Block-based environments are today commonly used for introductory programming activities like those that are part of the Hour of Code campaign, which reaches millions of students. These activities typically consist of a static series of problems. Our aim is to make this type of activities more efficient by incorporating adaptive behavior. In this work, we discuss steps towards this goal, specifically a proposal and implementation of a programming game that supports both elementary problems and interesting programming challenges and thus provides an environment for meaningful adaptation. We also discuss methods of adaptivity and the issue of evaluating student performance while solving a problem.
{"title":"Towards making block-based programming activities adaptive","authors":"Tomáš Effenberger, Radek Pelánek","doi":"10.1145/3231644.3231670","DOIUrl":"https://doi.org/10.1145/3231644.3231670","url":null,"abstract":"Block-based environments are today commonly used for introductory programming activities like those that are part of the Hour of Code campaign, which reaches millions of students. These activities typically consist of a static series of problems. Our aim is to make this type of activities more efficient by incorporating adaptive behavior. In this work, we discuss steps towards this goal, specifically a proposal and implementation of a programming game that supports both elementary problems and interesting programming challenges and thus provides an environment for meaningful adaptation. We also discuss methods of adaptivity and the issue of evaluating student performance while solving a problem.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86854123","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 paper describes ClassDB, a free and open source system to enable large-scale learning of data management. ClassDB is different from existing solutions in that the same system supports a wide range of data-management topics from introductory SQL to advanced "native analytics" where code in SQL and non-SQL languages (Python and R) run inside a database management system. Each student/team maintains their own sandbox which instructors can read and provide feedback. Both students and instructors can review activity logs to analyze progress and determine future course of action. ClassDB is currently in its second pilot and is scheduled for a larger trial later this year. After the trials, ClassDB will be made available to about 4,000 students in the university system, which comprises four universities and 12 community colleges. ClassDB is built in collaboration with students employing modern DevOps processes. Its source code and documentation are available in a public GitHub repository. ClassDB is work in progress.
{"title":"Toward a large-scale open learning system for data management","authors":"S. Murthy, Andrew Figueroa, Steven Rollo","doi":"10.1145/3231644.3231673","DOIUrl":"https://doi.org/10.1145/3231644.3231673","url":null,"abstract":"This paper describes ClassDB, a free and open source system to enable large-scale learning of data management. ClassDB is different from existing solutions in that the same system supports a wide range of data-management topics from introductory SQL to advanced \"native analytics\" where code in SQL and non-SQL languages (Python and R) run inside a database management system. Each student/team maintains their own sandbox which instructors can read and provide feedback. Both students and instructors can review activity logs to analyze progress and determine future course of action. ClassDB is currently in its second pilot and is scheduled for a larger trial later this year. After the trials, ClassDB will be made available to about 4,000 students in the university system, which comprises four universities and 12 community colleges. ClassDB is built in collaboration with students employing modern DevOps processes. Its source code and documentation are available in a public GitHub repository. ClassDB is work in progress.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89620522","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}
Personalized educational systems adapt their behavior based on student performance. Most student modeling techniques, which are used for guiding the adaptation, utilize only the correctness of student's answers. However, other data about performance are typically available. In this work we focus on response times and wrong answers as these aspects of performance are available in most systems. We analyze data from several types of exercises and domains (mathematics, spelling, grammar). The results suggest that wrong answers are more informative than response times. Based on our results we propose a classification of student performance into several categories.
{"title":"Exploring the utility of response times and wrong answers for adaptive learning","authors":"Radek Pelánek","doi":"10.1145/3231644.3231675","DOIUrl":"https://doi.org/10.1145/3231644.3231675","url":null,"abstract":"Personalized educational systems adapt their behavior based on student performance. Most student modeling techniques, which are used for guiding the adaptation, utilize only the correctness of student's answers. However, other data about performance are typically available. In this work we focus on response times and wrong answers as these aspects of performance are available in most systems. We analyze data from several types of exercises and domains (mathematics, spelling, grammar). The results suggest that wrong answers are more informative than response times. Based on our results we propose a classification of student performance into several categories.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73767220","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}
Viktoria Pammer-Schindler, S. Thalmann, Angela Fessl, Julia Füssel
Traditionally, professional learning for senior professionals is organized around face-2-face trainings. Virtual trainings seem to offer an opportunity to reduce costs related to travel and travel time. In this paper we present a comparative case study that investigates the differences between traditional face-2-face trainings in physical reality, and virtual trainings via WebEx. Our goal is to identify how the way of communication impacts interaction between trainees, between trainees and trainers, and how it impacts interruptions. We present qualitative results from observations and interviews of three cases in different setups (traditional classroom, web-based with all participants co-located, web-based with all participants at different locations) and with overall 25 training participants and three trainers. The study is set within one of the Big Four global auditing companies, with advanced senior auditors as learning cohort.
{"title":"Virtualizing face-2-face trainings for training senior professionals: a comparative case study on financial auditors","authors":"Viktoria Pammer-Schindler, S. Thalmann, Angela Fessl, Julia Füssel","doi":"10.1145/3231644.3231695","DOIUrl":"https://doi.org/10.1145/3231644.3231695","url":null,"abstract":"Traditionally, professional learning for senior professionals is organized around face-2-face trainings. Virtual trainings seem to offer an opportunity to reduce costs related to travel and travel time. In this paper we present a comparative case study that investigates the differences between traditional face-2-face trainings in physical reality, and virtual trainings via WebEx. Our goal is to identify how the way of communication impacts interaction between trainees, between trainees and trainers, and how it impacts interruptions. We present qualitative results from observations and interviews of three cases in different setups (traditional classroom, web-based with all participants co-located, web-based with all participants at different locations) and with overall 25 training participants and three trainers. The study is set within one of the Big Four global auditing companies, with advanced senior auditors as learning cohort.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89039458","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}
Avneesh Sarwate, Creston Brunch, Jason Freeman, S. Siva
This paper explores some of the challenges posed by automated grading of programming assignments in a STEAM (Science, Technology, Engineering, Art, and Math) based curriculum, as well as how these challenges are addressed in the automatic grading processes used in EarSketch, a music-based educational programming environment developed at Georgia Tech. This work-in-progress paper reviews common strategies for grading programming assignments at scale and discusses how they are combined in EarSketch to evaluate open ended STEAM-focused assignments.
{"title":"Grading at scale in earsketch","authors":"Avneesh Sarwate, Creston Brunch, Jason Freeman, S. Siva","doi":"10.1145/3231644.3231708","DOIUrl":"https://doi.org/10.1145/3231644.3231708","url":null,"abstract":"This paper explores some of the challenges posed by automated grading of programming assignments in a STEAM (Science, Technology, Engineering, Art, and Math) based curriculum, as well as how these challenges are addressed in the automatic grading processes used in EarSketch, a music-based educational programming environment developed at Georgia Tech. This work-in-progress paper reviews common strategies for grading programming assignments at scale and discusses how they are combined in EarSketch to evaluate open ended STEAM-focused assignments.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"373 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76413755","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}
If a MOOC is to qualify for equal credit as an existing on-campus offering, students must achieve comparable outcomes, both educational and attitudinal. We have built a MOOC for teaching CS1 with the intent of offering it for degree credit. To test its eligibility for credit, we delivered it as an online for-credit course for two semesters to 197 on-campus students who selected the online version rather than a traditional version. We compared the demographics, outcomes, and experiences of these students to the 715 students in the traditional version. We found the online students more likely to be older; to be underrepresented minorities; and to have previously failed a CS class. We then found that our online students attained comparable learning outcomes to students in the traditional section. Finally, we found that our online students perceived the online course quality more positively and required less time to achieve those comparable learning outcomes.
{"title":"Toward CS1 at scale: building and testing a MOOC-for-credit candidate","authors":"David A. Joyner","doi":"10.1145/3231644.3231665","DOIUrl":"https://doi.org/10.1145/3231644.3231665","url":null,"abstract":"If a MOOC is to qualify for equal credit as an existing on-campus offering, students must achieve comparable outcomes, both educational and attitudinal. We have built a MOOC for teaching CS1 with the intent of offering it for degree credit. To test its eligibility for credit, we delivered it as an online for-credit course for two semesters to 197 on-campus students who selected the online version rather than a traditional version. We compared the demographics, outcomes, and experiences of these students to the 715 students in the traditional version. We found the online students more likely to be older; to be underrepresented minorities; and to have previously failed a CS class. We then found that our online students attained comparable learning outcomes to students in the traditional section. Finally, we found that our online students perceived the online course quality more positively and required less time to achieve those comparable learning outcomes.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74146653","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}
A typical problem in MOOCs is the missing opportunity for course conductors to individually support students in overcoming their problems and misconceptions. This paper presents the results of automatically intervening on struggling students during programming exercises and offering peer feedback and tailored bonus exercises. To improve learning success, we do not want to abolish instructionally desired trial and error but reduce extensive struggle and demotivation. Therefore, we developed adaptive automatic just-in-time interventions to encourage students to ask for help if they require considerably more than average working time to solve an exercise. Additionally, we offered students bonus exercises tailored for their individual weaknesses. The approach was evaluated within a live course with over 5,000 active students via a survey and metrics gathered alongside. Results show that we can increase the call outs for help by up to 66% and lower the dwelling time until issuing action. Learnings from the experiments can further be used to pinpoint course material to be improved and tailor content to be audience specific.
{"title":"Effects of automated interventions in programming assignments: evidence from a field experiment","authors":"Ralf Teusner, Thomas Hille, T. Staubitz","doi":"10.1145/3231644.3231650","DOIUrl":"https://doi.org/10.1145/3231644.3231650","url":null,"abstract":"A typical problem in MOOCs is the missing opportunity for course conductors to individually support students in overcoming their problems and misconceptions. This paper presents the results of automatically intervening on struggling students during programming exercises and offering peer feedback and tailored bonus exercises. To improve learning success, we do not want to abolish instructionally desired trial and error but reduce extensive struggle and demotivation. Therefore, we developed adaptive automatic just-in-time interventions to encourage students to ask for help if they require considerably more than average working time to solve an exercise. Additionally, we offered students bonus exercises tailored for their individual weaknesses. The approach was evaluated within a live course with over 5,000 active students via a survey and metrics gathered alongside. Results show that we can increase the call outs for help by up to 66% and lower the dwelling time until issuing action. Learnings from the experiments can further be used to pinpoint course material to be improved and tailor content to be audience specific.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80557055","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}
C. Edwards, Wayne Holmes, Denise Whitelock, A. Okada
Trust is a fundamental prerequisite for the success of any technological development, especially in education. Without the trust of stakeholders, educational technologies (however effective they might be) can fail to be taken up at scale. This work-in-progress paper reports on a study that investigated the trust students have in tools developed to support e-authentication for online assessments in Higher Education. The study, part of the EU-funded TeSLA project (http://tesla-project.eu), involved almost 500 students from the Open University, UK. Students were asked their views on trust and other issues before and after they used a tool developed to authenticate student identity in online assessments. A key finding is that, after using the tools, participants marginally increased their trust in online assessments, with the majority also reporting that they also trusted how the institution would use the outcomes.
{"title":"Student trust in e-authentication","authors":"C. Edwards, Wayne Holmes, Denise Whitelock, A. Okada","doi":"10.1145/3231644.3231700","DOIUrl":"https://doi.org/10.1145/3231644.3231700","url":null,"abstract":"Trust is a fundamental prerequisite for the success of any technological development, especially in education. Without the trust of stakeholders, educational technologies (however effective they might be) can fail to be taken up at scale. This work-in-progress paper reports on a study that investigated the trust students have in tools developed to support e-authentication for online assessments in Higher Education. The study, part of the EU-funded TeSLA project (http://tesla-project.eu), involved almost 500 students from the Open University, UK. Students were asked their views on trust and other issues before and after they used a tool developed to authenticate student identity in online assessments. A key finding is that, after using the tools, participants marginally increased their trust in online assessments, with the majority also reporting that they also trusted how the institution would use the outcomes.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78243340","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}
Support of discussion based learning at scale benefits from automated analysis of discussion for enabling effective assignment of students to project teams, for triggering dynamic support of group learning processes, and for assessment of those learning processes. A major limitation of much past work in machine learning applied to automated analysis of discussion is the failure of the models to generalize to data outside of the parameters of the context in which the training data was collected. This limitation means that a separate training effort must be undertaken for each domain in which the models will be used. This paper focuses on a specific construct of discussion based learning referred to as Transactivity and provides a novel machine learning approach with performance that exceeds state-of-the-art performance within the same domain in which it was trained and a new domain, and does not suffer any reduction in performance when transferring to the new domain. These results stand as an advance over past work on automated detection of Transactivity and increase the value of trained models for supporting group learning at scale. Implications for practice in at-scale learning environments are discussed.
{"title":"Towards domain general detection of transactive knowledge building behavior","authors":"James Fiacco, C. Rosé","doi":"10.1145/3231644.3231655","DOIUrl":"https://doi.org/10.1145/3231644.3231655","url":null,"abstract":"Support of discussion based learning at scale benefits from automated analysis of discussion for enabling effective assignment of students to project teams, for triggering dynamic support of group learning processes, and for assessment of those learning processes. A major limitation of much past work in machine learning applied to automated analysis of discussion is the failure of the models to generalize to data outside of the parameters of the context in which the training data was collected. This limitation means that a separate training effort must be undertaken for each domain in which the models will be used. This paper focuses on a specific construct of discussion based learning referred to as Transactivity and provides a novel machine learning approach with performance that exceeds state-of-the-art performance within the same domain in which it was trained and a new domain, and does not suffer any reduction in performance when transferring to the new domain. These results stand as an advance over past work on automated detection of Transactivity and increase the value of trained models for supporting group learning at scale. Implications for practice in at-scale learning environments are discussed.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81473309","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 present a mobile game app (EUR Game) that has been designed to complement teaching and learning in higher education. The mobile game app can be used by teachers to gauge how well students are meeting the learning objectives. Teachers can use the information to provide 'just-in-time' support and adapt their lessons accordingly. For the students, the game app is a study tool that can be used to test their own understanding and monitor their study progress. This, in turn, supports students' self-regulated learning. Gamification elements are also included in the game app to enhance the learning experience. During the demonstration, participants will experience the features of the game app and be engaged in an interactive session to explore the possible ways to use the mobile game app to support teaching and learning.
{"title":"Gamifying higher education: enhancing learning with mobile game app","authors":"Farshida Zafar, Jacqueline Wong, Mohammad Khalil","doi":"10.1145/3231644.3231686","DOIUrl":"https://doi.org/10.1145/3231644.3231686","url":null,"abstract":"We present a mobile game app (EUR Game) that has been designed to complement teaching and learning in higher education. The mobile game app can be used by teachers to gauge how well students are meeting the learning objectives. Teachers can use the information to provide 'just-in-time' support and adapt their lessons accordingly. For the students, the game app is a study tool that can be used to test their own understanding and monitor their study progress. This, in turn, supports students' self-regulated learning. Gamification elements are also included in the game app to enhance the learning experience. During the demonstration, participants will experience the features of the game app and be engaged in an interactive session to explore the possible ways to use the mobile game app to support teaching and learning.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"62 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86641163","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：