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Reducing Workload in Short Answer Grading Using Machine Learning 使用机器学习减少简答评分的工作量
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-02-28 DOI: 10.1007/s40593-022-00322-1
Rebecka Weegar, P. Idestam-Almquist
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引用次数: 4
Correction to: Utilizing a Pretrained Language Model (BERT) to Classify Preservice Physics Teachers’ Written Refections 更正:利用预训练语言模型(BERT)对防腐物理教师的书面参考进行分类
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-02-14 DOI: 10.1007/s40593-023-00330-9
P. Wulff, Lukas Mientus, Ann I. Nowak, Andreas Borowski
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引用次数: 1
A Step-Based Tutoring System to Teach Underachieving Students How to Construct Algebraic Models 一个循序渐进的辅导系统教成绩不佳的学生如何构造代数模型
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-31 DOI: 10.1007/s40593-023-00328-3
K. VanLehn, Fabio Milner, Chandrani Banerjee, Jon Wetzel
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引用次数: 0
Machine Learning for All!—Introducing Machine Learning in Middle and High School 全民机器学习!-在初中和高中引入机器学习
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-25 DOI: 10.1007/s40593-022-00325-y
Ramon Mayor Martins, C. G. von Wangenheim, Marcelo Fernando Rauber, J. Hauck
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引用次数: 3
How Personalization Affects Motivation in Gamified Review Assessments. 游戏化复习评估中的个性化如何影响学习动机?
IF 4.7 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-10 DOI: 10.1007/s40593-022-00326-x
Luiz Rodrigues, Paula T Palomino, Armando M Toda, Ana C T Klock, Marcela Pessoa, Filipe D Pereira, Elaine H T Oliveira, David F Oliveira, Alexandra I Cristea, Isabela Gasparini, Seiji Isotani

Personalized gamification aims to address shortcomings of the one-size-fits-all (OSFA) approach in improving students' motivations throughout the learning process. However, studies still focus on personalizing to a single user dimension, ignoring multiple individual and contextual factors that affect user motivation. Unlike prior research, we address this issue by exploring multidimensional personalization compared to OSFA based on a multi-institution sample. Thus, we conducted a controlled experiment in three institutions, comparing gamification designs (OSFA and Personalized to the learning task and users' gaming habits/preferences and demographics) in terms of 58 students' motivations to complete assessments for learning. Our results suggest no significant differences among OSFA and Personalized designs, despite suggesting user motivation depended on fewer user characteristics when using personalization. Additionally, exploratory analyses suggest personalization was positive for females and those holding a technical degree, but negative for those who prefer adventure games and those who prefer single-playing. Our contribution benefits designers, suggesting how personalization works; practitioners, demonstrating to whom the personalization strategy was more or less suitable; and researchers, providing future research directions.

Supplementary information: The online version contains supplementary material available at 10.1007/s40593-022-00326-x.

个性化游戏化旨在解决 "一刀切"(OSFA)方法的不足,在整个学习过程中提高学生的学习动机。然而,目前的研究仍然只关注单一用户维度的个性化,而忽略了影响用户学习动机的多种个体因素和情境因素。与之前的研究不同,我们在多机构样本的基础上,探讨了多维度个性化与 OSFA 的比较,从而解决了这一问题。因此,我们在三所院校进行了一项对照实验,比较了游戏化设计(OSFA 和根据学习任务、用户的游戏习惯/偏好和人口统计学特征进行的个性化)对 58 名学生完成学习评估的激励作用。我们的研究结果表明,OSFA 和个性化设计之间没有明显差异,尽管在使用个性化设计时,用户动机取决于较少的用户特征。此外,探索性分析表明,个性化设计对女性和拥有技术学位的人来说是积极的,但对喜欢冒险游戏的人和喜欢单机游戏的人来说是消极的。我们的贡献对设计者、实践者和研究者都有益处,设计者提出了个性化是如何发挥作用的;实践者说明了个性化策略对哪些人更适合或不太适合;研究者提供了未来的研究方向:在线版本包含补充材料,可查阅 10.1007/s40593-022-00326-x。
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引用次数: 0
Artificial Intelligence in Education: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings 教育中的人工智能:第24届国际会议,AIED 2023,日本东京,2023年7月3-7日,论文集
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2023-01-01 DOI: 10.1007/978-3-031-36272-9
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引用次数: 0
The Introduction of Artificial Intelligence in Diagnostic Radiology Curricula: a Text and Opinion Systematic Review 人工智能在放射诊断学课程中的引入:文本与观点系统综述
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-12-08 DOI: 10.1007/s40593-022-00324-z
G. N. M. Santos, H. E. C. da Silva, Paulo Tadeu Figueiredo, C. R. Mesquita, N. Melo, C. Stefani, A. Leite
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引用次数: 0
WhatsApp Discourse Throughout COVID-19: Towards Computerized Evaluation of the Development of a STEM Teachers Professional Learning Community. WhatsApp 话语贯穿 COVID-19:对 STEM 教师专业学习社区的发展进行计算机化评估。
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-12-08 DOI: 10.1007/s40593-022-00320-3
Zahava Scherz, Asaf Salman, Giora Alexandron, Yael Shwartz

This two-year study followed a professional learning community (PLC) of STEM Teachers Leaders, referred to as L-PLC. The onset of the COVID-19 pandemic accelerated changes in the focus of many professional development frameworks from face-to-face to online communication. We sought for new ways and tools to follow the professional development and the dynamics in our L-PLC. In particular, we explored professional knowledge development and social interactions, as derived from its WhatsApp group (43-48 participants) discourse, before and during the COVID-19 pandemic. Data were extracted from 6599 WhatsApp messages issued during four consecutive semesters (March 2019-March 2021), as well as from participant background questionnaires. The analysis incorporated both structure and content examination of the L-PLC WhatsApp discourse, using social network analysis (SNA), and a distinctive coding scheme followed by statistical analysis, heat map, and bar graph visualizations. These provided insights into whole group (macro), subgroups (meso), and individual (micro) profiles. The results indicated that over time, the participants gradually began to use the WhatsApp platform for professional purposes on top of its initial administrative intention. Moreover, the pandemic seemed to lead to a unique adjustment process, denoted by enhanced professional interactions, regarding content knowledge, professional content knowledge, and technological knowledge, and also accelerated the development of productive community behaviors, such as sharing and social support. The research approach enabled us to detect changes in key PLC characteristics, follow their dynamics under the influence of chaotic changes and navigate the community accordingly. Taken together, WhatsApp exchanges can serve as a rich source of data for a noninvasive continuous evaluation of group processes and progress.

Supplementary information: The online version contains supplementary material available at 10.1007/s40593-022-00320-3.

这项为期两年的研究跟踪了一个由 STEM 教师领导者组成的专业学习社区(PLC),简称为 L-PLC。COVID-19 大流行病的爆发加速了许多专业发展框架的重点从面对面交流向在线交流的转变。我们寻求新的方法和工具来跟踪 L-PLC 的专业发展和动态。特别是,在 COVID-19 大流行之前和期间,我们从 WhatsApp 群组(43-48 名参与者)的话语中探索了专业知识的发展和社会互动。我们从连续四个学期(2019 年 3 月至 2021 年 3 月)发布的 6599 条 WhatsApp 消息以及参与者背景调查问卷中提取了数据。分析结合了对 L-PLC WhatsApp 话语的结构和内容检查,使用了社交网络分析(SNA)和独特的编码方案,随后进行了统计分析、热图和条形图可视化。这些方法提供了对整个群体(宏观)、子群体(中观)和个人(微观)概况的洞察。研究结果表明,随着时间的推移,参与者逐渐开始将 WhatsApp 平台用于专业用途,而非最初的行政用途。此外,大流行似乎导致了一个独特的适应过程,表现为内容知识、专业内容知识和技术知识方面的专业互动得到加强,同时也加速了生产性社区行为的发展,如分享和社会支持。这种研究方法使我们能够发现 PLC 关键特征的变化,跟踪其在混乱变化影响下的动态变化,并相应地引导社区。总之,WhatsApp 交流可作为丰富的数据来源,用于对群体进程和进展进行非侵入式的持续评估:在线版本包含补充材料,可查阅 10.1007/s40593-022-00320-3。
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引用次数: 0
Using Automated Planning to Provide Feedback during Collaborative Problem-Solving 在协作解决问题的过程中使用自动化计划提供反馈
IF 4.9 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-12-01 DOI: 10.1007/s40593-022-00321-2
Matías Rojas, Cristian Sáez, Jorge A. Baier, M. Nussbaum, Orlando Guerrero, María Fernanda Rodríguez
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引用次数: 0
A Survey of Current Machine Learning Approaches to Student Free-Text Evaluation for Intelligent Tutoring. 当前用于智能辅导的学生自由文本评估的机器学习方法调查。
IF 4.7 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2022-11-28 DOI: 10.1007/s40593-022-00323-0
Xiaoyu Bai, Manfred Stede

Recent years have seen increased interests in applying the latest technological innovations, including artificial intelligence (AI) and machine learning (ML), to the field of education. One of the main areas of interest to researchers is the use of ML to assist teachers in assessing students' work on the one hand and to promote effective self-tutoring on the other hand. In this paper, we present a survey of the latest ML approaches to the automated evaluation of students' natural language free-text, including both short answers to questions and full essays. Existing systematic literature reviews on the subject often emphasise an exhaustive and methodical study selection process and do not provide much detail on individual studies or a technical background to the task. In contrast, we present an accessible survey of the current state-of-the-art in student free-text evaluation and target a wider audience that is not necessarily familiar with the task or with ML-based text analysis in natural language processing (NLP). We motivate and contextualise the task from an application perspective, illustrate popular feature-based and neural model architectures and present a selection of the latest work in the area. We also remark on trends and challenges in the field.

近年来,将人工智能(AI)和机器学习(ML)等最新技术创新应用于教育领域的兴趣与日俱增。研究人员感兴趣的主要领域之一是利用 ML 一方面协助教师评估学生的作业,另一方面促进有效的自我辅导。在本文中,我们介绍了对学生的自然语言自由文本(包括简短的问题答案和完整的文章)进行自动评估的最新 ML 方法。有关该主题的现有系统性文献综述通常强调详尽、有条不紊的研究选择过程,并不提供有关单项研究或任务技术背景的详细信息。与此相反,我们对当前学生自由文本评价的最新进展进行了调查,并将目标对准了不一定熟悉该任务或自然语言处理(NLP)中基于 ML 的文本分析的广大读者。我们从应用的角度对任务进行了激励和背景分析,说明了流行的基于特征和神经模型的架构,并介绍了该领域的最新研究成果。我们还对该领域的趋势和挑战进行了评论。
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引用次数: 0
期刊
International Journal of Artificial Intelligence in Education
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