Problem. Educational research identifies answerable questions, but often does not address the problems K-12 teachers identify as important. Further, academic research findings can be difficult for teachers to apply to their practices and unique contexts. Currently, little research exists on the lived experiences of primary and secondary instructors who teach computer science (CS) or computational thinking (CT) and also on the specific problems of practice teachers face when teaching CS. Research Question. What problems of practice do K-12 teachers face when teaching CS/CT? Method. Data for this qualitative study was collected using an online questionnaire distributed to teachers internationally. CS/CT teachers responded to an open-ended prompt asking for problems related to teaching CS. The data was analyzed using descriptive first-round coding and focused second-round coding. Validity was established through collaborative coding. Analysis was theorized using locus of control. Findings. Problems with students encompassed behavioral, cognitive, and attitudinal issues, as well as lack of home support or resources. Teachers identified many problems of policy notably stemming from lack of resources or support from administrators. A smaller number of challenges, such as lack of content knowledge, were situated within teachers themselves. While some problems such as student motivation are general, a number of responses identified unique challenges in CS education compared to other disciplines. Implications. Identifying problems faced by teachers can guide professional development offerings, help researchers develop studies that would result in meaningful improvement to CS education, and suggest policy decisions which would result in better outcomes for students.
{"title":"Solve This! K-12 CS Education Teachers’ Problems of Practice","authors":"Michèlle Friend, Monica Mcgill, Anni Reinking","doi":"10.1145/3564721.3564738","DOIUrl":"https://doi.org/10.1145/3564721.3564738","url":null,"abstract":"Problem. Educational research identifies answerable questions, but often does not address the problems K-12 teachers identify as important. Further, academic research findings can be difficult for teachers to apply to their practices and unique contexts. Currently, little research exists on the lived experiences of primary and secondary instructors who teach computer science (CS) or computational thinking (CT) and also on the specific problems of practice teachers face when teaching CS. Research Question. What problems of practice do K-12 teachers face when teaching CS/CT? Method. Data for this qualitative study was collected using an online questionnaire distributed to teachers internationally. CS/CT teachers responded to an open-ended prompt asking for problems related to teaching CS. The data was analyzed using descriptive first-round coding and focused second-round coding. Validity was established through collaborative coding. Analysis was theorized using locus of control. Findings. Problems with students encompassed behavioral, cognitive, and attitudinal issues, as well as lack of home support or resources. Teachers identified many problems of policy notably stemming from lack of resources or support from administrators. A smaller number of challenges, such as lack of content knowledge, were situated within teachers themselves. While some problems such as student motivation are general, a number of responses identified unique challenges in CS education compared to other disciplines. Implications. Identifying problems faced by teachers can guide professional development offerings, help researchers develop studies that would result in meaningful improvement to CS education, and suggest policy decisions which would result in better outcomes for students.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122881750","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}
Self-efficacy is an important construct in education, as it can influence (among other aspects) perseverance, engagement and success on educational tasks. As such, a student’s Computational Thinking (CT) self-efficacy can have an important influence on, and may be a predictor for, the development and use of CT skills. This poster abstract provides the details of an in-progress study in which we develop a scale to measure CT self-efficacy in different contexts.
{"title":"Developing a Self-efficacy Scale for Computational Thinking (CT-SES)","authors":"Imke De Jong, J. Jeuring","doi":"10.1145/3564721.3565954","DOIUrl":"https://doi.org/10.1145/3564721.3565954","url":null,"abstract":"Self-efficacy is an important construct in education, as it can influence (among other aspects) perseverance, engagement and success on educational tasks. As such, a student’s Computational Thinking (CT) self-efficacy can have an important influence on, and may be a predictor for, the development and use of CT skills. This poster abstract provides the details of an in-progress study in which we develop a scale to measure CT self-efficacy in different contexts.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115849259","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}
Museums and exhibitions are recognised as places of informal learning. Visitors with different interests and prior knowledge have access to a variety of learning opportunities provided by exhibits. Empirical findings on possible learning outcomes in comparable environments, such as science centres, exhibitions with themes of natural science, art galleries, or historical sites, are well examined. However, little is known about learning outcomes of visitors to computer science museums and exhibitions (CSME). In order to gain initial insights which aspects, especially computer science related aspects, visitors to CSME remember, we surveyed adult visitors’ long-term memories linked to exhibits in three German CSME. We used an online questionnaire distributed to users of the museums’ communication channels. The written responses refer to visits dating back a few months to several years ago. We used a deductive-inductive coding approach to identify text passages relating to possible learning outcomes and reasons for the vividness of the memories. The results show that participants are able to recall an overview gained of a collection of exhibits as well as concrete information conveyed by the exhibits. In some cases, the reports refer to sensory impressions, emotions, personal evaluations, or to auto-biographical aspects as well as accompanying visitors. As museums provide an environment with a broad variety of mediation approaches and displayed content, the discussion focuses on how long-term memories may offer a new perspective on visits to CSME and computer science related learning opportunities.
{"title":"Informal Learning in Computer Science Museums and Exhibitions: What Visitors’ Long-Term Memories Can Tell Us About Exhibits as Possible Learning Opportunities","authors":"Fabian Graap","doi":"10.1145/3564721.3564737","DOIUrl":"https://doi.org/10.1145/3564721.3564737","url":null,"abstract":"Museums and exhibitions are recognised as places of informal learning. Visitors with different interests and prior knowledge have access to a variety of learning opportunities provided by exhibits. Empirical findings on possible learning outcomes in comparable environments, such as science centres, exhibitions with themes of natural science, art galleries, or historical sites, are well examined. However, little is known about learning outcomes of visitors to computer science museums and exhibitions (CSME). In order to gain initial insights which aspects, especially computer science related aspects, visitors to CSME remember, we surveyed adult visitors’ long-term memories linked to exhibits in three German CSME. We used an online questionnaire distributed to users of the museums’ communication channels. The written responses refer to visits dating back a few months to several years ago. We used a deductive-inductive coding approach to identify text passages relating to possible learning outcomes and reasons for the vividness of the memories. The results show that participants are able to recall an overview gained of a collection of exhibits as well as concrete information conveyed by the exhibits. In some cases, the reports refer to sensory impressions, emotions, personal evaluations, or to auto-biographical aspects as well as accompanying visitors. As museums provide an environment with a broad variety of mediation approaches and displayed content, the discussion focuses on how long-term memories may offer a new perspective on visits to CSME and computer science related learning opportunities.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131841232","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 curriculum for an introductory module on programming for Finnish upper secondary school was developed in collaboration with tertiary education. The module was refined over three school years and the attitudes and progress of the students were evaluated through pre-course and post-course questionnaires. Interviews with the responsible teachers were also used for the evaluation. The early results indicate that such a module can be successfully implemented with limited resources and also point to the large informal influence of the national standardized matriculation exam on student interest for and school implementation of such a module.
{"title":"A Case Study on Programming in Finnish General Upper Secondary Education","authors":"Markus Norrby, N. Palmberg, Ray Pörn","doi":"10.1145/3564721.3565959","DOIUrl":"https://doi.org/10.1145/3564721.3565959","url":null,"abstract":"A curriculum for an introductory module on programming for Finnish upper secondary school was developed in collaboration with tertiary education. The module was refined over three school years and the attitudes and progress of the students were evaluated through pre-course and post-course questionnaires. Interviews with the responsible teachers were also used for the evaluation. The early results indicate that such a module can be successfully implemented with limited resources and also point to the large informal influence of the national standardized matriculation exam on student interest for and school implementation of such a module.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115205536","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}
Higher education institutions (HEIs) internationally are increasingly recognising the importance of understanding student study choices. This is especially true in the field of computing in which skills shortages in the labour market are high but so too are student dropout rates. Loss of interest in the computing field has been reported among the reasons for students dropping out. The aim of this study is to offer a fresh perspective of the factors influencing undergraduate student’s interest and choice of specialisation in computing. Previous studies have mainly focused on five ACM-identified computing disciplines: Computer Science, Information Systems, Information Technology, Computer Engineering and Software Engineering. With the ever-growing nature of computing, two more disciplines recently emerged: Cybersecurity and Data Science. HEIs continuously endeavour to expand computing programmes into specialist areas within these disciplines such as machine learning, artificial intelligence, gaming, robotics and creative computing. For prospective students, this maze of options can make for a difficult decision. 137 first-year computing students were invited to participate in a mixed-methods survey to explore their choices around cybersecurity and other newer specialisations. The results of the survey were matched with findings from recent literature, and show that personal interest, family, media, career prospects and prior experiences still influence student choices, with media appearing to have a greater impact compared to earlier studies. HEIs can use this when developing effective recruitment strategies in computing.
{"title":"With a Rise in Computing Disciplines Comes a Greater Choice of Computing Degrees in Higher Education","authors":"Fiona Redmond","doi":"10.1145/3564721.3565946","DOIUrl":"https://doi.org/10.1145/3564721.3565946","url":null,"abstract":"Higher education institutions (HEIs) internationally are increasingly recognising the importance of understanding student study choices. This is especially true in the field of computing in which skills shortages in the labour market are high but so too are student dropout rates. Loss of interest in the computing field has been reported among the reasons for students dropping out. The aim of this study is to offer a fresh perspective of the factors influencing undergraduate student’s interest and choice of specialisation in computing. Previous studies have mainly focused on five ACM-identified computing disciplines: Computer Science, Information Systems, Information Technology, Computer Engineering and Software Engineering. With the ever-growing nature of computing, two more disciplines recently emerged: Cybersecurity and Data Science. HEIs continuously endeavour to expand computing programmes into specialist areas within these disciplines such as machine learning, artificial intelligence, gaming, robotics and creative computing. For prospective students, this maze of options can make for a difficult decision. 137 first-year computing students were invited to participate in a mixed-methods survey to explore their choices around cybersecurity and other newer specialisations. The results of the survey were matched with findings from recent literature, and show that personal interest, family, media, career prospects and prior experiences still influence student choices, with media appearing to have a greater impact compared to earlier studies. HEIs can use this when developing effective recruitment strategies in computing.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114879003","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}
Siddharth Prasad, B. Greenman, Tim Nelson, J. Wrenn, S. Krishnamurthi
Novice programmers often begin coding with a poor understanding of the task at hand and end up solving the wrong problem. A promising way to put novices on the right track is to have them write examples first, before coding, and provide them with feedback by evaluating the examples on a suite of chaff implementations that are flawed in subtle ways. This feedback, however, is only as good as the chaffs themselves. Instructors must anticipate misconceptions and avoid expert blind spots to make a useful suite of chaffs. This paper conjectures that novices’ incorrect examples are a rich source of insight and presents a classsourcing method for identifying misconceptions. First off, we identify incorrect examples using known, correct wheat implementations. The method is to manually cluster incorrect examples by semantic similarity, summarize each cluster with a potential misconception, and use the analysis to generate chaffs—thereby deriving a useful by-product (hay) from examples that fail the wheats. Classsourced misconceptions have revealed expert blind spots and drawn attention to chaffs that seldom arose in practice, one of which had an undiscovered bug.
{"title":"Making Hay from Wheats: A Classsourcing Method to Identify Misconceptions","authors":"Siddharth Prasad, B. Greenman, Tim Nelson, J. Wrenn, S. Krishnamurthi","doi":"10.1145/3564721.3564726","DOIUrl":"https://doi.org/10.1145/3564721.3564726","url":null,"abstract":"Novice programmers often begin coding with a poor understanding of the task at hand and end up solving the wrong problem. A promising way to put novices on the right track is to have them write examples first, before coding, and provide them with feedback by evaluating the examples on a suite of chaff implementations that are flawed in subtle ways. This feedback, however, is only as good as the chaffs themselves. Instructors must anticipate misconceptions and avoid expert blind spots to make a useful suite of chaffs. This paper conjectures that novices’ incorrect examples are a rich source of insight and presents a classsourcing method for identifying misconceptions. First off, we identify incorrect examples using known, correct wheat implementations. The method is to manually cluster incorrect examples by semantic similarity, summarize each cluster with a potential misconception, and use the analysis to generate chaffs—thereby deriving a useful by-product (hay) from examples that fail the wheats. Classsourced misconceptions have revealed expert blind spots and drawn attention to chaffs that seldom arose in practice, one of which had an undiscovered bug.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116900765","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}
As the enrollments in CS courses continue to increase, the need to grade students’ submissions and provide effective feedback promptly at scale is a growing challenge for CS educators. Many autograding solutions have been introduced to address this issue. However, there are multiple barriers to adopting these solutions, including requiring significant changes in a course’s workflow, setup processes requiring extensive IT support, and, more importantly, the learning curve for instructors. These inhibit instructors’ ability to use autograding solutions effectively. In this demo, we present Unison Live, an automated feedback and grading web application that integrates with LTI (learning-tools-interoperability) compliant learning management systems (LMSs) like Canvas. With its use, instructors can enable autograding instructions on their existing assignments in their CS1/2 courses through an intuitive user interface without changing course specifications. Students submit their program files on the LMS and receive instant feedback and grade reports. Unison Live currently supports programming languages like Python, C++, and MATLAB. After the submission deadline, instructors receive auto-generated code similarity reports and aggregate behavioral analytics on student submissions. We believe that an app like this will not only address the logistical issues related to grading but also pedagogically support the integration of formative & optional programming assignments that students can practice at their own pace and receive feedback. More details on Unison Live are available on https://www.unisonlive.io/
{"title":"Unison Live: Automated Feedback, Grading, and Analytics LTI Application","authors":"Ashish Aggarwal, Aeyzechiah Vasquez","doi":"10.1145/3564721.3565960","DOIUrl":"https://doi.org/10.1145/3564721.3565960","url":null,"abstract":"As the enrollments in CS courses continue to increase, the need to grade students’ submissions and provide effective feedback promptly at scale is a growing challenge for CS educators. Many autograding solutions have been introduced to address this issue. However, there are multiple barriers to adopting these solutions, including requiring significant changes in a course’s workflow, setup processes requiring extensive IT support, and, more importantly, the learning curve for instructors. These inhibit instructors’ ability to use autograding solutions effectively. In this demo, we present Unison Live, an automated feedback and grading web application that integrates with LTI (learning-tools-interoperability) compliant learning management systems (LMSs) like Canvas. With its use, instructors can enable autograding instructions on their existing assignments in their CS1/2 courses through an intuitive user interface without changing course specifications. Students submit their program files on the LMS and receive instant feedback and grade reports. Unison Live currently supports programming languages like Python, C++, and MATLAB. After the submission deadline, instructors receive auto-generated code similarity reports and aggregate behavioral analytics on student submissions. We believe that an app like this will not only address the logistical issues related to grading but also pedagogically support the integration of formative & optional programming assignments that students can practice at their own pace and receive feedback. More details on Unison Live are available on https://www.unisonlive.io/","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128927518","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}
Designing assessments in classroom contexts or having them generated automatically requires - among other things - knowledge about the difficulty of what is assessed. Estimates of difficulty can be derived empirically, usually by piloting items, or theoretically from models. Empirical results, in turn, can inform theory and refine models. In this article, we compare four methods of estimating the item difficulty for a typical topic of introductory programming courses: control flow. For a given set of items that have been tested empirically, we also collected expert ratings and additionally applied measures of code complexity both from software engineering and from computer science education research The results show that there is some overlap between empirical results and theoretical predictions. However, for the simple item format that we have been using, the models all fall short in offering enough explanatory power regarding the observed variance in difficulty. Empirical difficulty in turn can serve as the basis for rules that can be used for item generation in the future.
{"title":"Comparing estimates of difficulty of programming constructs","authors":"M. Bastian, A. Mühling","doi":"10.1145/3564721.3565950","DOIUrl":"https://doi.org/10.1145/3564721.3565950","url":null,"abstract":"Designing assessments in classroom contexts or having them generated automatically requires - among other things - knowledge about the difficulty of what is assessed. Estimates of difficulty can be derived empirically, usually by piloting items, or theoretically from models. Empirical results, in turn, can inform theory and refine models. In this article, we compare four methods of estimating the item difficulty for a typical topic of introductory programming courses: control flow. For a given set of items that have been tested empirically, we also collected expert ratings and additionally applied measures of code complexity both from software engineering and from computer science education research The results show that there is some overlap between empirical results and theoretical predictions. However, for the simple item format that we have been using, the models all fall short in offering enough explanatory power regarding the observed variance in difficulty. Empirical difficulty in turn can serve as the basis for rules that can be used for item generation in the future.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132116456","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}
Computer science and programming achievement standards for the three final years of high school were introduced in New Zealand in 2011. More than a decade after the introduction, the experiences of teachers who play a key role in the successful implementation have been largely overlooked. However, listening to teachers’ experiences with preparing, teaching and assessing these standards enables us to discover barriers and challenges, along with solutions suggested to overcome them. We conducted an online survey in 2021 and received responses from 91 teachers in New Zealand who have been involved with the new standards. Our analysis identified barriers and related challenges for both teachers and students, falling in to five main categories: standard content, resources, support, assessment, and student centered factors. In many cases these barriers hinder successful implementation and student participation. From the suggestions made we deduce actions which could be taken by schools as well as factors beyond the schools’ control to address these barriers. The survey showed that the majority of the teachers were not satisfied with the time available for upskilling, and the guidance available on marking assessments. Lack of support materials was also considered to be a pressing issue. The key suggestions that emerged were inclusion of more practical work instead of theory and writing, and more time for teachers to practise the subject. With this study we provide a subjective evaluation of the current situation in New Zealand suggesting some improvements for the future which we trust will be valuable to researchers in other countries undergoing similar implementations.
{"title":"Teachers’ Views on the Implementation of a New High School Computing Curriculum","authors":"Chamindi K. Samarasekara, Claudia Ott, A. Robins","doi":"10.1145/3564721.3564732","DOIUrl":"https://doi.org/10.1145/3564721.3564732","url":null,"abstract":"Computer science and programming achievement standards for the three final years of high school were introduced in New Zealand in 2011. More than a decade after the introduction, the experiences of teachers who play a key role in the successful implementation have been largely overlooked. However, listening to teachers’ experiences with preparing, teaching and assessing these standards enables us to discover barriers and challenges, along with solutions suggested to overcome them. We conducted an online survey in 2021 and received responses from 91 teachers in New Zealand who have been involved with the new standards. Our analysis identified barriers and related challenges for both teachers and students, falling in to five main categories: standard content, resources, support, assessment, and student centered factors. In many cases these barriers hinder successful implementation and student participation. From the suggestions made we deduce actions which could be taken by schools as well as factors beyond the schools’ control to address these barriers. The survey showed that the majority of the teachers were not satisfied with the time available for upskilling, and the guidance available on marking assessments. Lack of support materials was also considered to be a pressing issue. The key suggestions that emerged were inclusion of more practical work instead of theory and writing, and more time for teachers to practise the subject. With this study we provide a subjective evaluation of the current situation in New Zealand suggesting some improvements for the future which we trust will be valuable to researchers in other countries undergoing similar implementations.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133155274","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}
While recent research has explored how to keep learners engaged in massive open online courses (MOOCs), motivating sustained engagement in small private online courses (SPOCs) has received less attention. The recent rise in SPOC offerings by academic institutions significantly increases the need to better understand how best to motivate and support learners in these communities, which can be differentiated from those in MOOCs by their smaller size, fixed timeframe, instructor presence, and more predictable learning paths. This paper describes a system, “Small Private Online Course Keeper” (SPOCK), designed to support communities of learners in SPOCs and presents an experience report detailing its usage in five SPOCs taught over two semesters. This report focuses on the ability of SPOCK to motivate sustained lecture video engagement through two primary features: 1) a credit and reward system, and 2) timeline-anchored, “pseudo-anonymous” comments and replies. The credit and reward feature motivated system usage by 77% of survey respondents, and its pseudo-anonymity feature motivated system usage by 63% of respondents. Usage data reflects these reported levels of motivation, with 82% of students earning more than the minimum number of credits expected and over 63% of students voluntarily submitting comments or replies, 72% of which were posted anonymously. Further analysis showed that students earned credits steadily throughout the semesters through a variety of means, and their posts were found to be positive in tone and on-topic.
{"title":"A System to Motivate Sustained Lecture Video Engagement in Small Private Online Courses","authors":"Ryan Hardt","doi":"10.1145/3564721.3564735","DOIUrl":"https://doi.org/10.1145/3564721.3564735","url":null,"abstract":"While recent research has explored how to keep learners engaged in massive open online courses (MOOCs), motivating sustained engagement in small private online courses (SPOCs) has received less attention. The recent rise in SPOC offerings by academic institutions significantly increases the need to better understand how best to motivate and support learners in these communities, which can be differentiated from those in MOOCs by their smaller size, fixed timeframe, instructor presence, and more predictable learning paths. This paper describes a system, “Small Private Online Course Keeper” (SPOCK), designed to support communities of learners in SPOCs and presents an experience report detailing its usage in five SPOCs taught over two semesters. This report focuses on the ability of SPOCK to motivate sustained lecture video engagement through two primary features: 1) a credit and reward system, and 2) timeline-anchored, “pseudo-anonymous” comments and replies. The credit and reward feature motivated system usage by 77% of survey respondents, and its pseudo-anonymity feature motivated system usage by 63% of respondents. Usage data reflects these reported levels of motivation, with 82% of students earning more than the minimum number of credits expected and over 63% of students voluntarily submitting comments or replies, 72% of which were posted anonymously. Further analysis showed that students earned credits steadily throughout the semesters through a variety of means, and their posts were found to be positive in tone and on-topic.","PeriodicalId":149708,"journal":{"name":"Proceedings of the 22nd Koli Calling International Conference on Computing Education Research","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115224725","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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