Pub Date : 2023-02-28DOI: 10.1007/s40593-022-00322-1
Rebecka Weegar, P. Idestam-Almquist
{"title":"Reducing Workload in Short Answer Grading Using Machine Learning","authors":"Rebecka Weegar, P. Idestam-Almquist","doi":"10.1007/s40593-022-00322-1","DOIUrl":"https://doi.org/10.1007/s40593-022-00322-1","url":null,"abstract":"","PeriodicalId":46637,"journal":{"name":"International Journal of Artificial Intelligence in Education","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42591469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-14DOI: 10.1007/s40593-023-00330-9
P. Wulff, Lukas Mientus, Ann I. Nowak, Andreas Borowski
{"title":"Correction to: Utilizing a Pretrained Language Model (BERT) to Classify Preservice Physics Teachers’ Written Refections","authors":"P. Wulff, Lukas Mientus, Ann I. Nowak, Andreas Borowski","doi":"10.1007/s40593-023-00330-9","DOIUrl":"https://doi.org/10.1007/s40593-023-00330-9","url":null,"abstract":"","PeriodicalId":46637,"journal":{"name":"International Journal of Artificial Intelligence in Education","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2023-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49514561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-31DOI: 10.1007/s40593-023-00328-3
K. VanLehn, Fabio Milner, Chandrani Banerjee, Jon Wetzel
{"title":"A Step-Based Tutoring System to Teach Underachieving Students How to Construct Algebraic Models","authors":"K. VanLehn, Fabio Milner, Chandrani Banerjee, Jon Wetzel","doi":"10.1007/s40593-023-00328-3","DOIUrl":"https://doi.org/10.1007/s40593-023-00328-3","url":null,"abstract":"","PeriodicalId":46637,"journal":{"name":"International Journal of Artificial Intelligence in Education","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47324087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-25DOI: 10.1007/s40593-022-00325-y
Ramon Mayor Martins, C. G. von Wangenheim, Marcelo Fernando Rauber, J. Hauck
{"title":"Machine Learning for All!—Introducing Machine Learning in Middle and High School","authors":"Ramon Mayor Martins, C. G. von Wangenheim, Marcelo Fernando Rauber, J. Hauck","doi":"10.1007/s40593-022-00325-y","DOIUrl":"https://doi.org/10.1007/s40593-022-00325-y","url":null,"abstract":"","PeriodicalId":46637,"journal":{"name":"International Journal of Artificial Intelligence in Education","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46082263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-10DOI: 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.
{"title":"How Personalization Affects Motivation in Gamified Review Assessments.","authors":"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","doi":"10.1007/s40593-022-00326-x","DOIUrl":"10.1007/s40593-022-00326-x","url":null,"abstract":"<p><p>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 (<i>OSFA</i> and <i>Personalized</i> 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.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s40593-022-00326-x.</p>","PeriodicalId":46637,"journal":{"name":"International Journal of Artificial Intelligence in Education","volume":" ","pages":"1-38"},"PeriodicalIF":4.7,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9838401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9147117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1007/978-3-031-36272-9
{"title":"Artificial Intelligence in Education: 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings","authors":"","doi":"10.1007/978-3-031-36272-9","DOIUrl":"https://doi.org/10.1007/978-3-031-36272-9","url":null,"abstract":"","PeriodicalId":46637,"journal":{"name":"International Journal of Artificial Intelligence in Education","volume":"29 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80931302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-08DOI: 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
{"title":"The Introduction of Artificial Intelligence in Diagnostic Radiology Curricula: a Text and Opinion Systematic Review","authors":"G. N. M. Santos, H. E. C. da Silva, Paulo Tadeu Figueiredo, C. R. Mesquita, N. Melo, C. Stefani, A. Leite","doi":"10.1007/s40593-022-00324-z","DOIUrl":"https://doi.org/10.1007/s40593-022-00324-z","url":null,"abstract":"","PeriodicalId":46637,"journal":{"name":"International Journal of Artificial Intelligence in Education","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48790910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"WhatsApp Discourse Throughout COVID-19: Towards Computerized Evaluation of the Development of a STEM Teachers Professional Learning Community.","authors":"Zahava Scherz, Asaf Salman, Giora Alexandron, Yael Shwartz","doi":"10.1007/s40593-022-00320-3","DOIUrl":"10.1007/s40593-022-00320-3","url":null,"abstract":"<p><p>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.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s40593-022-00320-3.</p>","PeriodicalId":46637,"journal":{"name":"International Journal of Artificial Intelligence in Education","volume":" ","pages":"1-25"},"PeriodicalIF":4.9,"publicationDate":"2022-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9734941/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10404413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1007/s40593-022-00321-2
Matías Rojas, Cristian Sáez, Jorge A. Baier, M. Nussbaum, Orlando Guerrero, María Fernanda Rodríguez
{"title":"Using Automated Planning to Provide Feedback during Collaborative Problem-Solving","authors":"Matías Rojas, Cristian Sáez, Jorge A. Baier, M. Nussbaum, Orlando Guerrero, María Fernanda Rodríguez","doi":"10.1007/s40593-022-00321-2","DOIUrl":"https://doi.org/10.1007/s40593-022-00321-2","url":null,"abstract":"","PeriodicalId":46637,"journal":{"name":"International Journal of Artificial Intelligence in Education","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44405610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-28DOI: 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。
{"title":"A Survey of Current Machine Learning Approaches to Student Free-Text Evaluation for Intelligent Tutoring.","authors":"Xiaoyu Bai, Manfred Stede","doi":"10.1007/s40593-022-00323-0","DOIUrl":"10.1007/s40593-022-00323-0","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":46637,"journal":{"name":"International Journal of Artificial Intelligence in Education","volume":" ","pages":"1-39"},"PeriodicalIF":4.7,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9644494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}