Computers and Education Open最新文献_第3页

Learning in hybrid times: Comparing student experiences in traditional and GenAI-supported instruction 混合时代的学习：比较学生在传统和人工智能支持教学中的经验

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2025.100313

Meital Amzalag , Dizza Beimel , Rina Zviel-Girshin

The emergence of generative artificial intelligence (GenAI) tools, such as ChatGPT, is reshaping the landscape of higher education by introducing new opportunities for learner autonomy, flexibility, and engagement. While extensive research has explored GenAI’s technical capabilities and ethical implications, limited attention has been paid to students’ subjective learning experiences with AI-supported instruction. This study investigates how undergraduate students perceive and experience two instructional modes in a database management course: traditional lecturer-led instruction and GenAI-supported self-regulated learning. Sixty-eight second-year engineering students participated in the study, providing qualitative insights through open-ended survey responses. The findings reveal key cognitive, emotional, and strategic differences between the two approaches: traditional instruction fostered structure, immediate feedback, and emotional reassurance, while GenAI-supported learning promoted autonomy and exploration but raised concerns regarding reliability and critical thinking. Importantly, students did not view these modes as mutually exclusive, but rather as complementary components of a broader “learning puzzle,” balancing the stability of face-to-face instruction with the adaptability of GenAI to support diverse learning needs. This study contributes to a deeper understanding of hybrid learning environments and underscores the importance of nuanced, learner-centered integration of AI technologies in higher education.

ChatGPT等生成式人工智能（GenAI）工具的出现，通过为学习者的自主性、灵活性和参与度带来新的机会，正在重塑高等教育的格局。虽然广泛的研究探索了GenAI的技术能力和伦理影响，但对学生在人工智能支持教学中的主观学习体验的关注有限。本研究调查了本科生如何感知和体验数据库管理课程的两种教学模式：传统的讲师主导教学和genai支持的自主学习。68名二年级工程专业的学生参与了这项研究，通过开放式的调查回答提供定性的见解。研究结果揭示了两种方法在认知、情感和策略上的关键差异：传统教学促进结构、即时反馈和情感安慰，而基因人工智能支持的学习促进自主和探索，但增加了对可靠性和批判性思维的担忧。重要的是，学生们并不认为这些模式是相互排斥的，而是将其视为更广泛的“学习难题”的互补组成部分，在面对面教学的稳定性和GenAI的适应性之间取得平衡，以支持不同的学习需求。这项研究有助于更深入地理解混合学习环境，并强调了在高等教育中细致入微、以学习者为中心的人工智能技术集成的重要性。

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引用次数: 0

AI literacy, educational level, and parenting self-efficacy of children’s education among parents of primary school students 小学生家长人工智能素养、受教育程度与子女教育的父母自我效能感

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2025.100318

Jiaqi Guo , Tammy Sheung-Ting Law , Shen Qiao , Susanna Siu-sze Yeung

Parenting self-efficacy, representing parents’ confidence in their capability to effectively handle parenting responsibilities and obstacles, is an important determinant of parenting quality. Parents’ educational level is known to be an important predictor of parenting self-efficacy. Nowadays, children are exposed to Artificial Intelligence (AI) from an early age, especially in their learning process. Parents’ AI literacy emerges as an important factor that could contribute to parenting self-efficacy of children’s education. However, no previous studies have investigated how parents’ AI literacy is related to parenting self-efficacy. The quantitative study examined the associations among parents’ AI literacy, educational level, and parenting self-efficacy of children’s education. Data were collected from 160 parents of primary school students in Hong Kong through online surveys. Results showed significant relationships among parents’ AI literacy, educational level, and parenting self-efficacy of children’s education. The association between parents’ educational level and parenting self-efficacy was partially mediated by the subfactors of AI literacy. The study extended the literature by investigating AI literacy from the parents’ side and highlighted the importance of parents’ AI literacy in parenting regarding children’s education. It had implications for practitioners and policymakers to develop intervention programs to help parents improve AI literacy.

育儿自我效能感是衡量育儿质量的一个重要决定因素，它代表父母对自己有效处理育儿责任和障碍的能力的信心。众所周知，父母的教育水平是父母自我效能感的重要预测指标。如今，孩子们从小就接触到人工智能（AI），尤其是在学习过程中。父母的人工智能素养成为影响子女教育父母自我效能感的重要因素。然而，之前没有研究调查过父母的人工智能素养与父母自我效能感之间的关系。定量研究考察了父母的人工智能素养、教育水平和子女教育的父母自我效能感之间的关系。通过网上调查，收集了160名香港小学生家长的数据。结果显示，父母的人工智能素养、受教育程度和子女教育的父母自我效能感之间存在显著的相关关系。父母受教育水平与父母自我效能感之间的关系被人工智能素养的子因素部分中介。该研究扩展了文献，从父母的角度调查了人工智能素养，并强调了父母的人工智能素养在养育子女教育中的重要性。这对从业者和政策制定者制定干预计划以帮助父母提高人工智能素养具有重要意义。

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引用次数: 0

Enhancing enthusiasm for STEM education with AI: Domain-specific chatbot as personalized learning assistant 用人工智能提高STEM教育的热情：特定领域的聊天机器人作为个性化学习助手

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2025.100315

Cilia Ricarda Rücker, Sebastian Becker-Genschow

Generative AI has been increasingly integrated into educational settings for its potential to facilitate personalized learning. However, evidence-based implementation concepts remain essential to realize its pedagogical value. Therefore, this study investigates the educational efficacy of ADA, a domain-specific AI chatbot customized for secondary mathematics education, in enhancing learning outcomes and student engagement. A cluster-randomized controlled study was conducted with 195 ninth-grade students from German secondary schools in authentic classroom settings. The objective of the study was to compare ADA's personalized support against conventional differentiation materials. The intervention targeted the Heron method for estimating square roots in a single-lesson intervention with a pre-post assessment. As one of the few cluster-randomized controlled studies on custom AI chatbots in secondary mathematics education, this investigation assessed both affective and cognitive aspects of learning simultaneously to provide a comprehensive research picture. The findings indicated a high degree of student acceptance across the dimensions of the Technology Acceptance Model. Additionally, situational interest significantly improved in the chatbot condition. The study found that trends in performance and emotional responses were favorable, while cognitive load increased slightly. The findings emphasize the evident potential of customized AI chatbots to supplement differentiated instruction in the domain of mathematics education. They provide a critical foundation for future research investigating the potential of AI chatbots to enhance student learning through careful integration and customization.

生成式人工智能因其促进个性化学习的潜力而越来越多地融入教育环境。然而，基于证据的实施概念仍然是实现其教学价值的必要条件。因此，本研究调查了ADA（一种为中学数学教育定制的特定领域的AI聊天机器人）在提高学习成果和学生参与度方面的教育功效。本研究以195名德国中学九年级学生为研究对象，在真实的课堂环境中进行整群随机对照研究。该研究的目的是比较ADA的个性化支持与传统分化材料。干预的目标是在单课干预中估计平方根的Heron方法，并进行前后评估。作为为数不多的针对中学数学教育中定制AI聊天机器人的集群随机对照研究之一，本研究同时评估了学习的情感和认知方面，以提供全面的研究图景。研究结果表明，在技术接受模型的各个维度上，学生的接受程度都很高。此外，在聊天机器人条件下，情境兴趣显著提高。研究发现，表现和情绪反应的趋势是有利的，而认知负荷略有增加。研究结果强调了定制人工智能聊天机器人在数学教育领域补充差异化教学方面的明显潜力。它们为未来研究人工智能聊天机器人的潜力提供了重要的基础，通过仔细整合和定制来增强学生的学习。

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引用次数: 0

Special Issue: Datafied by default: Examining the intersect between children's digital rights and education 特刊：默认数据化：审视儿童数字权利与教育之间的交集

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2024.100237

Tiffani Apps , Karley Beckman , Rebecca Ng

引用次数: 0

Does ChatGPT-enhanced collaborative learning foster critical thinking in education? A Bloom’s Taxonomy perspective chatgpt增强的协作学习在教育中培养批判性思维吗？Bloom的分类法透视图

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2025.100316

Abdhy Aulia Adnans , Yithro Serang , Ixora Javanisa Eunike , Andri Dayarana K. Silalahi

This study examines how ChatGPT-supported collaborative learning influences critical thinking in health education using Bloom’s Taxonomy. Purposive sampling was used to collect data from 665 Indonesian health students through an online survey. Partial Least Square – Structural Equation Modelling (PLS-SEM) assessed the direct effects of cognitive processes on critical thinking. Necessary Condition Analysis (NCA) identified essential cognitive conditions, while fuzzy sets qualitative comparative analysis (fsQCA) explored different cognitive pathways leading to high or low critical thinking. Collaborative learning significantly enhances understanding, applying, and remembering. Understanding has the strongest effect on critical thinking, while applying and remembering have moderate effects. These findings suggest that deep comprehension drives analytical reasoning, whereas applying and remembering serve complementary roles. NCA confirms that understanding and applying are necessary for fostering critical thinking, while remembering plays a supporting role. fsQCA results indicate that students who combine deep understanding with memory retention exhibit strong critical thinking. In contrast, students who rely solely on remembering without comprehension or application struggle to develop higher-order reasoning. This study reveals that ChatGPT does not inherently enhance critical thinking but must be integrated into structured collaborative learning. Effective AI-assisted education requires active discussion, application, and critical evaluation of AI-generated insights. These findings offer a framework for optimizing AI-driven health education to support both knowledge acquisition and analytical reasoning in clinical decision-making.

本研究使用Bloom分类法考察了chatgpt支持的协作学习如何影响健康教育中的批判性思维。采用有目的抽样方法，对665名印尼卫生专业学生进行在线调查。偏最小二乘-结构方程模型（PLS-SEM）评估了认知过程对批判性思维的直接影响。必要条件分析（NCA）确定了必要的认知条件，而模糊集定性比较分析（fsQCA）探索了导致高批判性思维和低批判性思维的不同认知途径。协作学习可以显著提高理解、应用和记忆能力。理解对批判性思维的影响最大，而运用和记忆的影响一般。这些发现表明，深度理解驱动分析推理，而应用和记忆则起到互补的作用。NCA证实理解和应用对于培养批判性思维是必要的，而记忆起着辅助作用。fsQCA结果表明，将深刻理解与记忆保持相结合的学生表现出较强的批判性思维。相比之下，仅仅依靠记忆而不理解或应用的学生很难发展高阶推理。这项研究表明，ChatGPT本身并不能增强批判性思维，但必须将其整合到结构化的协作学习中。有效的人工智能辅助教育需要对人工智能产生的见解进行积极的讨论、应用和批判性评估。这些发现为优化人工智能驱动的健康教育提供了一个框架，以支持临床决策中的知识获取和分析推理。

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引用次数: 0

Generative AI chatbots in higher education: Student experiences and perceived ethical challenges 高等教育中的生成式AI聊天机器人：学生体验和感知到的伦理挑战

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-12-01 DOI: 10.1016/j.caeo.2025.100311

Neda Hadinejad , Katarina Sperling , Cormac McGrath

The integration of generative artificial intelligence (GAI) chatbots in higher education has introduced both opportunities and challenges for students’ academic practices. To date, most qualitative research has examined the implications of this phenomenon for teaching practices and the experiences of teachers. This explorative study investigates how students use GAI chatbots in their studies and how they evaluate AI generated text, particularly in relation to academic integrity, reliability, and ethical norms. Using a qualitative research approach, we conducted semi-structured interviews with higher education students to a) examine students’ experiences of using GAI chatbots in their academic studies, and b) to explore how students perceive the ethical impact of using chatbots on their studies. The findings reveal that while students use GAI chatbots to support writing, idea generation, and language improvement, they also face challenges related to plagiarism concerns, the reliability of AI-generated content, and the lack of clear institutional guidelines on responsible AI use. Additionally, the study highlights how students develop individual strategies to navigate these challenges, including seeking validation from educators and critically assessing AI-generated outputs. The findings emphasize the need for clearer academic policies and ethical frameworks to support students in making informed and responsible decisions about the use of GAI chatbots in higher education.

生成式人工智能（GAI）聊天机器人在高等教育中的整合为学生的学术实践带来了机遇和挑战。迄今为止，大多数定性研究都考察了这一现象对教学实践和教师经验的影响。这项探索性研究调查了学生如何在学习中使用人工智能聊天机器人，以及他们如何评估人工智能生成的文本，特别是在学术诚信、可靠性和道德规范方面。采用定性研究方法，我们对高等教育学生进行了半结构化访谈，以a)检查学生在学术研究中使用GAI聊天机器人的经历，以及b)探索学生如何看待使用聊天机器人对他们学习的道德影响。研究结果显示，虽然学生们使用人工智能聊天机器人来支持写作、创意产生和语言提高，但他们也面临着与剽窃问题、人工智能生成内容的可靠性以及缺乏明确的负责任的人工智能使用制度指南相关的挑战。此外，该研究还强调了学生如何制定个人策略来应对这些挑战，包括寻求教育工作者的认可和批判性地评估人工智能生成的输出。研究结果强调，需要制定更清晰的学术政策和道德框架，以支持学生在高等教育中使用GAI聊天机器人时做出明智和负责任的决定。

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引用次数: 0

Conceptualizing pre-service teachers' readiness for AI integration into teaching practices: An intelligent-TPACK approach 概念化职前教师将人工智能融入教学实践的准备：一种智能tpack方法

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-29 DOI: 10.1016/j.caeo.2025.100320

José Reyes-Rojas , Brayan Díaz , Camila Ruz-Reveco , Angela Castro , David Reyes-González

Pre-service teachers can play a crucial role in integrating AI-based tools into the new educational landscape. However, there is a need to validate specialized instruments, apply current conceptualizations such as intelligent-TPACK, and address ethical issues, as pre-service teachers are often overlooked in the development of tools for AI integration. To address these gaps, we adapted a previously existing instrument designed for in-service teachers to measure pre-service teachers’ integration of AI within their training context. We conducted a quantitative cross-sectional survey with a total of 366 pre-service teachers to evaluate the adapted intelligent-TPACK instrument and examine participants' demographic characteristics related to the framework dimensions. Data analysis included a Confirmatory Factor Analysis to assess the factor model of the adapted instrument, followed by correlations to compare participant variables such as gender, type of university, and stage in the training program with the Intelligent-TPACK model factors. To investigate the differences among groups, the nonparametric ANCOVA test (Quade test) was utilized, enabling the control of covariates like age and academic progress level to ensure comparability across the dimensions of the Intelligent-TPACK model. Findings reveal a high fit of the Intelligent-TPACK model for pre-service teachers (CFI=0.997; TLI=0.997). The data also shows statistically significant effects related to academic progress level and type of institution, while factors -gender, geographic location, and type of major- did not demonstrate noteworthy differences. These results highlight key areas for future curriculum development and support for pre-service teachers in integrating AI education.

职前教师可以在将基于人工智能的工具整合到新的教育环境中发挥关键作用。然而，有必要验证专门的工具，应用当前的概念，如智能tpack，并解决道德问题，因为在人工智能集成工具的开发中，职前教师经常被忽视。为了解决这些差距，我们采用了先前为在职教师设计的工具，以衡量职前教师在培训环境中整合人工智能的情况。我们对366名职前教师进行了一项定量横断面调查，以评估适应性智能tpack工具，并检查参与者与框架维度相关的人口统计学特征。数据分析包括验证性因素分析，以评估适应工具的因素模型，然后将参与者变量（如性别、大学类型和培训计划阶段）与Intelligent-TPACK模型因素进行相关性比较。为了研究组间差异，采用非参数ANCOVA检验（Quade检验），控制协变量，如年龄和学业进步水平，以确保智能- tpack模型各维度的可比性。结果显示，智能- tpack模型对职前教师具有较高的拟合性（CFI=0.997； TLI=0.997）。数据还显示，在统计上，学业进步水平和机构类型对学生的影响显著，而性别、地理位置和专业类型等因素并没有显示出显著的差异。这些结果突出了未来课程开发的关键领域，并支持职前教师整合人工智能教育。

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引用次数: 0

The perceived importance of active learning techniques in online STEM courses 主动学习技术在在线STEM课程中的重要性

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-19 DOI: 10.1016/j.caeo.2025.100309

Montgomery Van Wart , Mirand McIntyre , Jing Zhang , Pamela Medina , Anna Ni , Lewis Njualem

It is widely acknowledged that active learning strategies increase engagement and long-term retention, while reducing attrition and frustration of students with less academic preparation and self-efficacy. Promoting active learning methods in STEM has been a long-term project in higher education. This study examines the perceptions of active learning techniques in online STEM education, leveraging a large, diverse sample (N = 727) across four STEM fields. The post-pandemic context of the study offers unique insights into how students and faculty perceive the effectiveness of various active learning methods in a rapidly changing educational environment. For eight of the nine methods studied, more than half of students and faculty found each active learning strategy to be helpful for online learning achievement. On average, both students and faculty found active learning methods to be modestly more important in online courses than face-to-face courses. A novel finding that was striking was that by a wide margin, both students and faculty perceived requiring activities more helpful than offering them on an optional basis. This implies that active learning methods become a meaningful portion of the course grade. However, faculty and students disagree on how heavily such activities should contribute to course grades. On average, students believe about half of their grade (52%) should comprise active learning activities, whereas faculty report that 32% of grades in their courses come from formative active learning assessments. The implications of activity-based STEM learning in online courses are discussed.

人们普遍认为，主动学习策略可以提高参与度和长期记忆力，同时减少学业准备不足和自我效能低下的学生的流失和挫折感。在STEM中推广主动学习方法一直是高等教育的一个长期课题。本研究考察了在线STEM教育中主动学习技术的看法，利用了四个STEM领域的大量不同样本（N = 727）。该研究的大流行后背景为学生和教师如何在快速变化的教育环境中感知各种主动学习方法的有效性提供了独特的见解。对于所研究的九种方法中的八种，超过一半的学生和教师发现每种主动学习策略都有助于在线学习成绩。平均而言，学生和教师都认为主动学习方法在在线课程中比面对面课程更重要。一项引人注目的新发现是，在很大程度上，学生和教师都认为必修活动比提供可选活动更有帮助。这意味着主动学习方法成为课程成绩中有意义的一部分。然而，教师和学生对这些活动对课程成绩的影响有多大存在分歧。平均而言，学生认为大约一半的成绩（52%）应该包括主动学习活动，而教师报告说，他们课程中32%的成绩来自形成性主动学习评估。讨论了在线课程中基于活动的STEM学习的含义。

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引用次数: 0

AI-driven gamified speech training for primary students: framework and evaluation 基于ai的小学生游戏化语音训练：框架与评价

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-17 DOI: 10.1016/j.caeo.2025.100312

Xing Sun , Zi-Xiang Xu , Ling-Chen Meng , Ding-Nan Shi

Traditional public speaking education often suffers from limited learner engagement, delayed formative feedback, and a lack of interactive and adaptive training environments. This study proposes an AI-driven gamified speech learning framework (AI-GSLF), which combines real-time feedback technologies with motivational game design principles to address these issues. Based on this framework, a serious game—Strongest Speech Streamer—was developed using the Godot engine. The system integrates automatic speech recognition, sentiment analysis, and a novel speech rate detection algorithm to provide immediate feedback, helping learners adjust pacing, reduce anxiety, and enhance fluency during practice. A true experimental design was employed, involving 57 primary school students randomly assigned to either the experimental group using the gamified system or a control group following traditional methods over one month. Quantitative results showed that the experimental group demonstrated statistically significant improvements in motivation, confidence, speech accuracy, and delivery fluency. To our knowledge, few prior studies have integrated real-time AI feedback with systematic gamification for primary-level formal speech training. Findings support the potential of AI-GSLF as an effective, scalable approach to enhancing student performance and engagement in public speaking education.

传统的公共演讲教育往往存在学习者参与度有限、形成性反馈滞后、缺乏互动性和适应性训练环境等问题。本研究提出了一个人工智能驱动的游戏化语音学习框架（AI-GSLF），它将实时反馈技术与动机游戏设计原则相结合，以解决这些问题。基于这个框架，我们使用Godot引擎开发了一款严肃的游戏——《最强的语音流》。该系统集成了自动语音识别、情感分析和一种新颖的语音率检测算法，提供即时反馈，帮助学习者在练习中调整节奏，减少焦虑，提高流利度。采用真正的实验设计，在一个月的时间里，57名小学生被随机分配到使用游戏化系统的实验组和使用传统方法的对照组。定量结果显示，实验组在动机、信心、语言准确性和表达流畅性方面表现出统计学上显著的改善。据我们所知，之前很少有研究将实时人工智能反馈与系统游戏化相结合，用于初级水平的正式语音训练。研究结果支持AI-GSLF作为一种有效的、可扩展的方法来提高学生在公共演讲教育中的表现和参与度的潜力。

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引用次数: 0

Room for collaboration: Analyzing group learning in spatial digital learning environments 协作空间：分析空间数字学习环境中的小组学习

IF 5.7 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computers and Education Open

Pub Date : 2025-11-17 DOI: 10.1016/j.caeo.2025.100308

Michael Montag , Heinrich Söbke , Mario Wolf , Florian Wehking , Eckhard Kraft

Collaboration within learning activities, i.e., solving tasks together, is one of the effective elements for designing learning activities. Collaboration is also seen as conducive to learning in spatial digital learning environments (SDLE), such as virtual reality environments. However, less is known about the detailed design of collaboration, such as learning requirements and specific interaction between learners. Thus, this evaluation study examines the groupwise exploration of SDLEs using the example of a 360°-based digital representation of a waterworks. The study combines ecological authenticity with a multidimensional assessment approach, examining motivation, emotions, cognitive load, and social presence in authentic group learning contexts. We used standardized instruments to collect data regarding motivation and emotion as learning requirements, social presence and cognitive load. A pre- and post-test supplemented the data, as did semi-structured interviews. T-tests show that learning in groups can be more stressful for learners but leads to a more positive affect overall (Collaborative Load–Motivation Trade-off Principle). Further, we found that pairs achieve better learning outcomes than triads. A side finding revealed that learning in face-to-face settings appears to be more effective than online learning. The study contributes to an informed instructional design of learning in SDLEs and thus might advance this valuable learning technology.

学习活动中的协作，即共同解决任务，是设计学习活动的有效要素之一。协作也被视为有助于在空间数字学习环境（SDLE）中学习，如虚拟现实环境。然而，对协作的详细设计知之甚少，例如学习需求和学习者之间的具体交互。因此，本评估研究以自来水厂的360°数字表示为例，考察了SDLEs的分组探索。本研究将生态真实性与多维评估方法相结合，考察了真实群体学习情境中的动机、情绪、认知负荷和社会存在。我们使用标准化的工具来收集动机和情绪作为学习要求、社会存在和认知负荷的数据。前测和后测补充了数据，半结构化访谈也是如此。t检验表明，小组学习对学习者来说压力更大，但总体上产生了更积极的影响（协作负载-动机权衡原则）。此外，我们发现结对学习比三合学习取得更好的学习效果。另一项研究发现，面对面学习似乎比在线学习更有效。该研究有助于在特殊语言学习环境中进行明智的教学设计，从而可能推进这一有价值的学习技术。

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引用次数: 0