Pub Date : 2024-01-30DOI: 10.1109/TLT.2024.3360121
Thomas Sergent;Morgane Daniel;François Bouchet;Thibault Carron
Self-regulated learning (SRL) skills are critical for students of all ages to maximize their learning. Two key processes of SRL are being aware of one's performance (self-evaluation) and believing in one's capabilities to produce given attainments (self-efficacy). To assess and improve these capabilities in young children (5–8), we use a literacy web application, where we introduced two randomly triggered prompts to evaluate perceived difficulty and desired difficulty. Comparing students' actual performance with their responses to self-regulatory prompts provides information about their ability to self-regulate their learning, in particular their self-evaluation and self-efficacy. The novelty of this work resides in studying the SRL of young children (5–8) in digital learning environments while learning another task (reading in our case), measuring and improving some SRL abilities themselves and not only measuring and improving academic results in other tasks, and the large number of students on which the studies were carried (over 400 000). Using 15 982 994 responses from 467 116 students, we first measured two types of SRL deficits, and then, we assessed how a scaffolding and remediation strategy can reduce these deficits. In Study 1, we compare a group receiving remediation feedback to a control group, whereas in Study 2, we determine the impact of age and level on the remediation efficiency. Our contribution is twofold: a method to address on the long term a deficit in self-evaluation or in self-efficacy in a digital learning environment, and a corroboration of the fact that students who are academically at risk lack self-efficacy and avoid tackling challenging exercises compared with their level. We, therefore, recommend that digital learning environments integrate an overlay of SRL, such as self-evaluation and self-efficacy remediation loops, especially for younger students and students who are struggling academically. We included notes for educational practitioners in this article for this purpose.
{"title":"How Can Self-Evaluation and Self-Efficacy Skills of Young Learners be Scaffolded in a Web Application?","authors":"Thomas Sergent;Morgane Daniel;François Bouchet;Thibault Carron","doi":"10.1109/TLT.2024.3360121","DOIUrl":"10.1109/TLT.2024.3360121","url":null,"abstract":"Self-regulated learning (SRL) skills are critical for students of all ages to maximize their learning. Two key processes of SRL are being aware of one's performance (self-evaluation) and believing in one's capabilities to produce given attainments (self-efficacy). To assess and improve these capabilities in young children (5–8), we use a literacy web application, where we introduced two randomly triggered prompts to evaluate perceived difficulty and desired difficulty. Comparing students' actual performance with their responses to self-regulatory prompts provides information about their ability to self-regulate their learning, in particular their self-evaluation and self-efficacy. The novelty of this work resides in studying the SRL of young children (5–8) in digital learning environments while learning another task (reading in our case), measuring and improving some SRL abilities themselves and not only measuring and improving academic results in other tasks, and the large number of students on which the studies were carried (over 400 000). Using 15 982 994 responses from 467 116 students, we first measured two types of SRL deficits, and then, we assessed how a scaffolding and remediation strategy can reduce these deficits. In Study 1, we compare a group receiving remediation feedback to a control group, whereas in Study 2, we determine the impact of age and level on the remediation efficiency. Our contribution is twofold: a method to address on the long term a deficit in self-evaluation or in self-efficacy in a digital learning environment, and a corroboration of the fact that students who are academically at risk lack self-efficacy and avoid tackling challenging exercises compared with their level. We, therefore, recommend that digital learning environments integrate an overlay of SRL, such as self-evaluation and self-efficacy remediation loops, especially for younger students and students who are struggling academically. We included notes for educational practitioners in this article for this purpose.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1184-1197"},"PeriodicalIF":3.7,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139945561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Metaverse has evolved as one of the popular research agenda that let users learn, socialize, and collaborate in a networked 3-D immersive virtual world. Due to the rich multimedia streaming capability and immersive user experience with high-speed communication, the metaverse is an ideal model for education, training, and skill development tasks. To facilitate research in this area, we provide a comprehensive review of the various educational use cases and explore how enabling technologies, such as extended reality and the Internet of Everything will play a major role in educational services in future metaverses. Then, we provide an overview of metaverse-based educational applications focusing on education, training, and skill development and analyze the technologies they are built upon. We identify common research problems and future research directions in the domain. This article also identifies core ethical considerations of metaverse for education and potential pitfalls. We believe this survey can fully demonstrate the versatility of metaverse-driven education, which could serve as a potential guideline for the researchers.
{"title":"Advancing Education Through Extended Reality and Internet of Everything Enabled Metaverses: Applications, Challenges, and Open Issues","authors":"Senthil Kumar Jagatheesaperumal;Kashif Ahmad;Ala Al-Fuqaha;Junaid Qadir","doi":"10.1109/TLT.2024.3358859","DOIUrl":"10.1109/TLT.2024.3358859","url":null,"abstract":"Metaverse has evolved as one of the popular research agenda that let users learn, socialize, and collaborate in a networked 3-D immersive virtual world. Due to the rich multimedia streaming capability and immersive user experience with high-speed communication, the metaverse is an ideal model for education, training, and skill development tasks. To facilitate research in this area, we provide a comprehensive review of the various educational use cases and explore how enabling technologies, such as extended reality and the Internet of Everything will play a major role in educational services in future metaverses. Then, we provide an overview of metaverse-based educational applications focusing on education, training, and skill development and analyze the technologies they are built upon. We identify common research problems and future research directions in the domain. This article also identifies core ethical considerations of metaverse for education and potential pitfalls. We believe this survey can fully demonstrate the versatility of metaverse-driven education, which could serve as a potential guideline for the researchers.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1120-1139"},"PeriodicalIF":3.7,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10415252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139945439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-01-26DOI: 10.1109/TLT.2024.3358800
Mario Vallarino;Saverio Iacono;Edoardo Bellanti;Gianni V. Vercelli
This article introduces a novel approach to remote laboratory instruction, specifically designed for teaching three-dimensional modeling using Blender software. The lab uses virtual machines to provide students with the necessary computational power to carry out the course activities, along with the correct version of the software. The flipped remote lab approach combines the elements of flipped classroom and peer assessment, making it suitable for face-to-face, totally online, or hybrid classes. Prior to each of the two lectures, students begin to practice by replicating the instructor's demonstrations in a set of concise tutorials. Upon completion of the assigned tasks, students carry out self-assessments of their own modeling, in addition to assessing two models created by their peers. A rubric comprising three questions facilitates the assessment process and allows providing feedback on each response. During the subsequent lecture, students work together with the instructor to address challenges encountered in their modeling, exploring also the advanced aspects of software usage that time constraints preclude in a traditional setting. The analysis of the flipped remote lab results reveals that student responses in peer-assessment activities are relevant to the posed questions. Moreover, the students who realized the models demonstrated a comparable level of rigor in self-assessment as their mates who reviewed their works. While students express a high degree of appreciation for the laboratory activities, a notable concern is the highlighted heavy workload. Increasing the allocated time for task completion can help mitigate the workload impact. The article concludes with insights gained from the implementation of the flipped remote lab approach.
{"title":"A Flipped Remote Lab: Using a Peer-Assessment Tool for Learning 3-D Modeling","authors":"Mario Vallarino;Saverio Iacono;Edoardo Bellanti;Gianni V. Vercelli","doi":"10.1109/TLT.2024.3358800","DOIUrl":"10.1109/TLT.2024.3358800","url":null,"abstract":"This article introduces a novel approach to remote laboratory instruction, specifically designed for teaching three-dimensional modeling using Blender software. The lab uses virtual machines to provide students with the necessary computational power to carry out the course activities, along with the correct version of the software. The flipped remote lab approach combines the elements of flipped classroom and peer assessment, making it suitable for face-to-face, totally online, or hybrid classes. Prior to each of the two lectures, students begin to practice by replicating the instructor's demonstrations in a set of concise tutorials. Upon completion of the assigned tasks, students carry out self-assessments of their own modeling, in addition to assessing two models created by their peers. A rubric comprising three questions facilitates the assessment process and allows providing feedback on each response. During the subsequent lecture, students work together with the instructor to address challenges encountered in their modeling, exploring also the advanced aspects of software usage that time constraints preclude in a traditional setting. The analysis of the flipped remote lab results reveals that student responses in peer-assessment activities are relevant to the posed questions. Moreover, the students who realized the models demonstrated a comparable level of rigor in self-assessment as their mates who reviewed their works. While students express a high degree of appreciation for the laboratory activities, a notable concern is the highlighted heavy workload. Increasing the allocated time for task completion can help mitigate the workload impact. The article concludes with insights gained from the implementation of the flipped remote lab approach.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1140-1154"},"PeriodicalIF":3.7,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139945546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-26DOI: 10.1109/TLT.2024.3358864
Hani Y. Ayyoub;Omar S. Al-Kadi
Education is a dynamic field that must be adaptable to sudden changes and disruptions caused by events like pandemics, war, and natural disasters related to climate change. When these events occur, traditional classrooms with traditional or blended delivery can shift to fully online learning, which requires an efficient learning environment that meets students’ needs. While learning management systems support teachers’ productivity and creativity, they typically provide the same content to all learners in a course, ignoring their unique learning styles. To address this issue, we propose a semisupervised machine learning approach that detects students’ learning styles using a data mining technique. We use the commonly used Felder-Silverman learning style model and demonstrate that our semisupervised method can produce reliable classification models with few labeled data. We evaluate our approach on two different courses and achieve an accuracy of 88.83% and 77.35%, respectively. Our work shows that educational data mining and semisupervised machine learning techniques can identify different learning styles and create a personalized learning environment.
{"title":"Learning Style Identification Using Semisupervised Self-Taught Labeling","authors":"Hani Y. Ayyoub;Omar S. Al-Kadi","doi":"10.1109/TLT.2024.3358864","DOIUrl":"https://doi.org/10.1109/TLT.2024.3358864","url":null,"abstract":"Education is a dynamic field that must be adaptable to sudden changes and disruptions caused by events like pandemics, war, and natural disasters related to climate change. When these events occur, traditional classrooms with traditional or blended delivery can shift to fully online learning, which requires an efficient learning environment that meets students’ needs. While learning management systems support teachers’ productivity and creativity, they typically provide the same content to all learners in a course, ignoring their unique learning styles. To address this issue, we propose a semisupervised machine learning approach that detects students’ learning styles using a data mining technique. We use the commonly used Felder-Silverman learning style model and demonstrate that our semisupervised method can produce reliable classification models with few labeled data. We evaluate our approach on two different courses and achieve an accuracy of 88.83% and 77.35%, respectively. Our work shows that educational data mining and semisupervised machine learning techniques can identify different learning styles and create a personalized learning environment.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1093-1106"},"PeriodicalIF":3.7,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-18DOI: 10.1109/TLT.2024.3355791
Marc Burchart;Joerg M. Haake
In distance education courses with a large number of students and groups, the organization and facilitation of collaborative writing tasks are challenging. Teachers need support for planning, specification, execution, monitoring, and evaluation of collaborative writing tasks in their course. This requires a collaborative learning platform for coordinating all of the different phases in the writing process. In order to enable the design of such a platform, we created a process model of collaborative writing tasks that is based on the identification of participants, activities, phases, and orchestration from the literature. This model may serve as a basis for teachers to specify the instances for such tasks and can be used to determine the functional requirements needed for supporting model-compliant tasks on a collaborative learning platform. We present a general architecture for a platform of this kind that is independent of a concrete learning management systerm (LMS) system or shared editor and demonstrate its implementation using Moodle, Etherpad Lite, and Docker. The platform makes it easier for teachers to create groups and automatically assign members to collaborative workspaces. It enables asynchronous as well as synchronous text editing and communication. It also respects the European information security and data protection requirements and helps teachers monitor both the writing and reviewing activities. The platform was evaluated over a period of three semesters in distance learning courses with more than 4500 students. It proved a scalable and robust environment for coordinating the collaborative writing process of teachers and students and enables analysis of collaborative writing behavior by teachers and researchers.
在有大量学生和小组的远程教育课程中，组织和促进协作写作任务具有挑战性。教师需要在课程中为协作写作任务的规划、规范、执行、监控和评估提供支持。这就需要一个协作学习平台来协调写作过程中的所有不同阶段。为了能够设计这样一个平台，我们根据文献中对参与者、活动、阶段和协调的识别，创建了一个协作写作任务的过程模型。该模型可作为教师指定此类任务实例的基础，并可用于确定在协作学习平台上支持符合模型的任务所需的功能要求。我们提出了一个独立于具体学习管理系统（LMS）或共享编辑器的此类平台的总体架构，并使用 Moodle、Etherpad Lite 和 Docker 演示了其实现。该平台使教师更容易创建群组，并自动将成员分配到协作工作空间。它既能实现异步文本编辑，也能实现同步文本编辑和交流。该平台还遵守欧洲信息安全和数据保护要求，帮助教师监控写作和审阅活动。该平台在有 4500 多名学生参加的远程学习课程中进行了为期三个学期的评估。事实证明，该平台是一个可扩展的、稳健的环境，可以协调教师和学生的协作写作过程，并使教师和研究人员能够对协作写作行为进行分析。
{"title":"Supporting Collaborative Writing Tasks in Large-Scale Distance Education","authors":"Marc Burchart;Joerg M. Haake","doi":"10.1109/TLT.2024.3355791","DOIUrl":"https://doi.org/10.1109/TLT.2024.3355791","url":null,"abstract":"In distance education courses with a large number of students and groups, the organization and facilitation of collaborative writing tasks are challenging. Teachers need support for planning, specification, execution, monitoring, and evaluation of collaborative writing tasks in their course. This requires a collaborative learning platform for coordinating all of the different phases in the writing process. In order to enable the design of such a platform, we created a process model of collaborative writing tasks that is based on the identification of participants, activities, phases, and orchestration from the literature. This model may serve as a basis for teachers to specify the instances for such tasks and can be used to determine the functional requirements needed for supporting model-compliant tasks on a collaborative learning platform. We present a general architecture for a platform of this kind that is independent of a concrete learning management systerm (LMS) system or shared editor and demonstrate its implementation using Moodle, Etherpad Lite, and Docker. The platform makes it easier for teachers to create groups and automatically assign members to collaborative workspaces. It enables asynchronous as well as synchronous text editing and communication. It also respects the European information security and data protection requirements and helps teachers monitor both the writing and reviewing activities. The platform was evaluated over a period of three semesters in distance learning courses with more than 4500 students. It proved a scalable and robust environment for coordinating the collaborative writing process of teachers and students and enables analysis of collaborative writing behavior by teachers and researchers.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1051-1068"},"PeriodicalIF":3.7,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139676134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intact graphic execution ability is considered an important gateway to one's academic success. It is often reported that the graphic execution ability of neurotypical children and those having autism, i.e., children with autism spectrum disorder (ASD) is differentiated. Although insightful, these reports had been mostly for text handwriting task that is not language agnostic (with observations related to a writer's language skills), thereby emphasizing the importance of assessing graphic execution ability during language-agnostic graphic task. The assessment can be carried out in terms of kinetic (e.g., grip force exerted on the pen barrel, in-hand manipulation of the pen, etc.) and kinematic (e.g., speed of the pen-tip, pen-tip pressure, etc.) attributes. Given the importance of assessing one's graphic execution ability and the use of language-agnostic platform, in our study, we present the design of a language-agnostic digitized platform that can assess the kinematic and kinetic attributes of one's graphic execution ability. We included shape drawing (with varying turns) as the graphic task. Also, we carried out a study in which 25 neurotypical children and 25 children with ASD took part. Results indicated that, irrespective of the shape being drawn, the kinematic and kinetic attributes of graphic execution were differentiated between the two participant groups. Our system has the potential to help teachers, therapists, etc., to assess one's graphic execution ability and adopt timely strategies addressing the difficulties (if any) faced by a child.
{"title":"Assessment of Kinematic and Kinetic Attributes of Graphic Execution of Children With Autism and Typically Developing Children Using a Digitized Platform","authors":"Pragya Verma;Niravkumar Patel;Prachi Sharma;Manasi Anand Kanetkar;Madhu Singh;Uttama Lahiri","doi":"10.1109/TLT.2024.3355793","DOIUrl":"https://doi.org/10.1109/TLT.2024.3355793","url":null,"abstract":"Intact graphic execution ability is considered an important gateway to one's academic success. It is often reported that the graphic execution ability of neurotypical children and those having autism, i.e., children with autism spectrum disorder (ASD) is differentiated. Although insightful, these reports had been mostly for text handwriting task that is not language agnostic (with observations related to a writer's language skills), thereby emphasizing the importance of assessing graphic execution ability during language-agnostic graphic task. The assessment can be carried out in terms of kinetic (e.g., grip force exerted on the pen barrel, in-hand manipulation of the pen, etc.) and kinematic (e.g., speed of the pen-tip, pen-tip pressure, etc.) attributes. Given the importance of assessing one's graphic execution ability and the use of language-agnostic platform, in our study, we present the design of a language-agnostic digitized platform that can assess the kinematic and kinetic attributes of one's graphic execution ability. We included shape drawing (with varying turns) as the graphic task. Also, we carried out a study in which 25 neurotypical children and 25 children with ASD took part. Results indicated that, irrespective of the shape being drawn, the kinematic and kinetic attributes of graphic execution were differentiated between the two participant groups. Our system has the potential to help teachers, therapists, etc., to assess one's graphic execution ability and adopt timely strategies addressing the difficulties (if any) faced by a child.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1082-1092"},"PeriodicalIF":3.7,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139700457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-16DOI: 10.1109/TLT.2024.3355015
Chad C. Tossell;Nathan L. Tenhundfeld;Ali Momen;Katrina Cooley;Ewart J. de Visser
This article examined student experiences before and after an essay writing assignment that required the use of ChatGPT within an undergraduate engineering course. Utilizing a pre–post study design, we gathered data from 24 participants to evaluate ChatGPT's support for both completing and grading an essay assignment, exploring its educational value and impact on the learning process. Our quantitative and thematic analyses uncovered that ChatGPT did not simplify the writing process. Instead, the tool transformed the student learning experience yielding mixed responses. Participants reported finding ChatGPT valuable for learning, and their comfort with its ethical and benevolent aspects increased postuse. Concerns with ChatGPT included poor accuracy and limited feedback on the confidence of its output. Students preferred instructors to use ChatGPT to help grade their assignments, with appropriate oversight. They did not trust ChatGPT to grade by itself. Student views of ChatGPT evolved from a perceived “cheating tool” to a collaborative resource that requires human oversight and calibrated trust. Implications for writing, education, and trust in artificial intelligence are discussed.
{"title":"Student Perceptions of ChatGPT Use in a College Essay Assignment: Implications for Learning, Grading, and Trust in Artificial Intelligence","authors":"Chad C. Tossell;Nathan L. Tenhundfeld;Ali Momen;Katrina Cooley;Ewart J. de Visser","doi":"10.1109/TLT.2024.3355015","DOIUrl":"https://doi.org/10.1109/TLT.2024.3355015","url":null,"abstract":"This article examined student experiences before and after an essay writing assignment that required the use of ChatGPT within an undergraduate engineering course. Utilizing a pre–post study design, we gathered data from 24 participants to evaluate ChatGPT's support for both completing and grading an essay assignment, exploring its educational value and impact on the learning process. Our quantitative and thematic analyses uncovered that ChatGPT did not simplify the writing process. Instead, the tool transformed the student learning experience yielding mixed responses. Participants reported finding ChatGPT valuable for learning, and their comfort with its ethical and benevolent aspects increased postuse. Concerns with ChatGPT included poor accuracy and limited feedback on the confidence of its output. Students preferred instructors to use ChatGPT to help grade their assignments, with appropriate oversight. They did not trust ChatGPT to grade by itself. Student views of ChatGPT evolved from a perceived “cheating tool” to a collaborative resource that requires human oversight and calibrated trust. Implications for writing, education, and trust in artificial intelligence are discussed.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1069-1081"},"PeriodicalIF":3.7,"publicationDate":"2024-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10400910","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139700502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-01-15DOI: 10.1109/TLT.2024.3354128
David P. Reid;Timothy D. Drysdale
The designs of many student-facing learning analytics (SFLA) dashboards are insufficiently informed by educational research and lack rigorous evaluation in authentic learning contexts, including during remote laboratory practical work. In this article, we present and evaluate an SFLA dashboard designed using the principles of formative assessment to provide feedback to students during remote lab activities. Feedback is based upon graphical visualizations of student actions performed during lab tasks and comparison to expected procedures using TaskCompare—our custom, asymmetric graph dissimilarity measure that distinguishes students who miss expected actions from those who perform additional actions, a capability missing in existing graph distance (symmetrical dissimilarity) measures. Using a total of $N = 235$ student graphs collected during authentic learning in two different engineering courses, we describe the validation of TaskCompare and evaluate the impact of the SFLA dashboard on task completion during remote lab activities. In addition, we use components of the motivated strategies for learning questionnaire as covariates for propensity score matching to account for potential bias in self-selection of use of the dashboard. We find that those students who used the SFLA dashboard achieved significantly better task completion rate (nearly double) than those who did not, with a significant difference in TaskCompare score between the two groups (Mann–Whitney $U = 453.5$ , $p < 0.01$ and Cliff's $delta = 0.43$ , large effect size). This difference remains after accounting for self-selection. We also report that students' positive rating of the usefulness of the SFLA dashboard for completing lab work is significantly above a neutral response ( $S = 21.0$ and $p < 0.01$ ). These findings provide evidence that our SFLA dashboard is an effective means of providing formative assessment during remote laboratory activities.
{"title":"Student-Facing Learning Analytics Dashboard for Remote Lab Practical Work","authors":"David P. Reid;Timothy D. Drysdale","doi":"10.1109/TLT.2024.3354128","DOIUrl":"https://doi.org/10.1109/TLT.2024.3354128","url":null,"abstract":"The designs of many student-facing learning analytics (SFLA) dashboards are insufficiently informed by educational research and lack rigorous evaluation in authentic learning contexts, including during remote laboratory practical work. In this article, we present and evaluate an SFLA dashboard designed using the principles of formative assessment to provide feedback to students during remote lab activities. Feedback is based upon graphical visualizations of student actions performed during lab tasks and comparison to expected procedures using TaskCompare—our custom, asymmetric graph dissimilarity measure that distinguishes students who miss expected actions from those who perform additional actions, a capability missing in existing graph distance (symmetrical dissimilarity) measures. Using a total of \u0000<inline-formula><tex-math>$N = 235$</tex-math></inline-formula>\u0000 student graphs collected during authentic learning in two different engineering courses, we describe the validation of TaskCompare and evaluate the impact of the SFLA dashboard on task completion during remote lab activities. In addition, we use components of the motivated strategies for learning questionnaire as covariates for propensity score matching to account for potential bias in self-selection of use of the dashboard. We find that those students who used the SFLA dashboard achieved significantly better task completion rate (nearly double) than those who did not, with a significant difference in TaskCompare score between the two groups (Mann–Whitney \u0000<inline-formula><tex-math>$U = 453.5$</tex-math></inline-formula>\u0000, \u0000<inline-formula><tex-math>$p < 0.01$</tex-math></inline-formula>\u0000 and Cliff's \u0000<inline-formula><tex-math>$delta = 0.43$</tex-math></inline-formula>\u0000, large effect size). This difference remains after accounting for self-selection. We also report that students' positive rating of the usefulness of the SFLA dashboard for completing lab work is significantly above a neutral response (\u0000<inline-formula><tex-math>$S = 21.0$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$p < 0.01$</tex-math></inline-formula>\u0000). These findings provide evidence that our SFLA dashboard is an effective means of providing formative assessment during remote laboratory activities.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1037-1050"},"PeriodicalIF":3.7,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139676214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-12DOI: 10.1109/TLT.2024.3352743
Chen Li;Yue Jiang;Peter H. F. Ng;Yixin Dai;Francis Cheung;Henry C. B. Chan;Ping Li
Computer-supported collaborative learning aims to use information technologies to support collaborative knowledge construction by practicing the relevant pedagogical approaches, especially in the distance learning setting. The enabling technologies are fast advancing, and the need for solutions during the COVID-19 global pandemic led to the emergence of the Edu-Metaverse, which is conceptualized as a collection of networked virtual worlds (i.e., the Metaverse) for learning. There is a great necessity to investigate how these more recent enabling technologies can support collaborative learning. This empirical study aims to collect both quantitative and qualitative results to fill the knowledge gaps. Specifically, 20 undergraduate students (three females and 17 males) taking the Game Design and Development course voluntarily participated in this study. The participants used three representative collaboration platforms (i.e., AltSpace, Gather, and ZOOM) in our laboratory for discussing three course-specific topics, simulating the undertaking of collaborative learning tasks in the distance learning setting. The results suggest that the participants were more engaged in the learning activities using the Metaverse platforms that offer avatar-mediated communications and collaborations (i.e., AltSpace and Gather). These platforms also gave the participants a stronger sense of copresence and belonging to the learning community. Potential improvements to the usability and the participants' feedback are also discussed in the article. We hope the results can contribute to the fast-growing use of the Metaverse-enabling technologies for educational purposes.
{"title":"Collaborative Learning in the Edu-Metaverse Era: An Empirical Study on the Enabling Technologies","authors":"Chen Li;Yue Jiang;Peter H. F. Ng;Yixin Dai;Francis Cheung;Henry C. B. Chan;Ping Li","doi":"10.1109/TLT.2024.3352743","DOIUrl":"https://doi.org/10.1109/TLT.2024.3352743","url":null,"abstract":"Computer-supported collaborative learning aims to use information technologies to support collaborative knowledge construction by practicing the relevant pedagogical approaches, especially in the distance learning setting. The enabling technologies are fast advancing, and the need for solutions during the COVID-19 global pandemic led to the emergence of the Edu-Metaverse, which is conceptualized as a collection of networked virtual worlds (i.e., the Metaverse) for learning. There is a great necessity to investigate how these more recent enabling technologies can support collaborative learning. This empirical study aims to collect both quantitative and qualitative results to fill the knowledge gaps. Specifically, 20 undergraduate students (three females and 17 males) taking the Game Design and Development course voluntarily participated in this study. The participants used three representative collaboration platforms (i.e., AltSpace, Gather, and ZOOM) in our laboratory for discussing three course-specific topics, simulating the undertaking of collaborative learning tasks in the distance learning setting. The results suggest that the participants were more engaged in the learning activities using the Metaverse platforms that offer avatar-mediated communications and collaborations (i.e., AltSpace and Gather). These platforms also gave the participants a stronger sense of copresence and belonging to the learning community. Potential improvements to the usability and the participants' feedback are also discussed in the article. We hope the results can contribute to the fast-growing use of the Metaverse-enabling technologies for educational purposes.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"1107-1119"},"PeriodicalIF":3.7,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10399380","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139738983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 : 2024-01-10DOI: 10.1109/TLT.2023.3346645
Gina George;Anisha M. Lal
E-learning is increasingly being used by students in the higher education level for their university credit purpose and some for improving their knowledge. E-learning is also used for skill enhancement purpose by organizations. Due to the availability of wide-ranging options, recommender systems that provide personalized suggestions are much needed. The proposed methodology takes advantage of compact prediction tree (CPT), a popular sequence prediction algorithm. In this article, a new prediction model based on applying CPT over similar students which is found in a novel manner is proposed. The aim of the work is to recommend courses to students at university level. The methodology was evaluated in terms of accuracy and results show the proposed work performs better than applying only CPT, when applying fuzzy C-means with CPT, and when applying k nearest neighbors with CPT.
{"title":"PERKC: Personalized kNN With CPT for Course Recommendations in Higher Education","authors":"Gina George;Anisha M. Lal","doi":"10.1109/TLT.2023.3346645","DOIUrl":"https://doi.org/10.1109/TLT.2023.3346645","url":null,"abstract":"E-learning is increasingly being used by students in the higher education level for their university credit purpose and some for improving their knowledge. E-learning is also used for skill enhancement purpose by organizations. Due to the availability of wide-ranging options, recommender systems that provide personalized suggestions are much needed. The proposed methodology takes advantage of compact prediction tree (CPT), a popular sequence prediction algorithm. In this article, a new prediction model based on applying CPT over similar students which is found in a novel manner is proposed. The aim of the work is to recommend courses to students at university level. The methodology was evaluated in terms of accuracy and results show the proposed work performs better than applying only CPT, when applying fuzzy C-means with CPT, and when applying k nearest neighbors with CPT.","PeriodicalId":49191,"journal":{"name":"IEEE Transactions on Learning Technologies","volume":"17 ","pages":"885-892"},"PeriodicalIF":3.7,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139419476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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