G. Fernandez-Nieto, Roberto Martinez-Maldonado, Vanessa Echeverría, Kirsty Kitto, D. Gašević, S. Buckingham Shum
{"title":"Data Storytelling Editor: A Teacher-Centred Tool for Customising Learning Analytics Dashboard Narratives","authors":"G. Fernandez-Nieto, Roberto Martinez-Maldonado, Vanessa Echeverría, Kirsty Kitto, D. Gašević, S. Buckingham Shum","doi":"10.1145/3636555.3636930","DOIUrl":"https://doi.org/10.1145/3636555.3636930","url":null,"abstract":"","PeriodicalId":517868,"journal":{"name":"Proceedings of the 14th Learning Analytics and Knowledge Conference","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140283697","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}
During middle school, students’ learning experiences begin to influence their future decisions about college enrollment and career selection. Prior research indicates that both knowledge gained and the disengagement and affect experienced during this period are predictors of these future outcomes. However, this past research has investigated affect, disengagement, and knowledge in an overall fashion – looking at the average manifestation of these constructs across all topics studied across a year of mathematics. It may be that some mathematics topics are more associated with these outcomes than others. In this study, we use data from middle school students interacting with a digital mathematics learning platform, to analyze the interplay of these features across different topic areas. Our findings show that mastering Functions is the most important predictor of both college enrollment and STEM career selection, while the importance of knowing other topic areas varies across the two outcomes. Furthermore, while subject knowledge tends to be the most relevant predictor for general college enrollment, affective states, especially confusion and engaged concentration, become more important for predicting STEM career selection.
{"title":"Long-Term Prediction from Topic-Level Knowledge and Engagement in Mathematics Learning","authors":"Andres Felipe Zambrano, Ryan S. Baker","doi":"10.1145/3636555.3636851","DOIUrl":"https://doi.org/10.1145/3636555.3636851","url":null,"abstract":"During middle school, students’ learning experiences begin to influence their future decisions about college enrollment and career selection. Prior research indicates that both knowledge gained and the disengagement and affect experienced during this period are predictors of these future outcomes. However, this past research has investigated affect, disengagement, and knowledge in an overall fashion – looking at the average manifestation of these constructs across all topics studied across a year of mathematics. It may be that some mathematics topics are more associated with these outcomes than others. In this study, we use data from middle school students interacting with a digital mathematics learning platform, to analyze the interplay of these features across different topic areas. Our findings show that mastering Functions is the most important predictor of both college enrollment and STEM career selection, while the importance of knowing other topic areas varies across the two outcomes. Furthermore, while subject knowledge tends to be the most relevant predictor for general college enrollment, affective states, especially confusion and engaged concentration, become more important for predicting STEM career selection.","PeriodicalId":517868,"journal":{"name":"Proceedings of the 14th Learning Analytics and Knowledge Conference","volume":"224 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140283667","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}
Riordan Alfredo, Vanessa Echeverría, Yueqiao Jin, Z. Swiecki, D. Gašević, Roberto Martinez-Maldonado
{"title":"SLADE: A Method for Designing Human-Centred Learning Analytics Systems","authors":"Riordan Alfredo, Vanessa Echeverría, Yueqiao Jin, Z. Swiecki, D. Gašević, Roberto Martinez-Maldonado","doi":"10.1145/3636555.3636847","DOIUrl":"https://doi.org/10.1145/3636555.3636847","url":null,"abstract":"","PeriodicalId":517868,"journal":{"name":"Proceedings of the 14th Learning Analytics and Knowledge Conference","volume":"224 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140283840","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}
Hai Li, Chenglu Li, Wanli Xing, Sami Baral, Neil Heffernan
{"title":"Automated Feedback for Student Math Responses Based on Multi-Modality and Fine-Tuning","authors":"Hai Li, Chenglu Li, Wanli Xing, Sami Baral, Neil Heffernan","doi":"10.1145/3636555.3636860","DOIUrl":"https://doi.org/10.1145/3636555.3636860","url":null,"abstract":"","PeriodicalId":517868,"journal":{"name":"Proceedings of the 14th Learning Analytics and Knowledge Conference","volume":"36 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140283790","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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