This demo will present the implementation of an A.I. powered web application designed to assist students with creation of a homework and study schedule. The intent of this project is to use key principles from Self-Regulated Learning and Cognitive Load Theory to translate the large, abstract problem of "creating a study and homework schedule from scratch" into a structured, repeatable set of review tasks. The system then uses a constraint satisfaction AI agent to recommend a weekly schedule of activities that supports the student to achieve the specified goals.
{"title":"Demo of JARET: A.I. Powered Web App for Goal Review and Time Management","authors":"Andrew Schwabe","doi":"10.1145/3386527.3405954","DOIUrl":"https://doi.org/10.1145/3386527.3405954","url":null,"abstract":"This demo will present the implementation of an A.I. powered web application designed to assist students with creation of a homework and study schedule. The intent of this project is to use key principles from Self-Regulated Learning and Cognitive Load Theory to translate the large, abstract problem of \"creating a study and homework schedule from scratch\" into a structured, repeatable set of review tasks. The system then uses a constraint satisfaction AI agent to recommend a weekly schedule of activities that supports the student to achieve the specified goals.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"133 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86253190","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}
Peter Brusilovsky, K. Koedinger, David A. Joyner, T. Price
The goal of this workshop is to bring together the existing community of researchers working on Infrastructure Design for Data-Intensive Research in Computer Science Education and a community of Learning at Scale researchers focused on Computer Science Education. While both communities share many similar goals and could greatly benefit from each other work, the interaction between the communities is small. We hope that the proposed workshop will be instrumental in bringing together like-minded researchers from different communities, establishing collaboration, and expanding the scope of infrastructure project to address critical scaling issues.
{"title":"Building an Infrastructure for Computer Science Education Research and Practice at Scale","authors":"Peter Brusilovsky, K. Koedinger, David A. Joyner, T. Price","doi":"10.1145/3386527.3405936","DOIUrl":"https://doi.org/10.1145/3386527.3405936","url":null,"abstract":"The goal of this workshop is to bring together the existing community of researchers working on Infrastructure Design for Data-Intensive Research in Computer Science Education and a community of Learning at Scale researchers focused on Computer Science Education. While both communities share many similar goals and could greatly benefit from each other work, the interaction between the communities is small. We hope that the proposed workshop will be instrumental in bringing together like-minded researchers from different communities, establishing collaboration, and expanding the scope of infrastructure project to address critical scaling issues.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81022583","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 work reports on progress made towards building an equitable model to predict the success of an applicant to Georgia Tech's Online Master's in Analytics program. As a first step, we have collected and processed data on 9,044 applications and have trained a predictive model with a ROC-AUC score of 0.81, which predicts whether an applicant would be admitted to the program. Our next steps will include using applicant data to model the successful completion of the Analytics program's three core courses, graduation, and finally job placement. In addition, we plan to expand our feature processing and incorporate techniques to ensure that our models do not discriminate based on demographic factors. In the long run, we hope that the results of this study can be used to improve the course contents, selection of offered courses, and prerequisite training, and even give guidance toward the selection of the applicants.
{"title":"Predicting Applicant Admission Status for Georgia Tech's Online Master's in Analytics Program","authors":"S. Staudaher, Jeonghyun Lee, F. Soleimani","doi":"10.1145/3386527.3406735","DOIUrl":"https://doi.org/10.1145/3386527.3406735","url":null,"abstract":"This work reports on progress made towards building an equitable model to predict the success of an applicant to Georgia Tech's Online Master's in Analytics program. As a first step, we have collected and processed data on 9,044 applications and have trained a predictive model with a ROC-AUC score of 0.81, which predicts whether an applicant would be admitted to the program. Our next steps will include using applicant data to model the successful completion of the Analytics program's three core courses, graduation, and finally job placement. In addition, we plan to expand our feature processing and incorporate techniques to ensure that our models do not discriminate based on demographic factors. In the long run, we hope that the results of this study can be used to improve the course contents, selection of offered courses, and prerequisite training, and even give guidance toward the selection of the applicants.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84016222","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}
Ashank Verma, Timothy Bretl, Matthew West, C. Zilles
In this paper, we study a computerized exam system that allows students to attempt the same question multiple times. This system permits students either to receive feedback on their submitted answer immediately or to defer the feedback and grade questions in bulk. An analysis of student behavior in three courses across two semesters found similar student behaviors across courses and student groups. We found that only a small minority of students used the deferred feedback option. A clustering analysis that considered both when students chose to receive feedback and either to immediately retry incorrect problems or to attempt other unfinished problems identified four main student strategies. These strategies were correlated to statistically significant differences in exam scores, but it was not clear if some strategies improved outcomes or if stronger students tended to prefer certain strategies.
{"title":"A Quantitative Analysis of When Students Choose to Grade Questions on Computerized Exams with Multiple Attempts","authors":"Ashank Verma, Timothy Bretl, Matthew West, C. Zilles","doi":"10.1145/3386527.3406740","DOIUrl":"https://doi.org/10.1145/3386527.3406740","url":null,"abstract":"In this paper, we study a computerized exam system that allows students to attempt the same question multiple times. This system permits students either to receive feedback on their submitted answer immediately or to defer the feedback and grade questions in bulk. An analysis of student behavior in three courses across two semesters found similar student behaviors across courses and student groups. We found that only a small minority of students used the deferred feedback option. A clustering analysis that considered both when students chose to receive feedback and either to immediately retry incorrect problems or to attempt other unfinished problems identified four main student strategies. These strategies were correlated to statistically significant differences in exam scores, but it was not clear if some strategies improved outcomes or if stronger students tended to prefer certain strategies.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90159857","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}
Anish Khazane, Jia Mao, India Irish, Rocko Graziano, Thad Starner
As online educational programs scale, monitoring peer collaboration in platforms like BlueJeans for plagiarism becomes difficult. Recent studies indicate that students are less likely to cheat if presented with direct warning messages prior to engaging in online activities. In this work, we present Bluejeans codE Leak deTection (BELT), a system that monitors online BlueJeans meetings for shared code and sends timely warning messages to meeting participants. To test BELT's robustness as an online proctor, we evaluate its code-text disambiguation, code detection from images of varying quality, and code detection from videos of varying resolution. We conclude this work by pinpointing areas of improvement and briefly discuss possible extensions for future work.
{"title":"BELT","authors":"Anish Khazane, Jia Mao, India Irish, Rocko Graziano, Thad Starner","doi":"10.1145/3386527.3406727","DOIUrl":"https://doi.org/10.1145/3386527.3406727","url":null,"abstract":"As online educational programs scale, monitoring peer collaboration in platforms like BlueJeans for plagiarism becomes difficult. Recent studies indicate that students are less likely to cheat if presented with direct warning messages prior to engaging in online activities. In this work, we present Bluejeans codE Leak deTection (BELT), a system that monitors online BlueJeans meetings for shared code and sends timely warning messages to meeting participants. To test BELT's robustness as an online proctor, we evaluate its code-text disambiguation, code detection from images of varying quality, and code detection from videos of varying resolution. We conclude this work by pinpointing areas of improvement and briefly discuss possible extensions for future work.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89490634","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}
Crowdsourcing has shown promise in education domains. For example, researchers have leveraged the wisdom of the crowd to process grading in MOOCs and develop learning resources. An untapped domain is harnessing the crowd to systematically process educational data in classrooms -- data that provide key instructional insights but take time to process, such as paper-based assessments. In this paper, we describe an experiment of a crowdsourcing task to effectively process classroom-based data and explore the potential of crowdsourcing as a learning opportunity for the crowdworkers. We discuss implications for designing crowdsourced assessment tasks to yield both high quality output and enriching learning experiences for those involved in the crowdsourcing task.
{"title":"Where's the Learning in Education Crowdsourcing?","authors":"Ha Nguyen, June Ahn, William Young, Fabio Campos","doi":"10.1145/3386527.3406734","DOIUrl":"https://doi.org/10.1145/3386527.3406734","url":null,"abstract":"Crowdsourcing has shown promise in education domains. For example, researchers have leveraged the wisdom of the crowd to process grading in MOOCs and develop learning resources. An untapped domain is harnessing the crowd to systematically process educational data in classrooms -- data that provide key instructional insights but take time to process, such as paper-based assessments. In this paper, we describe an experiment of a crowdsourcing task to effectively process classroom-based data and explore the potential of crowdsourcing as a learning opportunity for the crowdworkers. We discuss implications for designing crowdsourced assessment tasks to yield both high quality output and enriching learning experiences for those involved in the crowdsourcing task.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"104 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75987058","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}
Explanations are used to provide an understanding of a concept, procedure, or reasoning to others. Although explanations are present online ubiquitously within textbooks, discussion forums, and many more, there is no way to mine them automatically to assist learners in seeking an explanation. To address this problem, we propose the task of Explanation Mining. To mine explanations of educational concepts, we propose a baseline approach based on the Language Modeling approach of information retrieval. Preliminary results suggest that incorporating knowledge from a model trained on the ELI5 (Explain Like I'm Five) dataset in the form of a document prior helps increase the performance of a standard retrieval model. This is encouraging because our method requires minimal in-domain supervision, as a result, it can be deployed for multiple online courses. We also suggest some interesting future work in the computational analysis of explanations.
解释用于向他人提供对概念、程序或推理的理解。尽管在线教科书、讨论论坛和其他很多地方都有解释，但没有办法自动挖掘它们来帮助学习者寻找解释。为了解决这个问题，我们提出了解释挖掘的任务。为了挖掘教育概念的解释，我们提出了一种基于信息检索的语言建模方法的基线方法。初步结果表明，将ELI5 (Explain Like I’m Five)数据集上训练的模型中的知识以文档的形式合并在一起，有助于提高标准检索模型的性能。这是令人鼓舞的，因为我们的方法需要最少的域内监督，因此，它可以部署到多个在线课程中。我们还建议在解释的计算分析方面进行一些有趣的未来工作。
{"title":"Explanation Mining","authors":"Bhavya, Chengxiang Zhai","doi":"10.1145/3386527.3406738","DOIUrl":"https://doi.org/10.1145/3386527.3406738","url":null,"abstract":"Explanations are used to provide an understanding of a concept, procedure, or reasoning to others. Although explanations are present online ubiquitously within textbooks, discussion forums, and many more, there is no way to mine them automatically to assist learners in seeking an explanation. To address this problem, we propose the task of Explanation Mining. To mine explanations of educational concepts, we propose a baseline approach based on the Language Modeling approach of information retrieval. Preliminary results suggest that incorporating knowledge from a model trained on the ELI5 (Explain Like I'm Five) dataset in the form of a document prior helps increase the performance of a standard retrieval model. This is encouraging because our method requires minimal in-domain supervision, as a result, it can be deployed for multiple online courses. We also suggest some interesting future work in the computational analysis of explanations.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73251132","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}
David A. Joyner, M. Carlon, J. Cross, Eduardo Corpeño, Rocael Hernández, Oscar Rodas, Dhawal Shah, Manoel Cortes Mendez, T. Staubitz, José A. Ruipérez Valiente
This workshop proposes specifically soliciting contributions and presentations from initiatives, programs, and platforms around the world. While many of these may already be presented at the full conference, we are also interested in more casual experience reports, case studies, and background presentations from individuals more closely acquainted with how learning at scale initiatives-including MOOCs, for-credit degree programs, informal learning environments, government initiatives, and so on-have unique needs and opportunities based on their local context. We refer to this as Global Learning @ Scale. For the purposes of this workshop, we take two views of Global Learning @ Scale.
{"title":"Global Learning @ Scale","authors":"David A. Joyner, M. Carlon, J. Cross, Eduardo Corpeño, Rocael Hernández, Oscar Rodas, Dhawal Shah, Manoel Cortes Mendez, T. Staubitz, José A. Ruipérez Valiente","doi":"10.1145/3386527.3405956","DOIUrl":"https://doi.org/10.1145/3386527.3405956","url":null,"abstract":"This workshop proposes specifically soliciting contributions and presentations from initiatives, programs, and platforms around the world. While many of these may already be presented at the full conference, we are also interested in more casual experience reports, case studies, and background presentations from individuals more closely acquainted with how learning at scale initiatives-including MOOCs, for-credit degree programs, informal learning environments, government initiatives, and so on-have unique needs and opportunities based on their local context. We refer to this as Global Learning @ Scale. For the purposes of this workshop, we take two views of Global Learning @ Scale.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86030365","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}
Massive Open Online Courses (MOOCs) provide the opportunity to offer free and open education at scale. Thousands of students with different social and cultural backgrounds from all over the world can enroll for a course. This diverse audience comes with varying motivations and intentions from their personal or professional life. However, course instructors cannot offer individual support and guidance at this scale and therefore usually provide a one-size-fits-all approach. Students have to follow weekly-structured courses and their success is measured with the achievement of a certificate at the end. To better address the varying learning needs, technical support for goal-oriented and self-regulated learning is desired but very limited to date. Both learning strategies are proven to be key factors for students' achievement in large-scale online learning environments. Therefore, this paper presents a continuative study of personalized learning objectives in MOOCs to encourage goal-oriented and self-regulated learning. Based on the previously well-perceived acceptance and usefulness of the concept of personalized learning objectives, this study examines which learners select an objective and how successful they complete objectives. Concerning the learners' socio-demographic and geographical background, we could not identify any practical significant difference between students with selected learning objectives and the total course population. However, we have identified promising objective achievement rates, and we have observed a practical significant improvement of the certification rates comparing the total course population and students who selected an objective that included a graded certificate. This has also demonstrated a method for calculating more reasonable completion rates in MOOCs.
{"title":"Students' Achievement of Personalized Learning Objectives in MOOCs","authors":"Tobias Rohloff, Dominic Sauer, C. Meinel","doi":"10.1145/3386527.3405918","DOIUrl":"https://doi.org/10.1145/3386527.3405918","url":null,"abstract":"Massive Open Online Courses (MOOCs) provide the opportunity to offer free and open education at scale. Thousands of students with different social and cultural backgrounds from all over the world can enroll for a course. This diverse audience comes with varying motivations and intentions from their personal or professional life. However, course instructors cannot offer individual support and guidance at this scale and therefore usually provide a one-size-fits-all approach. Students have to follow weekly-structured courses and their success is measured with the achievement of a certificate at the end. To better address the varying learning needs, technical support for goal-oriented and self-regulated learning is desired but very limited to date. Both learning strategies are proven to be key factors for students' achievement in large-scale online learning environments. Therefore, this paper presents a continuative study of personalized learning objectives in MOOCs to encourage goal-oriented and self-regulated learning. Based on the previously well-perceived acceptance and usefulness of the concept of personalized learning objectives, this study examines which learners select an objective and how successful they complete objectives. Concerning the learners' socio-demographic and geographical background, we could not identify any practical significant difference between students with selected learning objectives and the total course population. However, we have identified promising objective achievement rates, and we have observed a practical significant improvement of the certification rates comparing the total course population and students who selected an objective that included a graded certificate. This has also demonstrated a method for calculating more reasonable completion rates in MOOCs.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88348720","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}
Effective driver education techniques can greatly benefit from the use of state-of-the-art technologies for driving training and tutoring in classroom environment. Such environment includes simulation systems that are designed based on the Intelligent Tutoring System concepts and framework. This research analyzed simulator data for both simulation and vehicle environments to identify factors for driver training guidelines. Based on the results of this study, one of the recommendations is that current ITS based driver training systems be calibrated to accurately measure the steering input which is found to be the most significant parameter influencing time headway (distances between simulated vehicles). The findings also support the modern intelligent tutoring system used at scale that leverages human feedback to improve their design.
{"title":"Understanding the Implications of the Use of Intelligent Tutoring Systems in Driver Training","authors":"Al-Ahad Ekram","doi":"10.1145/3386527.3406756","DOIUrl":"https://doi.org/10.1145/3386527.3406756","url":null,"abstract":"Effective driver education techniques can greatly benefit from the use of state-of-the-art technologies for driving training and tutoring in classroom environment. Such environment includes simulation systems that are designed based on the Intelligent Tutoring System concepts and framework. This research analyzed simulator data for both simulation and vehicle environments to identify factors for driver training guidelines. Based on the results of this study, one of the recommendations is that current ITS based driver training systems be calibrated to accurately measure the steering input which is found to be the most significant parameter influencing time headway (distances between simulated vehicles). The findings also support the modern intelligent tutoring system used at scale that leverages human feedback to improve their design.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88453544","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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