Numerous studies have successfully adopted learning analytics techniques such as machine learning (ML) to address educational issues. However, limited research has addressed the problem of algorithmic bias in ML. In the few attempts to develop strategies to concretely mitigate algorithmic bias in education, the focus has been on debiasing ML models with single group membership. This study aimed to propose an algorithmic strategy to mitigate bias in a multi-group context. The results showed that our proposed model could effectively reduce algorithmic bias in a multi-group setting while retaining competitive accuracy. The findings implied that there could be a paradigm shift from focusing on debiasing a single group to multiple groups in educational attempts on ML.
{"title":"Predicting Students’ Algebra I Performance using Reinforcement Learning with Multi-Group Fairness","authors":"Fan Zhang, Wanli Xing, Chenglu Li","doi":"10.1145/3576050.3576104","DOIUrl":"https://doi.org/10.1145/3576050.3576104","url":null,"abstract":"Numerous studies have successfully adopted learning analytics techniques such as machine learning (ML) to address educational issues. However, limited research has addressed the problem of algorithmic bias in ML. In the few attempts to develop strategies to concretely mitigate algorithmic bias in education, the focus has been on debiasing ML models with single group membership. This study aimed to propose an algorithmic strategy to mitigate bias in a multi-group context. The results showed that our proposed model could effectively reduce algorithmic bias in a multi-group setting while retaining competitive accuracy. The findings implied that there could be a paradigm shift from focusing on debiasing a single group to multiple groups in educational attempts on ML.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115310091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Louis-Vincent Poellhuber, Bruno Poellhuber, M. Desmarais, C. Léger, Normand Roy, Mathieu Manh-Chien Vu
Learning management systems provide a wide breadth of data waiting to be analyzed and utilized to enhance student and faculty experience in higher education. As universities struggle to support students’ engagement, success and retention, learning analytics is being used to build predictive models and develop dashboards to support learners and help them stay engaged, to help teachers identify students needing support, and to predict and prevent dropout. Learning with Big Data has its challenges, however: managing great quantities of data requires time and expertise. To predict students at risk, many institutions use machine learning algorithms with LMS data for a given course or type of course, but only a few are trying to make predictions for a large subset of courses. This begs the question: “How can student dropout be predicted on a very large set of courses in an institution Moodle LMS?” In this paper, we use automation to improve student dropout prediction for a very large subset of courses, by clustering them based on course design and similarity, then by automatically training, testing, and selecting machine learning algorithms for each cluster. We developed a promising methodology that outlines a basic framework that can be adjusted and optimized in many ways and that further studies can easily build on and improve.
{"title":"Cluster-Based Performance of Student Dropout Prediction as a Solution for Large Scale Models in a Moodle LMS","authors":"Louis-Vincent Poellhuber, Bruno Poellhuber, M. Desmarais, C. Léger, Normand Roy, Mathieu Manh-Chien Vu","doi":"10.1145/3576050.3576146","DOIUrl":"https://doi.org/10.1145/3576050.3576146","url":null,"abstract":"Learning management systems provide a wide breadth of data waiting to be analyzed and utilized to enhance student and faculty experience in higher education. As universities struggle to support students’ engagement, success and retention, learning analytics is being used to build predictive models and develop dashboards to support learners and help them stay engaged, to help teachers identify students needing support, and to predict and prevent dropout. Learning with Big Data has its challenges, however: managing great quantities of data requires time and expertise. To predict students at risk, many institutions use machine learning algorithms with LMS data for a given course or type of course, but only a few are trying to make predictions for a large subset of courses. This begs the question: “How can student dropout be predicted on a very large set of courses in an institution Moodle LMS?” In this paper, we use automation to improve student dropout prediction for a very large subset of courses, by clustering them based on course design and similarity, then by automatically training, testing, and selecting machine learning algorithms for each cluster. We developed a promising methodology that outlines a basic framework that can be adjusted and optimized in many ways and that further studies can easily build on and improve.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128502496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Ng, Yiming Liu, Didier S. Y. Chui, Jack C. H. Man, Xiao Hu
Existing learning analytics (LA) studies on self-regulated learning (SRL) have rarely focused on maker education that emphasizes student autonomy in their learning process. Towards using LA methods for generating evidence of SRL in maker-based courses, this study leverages logs of a learning management system (LMS) with its activity design aligned with the maker-based pedagogy. We explored frequencies and sequential patterns of students’ SRL behaviors as reflected in the LMS logs and their relations with learning performance. Adopting a mixed method approach, we collected and triangulated both quantitative (i.e., system logs, performance scores) and qualitative (i.e., student-written reflections) data sources from 104 students. Based on current LA-based SRL research, we developed an LMS log-based analytic framework to define the SRL phases and behaviors applicable to maker activities. Statistical, data mining, and qualitative analysis methods were conducted on 48,602 logged events and 131 excerpts extracted from student reflections. Results reveal that high-performing students demonstrated some SRL behaviors (e.g., Making Personal Plans, Evaluation) more frequently than their low-performing counterparts, yet the two groups showcased fairly similar sequences of SRL behaviors. Theoretical, methodological and pedagogical implications are drawn for LA-based SRL research and maker education.
{"title":"Leveraging LMS Logs to Analyze Self-Regulated Learning Behaviors in a Maker-based Course","authors":"J. Ng, Yiming Liu, Didier S. Y. Chui, Jack C. H. Man, Xiao Hu","doi":"10.1145/3576050.3576111","DOIUrl":"https://doi.org/10.1145/3576050.3576111","url":null,"abstract":"Existing learning analytics (LA) studies on self-regulated learning (SRL) have rarely focused on maker education that emphasizes student autonomy in their learning process. Towards using LA methods for generating evidence of SRL in maker-based courses, this study leverages logs of a learning management system (LMS) with its activity design aligned with the maker-based pedagogy. We explored frequencies and sequential patterns of students’ SRL behaviors as reflected in the LMS logs and their relations with learning performance. Adopting a mixed method approach, we collected and triangulated both quantitative (i.e., system logs, performance scores) and qualitative (i.e., student-written reflections) data sources from 104 students. Based on current LA-based SRL research, we developed an LMS log-based analytic framework to define the SRL phases and behaviors applicable to maker activities. Statistical, data mining, and qualitative analysis methods were conducted on 48,602 logged events and 131 excerpts extracted from student reflections. Results reveal that high-performing students demonstrated some SRL behaviors (e.g., Making Personal Plans, Evaluation) more frequently than their low-performing counterparts, yet the two groups showcased fairly similar sequences of SRL behaviors. Theoretical, methodological and pedagogical implications are drawn for LA-based SRL research and maker education.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130634140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given the growth in interest in human-centeredness within the learning analytics community - a workshop at LAK, a special issue in the Journal of Learning Analytics and multiple papers published on the topic - it seems an appropriate time to critically evaluate the popular design approach. Using a corpus of 165 publications that have substantial reference to both learning analytics and human-centeredness, the following paper delineates what is meant by "human-centered" and then discusses what the implications are for this approach. The conclusion reached through this analysis is that when authors refer to human-centeredness in learning analytics they are largely referring to stakeholder inclusion and the means by which this can be achieved (methodologically, politically and logistically). Furthermore, the justification for stakeholder inclusion is often coached in terms of its ability to develop more effective learning analytics applications along several dimensions (efficiency, efficacy, impact). With reference to human-centered design in other fields a discussion follows of the issues with such an approach and a prediction that LA will likely move toward a more neutral stance on stakeholder inclusion, as has occurred in both human-centered design and stakeholder engagement research in the past. A more stakeholder-neutral stance is defined as one in which stakeholder inclusion is one of many tools utilized in developing learning analytics applications.
{"title":"Learning Analytics and Stakeholder Inclusion: What do We Mean When We Say \"Human-Centered\"?","authors":"Charles Lang, Laura Davis","doi":"10.1145/3576050.3576110","DOIUrl":"https://doi.org/10.1145/3576050.3576110","url":null,"abstract":"Given the growth in interest in human-centeredness within the learning analytics community - a workshop at LAK, a special issue in the Journal of Learning Analytics and multiple papers published on the topic - it seems an appropriate time to critically evaluate the popular design approach. Using a corpus of 165 publications that have substantial reference to both learning analytics and human-centeredness, the following paper delineates what is meant by \"human-centered\" and then discusses what the implications are for this approach. The conclusion reached through this analysis is that when authors refer to human-centeredness in learning analytics they are largely referring to stakeholder inclusion and the means by which this can be achieved (methodologically, politically and logistically). Furthermore, the justification for stakeholder inclusion is often coached in terms of its ability to develop more effective learning analytics applications along several dimensions (efficiency, efficacy, impact). With reference to human-centered design in other fields a discussion follows of the issues with such an approach and a prediction that LA will likely move toward a more neutral stance on stakeholder inclusion, as has occurred in both human-centered design and stakeholder engagement research in the past. A more stakeholder-neutral stance is defined as one in which stakeholder inclusion is one of many tools utilized in developing learning analytics applications.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124508249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lixiang Yan, Roberto Martínez-Maldonado, Linxuan Zhao, Xinyu Li, D. Gašević
Homophily is a fundamental sociological theory that describes the tendency of individuals to interact with others who share similar attributes. This theory has shown evident relevance for studying collaborative learning and classroom orchestration in learning analytics research from a social constructivist perspective. Emerging advancements in multimodal learning analytics have shown promising results in capturing interaction data and generating socio-spatial analytics in physical learning spaces through computer vision and wearable positioning technologies. Yet, there are limited ways for analysing homophily (e.g., social network analysis; SNA), especially for unpacking the temporal connections between different homophilic behaviours. This paper presents a novel analytic approach, Social-epistemic Network Analysis (SeNA), for analysing homophily by combining social network analysis with epistemic network analysis to infuse socio-spatial analytics with temporal insights. The additional insights SeNA may offer over traditional approaches (e.g., SNA) were illustrated through analysing the homophily of 98 students in open learning spaces. The findings showed that SeNA could reveal significant behavioural differences in homophily between comparison groups across different learning designs, which were not accessible to SNA alone. The implications and limitations of SeNA in supporting future learning analytics research regarding homophily in physical learning spaces are also discussed.
{"title":"SeNA: Modelling Socio-spatial Analytics on Homophily by Integrating Social and Epistemic Network Analysis","authors":"Lixiang Yan, Roberto Martínez-Maldonado, Linxuan Zhao, Xinyu Li, D. Gašević","doi":"10.1145/3576050.3576054","DOIUrl":"https://doi.org/10.1145/3576050.3576054","url":null,"abstract":"Homophily is a fundamental sociological theory that describes the tendency of individuals to interact with others who share similar attributes. This theory has shown evident relevance for studying collaborative learning and classroom orchestration in learning analytics research from a social constructivist perspective. Emerging advancements in multimodal learning analytics have shown promising results in capturing interaction data and generating socio-spatial analytics in physical learning spaces through computer vision and wearable positioning technologies. Yet, there are limited ways for analysing homophily (e.g., social network analysis; SNA), especially for unpacking the temporal connections between different homophilic behaviours. This paper presents a novel analytic approach, Social-epistemic Network Analysis (SeNA), for analysing homophily by combining social network analysis with epistemic network analysis to infuse socio-spatial analytics with temporal insights. The additional insights SeNA may offer over traditional approaches (e.g., SNA) were illustrated through analysing the homophily of 98 students in open learning spaces. The findings showed that SeNA could reveal significant behavioural differences in homophily between comparison groups across different learning designs, which were not accessible to SNA alone. The implications and limitations of SeNA in supporting future learning analytics research regarding homophily in physical learning spaces are also discussed.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127910311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Song-Ae Han, Hyangeun Ji, Zilu Jiang, Michael West, Min Liu
We identified the nine most dominant massive open online course (MOOC) students’ needs by topic modeling and qualitative analysis of forum discussion posts (n = 3645) among students, staff, and instructors from 21 courses. We examined the implications of these needs using three main Universal Design for Learning (UDL) principles (representation, action and expression, and engagement). We then offered suggestions for what course providers can do to promote an equitable learning experience for MOOC students. The three suggestions are as follows: (1) providing tools such as a direct messaging application to encourage students’ socializing behaviors, (2) modifying course activities to promote more hands-on projects and sharing them, and (3) implementing a bidirectional channel, such as a natural language processing-based chatbot so that students can access useful information whenever they feel the need. We argue that it is critical to include minority students’ voices when examining needs in courses, and our methodology reflects this purpose. We also discuss how the UDL approach helped us recognize students’ needs, create more accessible MOOC learning experiences, and explore future research directions.
{"title":"What do students want to know while taking massive open online courses?: Examining massive open online course students’ needs based on online forum discussions from the Universal Design for Learning approach","authors":"Song-Ae Han, Hyangeun Ji, Zilu Jiang, Michael West, Min Liu","doi":"10.1145/3576050.3576072","DOIUrl":"https://doi.org/10.1145/3576050.3576072","url":null,"abstract":"We identified the nine most dominant massive open online course (MOOC) students’ needs by topic modeling and qualitative analysis of forum discussion posts (n = 3645) among students, staff, and instructors from 21 courses. We examined the implications of these needs using three main Universal Design for Learning (UDL) principles (representation, action and expression, and engagement). We then offered suggestions for what course providers can do to promote an equitable learning experience for MOOC students. The three suggestions are as follows: (1) providing tools such as a direct messaging application to encourage students’ socializing behaviors, (2) modifying course activities to promote more hands-on projects and sharing them, and (3) implementing a bidirectional channel, such as a natural language processing-based chatbot so that students can access useful information whenever they feel the need. We argue that it is critical to include minority students’ voices when examining needs in courses, and our methodology reflects this purpose. We also discuss how the UDL approach helped us recognize students’ needs, create more accessible MOOC learning experiences, and explore future research directions.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131560961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Students’ reading is an essential part of learning in college courses. However, many instructors are concerned that students do not complete assigned readings, and multiple studies have found evidence to support this concern. A handful of studies suggest adopting strategies to address students’ lack of reading. This research examines various instructional strategies and student eTextbook reading behaviors validated by page view data. Survey responses related to use of instructional strategies were collected. A total of 86 instructors from four public universities participated. Of these participants, 59 submitted the assigned reading pages for their courses. This resulted in reading data from 3,714 students which were examined in this study. The findings indicated that students read about 37% of the assigned pages on any given day during the semester. Also, of the students that read, two-thirds made at least one annotation and students tend to re-read the pages they annotated. Most importantly, student reading in the courses where strategies were used was almost three times higher than in the courses where no strategies were implemented.
{"title":"Instructional Strategies and Student eTextbook Reading","authors":"J. Russell, A. Smith, Salimma George, B. Damman","doi":"10.1145/3576050.3576086","DOIUrl":"https://doi.org/10.1145/3576050.3576086","url":null,"abstract":"Students’ reading is an essential part of learning in college courses. However, many instructors are concerned that students do not complete assigned readings, and multiple studies have found evidence to support this concern. A handful of studies suggest adopting strategies to address students’ lack of reading. This research examines various instructional strategies and student eTextbook reading behaviors validated by page view data. Survey responses related to use of instructional strategies were collected. A total of 86 instructors from four public universities participated. Of these participants, 59 submitted the assigned reading pages for their courses. This resulted in reading data from 3,714 students which were examined in this study. The findings indicated that students read about 37% of the assigned pages on any given day during the semester. Also, of the students that read, two-thirds made at least one annotation and students tend to re-read the pages they annotated. Most importantly, student reading in the courses where strategies were used was almost three times higher than in the courses where no strategies were implemented.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131872312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This case study examines an interactive, low barrier process, termed instructor-in-the-loop, by which an instructor defines and makes meaning from exploratory metrics and visualizations, and uses this multimodal information to improve a course iteratively. We present potentials for course improvement based on automated learning analytics insights related to students’ participation in small active learning sessions associated with a large lecture course. Automated analytics processes are essential for larger courses where engaging smaller groups is important to ensure participation and understanding, but monitoring a large total number of groups throughout an instructional experience becomes untenable for the instructor. Of interest is providing instructors with easy-to-digest summaries of group performance that do not require complex set up and knowledge of more advanced algorithmic approaches. We explore synthesizing metrics and visualizations as ways to engage instructors in meaning making of complex learning environments, but in a low barrier manner that provides insights quickly.
{"title":"Instructor-in-the-Loop Exploratory Analytics to Support Group Work","authors":"Armanda Lewis, X. Ochoa, R. Qamra","doi":"10.1145/3576050.3576093","DOIUrl":"https://doi.org/10.1145/3576050.3576093","url":null,"abstract":"This case study examines an interactive, low barrier process, termed instructor-in-the-loop, by which an instructor defines and makes meaning from exploratory metrics and visualizations, and uses this multimodal information to improve a course iteratively. We present potentials for course improvement based on automated learning analytics insights related to students’ participation in small active learning sessions associated with a large lecture course. Automated analytics processes are essential for larger courses where engaging smaller groups is important to ensure participation and understanding, but monitoring a large total number of groups throughout an instructional experience becomes untenable for the instructor. Of interest is providing instructors with easy-to-digest summaries of group performance that do not require complex set up and knowledge of more advanced algorithmic approaches. We explore synthesizing metrics and visualizations as ways to engage instructors in meaning making of complex learning environments, but in a low barrier manner that provides insights quickly.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114916857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With daily social media consumption among teens exceeding eight hours, it becomes increasingly important to raise awareness about the digital traces they leave behind. However, concepts of privacy literacy such as data and metadata can seem abstract and difficult to grasp. In this short research paper, we tackle this issue by designing, implementing and presenting an evaluation of a novel technology-enhanced pedagogical scenario for high school students. The scenario covers two main sessions. In a first session the SpeakUp social-media-like classroom interaction app is used to support a digitally mediated debate. In the second session, the actual learning traces from the digitally mediated debate are used as an object of study to enable students to reflect on the traces they leave behind on social media platforms. In order to enable this scenario we extended the existing SpeakUp app to the specifics of the context. The scenario was implemented and evaluated in real classrooms during a semester-long course on digital skills with 45 high school students. Our results show that the learning scenario is appreciated by students and even though non-STEM students might require more onboarding to be fully engaged in the digitally mediated debate, students from both STEM and non-STEM classes learn effectively. We discuss shortcomings and future research avenues.
{"title":"Fostering Privacy Literacy among High School Students by Leveraging Social Media Interaction and Learning Traces in the Classroom","authors":"Andrea Franco, A. Holzer","doi":"10.1145/3576050.3576153","DOIUrl":"https://doi.org/10.1145/3576050.3576153","url":null,"abstract":"With daily social media consumption among teens exceeding eight hours, it becomes increasingly important to raise awareness about the digital traces they leave behind. However, concepts of privacy literacy such as data and metadata can seem abstract and difficult to grasp. In this short research paper, we tackle this issue by designing, implementing and presenting an evaluation of a novel technology-enhanced pedagogical scenario for high school students. The scenario covers two main sessions. In a first session the SpeakUp social-media-like classroom interaction app is used to support a digitally mediated debate. In the second session, the actual learning traces from the digitally mediated debate are used as an object of study to enable students to reflect on the traces they leave behind on social media platforms. In order to enable this scenario we extended the existing SpeakUp app to the specifics of the context. The scenario was implemented and evaluated in real classrooms during a semester-long course on digital skills with 45 high school students. Our results show that the learning scenario is appreciated by students and even though non-STEM students might require more onboarding to be fully engaged in the digitally mediated debate, students from both STEM and non-STEM classes learn effectively. We discuss shortcomings and future research avenues.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114958019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We examined associations between mind wandering – where attention shifts from the task at hand to task-unrelated thoughts – and learning outcomes. Our data consisted of 177 students who self-reported mind wandering while reading five long, connected texts on scientific research methods and completed learning assessments targeting multiple depths of processing (rote, inference, integration) at different timescales (during and after reading each text, after reading all texts, and after a week-long delay). We found that mind wandering negatively predicted measures of factual, text-based (explicit) information and global integration of information across multiple parts of the text, but not measures requiring a local inference on a single sentence. Further, mind wandering only predicted comprehension measures assessed during the reading session and not after a week-long delay. Our findings provide important nuances to the established negative link between mind wandering and learning outcomes, which has predominantly focused on rote comprehension assessed during the learning session itself. Implications for interventions to address mind wandering during learning are discussed.
{"title":"Do Associations Between Mind Wandering and Learning from Complex Texts Vary by Assessment Depth and Time?","authors":"Megan Caruso, S. D’Mello","doi":"10.1145/3576050.3576084","DOIUrl":"https://doi.org/10.1145/3576050.3576084","url":null,"abstract":"We examined associations between mind wandering – where attention shifts from the task at hand to task-unrelated thoughts – and learning outcomes. Our data consisted of 177 students who self-reported mind wandering while reading five long, connected texts on scientific research methods and completed learning assessments targeting multiple depths of processing (rote, inference, integration) at different timescales (during and after reading each text, after reading all texts, and after a week-long delay). We found that mind wandering negatively predicted measures of factual, text-based (explicit) information and global integration of information across multiple parts of the text, but not measures requiring a local inference on a single sentence. Further, mind wandering only predicted comprehension measures assessed during the reading session and not after a week-long delay. Our findings provide important nuances to the established negative link between mind wandering and learning outcomes, which has predominantly focused on rote comprehension assessed during the learning session itself. Implications for interventions to address mind wandering during learning are discussed.","PeriodicalId":394433,"journal":{"name":"LAK23: 13th International Learning Analytics and Knowledge Conference","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124079069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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