Proceedings of the Eighth ACM Conference on Learning @ Scale最新文献_第2页

Combining Collaborative Reflection based on Worked-Out Examples with Problem-Solving Practice: Designing Collaborative Programming Projects for Learning at Scale 结合基于算例的协作反思与问题解决实践:设计大规模学习的协作编程项目

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460152

Sreecharan Sankaranarayanan, Siddharth Reddy Kandimalla, C. Bogart, R. C. Murray, Michael Hilton, M. Sakr, C. Rosé

{"title":"Combining Collaborative Reflection based on Worked-Out Examples with Problem-Solving Practice: Designing Collaborative Programming Projects for Learning at Scale","authors":"Sreecharan Sankaranarayanan, Siddharth Reddy Kandimalla, C. Bogart, R. C. Murray, Michael Hilton, M. Sakr, C. Rosé","doi":"10.1145/3430895.3460152","DOIUrl":"https://doi.org/10.1145/3430895.3460152","url":null,"abstract":"Computer science pedagogy has overwhelmingly favored problem-solving practice over methods of engagement like worked-out example study especially in advanced classes. This is due to the belief that while these alternative methods may improve student conceptual learning, they may leave them less able to perform on authentic problem-solving tasks from a lack of hands-on practice. In this paper, we perform a direct comparison of this trade-off in a synchronous collaborative programming project by adjusting the boundary between problem-solving and collaborative reflection based on a worked-out example while keeping the total time on task constant. We find that the more time students spent on worked example study, the more was the observed improvement in the pre- to post-test scores with no significant difference in performance on a subsequent problem-solving task. These results, therefore, challenge the dominant place of problem-solving practice in the advanced curricular context and inform the design of collaborative programming projects at scale.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116748640","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: 2

A Novel Framework for Discovering Cognitive Models of Learning 发现学习认知模型的新框架

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460156

Jinjin Zhao, Candace Thille, Neelesh Gattani, D. Zimmaro

{"title":"A Novel Framework for Discovering Cognitive Models of Learning","authors":"Jinjin Zhao, Candace Thille, Neelesh Gattani, D. Zimmaro","doi":"10.1145/3430895.3460156","DOIUrl":"https://doi.org/10.1145/3430895.3460156","url":null,"abstract":"A cognitive model is a descriptive account or computational representation of human thinking about a given concept, skill, or domain. A cognitive model of learning, includes both a way of organizing knowledge within a subject area and an account of how humans develop accurate and complete knowledge of that subject area. Learning designers engage in a variety of practices to unpack knowledge from subject matter experts and novices to develop cognitive models of learning and use those models to guide the design of instruction or instructional technologies. Traditional approaches to eliciting and organizing knowledge, such as conducting a cognitive task analysis (CTA) [10] with experts and novices, are labor-intensive and require specific expertise that many learning designers do not have. However, learning data generated from learners' interaction with the courses, reveal how humans think about and develop knowledge. We propose a novel framework that uses learning data to discover and refine cognitive models of learning. The framework includes a Variational Autoencoder (VAE) module and a Gaussian Mixture Model (GMM) module. We provide one case study in a corporate setting to demonstrate the effectiveness of the proposed framework compared to other approaches.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123859417","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: 1

Analyzing Patterns in Student SQL Solutions via Levenshtein Edit Distance 利用Levenshtein编辑距离分析学生SQL解决方案中的模式

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460979

Sophia Yang, Ziyuan Wei, Geoffrey L. Herman, Abdussalam Alawini

{"title":"Analyzing Patterns in Student SQL Solutions via Levenshtein Edit Distance","authors":"Sophia Yang, Ziyuan Wei, Geoffrey L. Herman, Abdussalam Alawini","doi":"10.1145/3430895.3460979","DOIUrl":"https://doi.org/10.1145/3430895.3460979","url":null,"abstract":"Structured Query Language (SQL), the standard language for relational database management systems, is an essential skill for software developers, data scientists, and professionals who need to interact with databases. SQL is highly structured and presents diverse ways for learners to acquire this skill. However, despite the significance of SQL to other related fields, little research has been done to understand how students learn SQL as they work on homework assignments. In this paper, we analyze students' SQL submissions to homework problems of the Database Systems course offered at the University of Illinois at Urbana-Champaign. For each student, we compute the Levenshtein Edit Distances between every submission and their final submission to understand how students reached their final solution and how they overcame any obstacles in their learning process. Our system visualizes the edit distances between students' submissions to a SQL problem, enabling instructors to identify interesting learning patterns and approaches. These findings will help instructors target their instruction in difficult SQL areas for the future and help students learn SQL more effectively.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124543793","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: 3

Co-Evolution of Human Capabilities and Intelligent Technologies for Digital Education 数字教育中人类能力与智能技术的共同进化

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3462227

Sanna Järvelä

引用次数: 0

Using Social Norms to Promote Actions Beyond the Course 利用社会规范促进课程之外的行动

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460144

J. Cho, Yue Li, Anne K. Armstrong, A. Russ, M. Krasny, René F. Kizilcec

引用次数: 0

Toward Reshaping the Syllabus for Education at Scale 面向规模化教育的教学大纲重塑

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460987

B. Eicher, David A. Joyner

引用次数: 4

Domain Experts' Interpretations of Assessment Bias in a Scaled, Online Computer Science Curriculum 领域专家对一个规模化在线计算机科学课程中评估偏差的解释

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460141

Benjamin Xie, Matthew J. Davidson, Baker Franke, Emily M. McLeod, Min Li, Amy J. Ko

{"title":"Domain Experts' Interpretations of Assessment Bias in a Scaled, Online Computer Science Curriculum","authors":"Benjamin Xie, Matthew J. Davidson, Baker Franke, Emily M. McLeod, Min Li, Amy J. Ko","doi":"10.1145/3430895.3460141","DOIUrl":"https://doi.org/10.1145/3430895.3460141","url":null,"abstract":"Understanding inequity at scale is necessary for designing equitable online learning experiences, but also difficult. Statistical techniques like differential item functioning (DIF) can help identify whether items/questions in an assessment exhibit potential bias by disadvantaging certain groups (e.g. whether item disadvantages woman vs man of equivalent knowledge). While testing companies typically use DIF to identify items to remove, we explored how domain-experts such as curriculum designers could use DIF to better understand how to design instructional materials to better serve students from diverse groups. Using Code.org's online Computer Science Discoveries (CSD) curriculum, we analyzed 139,097 responses from 19,617 students to identify DIF by gender and race in assessment items (e.g. multiple choice questions). Of the 17 items, we identified six that disadvantaged students who reported as female when compared to students who reported as non-binary or male. We also identified that most (13) items disadvantaged AHNP (African/Black, Hispanic/Latinx, Native American/Alaskan Native, Pacific Islander) students compared to WA (white, Asian) students. We then conducted a workshop and interviews with seven curriculum designers and found that they interpreted item bias relative to an intersection of item features and student identity, the broader curriculum, and differing uses for assessments. We interpreted these findings in the broader context of using data on assessment bias to inform domain-experts' efforts to design more equitable learning experiences.","PeriodicalId":125581,"journal":{"name":"Proceedings of the Eighth ACM Conference on Learning @ Scale","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130460972","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: 3

Generating Response-Specific Elaborated Feedback Using Long-Form Neural Question Answering 使用长形式神经问题回答生成特定响应的详细反馈

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460131

A. Olney

引用次数: 4

Personalized Lecture Recommendations to Facilitate Bite-Sized Learning 个性化的课程推荐，以促进循序渐进的学习

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460988

Meltem Tutar, Austin Wang, Gulsen Kutluoglu

引用次数: 1

Assessing Function Names and Quantifying the Relationship Between Identifiers and Their Functionality to Improve Them 评估函数名并量化标识符与其功能之间的关系以改进它们

Proceedings of the Eighth ACM Conference on Learning @ Scale Pub Date : 2021-06-08 DOI: 10.1145/3430895.3460161

Charis Charitsis, C. Piech, John C. Mitchell

引用次数: 3