{"title":"Trust, Sustainability and [email protected]","authors":"S. B. Shum","doi":"10.1145/3573051.3593375","DOIUrl":"https://doi.org/10.1145/3573051.3593375","url":null,"abstract":"","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"72 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77669272","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}
{"title":"L@S'22: Ninth ACM Conference on Learning @ Scale, New York City, NY, USA, June 1 - 3, 2022","authors":"","doi":"10.1145/3491140","DOIUrl":"https://doi.org/10.1145/3491140","url":null,"abstract":"","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89284433","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}
{"title":"L@S'21: Eighth ACM Conference on Learning @ Scale, Virtual Event, Germany, June 22-25, 2021","authors":"","doi":"10.1145/3430895","DOIUrl":"https://doi.org/10.1145/3430895","url":null,"abstract":"","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90038458","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 half-day workshop aims at collecting experiences of MOOC designers and MOOC educators to discuss what has been done to support SRL and what can be done to scaffold learners in MOOCs, particularly MOOCs for language learning purposes (LMOOCs). For this purpose, it is planned to come together with academicians and researchers working on related subjects within the scope of this workshop to develop a student support framework in large-scale learning environments. In this workshop, the participants should actively participate and take part in both the virtual session and the discussions that may emerge on social media (via Twitter). In line with this process, the main problems faced by individuals learning a foreign language with the help of LMOOCs will be examined following the experiences of the participants and the organizers.
{"title":"Supporting Learners' Self-regulation in LMOOCs: What Have We Done and How Far We Can Go?","authors":"Barbara Conde Gafaro, H. Yildiz","doi":"10.1145/3386527.3405931","DOIUrl":"https://doi.org/10.1145/3386527.3405931","url":null,"abstract":"This half-day workshop aims at collecting experiences of MOOC designers and MOOC educators to discuss what has been done to support SRL and what can be done to scaffold learners in MOOCs, particularly MOOCs for language learning purposes (LMOOCs). For this purpose, it is planned to come together with academicians and researchers working on related subjects within the scope of this workshop to develop a student support framework in large-scale learning environments. In this workshop, the participants should actively participate and take part in both the virtual session and the discussions that may emerge on social media (via Twitter). In line with this process, the main problems faced by individuals learning a foreign language with the help of LMOOCs will be examined following the experiences of the participants and the organizers.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84358202","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}
Hamid Karimi, Kaitlin T. Torphy, Tyler Derr, K. Frank, Jiliang Tang
Increasingly many teachers are turning to online social media to supplement educational resources and meet students' needs in the classrooms. The diffusion of information from online social media to the classroom is significantly faster than traditional curriculum-based approaches. However, this is contingent upon how well teachers across an online social media network are connected. To understand this, we perform a thorough and large-scale investigation of teacher connections in online social media, which is lacking in the literature. To make this feasible, we construct a large dataset of teachers on Pinterest, an image-based popular online social media. Our dataset includes 540 teachers across 5 states and 48 districts, thousands of connections they have established (either with their peers or some other Pinterest users), and all the resources they have shared in their accounts. Then, taking into account some crucial teacher-related attributes (e.g., their districts, grade levels, etc), we characterize direct and indirect teacher connections. Moreover, we compare the physical (face to face) and virtual (Pinterest) network of our surveyed teachers using several graph-related metrics. The finding in this study can serve as a basis to investigate teachers on social media in a deeper manner.
{"title":"Characterizing Teacher Connections in Online Social Media: A Case Study on Pinterest","authors":"Hamid Karimi, Kaitlin T. Torphy, Tyler Derr, K. Frank, Jiliang Tang","doi":"10.1145/3386527.3405941","DOIUrl":"https://doi.org/10.1145/3386527.3405941","url":null,"abstract":"Increasingly many teachers are turning to online social media to supplement educational resources and meet students' needs in the classrooms. The diffusion of information from online social media to the classroom is significantly faster than traditional curriculum-based approaches. However, this is contingent upon how well teachers across an online social media network are connected. To understand this, we perform a thorough and large-scale investigation of teacher connections in online social media, which is lacking in the literature. To make this feasible, we construct a large dataset of teachers on Pinterest, an image-based popular online social media. Our dataset includes 540 teachers across 5 states and 48 districts, thousands of connections they have established (either with their peers or some other Pinterest users), and all the resources they have shared in their accounts. Then, taking into account some crucial teacher-related attributes (e.g., their districts, grade levels, etc), we characterize direct and indirect teacher connections. Moreover, we compare the physical (face to face) and virtual (Pinterest) network of our surveyed teachers using several graph-related metrics. The finding in this study can serve as a basis to investigate teachers on social media in a deeper manner.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"82 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84412075","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 study examined two machine learning models for de- signing a learning analytics dashboard to assist teachers in facilitating problem-based learning. Specifically, we used BERT to automatically process a large amount of textual data to understand students' scientific argumentation. We then used Hidden Markov Model (HMM) to find students' cognitive state transition with time-series data. Preliminary results showed the models achieved high accuracy and were coherent with related theories, indicating the models can provide teachers with interpretable information to identify in-need students.
{"title":"Learning Analytics Dashboard for Problem-based Learning","authors":"Zilong Pan, Chenglu Li, Min Liu","doi":"10.1145/3386527.3406751","DOIUrl":"https://doi.org/10.1145/3386527.3406751","url":null,"abstract":"This study examined two machine learning models for de- signing a learning analytics dashboard to assist teachers in facilitating problem-based learning. Specifically, we used BERT to automatically process a large amount of textual data to understand students' scientific argumentation. We then used Hidden Markov Model (HMM) to find students' cognitive state transition with time-series data. Preliminary results showed the models achieved high accuracy and were coherent with related theories, indicating the models can provide teachers with interpretable information to identify in-need students.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85023973","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}
Raghav Apoorv, Akshay Dahiya, Uma Sreeram, Bharat Rahuldhev Patil, India Irish, Rocko Graziano, Thad Starner
Examinator compares pairs of take-home exams to select which should be manually checked for plagiarism. Examinator also generates a report with evidence for these cases using its metrics and those generated as a by-product of the commercial grading tool Gradescope. Examinator supports degree-seeking graduate programs (both online and on-campus) at a top computer science graduate institute in the United States. Since Spring 2019, Examinator has compared over 2 million pairs of exams from a popular Artificial Intelligence course, resulting in 56 cases being referred for discipline. Iterative development has improved the percentage of referrals of suggested cases from 15% to 25%.
{"title":"Examinator","authors":"Raghav Apoorv, Akshay Dahiya, Uma Sreeram, Bharat Rahuldhev Patil, India Irish, Rocko Graziano, Thad Starner","doi":"10.1145/3386527.3406723","DOIUrl":"https://doi.org/10.1145/3386527.3406723","url":null,"abstract":"Examinator compares pairs of take-home exams to select which should be manually checked for plagiarism. Examinator also generates a report with evidence for these cases using its metrics and those generated as a by-product of the commercial grading tool Gradescope. Examinator supports degree-seeking graduate programs (both online and on-campus) at a top computer science graduate institute in the United States. Since Spring 2019, Examinator has compared over 2 million pairs of exams from a popular Artificial Intelligence course, resulting in 56 cases being referred for discipline. Iterative development has improved the percentage of referrals of suggested cases from 15% to 25%.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84110902","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}
Kabdo Choi, Sally Chen, Hyungyu Shin, J. Son, Juho Kim
Planning a solution before writing code is essential in algorithmic problem-solving. However, novices often skip planning and jump straight into coding. Even if they set up a plan, some do not connect to their plan when writing code. Learners solving algorithmic problems often struggle with high-level components such as solution techniques and sub-problems, but existing representations that guide learners in planning, such as flowcharts, focus on presenting lower-level details. We use subgoal diagrams -- diagrams made of subgoal labels and the relationships between them -- as a representation that guides learners to focus on high-level plans when they develop solutions. We introduce AlgoPlan, an interface that enables learners to build their own subgoal diagram and use it to guide their problem-solving process. A preliminary study with seven students shows that subgoal diagrams help learners focus on high-level plans and connect these plans to their code.
{"title":"AlgoPlan","authors":"Kabdo Choi, Sally Chen, Hyungyu Shin, J. Son, Juho Kim","doi":"10.1145/3386527.3406750","DOIUrl":"https://doi.org/10.1145/3386527.3406750","url":null,"abstract":"Planning a solution before writing code is essential in algorithmic problem-solving. However, novices often skip planning and jump straight into coding. Even if they set up a plan, some do not connect to their plan when writing code. Learners solving algorithmic problems often struggle with high-level components such as solution techniques and sub-problems, but existing representations that guide learners in planning, such as flowcharts, focus on presenting lower-level details. We use subgoal diagrams -- diagrams made of subgoal labels and the relationships between them -- as a representation that guides learners to focus on high-level plans when they develop solutions. We introduce AlgoPlan, an interface that enables learners to build their own subgoal diagram and use it to guide their problem-solving process. A preliminary study with seven students shows that subgoal diagrams help learners focus on high-level plans and connect these plans to their code.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76541493","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}
Pub Date : 2020-08-12DOI: 10.1163/1574-9347_bnp_e1027550
Anne-Cécile Lefranc, David A. Joyner
{"title":"SAGA","authors":"Anne-Cécile Lefranc, David A. Joyner","doi":"10.1163/1574-9347_bnp_e1027550","DOIUrl":"https://doi.org/10.1163/1574-9347_bnp_e1027550","url":null,"abstract":"","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82849377","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}
Advancement in technology and innovation in teaching such as chatbot and extended reality can be daunting for teachers but as an educator, we need to leverage on these advancements to respond to the changes and challenges in the teaching and learning landscape. There are a number of tools available for teachers to use to overcome the challenges, and one of them is the application of artificial intelligence (AI) but creating a chatbot requires complex computer programming skills, and it is usually built from scratch to fit the intended educational purpose. This practice makes it difficult for teachers to adapt existing systems or to attempt in creating a similar version. In this workshop, we will be sharing our experiences gained from developing various chatbots for higher education using a commercial platform that can jumpstart your chatbot.
{"title":"Code Free Chatbot Development: An Easy Way to Jumpstart Your Chatbot!","authors":"C. Luo, V. Wong, D. Gonda","doi":"10.1145/3386527.3405932","DOIUrl":"https://doi.org/10.1145/3386527.3405932","url":null,"abstract":"Advancement in technology and innovation in teaching such as chatbot and extended reality can be daunting for teachers but as an educator, we need to leverage on these advancements to respond to the changes and challenges in the teaching and learning landscape. There are a number of tools available for teachers to use to overcome the challenges, and one of them is the application of artificial intelligence (AI) but creating a chatbot requires complex computer programming skills, and it is usually built from scratch to fit the intended educational purpose. This practice makes it difficult for teachers to adapt existing systems or to attempt in creating a similar version. In this workshop, we will be sharing our experiences gained from developing various chatbots for higher education using a commercial platform that can jumpstart your chatbot.","PeriodicalId":20608,"journal":{"name":"Proceedings of the Seventh ACM Conference on Learning @ Scale","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91546255","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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