Recent advances in generative artificial intelligence (AI) and multimodal learning analytics (MMLA) have allowed for new and creative ways of leveraging AI to support K12 students' collaborative learning in STEM+C domains. To date, there is little evidence of AI methods supporting students' collaboration in complex, open‐ended environments. AI systems are known to underperform humans in (1) interpreting students' emotions in learning contexts, (2) grasping the nuances of social interactions and (3) understanding domain‐specific information that was not well‐represented in the training data. As such, combined human and AI (ie, hybrid) approaches are needed to overcome the current limitations of AI systems. In this paper, we take a first step towards investigating how a human‐AI collaboration between teachers and researchers using an AI‐generated multimodal timeline can guide and support teachers' feedback while addressing students' STEM+C difficulties as they work collaboratively to build computational models and solve problems. In doing so, we present a framework characterizing the human component of our human‐AI partnership as a collaboration between teachers and researchers. To evaluate our approach, we present our timeline to a high school teacher and discuss the key insights gleaned from our discussions. Our case study analysis reveals the effectiveness of an iterative approach to using human‐AI collaboration to address students' STEM+C challenges: the teacher can use the AI‐generated timeline to guide formative feedback for students, and the researchers can leverage the teacher's feedback to help improve the multimodal timeline. Additionally, we characterize our findings with respect to two events of interest to the teacher: (1) when the students cross a <jats:italic>difficulty threshold,</jats:italic> and (2) the <jats:italic>point of intervention</jats:italic>, that is, when the teacher (or system) should intervene to provide effective feedback. It is important to note that the teacher explained that there should be a lag between (1) and (2) to give students a chance to resolve their own difficulties. Typically, such a lag is not implemented in computer‐based learning environments that provide feedback.<jats:label/><jats:boxed-text content-type="box" position="anchor"><jats:caption>Practitioner notes</jats:caption>What is already known about this topic <jats:list list-type="bullet"> <jats:list-item>Collaborative, open‐ended learning environments enhance students' STEM+C conceptual understanding and practice, but they introduce additional complexities when students learn concepts spanning multiple domains.</jats:list-item> <jats:list-item>Recent advances in generative AI and MMLA allow for integrating multiple datastreams to derive holistic views of students' states, which can support more informed feedback mechanisms to address students' difficulties in complex STEM+C environments.</jats:list-item> <jats:list-item>Hybrid human‐AI approache
{"title":"A multimodal approach to support teacher, researcher and AI collaboration in STEM+C learning environments","authors":"Clayton Cohn, Caitlin Snyder, Joyce Horn Fonteles, Ashwin T. S., Justin Montenegro, Gautam Biswas","doi":"10.1111/bjet.13518","DOIUrl":"https://doi.org/10.1111/bjet.13518","url":null,"abstract":"Recent advances in generative artificial intelligence (AI) and multimodal learning analytics (MMLA) have allowed for new and creative ways of leveraging AI to support K12 students' collaborative learning in STEM+C domains. To date, there is little evidence of AI methods supporting students' collaboration in complex, open‐ended environments. AI systems are known to underperform humans in (1) interpreting students' emotions in learning contexts, (2) grasping the nuances of social interactions and (3) understanding domain‐specific information that was not well‐represented in the training data. As such, combined human and AI (ie, hybrid) approaches are needed to overcome the current limitations of AI systems. In this paper, we take a first step towards investigating how a human‐AI collaboration between teachers and researchers using an AI‐generated multimodal timeline can guide and support teachers' feedback while addressing students' STEM+C difficulties as they work collaboratively to build computational models and solve problems. In doing so, we present a framework characterizing the human component of our human‐AI partnership as a collaboration between teachers and researchers. To evaluate our approach, we present our timeline to a high school teacher and discuss the key insights gleaned from our discussions. Our case study analysis reveals the effectiveness of an iterative approach to using human‐AI collaboration to address students' STEM+C challenges: the teacher can use the AI‐generated timeline to guide formative feedback for students, and the researchers can leverage the teacher's feedback to help improve the multimodal timeline. Additionally, we characterize our findings with respect to two events of interest to the teacher: (1) when the students cross a <jats:italic>difficulty threshold,</jats:italic> and (2) the <jats:italic>point of intervention</jats:italic>, that is, when the teacher (or system) should intervene to provide effective feedback. It is important to note that the teacher explained that there should be a lag between (1) and (2) to give students a chance to resolve their own difficulties. Typically, such a lag is not implemented in computer‐based learning environments that provide feedback.<jats:label/><jats:boxed-text content-type=\"box\" position=\"anchor\"><jats:caption>Practitioner notes</jats:caption>What is already known about this topic <jats:list list-type=\"bullet\"> <jats:list-item>Collaborative, open‐ended learning environments enhance students' STEM+C conceptual understanding and practice, but they introduce additional complexities when students learn concepts spanning multiple domains.</jats:list-item> <jats:list-item>Recent advances in generative AI and MMLA allow for integrating multiple datastreams to derive holistic views of students' states, which can support more informed feedback mechanisms to address students' difficulties in complex STEM+C environments.</jats:list-item> <jats:list-item>Hybrid human‐AI approache","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"17 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liz Ebersole, Teresa S. Foulger, Yi Jin, Daniel James Mourlam
Social media has been shown to be an efficient way to engage in networked participatory scholarship (NPS), which is defined as the use of online social networks to share and further develop scholarship. As leaders in the field, educational technology scholars should be at the forefront of this practice. We used social network analysis (SNA) to examine the structure and characteristics of the #TPACK Twitter network and to determine whether and how the users were engaging in value‐added NPS. Our findings revealed that the #TPACK Twitter network was loosely organized, and users were not very well connected outside of their clusters. Our findings also revealed that #TPACK tweets largely did not represent value‐added NPS. The majority of posts lacked useful context, were limited to merely sharing links to resources and did not establish meaningful interactions among users. The implications of this study provide a new direction for educational technology researchers and PK‐12 practitioners to approach social media from a value‐added standpoint and to apply value‐added NPS to improve their use of social media to advance research, enhance professional learning and forge closer ties between researchers and practitioners.<jats:label/><jats:boxed-text content-type="box" position="anchor"><jats:caption>Practitioner notes</jats:caption>What is already known about this topic <jats:list list-type="bullet"> <jats:list-item>Publication in high‐impact academic journals and measuring research impact through citations and journal indexing is still the dominant practice in research dissemination; however, there is growth in the use of alternative methods and the use of altmetrics to measure the impact of these methods.</jats:list-item> <jats:list-item>Scholars struggle with using social media in ways that align with networked participatory scholarship (NPS).</jats:list-item> <jats:list-item>Social network analysis (SNA) is the study of the structure and characteristics of the relationships that form in social networks, and can be used to fanalyze an online social network.</jats:list-item> </jats:list>What this paper adds <jats:list list-type="bullet"> <jats:list-item>Value‐added social learning theory and NPS can be used in SNA to both evaluate and inform scholars' social media practices.</jats:list-item> <jats:list-item>Educational technology scholars and practitioners struggle with using social media for value‐added NPS.</jats:list-item> <jats:list-item>Lost opportunities for value‐added NPS that were documented in this study include lack of engagement with the wider network, not sharing the role of spreading ideas and not making value‐added contributions.</jats:list-item> </jats:list>Implications for practice and policy <jats:list list-type="bullet"> <jats:list-item>Education scholars should use SNA as a tool to evaluate the level of value‐added NPS in the social media networks around areas of study they care about so that they can develop both personal and system
{"title":"Exploring Twitter as a social learning space for education scholars: An analysis of value‐added contributions to the #TPACK network","authors":"Liz Ebersole, Teresa S. Foulger, Yi Jin, Daniel James Mourlam","doi":"10.1111/bjet.13521","DOIUrl":"https://doi.org/10.1111/bjet.13521","url":null,"abstract":"Social media has been shown to be an efficient way to engage in networked participatory scholarship (NPS), which is defined as the use of online social networks to share and further develop scholarship. As leaders in the field, educational technology scholars should be at the forefront of this practice. We used social network analysis (SNA) to examine the structure and characteristics of the #TPACK Twitter network and to determine whether and how the users were engaging in value‐added NPS. Our findings revealed that the #TPACK Twitter network was loosely organized, and users were not very well connected outside of their clusters. Our findings also revealed that #TPACK tweets largely did not represent value‐added NPS. The majority of posts lacked useful context, were limited to merely sharing links to resources and did not establish meaningful interactions among users. The implications of this study provide a new direction for educational technology researchers and PK‐12 practitioners to approach social media from a value‐added standpoint and to apply value‐added NPS to improve their use of social media to advance research, enhance professional learning and forge closer ties between researchers and practitioners.<jats:label/><jats:boxed-text content-type=\"box\" position=\"anchor\"><jats:caption>Practitioner notes</jats:caption>What is already known about this topic <jats:list list-type=\"bullet\"> <jats:list-item>Publication in high‐impact academic journals and measuring research impact through citations and journal indexing is still the dominant practice in research dissemination; however, there is growth in the use of alternative methods and the use of altmetrics to measure the impact of these methods.</jats:list-item> <jats:list-item>Scholars struggle with using social media in ways that align with networked participatory scholarship (NPS).</jats:list-item> <jats:list-item>Social network analysis (SNA) is the study of the structure and characteristics of the relationships that form in social networks, and can be used to fanalyze an online social network.</jats:list-item> </jats:list>What this paper adds <jats:list list-type=\"bullet\"> <jats:list-item>Value‐added social learning theory and NPS can be used in SNA to both evaluate and inform scholars' social media practices.</jats:list-item> <jats:list-item>Educational technology scholars and practitioners struggle with using social media for value‐added NPS.</jats:list-item> <jats:list-item>Lost opportunities for value‐added NPS that were documented in this study include lack of engagement with the wider network, not sharing the role of spreading ideas and not making value‐added contributions.</jats:list-item> </jats:list>Implications for practice and policy <jats:list list-type=\"bullet\"> <jats:list-item>Education scholars should use SNA as a tool to evaluate the level of value‐added NPS in the social media networks around areas of study they care about so that they can develop both personal and system","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"17 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lili Yan, Breanne K. Litts, Melissa Tehee, Stuart Baggaley, Jennifer Jenkins
Although education is framed as a future‐oriented enterprise, we often fail to serve the diverse futurities of youth, particularly in formal learning environments. The cultural norms of formal learning environments are rooted in dominant ways of being and knowing and this shapes how learning environments and learning technologies can be designed. As a result, the futures youth can envision for themselves in these spaces are often static and limited by the dominant culture. As a move toward supporting youths' diverse cultural backgrounds and experiences, we ask how youth develop relationship with culture through creating culturally centred multimedia projects. Guided by a case study approach, we collected thirty‐six remixing multimedia projects from twelve sixth graders, who created these projects for three culturally centred learning activities over a school year. Findings share one case from each learning activity to demonstrate how students represent their relationships with culture through three forms of symbolising. Implications from this work reject the settled expectations of dominant culture in formal learning environments and, instead, invite youths' knowledges and experiences through remixing with multimedia.<jats:label/><jats:boxed-text content-type="box" position="anchor"><jats:caption>Practitioner notes</jats:caption>What is already known about this topic <jats:list list-type="bullet"> <jats:list-item>Formal learning environments are shaped by norms rooted in the dominant culture but are often assumed to be acultural spaces.</jats:list-item> <jats:list-item>Multimedia technologies have been leveraged to engage youth with culture in the classroom.</jats:list-item> <jats:list-item>Remixing is a sociocultural learning process that allows youth to reimagine their lived experiences.</jats:list-item> </jats:list>What this paper adds <jats:list list-type="bullet"> <jats:list-item>Sixth graders' relationships with culture were mediated by remixing with multimedia resources in a series of culturally centred multimedia projects.</jats:list-item> <jats:list-item>Forms of symbolising in students' remixing works reveal diverse relationships with their own culture and other cultures.</jats:list-item> <jats:list-item>Culturally centred multimedia projects afford the development of intertwined and reciprocal relationships with one's own culture and other cultures.</jats:list-item> </jats:list>Implications for practice <jats:list list-type="bullet"> <jats:list-item>Formal learning environments and embedded learning activities often operate on static or limited relationships between youth and their diverse range of cultural experiences.</jats:list-item> <jats:list-item>Engaging with multimedia projects can develop students' relationships with their own culture and other cultures in a reciprocal way.</jats:list-item> <jats:list-item>Supporting the development of diverse relationships with culture is crucial to designing a culturally centred learning env
{"title":"Youths' relationship with culture: Tracing sixth graders' learning through designing culturally centred multimedia projects","authors":"Lili Yan, Breanne K. Litts, Melissa Tehee, Stuart Baggaley, Jennifer Jenkins","doi":"10.1111/bjet.13520","DOIUrl":"https://doi.org/10.1111/bjet.13520","url":null,"abstract":"Although education is framed as a future‐oriented enterprise, we often fail to serve the diverse futurities of youth, particularly in formal learning environments. The cultural norms of formal learning environments are rooted in dominant ways of being and knowing and this shapes how learning environments and learning technologies can be designed. As a result, the futures youth can envision for themselves in these spaces are often static and limited by the dominant culture. As a move toward supporting youths' diverse cultural backgrounds and experiences, we ask how youth develop relationship with culture through creating culturally centred multimedia projects. Guided by a case study approach, we collected thirty‐six remixing multimedia projects from twelve sixth graders, who created these projects for three culturally centred learning activities over a school year. Findings share one case from each learning activity to demonstrate how students represent their relationships with culture through three forms of symbolising. Implications from this work reject the settled expectations of dominant culture in formal learning environments and, instead, invite youths' knowledges and experiences through remixing with multimedia.<jats:label/><jats:boxed-text content-type=\"box\" position=\"anchor\"><jats:caption>Practitioner notes</jats:caption>What is already known about this topic <jats:list list-type=\"bullet\"> <jats:list-item>Formal learning environments are shaped by norms rooted in the dominant culture but are often assumed to be acultural spaces.</jats:list-item> <jats:list-item>Multimedia technologies have been leveraged to engage youth with culture in the classroom.</jats:list-item> <jats:list-item>Remixing is a sociocultural learning process that allows youth to reimagine their lived experiences.</jats:list-item> </jats:list>What this paper adds <jats:list list-type=\"bullet\"> <jats:list-item>Sixth graders' relationships with culture were mediated by remixing with multimedia resources in a series of culturally centred multimedia projects.</jats:list-item> <jats:list-item>Forms of symbolising in students' remixing works reveal diverse relationships with their own culture and other cultures.</jats:list-item> <jats:list-item>Culturally centred multimedia projects afford the development of intertwined and reciprocal relationships with one's own culture and other cultures.</jats:list-item> </jats:list>Implications for practice <jats:list list-type=\"bullet\"> <jats:list-item>Formal learning environments and embedded learning activities often operate on static or limited relationships between youth and their diverse range of cultural experiences.</jats:list-item> <jats:list-item>Engaging with multimedia projects can develop students' relationships with their own culture and other cultures in a reciprocal way.</jats:list-item> <jats:list-item>Supporting the development of diverse relationships with culture is crucial to designing a culturally centred learning env","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"10 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142253941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Preparing preservice teachers (PSTs) to be able to notice, interpret, respond to and orchestrate student ideas—the core practices of responsive teaching—is a key goal for contemporary science and mathematics teacher education. This mixed‐methods study, employing a virtual reality (VR)‐supported simulation integrated with artificial intelligence (AI)‐powered virtual students, explored the frequent patterns of PSTs' talk moves as they attempted to orchestrate a responsive discussion, as well as the affordances and challenges of leveraging AI‐supported virtual simulation to enhance PSTs' responsive teaching skills. Sequential analysis of the talk moves of both PSTs (<jats:italic>n</jats:italic> = 24) and virtual students indicated that although PSTs did employ responsive talk moves, they encountered difficulties in transitioning from the authoritative, teacher‐centred teaching approach to a responsive way of teaching. The qualitative analysis with triangulated dialogue transcripts, observational field notes and semi‐structured interviews revealed participants' engagement in (1) orchestrating discussion by leveraging the design features of AI‐supported simulation, (2) iterative rehearsals through naturalistic and contextualized interactions and (3) exploring realism and boundaries in AI‐powered virtual students. The study findings provide insights into the potential of leveraging AI‐supported virtual simulation to improve PSTs' responsive teaching skills. The study also underscores the need for PSTs to engage in well‐designed pedagogical practices with adaptive and in situ support.<jats:label/><jats:boxed-text content-type="box" position="anchor"><jats:caption>Practitioner notes</jats:caption>What is already known about this topic <jats:list list-type="bullet"> <jats:list-item>Developing the teaching capacity of responsive teaching is an important goal for preservice teacher (PST) education. PSTs need systematic opportunities to build fluency in this approach.</jats:list-item> <jats:list-item>Virtual simulations can provide PSTs with the opportunities to practice interactive teaching and have been shown to improve their teaching skills.</jats:list-item> <jats:list-item>Artificial intelligence (AI)‐powered virtual students can be integrated into virtual simulations to enable interactive and authentic practice of teaching.</jats:list-item> </jats:list>What this paper adds <jats:list list-type="bullet"> <jats:list-item>AI‐supported simulation has the potential to support PSTs' responsive teaching skills.</jats:list-item> <jats:list-item>While PSTs enact responsive teaching talk moves, they struggle to enact those talk moves in challenging teaching scenarios due to limited epistemic and pedagogical resources.</jats:list-item> <jats:list-item>AI‐supported simulation affords iterative and contextualized opportunities for PSTs to practice responsive teaching talk moves; it challenges teachers to analyse student discourse and respond in real time.</jats:list
{"title":"Seeking to support preservice teachers' responsive teaching: Leveraging artificial intelligence‐supported virtual simulation","authors":"Nuodi Zhang, Fengfeng Ke, Chih‐Pu Dai, Sherry A. Southerland, Xin Yuan","doi":"10.1111/bjet.13522","DOIUrl":"https://doi.org/10.1111/bjet.13522","url":null,"abstract":"Preparing preservice teachers (PSTs) to be able to notice, interpret, respond to and orchestrate student ideas—the core practices of responsive teaching—is a key goal for contemporary science and mathematics teacher education. This mixed‐methods study, employing a virtual reality (VR)‐supported simulation integrated with artificial intelligence (AI)‐powered virtual students, explored the frequent patterns of PSTs' talk moves as they attempted to orchestrate a responsive discussion, as well as the affordances and challenges of leveraging AI‐supported virtual simulation to enhance PSTs' responsive teaching skills. Sequential analysis of the talk moves of both PSTs (<jats:italic>n</jats:italic> = 24) and virtual students indicated that although PSTs did employ responsive talk moves, they encountered difficulties in transitioning from the authoritative, teacher‐centred teaching approach to a responsive way of teaching. The qualitative analysis with triangulated dialogue transcripts, observational field notes and semi‐structured interviews revealed participants' engagement in (1) orchestrating discussion by leveraging the design features of AI‐supported simulation, (2) iterative rehearsals through naturalistic and contextualized interactions and (3) exploring realism and boundaries in AI‐powered virtual students. The study findings provide insights into the potential of leveraging AI‐supported virtual simulation to improve PSTs' responsive teaching skills. The study also underscores the need for PSTs to engage in well‐designed pedagogical practices with adaptive and in situ support.<jats:label/><jats:boxed-text content-type=\"box\" position=\"anchor\"><jats:caption>Practitioner notes</jats:caption>What is already known about this topic <jats:list list-type=\"bullet\"> <jats:list-item>Developing the teaching capacity of responsive teaching is an important goal for preservice teacher (PST) education. PSTs need systematic opportunities to build fluency in this approach.</jats:list-item> <jats:list-item>Virtual simulations can provide PSTs with the opportunities to practice interactive teaching and have been shown to improve their teaching skills.</jats:list-item> <jats:list-item>Artificial intelligence (AI)‐powered virtual students can be integrated into virtual simulations to enable interactive and authentic practice of teaching.</jats:list-item> </jats:list>What this paper adds <jats:list list-type=\"bullet\"> <jats:list-item>AI‐supported simulation has the potential to support PSTs' responsive teaching skills.</jats:list-item> <jats:list-item>While PSTs enact responsive teaching talk moves, they struggle to enact those talk moves in challenging teaching scenarios due to limited epistemic and pedagogical resources.</jats:list-item> <jats:list-item>AI‐supported simulation affords iterative and contextualized opportunities for PSTs to practice responsive teaching talk moves; it challenges teachers to analyse student discourse and respond in real time.</jats:list","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"128 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Markus Wolfgang Hermann Spitzer, Miguel Ruiz‐Garcia, Korbinian Moeller
<jats:label/>Research on fostering learning about percentages within intelligent tutoring systems (ITSs) is limited. Additionally, there is a lack of data‐driven approaches for improving the design of ITS to facilitate learning about percentages. To address these gaps, we first investigated whether students' understanding of basic mathematical skills (eg, arithmetic, measurement units and geometry) and fractions within an ITS predicts their understanding of percentages. We then applied a psychological network analysis to evaluate interdependencies within the data on 44 subtopics of basic mathematical concepts, fractions and percentages. We leveraged a large‐scale dataset consisting of 2798 students using the ITS <jats:italic>bettermarks</jats:italic> and working on approximately 4.1 million mathematical problems. We found that advanced arithmetic, measurement units, geometry and fraction understanding significantly predicted percentage understanding. Closer inspection indicated that percentage understanding was best predicted by problems sharing similar features, such as fraction word problems and fraction/natural number multiplication/division problems. Our findings suggest that practitioners and software developers may consider revising specific subtopics which share features with percentage problems for students struggling with percentages. More broadly, our study demonstrates how evaluating interdependencies between subtopics covered within an ITS as a data‐driven approach can provide practical insights for improving the design of ITSs.<jats:label/><jats:boxed-text content-type="box" position="anchor"><jats:caption>Practitioner notes</jats:caption>What is already known about this topic <jats:list list-type="bullet"> <jats:list-item>Longitudinal studies showed that basic mathematical skills predict fraction understanding.</jats:list-item> <jats:list-item>There is only limited evidence on whether similar predictions can be observed for percentage understanding—in general and within intelligent tutoring systems.</jats:list-item> <jats:list-item>Process data from such intelligent tutoring systems can be leveraged to pursue both educational research questions and optimizing digital learning software.</jats:list-item> <jats:list-item>Problems involving percentages typically are word problems requiring multiplications and/or divisions.</jats:list-item> </jats:list>What this paper adds <jats:list list-type="bullet"> <jats:list-item>Similar to the case of fractions, students' performance on advanced arithmetic, measurement units and geometry significantly predicted performance with percentages.</jats:list-item> <jats:list-item>Students' performance with fractions also predicted performance with percentages significantly.</jats:list-item> <jats:list-item>A psychological network analysis was applied to evaluate specific interdependencies between a range of subtopics (eg, <jats:italic>Multiplying and dividing fractions, Adding and subtracting fractions</
{"title":"Basic mathematical skills and fraction understanding predict percentage understanding: Evidence from an intelligent tutoring system","authors":"Markus Wolfgang Hermann Spitzer, Miguel Ruiz‐Garcia, Korbinian Moeller","doi":"10.1111/bjet.13517","DOIUrl":"https://doi.org/10.1111/bjet.13517","url":null,"abstract":"<jats:label/>Research on fostering learning about percentages within intelligent tutoring systems (ITSs) is limited. Additionally, there is a lack of data‐driven approaches for improving the design of ITS to facilitate learning about percentages. To address these gaps, we first investigated whether students' understanding of basic mathematical skills (eg, arithmetic, measurement units and geometry) and fractions within an ITS predicts their understanding of percentages. We then applied a psychological network analysis to evaluate interdependencies within the data on 44 subtopics of basic mathematical concepts, fractions and percentages. We leveraged a large‐scale dataset consisting of 2798 students using the ITS <jats:italic>bettermarks</jats:italic> and working on approximately 4.1 million mathematical problems. We found that advanced arithmetic, measurement units, geometry and fraction understanding significantly predicted percentage understanding. Closer inspection indicated that percentage understanding was best predicted by problems sharing similar features, such as fraction word problems and fraction/natural number multiplication/division problems. Our findings suggest that practitioners and software developers may consider revising specific subtopics which share features with percentage problems for students struggling with percentages. More broadly, our study demonstrates how evaluating interdependencies between subtopics covered within an ITS as a data‐driven approach can provide practical insights for improving the design of ITSs.<jats:label/><jats:boxed-text content-type=\"box\" position=\"anchor\"><jats:caption>Practitioner notes</jats:caption>What is already known about this topic <jats:list list-type=\"bullet\"> <jats:list-item>Longitudinal studies showed that basic mathematical skills predict fraction understanding.</jats:list-item> <jats:list-item>There is only limited evidence on whether similar predictions can be observed for percentage understanding—in general and within intelligent tutoring systems.</jats:list-item> <jats:list-item>Process data from such intelligent tutoring systems can be leveraged to pursue both educational research questions and optimizing digital learning software.</jats:list-item> <jats:list-item>Problems involving percentages typically are word problems requiring multiplications and/or divisions.</jats:list-item> </jats:list>What this paper adds <jats:list list-type=\"bullet\"> <jats:list-item>Similar to the case of fractions, students' performance on advanced arithmetic, measurement units and geometry significantly predicted performance with percentages.</jats:list-item> <jats:list-item>Students' performance with fractions also predicted performance with percentages significantly.</jats:list-item> <jats:list-item>A psychological network analysis was applied to evaluate specific interdependencies between a range of subtopics (eg, <jats:italic>Multiplying and dividing fractions, Adding and subtracting fractions</","PeriodicalId":48315,"journal":{"name":"British Journal of Educational Technology","volume":"23 1","pages":""},"PeriodicalIF":6.6,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142212638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}