开发、验证和特定维度项目都有详尽的说明。在当今多层面的人工智能环境中,这一全面的量表使个人具备了驾驭人工智能并批判性地参与其中所需的能力。
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引用次数: 0
Contextualized and Personalized Math Word Problem Generation in Authentic Contexts Using Generative Pre-trained Transformer and Its Influences on Geometry Learning 使用生成式预训练变换器在真实情境中生成情境化和个性化数学单词问题及其对几何学习的影响
IF 4.8 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2024-05-30 DOI: 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.
最近,出于教育目的对自动问题生成(AQG)进行了广泛研究。因此,我们提出了一种新的自动问题生成系统,利用生成预训练变换器(GPT)在真实语境中生成语境化和个性化的数学文字问题(MWP)。我们提出的系统包括:(1)通过 TensorFlow 的图像识别和 AR Core 的增强现实(AR)测量获取真实的情境信息;(2)基于教学提示的个性化机制,针对学习者的不同需求生成三种不同难度的问题;(3)通过 GPT 生成具有真实情境信息和个性化需求的 MWP。我们进行了一项有 52 名学生参与的准实验,以评估所提出的系统对几何学习成绩的影响。此外,我们还从真实情境、数学和反思行为等方面分析了学生的学习行为。研究结果表明,使用情境化和个性化的多工平台学习几何的学生,其几何学习成绩优于未使用情境化和个性化多工平台的学生。此外,研究还发现,学生理解问题中呈现的实际情况或情景的能力(问题情境理解)和学生从问题情境中识别相关信息的能力(识别情境信息)显著提高了他们的学习成绩。此外,学生应用数学概念和解决中等水平数学问题的能力也有助于提高学习成绩。同时,学习者对拟议系统在促进几何学习方面表现出积极的看法。因此,促进数学问题解决的真实情境设置是有益的。
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引用次数: 0
Integrating Artificial Intelligence and Computational Thinking in Educational Contexts: A Systematic Review of Instructional Design and Student Learning Outcomes 在教育环境中整合人工智能和计算思维:教学设计与学生学习成果的系统回顾
IF 4.8 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2024-05-30 DOI: 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。
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引用次数: 0
Collaborative Learning in K-12 Computational Thinking Education: A Systematic Review K-12 计算思维教育中的协作学习:系统回顾
IF 4.8 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2024-05-06 DOI: 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.
在过去十年中,计算思维(CT)教育越来越受到研究人员的关注。尽管许多综述提供了有关计算思维教学的综合信息,但很少有人特别关注协作学习(CL)策略。协作学习(CL)已在 CT 课程中广泛实施,并已成为最受教育工作者欢迎的教学法。因此,对 CT 课堂中的协作学习进行系统回顾,将为加强 CT 干预、提高教学质量的教学策略提供实用指导,最终有利于学生 CT 技能的发展。为了弥补这一不足,本研究考察了从 2006 年到 2022 年期间 43 项应用语言教学策略的实证研究。分析中发现了几个问题。首先,研究人员在提出研究问题、进行教学设计和采用研究方法时应用了多种理论和框架。其次,尽管小组构成在有效的学习活动中的重要性已得到公认,但大量研究并未提供学生如何分组的详细信息。第三,在 CT 课堂中发现了六种 CL 活动和教学设计。这些综合信息提供了有价值的见解,可以为未来的研究方向提供信息,并指导未来 CT 课堂中 CL 活动的设计和实施。
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引用次数: 0
Understanding the Characteristics of Students’ Behavioral Processes in Solving Computational Thinking Problems Based on the Behavioral Sequences 基于行为序列了解学生解决计算思维问题的行为过程特征
IF 4.8 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2024-04-30 DOI: 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.
以往的研究没有充分探讨学生在解决不同难度的计算思维(CT)问题时的行为过程,从而限制了对学生详细的计算思维发展特征的了解。本研究试图填补这一空白,采用游戏化的多难度 CT 项目来计算综合行为序列质量指标。然后,通过潜在特征分析,我们确定了行为过程的四个不同的潜在类别。然后,我们研究了这些类别在游戏中的表现差异,发现了每个类别的独特属性。无论项目难度如何,第一类学生始终表现出高质量、高效率的行为序列。与此相反,二班学生运用了大量的认知努力和试错策略,尽管行为序列质量较低,但仍取得了可接受的分数。三班学生在较简单的题目中表现出色,但在较复杂的题目中却乏善可陈。四班学生对高难度题目的积极性不高,往往很快就能猜出答案。此外,我们还研究了学生在游戏化项目中的表现及其行为过程班级对其外部 CT 测试成绩的预测价值。研究最后阐述了研究人员的理论意义和教师在 CT 培养方面的实践建议。
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引用次数: 0
A Study on ChatGPT-4 as an Innovative Approach to Enhancing English as a Foreign Language Writing Learning 将 ChatGPT-4 作为加强英语写作学习的创新方法的研究
IF 4.8 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2024-04-17 DOI: 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.
计算机辅助语言学习领域最近给英语作为外语(EFL)的写作带来了显著的变化。从2022年10月开始,不同学术领域的学生越来越依赖ChatGPT-4,将其作为应对EFL写作中特殊挑战的有用资源。本研究旨在调查 ChatGPT-4 在学生 EFL 写作中的使用和接受情况。为此,研究人员对阿尔及利亚一所私立学校的 76 名本科生进行了实验。参与者被随机分为两组:实验组(37 人)和对照组(39 人)。此外,还进行了问卷调查。结果显示,实验组(EG)的成绩优于对照组(CG)。此外,研究结果还显示,实验组学生的后测成绩优于对照组学生的前测成绩。研究结果还显示,实验组学生在感知有用性、感知易用性、态度和行为意向等方面都有显著改善。结果表明,ChatGPT-4 有助于提高学生的 EFL 写作技能,并最终被学生所接受。学生们似乎对 ChatGPT-4 特别感兴趣,因为它可以帮助他们将所学的 EFL 写作知识付诸实践。会议还提出了一些意见和建议。
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引用次数: 0
How to Make Computer-Based Feedback More Productive: The Power of Erroneous Solutions 如何让基于计算机的反馈更有成效?错误解决方案的力量
IF 4.8 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2024-04-15 DOI: 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.
本研究旨在拓展我们对如何在基于计算机的形成性评价环境中促进学生参与反馈过程的理解。在本研究中,我们设计了一种新的详细反馈类型,即正确答案和错误答案相结合,且错误答案与学生的初始答案相匹配。此外,我们还分析了这种反馈是否比其他三种包含不同复杂程度正确信息的反馈(即正确答案知识、解题线索或完整正确答案)具有更强的积极效果。正如预测的那样,获得正确和错误解决方案的学生会体验到更积极的反馈感知,感知到更低的外在认知负荷和更高的内在认知负荷,并取得更高的迁移成绩。这项研究首次提供了将学生的错误纳入反馈设计的积极影响的实证证据,这种新颖的见解可以扩展当前的理论,即如何优化反馈设计以促进学生积极处理和使用反馈。
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引用次数: 0
Exploring Gender Differences in Computational Thinking Among K-12 Students: A Meta-Analysis Investigating Influential Factors 探索 K-12 学生计算思维中的性别差异:调查影响因素的元分析
IF 4.8 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2024-04-04 DOI: 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.
本研究采用荟萃分析法,综合了 30 篇研究 K-12 学生计算思维(CT)性别差异的文章的研究结果。荟萃分析包括 51 个独立效应大小,涉及来自不同国家的 9181 名参与者,其中男性 4254 人,女性 4927 人。结果表明,尽管效应大小适中,但在 CT 方面存在明显的性别差异(Hedges's g = 0.091, p <.05),男性的 CT 分数高于女性。进一步的调节分析揭示了这些性别差异的多面性。具体来说,虽然性别差异在高中阶段变得显著,但最近的研究表明,随着社会的进步,K-12 学生的 CT 性别差异会逐渐缩小。此外,研究结果还显示了不同地理区域的性别差异。值得注意的是,虽然在 "东亚和太平洋 "地区,CT 的总体性别差异并不显著,但在 "北美 "和 "欧洲 "地区,性别差异有所扩大,男性得分高于女性。相反,在 "欧洲和中亚",这种性别差异呈现出不一致的结果,女性得分高于男性。重要的是,评估工具的类型对性别差异没有显著影响。最后,本研究提出了解决 CT 性别差距的建议,为促进教育领域的性别平等提供了宝贵的见解。
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引用次数: 0
Toward Artificial Intelligence-Human Paired Programming: A Review of the Educational Applications and Research on Artificial Intelligence Code-Generation Tools 迈向人工智能-人类配对编程:人工智能代码生成工具的教育应用与研究综述
IF 4.8 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2024-04-04 DOI: 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.
结对编程被认为是一种有效的编程教育方法,但两名程序员的同步协作涉及复杂的协调工作,因此这种方法难以在教育环境中广泛采用。人工智能(AI)代码生成工具在程序生成和自然语言理解方面能力突出,为与人类结对编程创造了有利条件。目前,一些较为成熟的工具正在逐步实现。本综述总结了人工智能辅助编程技术的教育应用和研究现状。通过对文献进行主题编码,现有研究主要集中在五个方面:底层技术和工具介绍、性能评估、潜在影响和应对策略、技术应用中的行为模式探索以及伦理和安全问题。通过对现有文献的系统分析,为未来编程 "人机配对 "实践的相关学术研究提供了以下启示:(1)在肯定人工智能代码生成工具价值的同时,明确其技术局限性和伦理风险;(2)开发适应性教学生态系统和教育模式,开展综合实证研究,探索人工智能与人机配对编程的效率机制;(3)进一步丰富研究方法的应用,将推测研究与实证研究相结合,将传统方法与新兴技术相结合。
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引用次数: 0
Crafting Compelling Argumentative Writing for Undergraduates: Exploring the Nexus of Digital Annotations, Conversational Agents, and Collaborative Concept Maps 为本科生创作令人信服的论证性写作:探索数字注释、对话代理和协作概念图之间的联系
IF 4.8 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Pub Date : 2024-04-03 DOI: 10.1177/07356331241242437
Randi Proska Sandra, Wu-Yuin Hwang, Afifah Zafirah, Uun Hariyanti, Engkizar Engkizar, Ahmaddul Hadi, Ahmad Fauzan
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.
论证性写作是本科生学术和科学写作中与批判性思维和解决问题能力相关的一个基本方面。然而,以往的研究表明,学生在议论文写作中面临着各种困难,如观点不明确、不合逻辑,论证结构不严谨,对问题、数据和证据的解释不平衡等。本研究旨在通过将计算机支持的论证写作工具,如注释、会话代理(CA)和协作概念图,整合到在线学习管理系统中,提高本科生的论证写作能力。由于研究是在 COVID-19 大流行期间进行的,这些工具可以支持有意义的学习活动和论证写作调查。研究人员将 60 名参与者分为实验组(30 人)和对照组(30 人)。结果显示,实验组的写作成绩优于对照组,三种工具有效地改善了论证写作的五个要素,包括主张、理由、证明、支持和反驳。此外,深入分析发现,协作概念图上的注释数量、有效的CA回应和论证节点能显著预测学生的论证写作发展。此外,学生认为这些工具能有效提高他们的议论文写作能力。
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引用次数: 0
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Journal of Educational Computing Research
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