Pub Date : 2024-12-05DOI: 10.1016/j.compedu.2024.105215
Yuyao Tong, Gaowei Chen, Morris Siu-Yung Jong
A substantial body of research has demonstrated the essential roles of collaboration and classroom discourse in student learning. However, low-achieving students often face the challenge of engaging in collaborative work and productive classroom discourse. This study aimed to examine how learning analytics can help scaffold low-achieving students' reflective activities to enhance their conception of collaboration and productive classroom discourse engagement. To meet this goal, we proposed a video-based analytics-supported formative feedback approach in which students use a video-based visual learning analytic tool to reflect on their classroom discourse collaboratively. Using a quasi-experimental design, four classes of 9th- and 10th-grade low-achieving students were divided into an experimental group (n = 48) and a comparison group (n = 42). The experimental students engaged in a knowledge-building environment augmented with the analytics-supported approach, while the comparison students studied in a regular knowledge-building environment. Mixed-method analysis revealed that students in the experimental group, relative to the comparison group, developed a deeper conception of collaboration and engaged more productively in classroom discussions. The results highlighted the relationship between students' conception of collaboration, productive classroom discourse engagement, and conceptual understanding. Qualitative analysis of the interview responses and group discussions provided evidence of how the analytics-supported approach facilitated students’ engagement in productive classroom discourse. Theoretically, this study advances the idea of productive classroom discourse by adding a layer of focusing on the student perspective in orchestrating productive whole-class discussions. Additionally, this study contributes to the current literature by proposing a novel learning analytics-supported approach to help low-achieving students develop their conception of collaboration and engage in productive classroom discourse.
{"title":"Video-based analytics-supported formative feedback for enhancing low-achieving students’ conception of collaboration and classroom discourse engagement","authors":"Yuyao Tong, Gaowei Chen, Morris Siu-Yung Jong","doi":"10.1016/j.compedu.2024.105215","DOIUrl":"https://doi.org/10.1016/j.compedu.2024.105215","url":null,"abstract":"A substantial body of research has demonstrated the essential roles of collaboration and classroom discourse in student learning. However, low-achieving students often face the challenge of engaging in collaborative work and productive classroom discourse. This study aimed to examine how learning analytics can help scaffold low-achieving students' reflective activities to enhance their conception of collaboration and productive classroom discourse engagement. To meet this goal, we proposed a video-based analytics-supported formative feedback approach in which students use a video-based visual learning analytic tool to reflect on their classroom discourse collaboratively. Using a quasi-experimental design, four classes of 9th- and 10th-grade low-achieving students were divided into an experimental group (<ce:italic>n</ce:italic> = 48) and a comparison group (<ce:italic>n</ce:italic> = 42). The experimental students engaged in a knowledge-building environment augmented with the analytics-supported approach, while the comparison students studied in a regular knowledge-building environment. Mixed-method analysis revealed that students in the experimental group, relative to the comparison group, developed a deeper conception of collaboration and engaged more productively in classroom discussions. The results highlighted the relationship between students' conception of collaboration, productive classroom discourse engagement, and conceptual understanding. Qualitative analysis of the interview responses and group discussions provided evidence of how the analytics-supported approach facilitated students’ engagement in productive classroom discourse. Theoretically, this study advances the idea of productive classroom discourse by adding a layer of focusing on the student perspective in orchestrating productive whole-class discussions. Additionally, this study contributes to the current literature by proposing a novel learning analytics-supported approach to help low-achieving students develop their conception of collaboration and engage in productive classroom discourse.","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"90 1","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888335","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}
Pub Date : 2024-12-03DOI: 10.1016/j.compedu.2024.105213
Changqin Huang, Yaxin Tu, Qiyun Wang, Mingxi Li, Tao He, Di Zhang
Engaging students in online discussion forums with social support holds significant potential for preventing and alleviating student burnout. However, the mechanisms by which different types of social support influence learning burnout remain poorly understood. Additionally, existing methods for detecting social support detection are limited in both practical application and theoretical advancement. This study addresses these gaps by developing a robust text classification model for social support and examining its effects on online learning burnout among learners with varying levels of self-regulated learning. We first developed a robust natural language processing model based on bidirectional encoder representations from transformers - bidirectional long short-term memory (BERT-Bi-LSTM) framework, trained on 11226 manually labeled posts from various course forums. This model was then applied to classify forum posts from an educational technology course over one semester. Multiple regression analysis revealed that informational support was negatively associated with two dimensions of learning burnout: emotional exhaustion and improper behavior, and emotional support was negatively correlated with emotional exhaustion and a low sense of achievement. Moreover, a moderating effect analysis indicated that self-regulated learning moderated the negative associations between informational support and improper behavior, as well as between emotional support and emotional exhaustion, with stronger effects observed among learners with lower self-regulated learning. These findings contribute to advancing automated content analysis of social support and provide actionable insights for mitigating student burnout through targeted social support.
{"title":"How does social support detected automatically in discussion forums relate to online learning burnout? The moderating role of students’ self-regulated learning","authors":"Changqin Huang, Yaxin Tu, Qiyun Wang, Mingxi Li, Tao He, Di Zhang","doi":"10.1016/j.compedu.2024.105213","DOIUrl":"https://doi.org/10.1016/j.compedu.2024.105213","url":null,"abstract":"Engaging students in online discussion forums with social support holds significant potential for preventing and alleviating student burnout. However, the mechanisms by which different types of social support influence learning burnout remain poorly understood. Additionally, existing methods for detecting social support detection are limited in both practical application and theoretical advancement. This study addresses these gaps by developing a robust text classification model for social support and examining its effects on online learning burnout among learners with varying levels of self-regulated learning. We first developed a robust natural language processing model based on bidirectional encoder representations from transformers - bidirectional long short-term memory (BERT-Bi-LSTM) framework, trained on 11226 manually labeled posts from various course forums. This model was then applied to classify forum posts from an educational technology course over one semester. Multiple regression analysis revealed that informational support was negatively associated with two dimensions of learning burnout: emotional exhaustion and improper behavior, and emotional support was negatively correlated with emotional exhaustion and a low sense of achievement. Moreover, a moderating effect analysis indicated that self-regulated learning moderated the negative associations between informational support and improper behavior, as well as between emotional support and emotional exhaustion, with stronger effects observed among learners with lower self-regulated learning. These findings contribute to advancing automated content analysis of social support and provide actionable insights for mitigating student burnout through targeted social support.","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"221 1","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788810","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}
The evolving landscape of educational technologies has ushered Virtual Reality (VR) in the forefront of higher education. As the COVID-19 pandemic propelled a rapid shift toward e-learning, the demand for high-quality distance education has surged, prompting an exploration of VR as a viable solution. While existing research indicates that VR supports student engagement and learning experiences compared with traditional teaching methods, the lack of shared pedagogical frameworks and systematic analyses of its applications leaves a deeper investigation of VR’s potentials and limitations in enhancing learning outcomes still unexplored. This paper presents a systematic literature review aimed at filling this gap by considering studies that evaluate VR-based teaching methods in comparison with traditional ones in higher education contexts in order to assess the strengths and weaknesses of this technology in improving students’ learning outcomes and achieve inclusive education. The analysis focuses on a set of dimensions including the adopted research design, participants’ characteristics, disciplinary field of application, VR technological features (i.e., immersivity, interactivity, operability, commercial availability, and presence of VR training), adopted teaching methodologies, assessed VR impact on learning outcomes and presence of studies involving students with disabilities or Specific Learning Disorders (SpLDs). Based on inclusion/exclusion criteria, a total of 71 studies of VR in higher education were analysed. Most of analysed studies employed quantitative methods (67%), while no qualitative studies were found. More than half of the studies were conducted with undergraduate students (61%). Most of the studies involved VR in STEM disciplines, with almost half of them concerning Health Sciences (45%). VR solutions were most frequently immersive (63%), predominantly using Oculus Rift and HTC Vive HMDs, interactive (59%), single-user (92%) and non-commercial (57%). Only a small portion of studies included a VR training in the research protocol (8%). Most of the studies compared lecture-based methodologies as control condition with active methodologies in the VR condition. Learning outcomes were positively influenced by immersivity, interactivity and active methodologies, although at different degrees. No study involved students with disabilities or SpLDs in the experimentation. By offering a multidimensional perspective on the application of VR in higher education contexts, the paper provides a valuable resource for educators, researchers, and policymakers navigating the dynamic intersection of VR and higher education.
{"title":"Virtual vs. Traditional Learning in Higher Education: A Systematic Review of Comparative Studies","authors":"Tommaso Santilli, Silvia Ceccacci, Maura Mengoni, Catia Giaconi","doi":"10.1016/j.compedu.2024.105214","DOIUrl":"https://doi.org/10.1016/j.compedu.2024.105214","url":null,"abstract":"The evolving landscape of educational technologies has ushered Virtual Reality (VR) in the forefront of higher education. As the COVID-19 pandemic propelled a rapid shift toward e-learning, the demand for high-quality distance education has surged, prompting an exploration of VR as a viable solution. While existing research indicates that VR supports student engagement and learning experiences compared with traditional teaching methods, the lack of shared pedagogical frameworks and systematic analyses of its applications leaves a deeper investigation of VR’s potentials and limitations in enhancing learning outcomes still unexplored. This paper presents a systematic literature review aimed at filling this gap by considering studies that evaluate VR-based teaching methods in comparison with traditional ones in higher education contexts in order to assess the strengths and weaknesses of this technology in improving students’ learning outcomes and achieve inclusive education. The analysis focuses on a set of dimensions including the adopted research design, participants’ characteristics, disciplinary field of application, VR technological features (i.e., immersivity, interactivity, operability, commercial availability, and presence of VR training), adopted teaching methodologies, assessed VR impact on learning outcomes and presence of studies involving students with disabilities or Specific Learning Disorders (SpLDs). Based on inclusion/exclusion criteria, a total of 71 studies of VR in higher education were analysed. Most of analysed studies employed quantitative methods (67%), while no qualitative studies were found. More than half of the studies were conducted with undergraduate students (61%). Most of the studies involved VR in STEM disciplines, with almost half of them concerning Health Sciences (45%). VR solutions were most frequently immersive (63%), predominantly using Oculus Rift and HTC Vive HMDs, interactive (59%), single-user (92%) and non-commercial (57%). Only a small portion of studies included a VR training in the research protocol (8%). Most of the studies compared lecture-based methodologies as control condition with active methodologies in the VR condition. Learning outcomes were positively influenced by immersivity, interactivity and active methodologies, although at different degrees. No study involved students with disabilities or SpLDs in the experimentation. By offering a multidimensional perspective on the application of VR in higher education contexts, the paper provides a valuable resource for educators, researchers, and policymakers navigating the dynamic intersection of VR and higher education.","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"82 1","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788839","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}
Pub Date : 2024-12-01DOI: 10.1016/j.compedu.2024.105211
Xingyu Geng, Yu-Sheng Su
Self-regulated learning has significant importance in blended learning, necessitating an exploration into the effects of metacognition on SRL. Furthermore, SRL exhibits interdependence, thus highlighting the urgent need for research that can capture the temporal processes of SRL in multi-task activities during blended learning. Over 18 weeks, 44 students participated in three SRL tasks designed for blended learning. Students completed a questionnaire assessing their metacognitive awareness at the end of the course. A two-step cluster analysis was employed to explore different metacognitive patterns among students: high metacognitive knowledge and regulation students and low metacognitive knowledge and regulation students. Furthermore, data from student's learning diaries were collected and coded based on the SRL model specific to blended learning. Epistemic Network Analysis, a computer-assisted learning analysis method, was employed to investigate the SRL process of students with different metacognitive patterns in various tasks of blended learning. First, the findings indicate that both types of students strive for performance achievement; however, students with high metacognitive knowledge and regulation primarily employ task strategies, while those with low metacognitive knowledge and regulation focus on time management. Moreover, a detailed centroid analysis conducted for each task revealed that students with high metacognitive knowledge and regulation initiate their SRL process in computer-assisted blended learning with self-efficacy and conclude it with goal setting. Conversely, students with low metacognitive knowledge and regulation commence their SRL process with self-efficacy and conclude it by employing strategies for self-evaluation. Finally, implications, limitations, and future research directions are discussed.
{"title":"The effects of different metacognitive patterns on students' self-regulated learning in blended learning","authors":"Xingyu Geng, Yu-Sheng Su","doi":"10.1016/j.compedu.2024.105211","DOIUrl":"https://doi.org/10.1016/j.compedu.2024.105211","url":null,"abstract":"Self-regulated learning has significant importance in blended learning, necessitating an exploration into the effects of metacognition on SRL. Furthermore, SRL exhibits interdependence, thus highlighting the urgent need for research that can capture the temporal processes of SRL in multi-task activities during blended learning. Over 18 weeks, 44 students participated in three SRL tasks designed for blended learning. Students completed a questionnaire assessing their metacognitive awareness at the end of the course. A two-step cluster analysis was employed to explore different metacognitive patterns among students: high metacognitive knowledge and regulation students and low metacognitive knowledge and regulation students. Furthermore, data from student's learning diaries were collected and coded based on the SRL model specific to blended learning. Epistemic Network Analysis, a computer-assisted learning analysis method, was employed to investigate the SRL process of students with different metacognitive patterns in various tasks of blended learning. First, the findings indicate that both types of students strive for performance achievement; however, students with high metacognitive knowledge and regulation primarily employ task strategies, while those with low metacognitive knowledge and regulation focus on time management. Moreover, a detailed centroid analysis conducted for each task revealed that students with high metacognitive knowledge and regulation initiate their SRL process in computer-assisted blended learning with self-efficacy and conclude it with goal setting. Conversely, students with low metacognitive knowledge and regulation commence their SRL process with self-efficacy and conclude it by employing strategies for self-evaluation. Finally, implications, limitations, and future research directions are discussed.","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"119 1","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816514","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}
Scholars have recommended the application of an intelligent tutoring and instant feedback system (ITIFS) to enhance students' motor skills performance by automatically evaluating their learning performance and providing personalized guidance and feedback. However, solely providing personalized evaluation and feedback may not necessarily attract students' active and sustained engagement in practice. In particular, it is difficult to arouse students' enthusiasm to participate in sports that require repetitive practice to improve their physical abilities and which involve less interaction with the environment and their opponents. To address this issue, grounded in self-determination theory (SDT), the present study integrated a gamification mechanism that aligned with students' psychological needs into an ITIFS. The gamification features included avatars, achievements (personal ratings and rankings), badges, levels, and social networks (group ratings and rankings). It aimed to attract students to engage continuously in practice, and to address the issue of students lacking motivation to engage in repeated practice. To investigate the effectiveness of the proposed method, a quasi-experimental research design was adopted, and the collected data were analyzed with analysis of covariance (ANCOVA), independent samples t tests and qualitative coding. Four classes of university students participated in the experiment. Two classes (N = 80) were the experimental group adopting the SDT-based gamified ITIFS (G-ITIFS), and the other two classes (N = 76) were the control group adopting the conventional ITIFS (C-ITIFS). The findings indicated that the experimental group showed significantly better yoga skills performance and learning engagement compared to the control group. Feedback from students also revealed that the gamification mechanism provided more excitement and had positive impacts, satisfying students’ psychological needs and reinforcing the learning benefits. The findings of the present study revealed that, from the perspective of SDT, incorporating gamification elements into the development of ITIFS could be a promising approach for physical education. Therefore, it is strongly encouraged that educators promote such a gamified intelligent tutoring mode in physical education curriculums as it is crucial to the development of students' physical and mental health, as well as to their enthusiasm to participate in sports.
{"title":"Effectiveness of gamified intelligent tutoring in physical education through the lens of self-determination theory","authors":"Lu-Ho Hsia, Yen-Nan Lin, Chung-Hisenh Lin, Gwo-Jen Hwang","doi":"10.1016/j.compedu.2024.105212","DOIUrl":"https://doi.org/10.1016/j.compedu.2024.105212","url":null,"abstract":"Scholars have recommended the application of an intelligent tutoring and instant feedback system (ITIFS) to enhance students' motor skills performance by automatically evaluating their learning performance and providing personalized guidance and feedback. However, solely providing personalized evaluation and feedback may not necessarily attract students' active and sustained engagement in practice. In particular, it is difficult to arouse students' enthusiasm to participate in sports that require repetitive practice to improve their physical abilities and which involve less interaction with the environment and their opponents. To address this issue, grounded in self-determination theory (SDT), the present study integrated a gamification mechanism that aligned with students' psychological needs into an ITIFS. The gamification features included avatars, achievements (personal ratings and rankings), badges, levels, and social networks (group ratings and rankings). It aimed to attract students to engage continuously in practice, and to address the issue of students lacking motivation to engage in repeated practice. To investigate the effectiveness of the proposed method, a quasi-experimental research design was adopted, and the collected data were analyzed with analysis of covariance (ANCOVA), independent samples <ce:italic>t</ce:italic> tests and qualitative coding. Four classes of university students participated in the experiment. Two classes (<ce:italic>N</ce:italic> = 80) were the experimental group adopting the SDT-based gamified ITIFS (G-ITIFS), and the other two classes (<ce:italic>N</ce:italic> = 76) were the control group adopting the conventional ITIFS (C-ITIFS). The findings indicated that the experimental group showed significantly better yoga skills performance and learning engagement compared to the control group. Feedback from students also revealed that the gamification mechanism provided more excitement and had positive impacts, satisfying students’ psychological needs and reinforcing the learning benefits. The findings of the present study revealed that, from the perspective of SDT, incorporating gamification elements into the development of ITIFS could be a promising approach for physical education. Therefore, it is strongly encouraged that educators promote such a gamified intelligent tutoring mode in physical education curriculums as it is crucial to the development of students' physical and mental health, as well as to their enthusiasm to participate in sports.","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"306 1","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788841","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}
Pub Date : 2024-11-26DOI: 10.1016/j.compedu.2024.105210
Sarit Barzilai, Marc Stadtler
Digital games can help students learn how to cope with misinformation. However, misinformation games typically include multiple game mechanics, making it hard to identify which mechanics contribute to learning. Hence, the aim of this study was to clarify how misinformation games promote learning by examining the effects of two promising misinformation game mechanics– simulating evaluation strategies and providing explanations about the types of misinformation encountered in the game. The participants were 132 8th and 9th-grade students who played different versions of a misinformation game that either simulated evaluation strategies or did not simulate them and either included misinformation explanations or did not include them. The results indicated that simulating evaluation strategies improved sharing and accuracy discernment following the game, but providing misinformation explanations did not. Simulating evaluation strategies also led to greater awareness of corroboration and sourcing strategies, whereas misinformation explanations only supported awareness of sourcing. Structural equation modeling revealed that simulating evaluation strategies led to more accurate sharing decisions in the game and that in-game sharing accuracy mediated the effect of simulating strategies on post-game accuracy and sharing discernment. These findings advance the understanding of misinformation game design by identifying game mechanics that are conducive to learning. The findings highlight that simulating evaluation strategies in a game can help prepare learners to cope with misinformation. More research is needed to explore the effective integration of misinformation explanations into such games.
{"title":"Learning to Evaluate (Mis)information in an Online Game: Strategies Matter!","authors":"Sarit Barzilai, Marc Stadtler","doi":"10.1016/j.compedu.2024.105210","DOIUrl":"https://doi.org/10.1016/j.compedu.2024.105210","url":null,"abstract":"Digital games can help students learn how to cope with misinformation. However, misinformation games typically include multiple game mechanics, making it hard to identify which mechanics contribute to learning. Hence, the aim of this study was to clarify how misinformation games promote learning by examining the effects of two promising misinformation game mechanics– simulating evaluation strategies and providing explanations about the types of misinformation encountered in the game. The participants were 132 8th and 9th-grade students who played different versions of a misinformation game that either simulated evaluation strategies or did not simulate them and either included misinformation explanations or did not include them. The results indicated that simulating evaluation strategies improved sharing and accuracy discernment following the game, but providing misinformation explanations did not. Simulating evaluation strategies also led to greater awareness of corroboration and sourcing strategies, whereas misinformation explanations only supported awareness of sourcing. Structural equation modeling revealed that simulating evaluation strategies led to more accurate sharing decisions in the game and that in-game sharing accuracy mediated the effect of simulating strategies on post-game accuracy and sharing discernment. These findings advance the understanding of misinformation game design by identifying game mechanics that are conducive to learning. The findings highlight that simulating evaluation strategies in a game can help prepare learners to cope with misinformation. More research is needed to explore the effective integration of misinformation explanations into such games.","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"34 1","pages":""},"PeriodicalIF":12.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788801","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}
Pub Date : 2024-11-23DOI: 10.1016/j.compedu.2024.105208
Ackermans Kevin , Marjoke Bakker , Anne-Marieke van Loon , Marijke Kral , Gino Camp
Introduction
Personalized learning, a topic that has garnered significant attention in education, is known for its potential to cater to student's unique needs and improve educational outcomes. However, most large-scale longitudinal studies on personalized learning have primarily focused on middle school students and above (age ≥11). This study, in contrast, delves into the uncharted territory of how personalized learning affects younger students (ages 7–12), a domain largely overlooked by large-scale studies.
Objective
To understand the effect of PL on young learners’ academic performance, metacognitive awareness, and motivation.
Method
Multidisciplinary design teams embedded personalized learning in eight participating elementary schools, resulting in personalized learning interventions tailored to each school in four subjects. The effects were measured over three years among 588 students and 82 teachers and analyzed using a Bayesian Gaussian regression with random intercept models and nested groups.
Results
We found significant evidence that the personalized learning interventions fostered academic performance in two of the four subjects: math and spelling. Regarding spelling, we found that the schools in which metacognitive skills were explicitly trained improved their students' spelling performance significantly compared to other schools. We found significant evidence suggesting that student ICT skills improved metacognitive awareness, intrinsic motivation, and math performance. We also found significant evidence that teachers' ICT skills support student metacognitive awareness. However, we could not confirm the theorized effect of personalized learning on metacognitive awareness or students’ intrinsic motivation.
Conclusion
Our study provides evidence-based recommendations for implementing personalized learning interventions in elementary schools, particularly for math and spelling. Finally, improving ICT skills among students and teachers benefits students in math and in their metacognitive skills.
{"title":"Young learners’ motivation, self-regulation and performance in personalized learning","authors":"Ackermans Kevin , Marjoke Bakker , Anne-Marieke van Loon , Marijke Kral , Gino Camp","doi":"10.1016/j.compedu.2024.105208","DOIUrl":"10.1016/j.compedu.2024.105208","url":null,"abstract":"<div><h3>Introduction</h3><div>Personalized learning, a topic that has garnered significant attention in education, is known for its potential to cater to student's unique needs and improve educational outcomes. However, most large-scale longitudinal studies on personalized learning have primarily focused on middle school students and above (age ≥11). This study, in contrast, delves into the uncharted territory of how personalized learning affects younger students (ages 7–12), a domain largely overlooked by large-scale studies.</div></div><div><h3>Objective</h3><div>To understand the effect of PL on young learners’ academic performance, metacognitive awareness, and motivation.</div></div><div><h3>Method</h3><div>Multidisciplinary design teams embedded personalized learning in eight participating elementary schools, resulting in personalized learning interventions tailored to each school in four subjects. The effects were measured over three years among 588 students and 82 teachers and analyzed using a Bayesian Gaussian regression with random intercept models and nested groups.</div></div><div><h3>Results</h3><div>We found significant evidence that the personalized learning interventions fostered academic performance in two of the four subjects: math and spelling. Regarding spelling, we found that the schools in which metacognitive skills were explicitly trained improved their students' spelling performance significantly compared to other schools. We found significant evidence suggesting that student ICT skills improved metacognitive awareness, intrinsic motivation, and math performance. We also found significant evidence that teachers' ICT skills support student metacognitive awareness. However, we could not confirm the theorized effect of personalized learning on metacognitive awareness or students’ intrinsic motivation.</div></div><div><h3>Conclusion</h3><div>Our study provides evidence-based recommendations for implementing personalized learning interventions in elementary schools, particularly for math and spelling. Finally, improving ICT skills among students and teachers benefits students in math and in their metacognitive skills.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"226 ","pages":"Article 105208"},"PeriodicalIF":8.9,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-21DOI: 10.1016/j.compedu.2024.105207
Olha Ketsman , Alissa Droog , Sumaiya Qazi
Advancing knowledge in educational technology requires a diverse array of research designs, methodologies, and data sources. Mixed methods research, particularly valuable for studying intricate issues, remains scarce in educational technology. The purpose of this study is to examine the prevalence of mixed methods research studies published in the top 10 educational technology journals between 2018 and 2022. By examining n = 2380 articles from top educational technology journals, this study aims to enhance awareness of mixed methodologies among educational technology researchers, highlighting common approaches and identifying gaps. Adhering to best practices for prevalence studies and utilizing methods from evidence synthesis, three authors screened and coded articles for quantitative, qualitative, mixed methods, or non-empirical studies through three stages of data analysis. Results indicate that mixed methods research is underrepresented, constituting only 12% of all published articles, the smallest proportion compared to other types. Furthermore, 64% of these mixed methods studies did not self-identify as such despite employing mixed methods approaches, revealing a gap in methodological understanding. Among those that did self-identify, 68% failed to specify the type of core mixed methods approach used, indicating further gaps. The prevalence of mixed methods research published in the top 10 educational technology journals in a 5-year period ranged between 5% and 15% and is low compared to other types of published studies in the field. Addressing the underrepresentation and improving the specificity of methodological reporting can enhance the overall quality of research in educational technology.
{"title":"Mapping the prevalence of mixed methods research in educational technology journals","authors":"Olha Ketsman , Alissa Droog , Sumaiya Qazi","doi":"10.1016/j.compedu.2024.105207","DOIUrl":"10.1016/j.compedu.2024.105207","url":null,"abstract":"<div><div>Advancing knowledge in educational technology requires a diverse array of research designs, methodologies, and data sources. Mixed methods research, particularly valuable for studying intricate issues, remains scarce in educational technology. The purpose of this study is to examine the prevalence of mixed methods research studies published in the top 10 educational technology journals between 2018 and 2022. By examining n = 2380 articles from top educational technology journals, this study aims to enhance awareness of mixed methodologies among educational technology researchers, highlighting common approaches and identifying gaps. Adhering to best practices for prevalence studies and utilizing methods from evidence synthesis, three authors screened and coded articles for quantitative, qualitative, mixed methods, or non-empirical studies through three stages of data analysis. Results indicate that mixed methods research is underrepresented, constituting only 12% of all published articles, the smallest proportion compared to other types. Furthermore, 64% of these mixed methods studies did not self-identify as such despite employing mixed methods approaches, revealing a gap in methodological understanding. Among those that did self-identify, 68% failed to specify the type of core mixed methods approach used, indicating further gaps. The prevalence of mixed methods research published in the top 10 educational technology journals in a 5-year period ranged between 5% and 15% and is low compared to other types of published studies in the field. Addressing the underrepresentation and improving the specificity of methodological reporting can enhance the overall quality of research in educational technology.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"226 ","pages":"Article 105207"},"PeriodicalIF":8.9,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743013","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}
Help-seeking is an active learning strategy tied to self-regulated learning (SRL), where learners seek assistance when facing challenges. They may seek help from teachers, peers, intelligent tu-tor systems, and more recently, generative artificial intelligence (AI). However, there is limited empirical research on how learners’ help-seeking process differs between generative AI and hu-man experts. To address this, we conducted a lab experiment with 38 university students tasked with essay writing and revising. The students were randomly divided into two groups: one seeking help from ChatGPT (AI Group) and the other from an experienced teacher (HE Group). To examine their help-seeking processes, we used a combination of statistical testing and process mining methods, analyzing multimodal data (e.g., trace data, eye-tracking data, and conversa-tional data). Our results indicated that the AI Group exhibited a nonlinear help-seeking process, such as skipping evaluation, differing significantly from the linear model observed in the HE Group which also aligned with classic help-seeking theory. Detailed analysis revealed that the AI Group asked more operational questions, showing pragmatic help-seeking activities, whereas the HE Group was more proactive in evaluating and processing received feedback. We discussed factors such as social pressure, metacognitive off-loading, and over-reliance on AI in these different help-seeking scenarios. More importantly, this study offers innovative insights and evidence, based on multimodal data, to better understand and scaffold learners learning with generative AI.
{"title":"Unpacking help-seeking process through multimodal learning analytics: A comparative study of ChatGPT vs Human expert","authors":"Angxuan Chen , Mengtong Xiang , Junyi Zhou , Jiyou Jia , Junjie Shang , Xinyu Li , Dragan Gašević , Yizhou Fan","doi":"10.1016/j.compedu.2024.105198","DOIUrl":"10.1016/j.compedu.2024.105198","url":null,"abstract":"<div><div>Help-seeking is an active learning strategy tied to self-regulated learning (SRL), where learners seek assistance when facing challenges. They may seek help from teachers, peers, intelligent tu-tor systems, and more recently, generative artificial intelligence (AI). However, there is limited empirical research on how learners’ help-seeking process differs between generative AI and hu-man experts. To address this, we conducted a lab experiment with 38 university students tasked with essay writing and revising. The students were randomly divided into two groups: one seeking help from ChatGPT (AI Group) and the other from an experienced teacher (HE Group). To examine their help-seeking processes, we used a combination of statistical testing and process mining methods, analyzing multimodal data (e.g., trace data, eye-tracking data, and conversa-tional data). Our results indicated that the AI Group exhibited a nonlinear help-seeking process, such as skipping evaluation, differing significantly from the linear model observed in the HE Group which also aligned with classic help-seeking theory. Detailed analysis revealed that the AI Group asked more operational questions, showing pragmatic help-seeking activities, whereas the HE Group was more proactive in evaluating and processing received feedback. We discussed factors such as social pressure, metacognitive off-loading, and over-reliance on AI in these different help-seeking scenarios. More importantly, this study offers innovative insights and evidence, based on multimodal data, to better understand and scaffold learners learning with generative AI.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"226 ","pages":"Article 105198"},"PeriodicalIF":8.9,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.compedu.2024.105197
Giorgio Di Pietro , Jonatan Castaño Muñoz
This paper presents a meta-analysis that investigates the impact that the educational use of digital technologies has on less advantaged students’ achievement. We use a comprehensive definition for this group of students that includes all students in less developed countries as well as more disadvantaged students in more developed countries. 740 estimates from 72 studies employing experimental and quasi-experimental research designs are collected. Overall, educational technology initiatives are found to have a small, positive, statistically significant effect that remains even after correcting for publication bias. Additionally, our results indicate that computer-assisted learning and behavioural interventions are more effective in raising the achievement of less advantaged students than simple access to technology. Interestingly, the effect of these two interventions appears to be of a similar magnitude. Finally, the use of digital technologies is associated with slightly greater achievements in math and science than humanities.
{"title":"A meta-analysis on the effect of technology on the achievement of less advantaged students","authors":"Giorgio Di Pietro , Jonatan Castaño Muñoz","doi":"10.1016/j.compedu.2024.105197","DOIUrl":"10.1016/j.compedu.2024.105197","url":null,"abstract":"<div><div>This paper presents a meta-analysis that investigates the impact that the educational use of digital technologies has on less advantaged students’ achievement. We use a comprehensive definition for this group of students that includes all students in less developed countries as well as more disadvantaged students in more developed countries. 740 estimates from 72 studies employing experimental and quasi-experimental research designs are collected. Overall, educational technology initiatives are found to have a small, positive, statistically significant effect that remains even after correcting for publication bias. Additionally, our results indicate that computer-assisted learning and behavioural interventions are more effective in raising the achievement of less advantaged students than simple access to technology. Interestingly, the effect of these two interventions appears to be of a similar magnitude. Finally, the use of digital technologies is associated with slightly greater achievements in math and science than humanities.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"226 ","pages":"Article 105197"},"PeriodicalIF":8.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142684348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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