Effective written communication is an essential skill which promotes educational success for undergraduates. However, undergraduate students, especially those in their first year at university, are unused to this form of writing. After their long experience with the schoolroom essay, for most undergraduates academic writing development is painstakingly slow. Thus, especially those with poor writing abilities, should write more to be better writers. Yet, the biggest impediment to more writing is that overburdened tutors would ask limited number of drafts from their students. Today, there exist powerful computational language technologies that could evaluate student writing, saving time and providing timely, speedy, reliable feedback which can support educators marking process. This paper motivates an updated visual analytics dashboard, XIPIt, to introduce a set of visual and writing analytics features embedded in a marking environment built on XIP output.
{"title":"XIPIt","authors":"Duygu Bektik","doi":"10.1145/3231644.3231696","DOIUrl":"https://doi.org/10.1145/3231644.3231696","url":null,"abstract":"Effective written communication is an essential skill which promotes educational success for undergraduates. However, undergraduate students, especially those in their first year at university, are unused to this form of writing. After their long experience with the schoolroom essay, for most undergraduates academic writing development is painstakingly slow. Thus, especially those with poor writing abilities, should write more to be better writers. Yet, the biggest impediment to more writing is that overburdened tutors would ask limited number of drafts from their students. Today, there exist powerful computational language technologies that could evaluate student writing, saving time and providing timely, speedy, reliable feedback which can support educators marking process. This paper motivates an updated visual analytics dashboard, XIPIt, to introduce a set of visual and writing analytics features embedded in a marking environment built on XIP output.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79349498","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 many strategies for protecting personal privacy have relied on regulatory frameworks, consent and anonymizing data, such approaches are not always effective. Frameworks and Terms and Conditions often lag user behaviour and advances in technology and software; consent can be provisional and fragile; and the anonymization of data may impede personalized learning. This paper reports on a dialogical multi-case study methodology of four Massive Open Online Course (MOOC) providers from different geopolitical and regulatory contexts. It explores how the providers (1) define 'personal data' and whether they acknowledge a category of 'special' or 'sensitive' data; (2) address the issue and scope of student consent (and define that scope); and (3) use student data in order to inform pedagogy and/or adapt the learning experience to personalise the context or to increase student retention and success rates. This study found that large amounts of personal data continue to be collected for purposes seemingly unrelated to the delivery and support of courses. The capacity for users to withdraw or withhold consent for the collection of certain categories of data such as sensitive personal data remains severely constrained. This paper proposes that user consent at the time of registration should be reconsidered, and that there is a particular need for consent when sensitive personal data are used to personalize learning, or for purposes outside the original intention of obtaining consent.
{"title":"The unbearable lightness of consent: mapping MOOC providers' response to consent","authors":"Mohammad Khalil, P. Prinsloo, Sharon Slade","doi":"10.1145/3231644.3231659","DOIUrl":"https://doi.org/10.1145/3231644.3231659","url":null,"abstract":"While many strategies for protecting personal privacy have relied on regulatory frameworks, consent and anonymizing data, such approaches are not always effective. Frameworks and Terms and Conditions often lag user behaviour and advances in technology and software; consent can be provisional and fragile; and the anonymization of data may impede personalized learning. This paper reports on a dialogical multi-case study methodology of four Massive Open Online Course (MOOC) providers from different geopolitical and regulatory contexts. It explores how the providers (1) define 'personal data' and whether they acknowledge a category of 'special' or 'sensitive' data; (2) address the issue and scope of student consent (and define that scope); and (3) use student data in order to inform pedagogy and/or adapt the learning experience to personalise the context or to increase student retention and success rates. This study found that large amounts of personal data continue to be collected for purposes seemingly unrelated to the delivery and support of courses. The capacity for users to withdraw or withhold consent for the collection of certain categories of data such as sensitive personal data remains severely constrained. This paper proposes that user consent at the time of registration should be reconsidered, and that there is a particular need for consent when sensitive personal data are used to personalize learning, or for purposes outside the original intention of obtaining consent.","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":"86971887","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}
Within the field of on-line tutoring systems for learning programming, such as Code.org's Hour of code, there is a trend to use previous student data to give hints. This paper shows that it is better to use expert knowledge to provide hints in environments such as Code.org's Hour of code. We present a heuristic-based approach to generating next-step hints. We use pattern matching algorithms to identify heuristics and apply each identified heuristic to an input program. We generate a next-step hint by selecting the highest scoring heuristic using a scoring function. By comparing our results with results of a previous experiment on Hour of code we show that a heuristics-based approach to providing hints gives results that are impossible to further improve. These basic heuristics are sufficient to efficiently mimic experts' next-step hints.
在学习编程的在线辅导系统领域，比如Code.org的“编程一小时”(Hour of code)，有一种趋势是使用以前学生的数据来提供提示。本文表明，在Code.org的代码一小时(Hour of code)等环境中，最好使用专家知识来提供提示。我们提出了一种基于启发式的方法来生成下一步提示。我们使用模式匹配算法来识别启发式，并将每个识别的启发式应用于输入程序。我们通过使用评分函数选择得分最高的启发式来生成下一步提示。通过将我们的结果与之前在Hour of code上的实验结果进行比较，我们发现基于启发式的方法提供提示的结果是不可能进一步改进的。这些基本的启发式方法足以有效地模仿专家的下一步提示。
{"title":"Use expert knowledge instead of data: generating hints for hour of code exercises","authors":"M. Buwalda, J. Jeuring, N. Naus","doi":"10.1145/3231644.3231690","DOIUrl":"https://doi.org/10.1145/3231644.3231690","url":null,"abstract":"Within the field of on-line tutoring systems for learning programming, such as Code.org's Hour of code, there is a trend to use previous student data to give hints. This paper shows that it is better to use expert knowledge to provide hints in environments such as Code.org's Hour of code. We present a heuristic-based approach to generating next-step hints. We use pattern matching algorithms to identify heuristics and apply each identified heuristic to an input program. We generate a next-step hint by selecting the highest scoring heuristic using a scoring function. By comparing our results with results of a previous experiment on Hour of code we show that a heuristics-based approach to providing hints gives results that are impossible to further improve. These basic heuristics are sufficient to efficiently mimic experts' next-step hints.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86429115","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}
Given that both computer scientists and educational researchers publish on the topic of massive open online courses (MOOCs), the research community should analyze how these disciplines approach the same topic. In order to promote productive dialogue within the community, we report on a bib-liometrics study of the growing MOOC literature and examine the potential interdisciplinarity of this research space. Drawing from 3,380 bibliographic items retrieved from Scopus, we conducted descriptive analyses on publication years, publication sources, disciplinary categories of publication sources, frequent keywords, leading authors, and cited references. We applied bibliographic coupling and network analysis to further investigate clusters of research topics in the MOOC literature. We found balanced representation of education and computer science within most topic clusters. However, integration could be further improved on, for example, by enhancing communication between the disciplines and broadening the scope of methods in specific studies.
{"title":"The potential of interdisciplinary in MOOC research: how do education and computer science intersect?","authors":"Kristine Lund, Bodong Chen, Sebastian Grauwin","doi":"10.1145/3231644.3231661","DOIUrl":"https://doi.org/10.1145/3231644.3231661","url":null,"abstract":"Given that both computer scientists and educational researchers publish on the topic of massive open online courses (MOOCs), the research community should analyze how these disciplines approach the same topic. In order to promote productive dialogue within the community, we report on a bib-liometrics study of the growing MOOC literature and examine the potential interdisciplinarity of this research space. Drawing from 3,380 bibliographic items retrieved from Scopus, we conducted descriptive analyses on publication years, publication sources, disciplinary categories of publication sources, frequent keywords, leading authors, and cited references. We applied bibliographic coupling and network analysis to further investigate clusters of research topics in the MOOC literature. We found balanced representation of education and computer science within most topic clusters. However, integration could be further improved on, for example, by enhancing communication between the disciplines and broadening the scope of methods in specific studies.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83943105","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}
R. W. Crues, Nigel Bosch, M. Perry, Lawrence Angrave, Najmuddin Shaik, S. Bhat
Massive open online courses (MOOCs) continue to see increasing enrollment and adoption by universities, although they are still not fully understood and could perhaps be significantly improved. For example, little is known about the relationships between the ways in which students choose to use MOOCs (e.g., sampling lecture videos, discussing topics with fellow students) and their overall level of engagement with the course, although these relationships are likely key to effective course implementation. In this paper we propose a multilevel definition of student engagement with MOOCs and explore the connections between engagement and students' behaviors across five unique courses. We modeled engagement using ordinal penalized logistic regression with the least absolute shrinkage and selection operator (LASSO), and found several predictors of engagement that were consistent across courses. In particular, we found that discussion activities (e.g., viewing forum posts) were positively related to engagement, whereas other types of student behaviors (e.g., attempting quizzes) were consistently related to less engagement with the course. Finally, we discuss implications of unexpected findings that replicated across courses, future work to explore these implications, and relevance of our findings for MOOC course design.
{"title":"Refocusing the lens on engagement in MOOCs","authors":"R. W. Crues, Nigel Bosch, M. Perry, Lawrence Angrave, Najmuddin Shaik, S. Bhat","doi":"10.1145/3231644.3231658","DOIUrl":"https://doi.org/10.1145/3231644.3231658","url":null,"abstract":"Massive open online courses (MOOCs) continue to see increasing enrollment and adoption by universities, although they are still not fully understood and could perhaps be significantly improved. For example, little is known about the relationships between the ways in which students choose to use MOOCs (e.g., sampling lecture videos, discussing topics with fellow students) and their overall level of engagement with the course, although these relationships are likely key to effective course implementation. In this paper we propose a multilevel definition of student engagement with MOOCs and explore the connections between engagement and students' behaviors across five unique courses. We modeled engagement using ordinal penalized logistic regression with the least absolute shrinkage and selection operator (LASSO), and found several predictors of engagement that were consistent across courses. In particular, we found that discussion activities (e.g., viewing forum posts) were positively related to engagement, whereas other types of student behaviors (e.g., attempting quizzes) were consistently related to less engagement with the course. Finally, we discuss implications of unexpected findings that replicated across courses, future work to explore these implications, and relevance of our findings for MOOC course design.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83452564","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}
M. Montebello, Petrilson Pinheiro, B. Cope, M. Kalantzis, Tabassum Amina, Duane Searsmith, D. Cao
Student performance over a course of an academic program can be significantly affected and positively influenced through a series of feedback processes by peers and tutors. Ideally, this feedback is structured and incremental, and as a consequence, data presents at large scale even in relatively small classes. In this paper, we investigate the effect of such processes as we analyze assessment data collected from online courses. We plan to fully analyze the massive dataset of over three and a half million granular data points generated to make the case for the scalability of these kinds of learning analytics. This could shed crucial light on assessment mechanism in MOOCs, as we continue to refine our processes in an effort to strike a balance of emphasis on formative in addition to summative assessment.
{"title":"The impact of the peer review process evolution on learner performance in e-learning environments","authors":"M. Montebello, Petrilson Pinheiro, B. Cope, M. Kalantzis, Tabassum Amina, Duane Searsmith, D. Cao","doi":"10.1145/3231644.3231693","DOIUrl":"https://doi.org/10.1145/3231644.3231693","url":null,"abstract":"Student performance over a course of an academic program can be significantly affected and positively influenced through a series of feedback processes by peers and tutors. Ideally, this feedback is structured and incremental, and as a consequence, data presents at large scale even in relatively small classes. In this paper, we investigate the effect of such processes as we analyze assessment data collected from online courses. We plan to fully analyze the massive dataset of over three and a half million granular data points generated to make the case for the scalability of these kinds of learning analytics. This could shed crucial light on assessment mechanism in MOOCs, as we continue to refine our processes in an effort to strike a balance of emphasis on formative in addition to summative assessment.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"108 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79219799","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}
Online learning platforms, such as edX, generate usage statistics data that can be valuable to educators. However, handling this raw data can prove challenging and time consuming for instructors and course designers. The raw data for the MIT courses running on the edX platform (MITx courses) are pre-processed and stored in a Google BigQuery database. We designed a tool based on Python and additional open-source Python packages such as Jupyter Notebook, to enable instructors to analyze their student data easily and securely. We expect that instructors would be encouraged to adopt more evidence-based teaching practices based on their interaction with the data.
{"title":"Managing and analyzing student learning data: a python-based solution for edX","authors":"Vita Lampietti, Anindya Roy, Sheryl Barnes","doi":"10.1145/3231644.3231706","DOIUrl":"https://doi.org/10.1145/3231644.3231706","url":null,"abstract":"Online learning platforms, such as edX, generate usage statistics data that can be valuable to educators. However, handling this raw data can prove challenging and time consuming for instructors and course designers. The raw data for the MIT courses running on the edX platform (MITx courses) are pre-processed and stored in a Google BigQuery database. We designed a tool based on Python and additional open-source Python packages such as Jupyter Notebook, to enable instructors to analyze their student data easily and securely. We expect that instructors would be encouraged to adopt more evidence-based teaching practices based on their interaction with the data.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85040767","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}
The global reach of online experiments and their wide adoption in fields ranging from political science to computer science poses an underexplored opportunity for learning at scale: the possibility of participants learning about the research to which they contribute data. We conducted three experiments on Amazon's Mechanical Turk to evaluate whether participants of paid online experiments are interested in learning about research, what information they find most interesting, and whether providing them with such information actually leads to learning gains. Our findings show that 40% of our participants on Mechanical Turk actively sought out post-experiment learning opportunities despite having already received their financial compensation. Participants expressed high interest in a range of research topics, including previous research and experimental design. Finally, we find that participants comprehend and accurately recall facts from post-experiment learning opportunities. Our findings suggest that Mechanical Turk can be a valuable platform for learning at scale and scientific outreach.
{"title":"The potential for scientific outreach and learning in mechanical turk experiments","authors":"Eunice Jun, Morelle S. Arian, Katharina Reinecke","doi":"10.1145/3231644.3231666","DOIUrl":"https://doi.org/10.1145/3231644.3231666","url":null,"abstract":"The global reach of online experiments and their wide adoption in fields ranging from political science to computer science poses an underexplored opportunity for learning at scale: the possibility of participants learning about the research to which they contribute data. We conducted three experiments on Amazon's Mechanical Turk to evaluate whether participants of paid online experiments are interested in learning about research, what information they find most interesting, and whether providing them with such information actually leads to learning gains. Our findings show that 40% of our participants on Mechanical Turk actively sought out post-experiment learning opportunities despite having already received their financial compensation. Participants expressed high interest in a range of research topics, including previous research and experimental design. Finally, we find that participants comprehend and accurately recall facts from post-experiment learning opportunities. Our findings suggest that Mechanical Turk can be a valuable platform for learning at scale and scientific outreach.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75071699","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 applies theory and methodology from the learning design literature to large-scale learning environments through quantitative modeling of the structure and design of Massive Open Online Courses. For two institutions of higher education, we automate the task of encoding pedagogy and learning design principles for 177 courses (which accounted for for nearly 4 million enrollments). Course materials from these MOOCs are parsed and abstracted into sequences of components, such as videos and problems. Our key contributions are (i) describing the parsing and abstraction of courses for quantitative analyses, (ii) the automated categorization of similar course designs, and (iii) the identification of key structural components that show relationships between categories and learning design principles. We employ two methods to categorize similar course designs---one aimed at clustering courses using transition probabilities and another using trajectory mining. We then proceed with an exploratory analysis of relationships between our categorization and learning outcomes.
{"title":"Toward large-scale learning design: categorizing course designs in service of supporting learning outcomes","authors":"Dan Davis, Daniel T. Seaton, C. Hauff, G. Houben","doi":"10.1145/3231644.3231663","DOIUrl":"https://doi.org/10.1145/3231644.3231663","url":null,"abstract":"This paper applies theory and methodology from the learning design literature to large-scale learning environments through quantitative modeling of the structure and design of Massive Open Online Courses. For two institutions of higher education, we automate the task of encoding pedagogy and learning design principles for 177 courses (which accounted for for nearly 4 million enrollments). Course materials from these MOOCs are parsed and abstracted into sequences of components, such as videos and problems. Our key contributions are (i) describing the parsing and abstraction of courses for quantitative analyses, (ii) the automated categorization of similar course designs, and (iii) the identification of key structural components that show relationships between categories and learning design principles. We employ two methods to categorize similar course designs---one aimed at clustering courses using transition probabilities and another using trajectory mining. We then proceed with an exploratory analysis of relationships between our categorization and learning outcomes.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"183 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74630196","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 analyzes the use of paper exams in college-level STEM courses. It leverages a unique dataset of nearly 1,800 exams, which were scanned into a web application, then processed by a team of annotators to yield a detailed snapshot of the way instructors currently structure exams. The focus of the investigation is on the variety of question formats, and how they are applied across different course topics. The analysis divides questions according to seven top-level categories, finding significant differences among these in terms of positioning, use across subjects, and student performance. The analysis also reveals a strong tendency within the collection for instructors to order questions from easier to harder. A linear mixed effects model is used to estimate the reliability of different question types. Long writing questions stand out for their high reliability, while binary and multiple choice questions have low reliability. The model suggests that over three multiple choice questions, or over five binary questions, are required to attain the same reliability as a single long writing question. A correlation analysis across seven response types finds that student abilities for different questions types exceed 70 percent for all pairs, although binary and multiple-choice questions stand out for having unusually low correlations with all other question types.
{"title":"How do professors format exams?: an analysis of question variety at scale","authors":"Paul Laskowski, Sergey Karayev, Marti A. Hearst","doi":"10.1145/3231644.3231667","DOIUrl":"https://doi.org/10.1145/3231644.3231667","url":null,"abstract":"This study analyzes the use of paper exams in college-level STEM courses. It leverages a unique dataset of nearly 1,800 exams, which were scanned into a web application, then processed by a team of annotators to yield a detailed snapshot of the way instructors currently structure exams. The focus of the investigation is on the variety of question formats, and how they are applied across different course topics. The analysis divides questions according to seven top-level categories, finding significant differences among these in terms of positioning, use across subjects, and student performance. The analysis also reveals a strong tendency within the collection for instructors to order questions from easier to harder. A linear mixed effects model is used to estimate the reliability of different question types. Long writing questions stand out for their high reliability, while binary and multiple choice questions have low reliability. The model suggests that over three multiple choice questions, or over five binary questions, are required to attain the same reliability as a single long writing question. A correlation analysis across seven response types finds that student abilities for different questions types exceed 70 percent for all pairs, although binary and multiple-choice questions stand out for having unusually low correlations with all other question types.","PeriodicalId":20634,"journal":{"name":"Proceedings of the Fifth Annual ACM Conference on Learning at Scale","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76245218","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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