Proceedings of the Second (2015) ACM Conference on Learning @ Scale最新文献

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Supporting Instructors in Collaborating with Researchers using MOOClets 支持教师使用moolet与研究人员合作

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-02-14 DOI: 10.2139/ssrn.2580666

J. Williams, Juho Kim, Brian Keegan

{"title":"Supporting Instructors in Collaborating with Researchers using MOOClets","authors":"J. Williams, Juho Kim, Brian Keegan","doi":"10.2139/ssrn.2580666","DOIUrl":"https://doi.org/10.2139/ssrn.2580666","url":null,"abstract":"Most education and workplace learning takes place in classroom contexts far removed from laboratories or field sites with special arrangements for scientific research. But digital online resources provide a novel opportunity for large-scale efforts to bridge the real-world and laboratory settings which support data collection and randomized A/B experiments comparing different versions of content or interactions [2]. However, there are substantial technological and practical barriers in aligning instructors and researchers to use learning technologies like blended lessons/exercises & MOOCs as both a service for students and a realistic context to conduct research. This paper explains how the concept of a \"MOOClet\" can facilitate research-practitioner collaborations. MOOClets [3] are defined as modular components of a digital resource that can be implemented in technology to: (1) allow modification to create multiple versions, (2) allow experimental comparison and personalization of different versions, (3) reliably specify what data are collected. We suggest a framework in which instructors specify what kinds of changes to lessons, exercises, and emails they would be willing to adopt, and what data they will collect and make available. Researchers can then: (1) specify or design experiments that compare the effects of different versions on quantifiable outcomes. (2) Explore algorithms for maximizing particular outcomes by choosing alternative versions of a MOOClet based on the input variables available. We present a prototype survey tool for instructors intended to facilitate practitioner-researcher matches and successful collaborations.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76037644","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}

引用次数: 4

Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions 数学语言处理:开放式数学问题的自动评分和反馈

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2015-01-18 DOI: 10.1145/2724660.2724664

Andrew S. Lan, Divyanshu Vats, Andrew E. Waters, Richard Baraniuk

{"title":"Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions","authors":"Andrew S. Lan, Divyanshu Vats, Andrew E. Waters, Richard Baraniuk","doi":"10.1145/2724660.2724664","DOIUrl":"https://doi.org/10.1145/2724660.2724664","url":null,"abstract":"While computer and communication technologies have provided effective means to scale up many aspects of education, the submission and grading of assessments such as homework assignments and tests remains a weak link. In this paper, we study the problem of automatically grading the kinds of open response mathematical questions that figure prominently in STEM (science, technology, engineering, and mathematics) courses. Our data-driven framework for mathematical language processing (MLP) leverages solution data from a large number of learners to evaluate the correctness of their solutions, assign partial-credit scores, and provide feedback to each learner on the likely locations of any errors. MLP takes inspiration from the success of natural language processing for text data and comprises three main steps. First, we convert each solution to an open response mathematical question into a series of numerical features. Second, we cluster the features from several solutions to uncover the structures of correct, partially correct, and incorrect solutions. We develop two different clustering approaches, one that leverages generic clustering algorithms and one based on Bayesian nonparametrics. Third, we automatically grade the remaining (potentially large number of) solutions based on their assigned cluster and one instructor-provided grade per cluster. As a bonus, we can track the cluster assignment of each step of a multistep solution and determine when it departs from a cluster of correct solutions, which enables us to indicate the likely locations of errors to learners. We test and validate MLP on real-world MOOC data to demonstrate how it can substantially reduce the human effort required in large-scale educational platforms.","PeriodicalId":20664,"journal":{"name":"Proceedings of the Second (2015) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77349500","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}

引用次数: 63

Effective Sampling for Large-scale Automated Writing Evaluation Systems 大规模自动写作评价系统的有效抽样

Proceedings of the Second (2015) ACM Conference on Learning @ Scale Pub Date : 2014-12-17 DOI: 10.1145/2724660.2724661

Nicholas Dronen, P. Foltz, Kyle Habermehl

引用次数: 17

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