We present LINK-REPORT, a distributed learning outcome analysis module that is integrated with the WISEngineering platform for supporting informal learning in engineering. LINK-REPORT provides a coherent workflow of outcome analysis: starting from development of learning outcome goals, to learner behavior collection, to automated grading of open ended short answer questions, and to report generation and aggregation. It generates learning data for research opportunities in modeling of learner traits.
{"title":"LINK-REPORT: Outcome Analysis of Informal Learning at Scale","authors":"Xiang Fu, Tyler Befferman, M. Burghardt","doi":"10.1145/2876034.2893407","DOIUrl":"https://doi.org/10.1145/2876034.2893407","url":null,"abstract":"We present LINK-REPORT, a distributed learning outcome analysis module that is integrated with the WISEngineering platform for supporting informal learning in engineering. LINK-REPORT provides a coherent workflow of outcome analysis: starting from development of learning outcome goals, to learner behavior collection, to automated grading of open ended short answer questions, and to report generation and aggregation. It generates learning data for research opportunities in modeling of learner traits.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83400429","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}
Massive online classes can benefit from peer interactions such as discussion, critique, or tutoring. However, to scaffold productive peer interactions, systems must be able to detect student behavior in interactions at scale, which is challenging when interactions occur over rich media like video. This paper introduces an imprecise yet simple browser-based conversational turn detector for video conversations. Turns are detected without accessing video or audio data. We show how this turn detector can find dominance in video-based conversations. In a case study with 1,027 students using Talkabout, a video-based discussion system for online classes, we show how detected conversational turn behavior correlates with participants' subjective experience in discussions and their final course grade.
{"title":"$1 Conversational Turn Detector: Measuring How Video Conversations Affect Student Learning in Online Classes","authors":"A. Stankiewicz, Chinmay Kulkarni","doi":"10.1145/2876034.2876048","DOIUrl":"https://doi.org/10.1145/2876034.2876048","url":null,"abstract":"Massive online classes can benefit from peer interactions such as discussion, critique, or tutoring. However, to scaffold productive peer interactions, systems must be able to detect student behavior in interactions at scale, which is challenging when interactions occur over rich media like video. This paper introduces an imprecise yet simple browser-based conversational turn detector for video conversations. Turns are detected without accessing video or audio data. We show how this turn detector can find dominance in video-based conversations. In a case study with 1,027 students using Talkabout, a video-based discussion system for online classes, we show how detected conversational turn behavior correlates with participants' subjective experience in discussions and their final course grade.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87299954","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 Elice, an online CS (computer science) education platform, and Elivate, a system for taking student learning data from Elice and infers their progress through an educational taxonomy tailored for programming education. Elice captures detailed student learning activities, such as the intermediate revisions of code as students make progress toward completing their programming exercises. With those data, Elivate recognizes each student's progression through an education taxonomy which organizes intermediate stages of learning such that the taxonomy can be used to evaluate student progress as well as to design and improve course materials and structure. With more than 240,000 intermediate source codes generated by 1,000 students, we demonstrate the practicality of the Elice and Elivate. We present case studies that confirm that categorizing student actions into the different steps of the taxonomy results in better understanding of the effect of TA's assist and student's performance.
{"title":"Elice: An online CS Education Platform to Understand How Students Learn Programming","authors":"Suin Kim, Jae Won Kim, Jungkook Park, Alice H. Oh","doi":"10.1145/2876034.2893420","DOIUrl":"https://doi.org/10.1145/2876034.2893420","url":null,"abstract":"We present Elice, an online CS (computer science) education platform, and Elivate, a system for taking student learning data from Elice and infers their progress through an educational taxonomy tailored for programming education. Elice captures detailed student learning activities, such as the intermediate revisions of code as students make progress toward completing their programming exercises. With those data, Elivate recognizes each student's progression through an education taxonomy which organizes intermediate stages of learning such that the taxonomy can be used to evaluate student progress as well as to design and improve course materials and structure. With more than 240,000 intermediate source codes generated by 1,000 students, we demonstrate the practicality of the Elice and Elivate. We present case studies that confirm that categorizing student actions into the different steps of the taxonomy results in better understanding of the effect of TA's assist and student's performance.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82273216","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}
Ben U. Gelman, Chris Beckley, A. Johri, C. Domeniconi, Seungwon Yang
Online communities continue to be an important resource for informal learning. Although many facets of online learning communities have been studied, we have limited understanding of how such communities grow over time to productively engage a large number of learners. In this paper we present a study of a large online community called Scratch which was created to help users learn software programming. We analyzed 5 years of data consisting of 1 million users and their 1.9 million projects. Examination of interactional patterns among highly active members of the community uncovered a markedly temporal dimension to participation. As membership of the Scratch online community grew over time, interest-based subcultures started to emerge. This pattern was uncovered even when clustering was based solely on social network of members. This process, which closely resembles urbanism or the growth of physically populated areas, allowed new members to combine their interests with programming.
{"title":"Online Urbanism: Interest-based Subcultures as Drivers of Informal Learning in an Online Community","authors":"Ben U. Gelman, Chris Beckley, A. Johri, C. Domeniconi, Seungwon Yang","doi":"10.1145/2876034.2876052","DOIUrl":"https://doi.org/10.1145/2876034.2876052","url":null,"abstract":"Online communities continue to be an important resource for informal learning. Although many facets of online learning communities have been studied, we have limited understanding of how such communities grow over time to productively engage a large number of learners. In this paper we present a study of a large online community called Scratch which was created to help users learn software programming. We analyzed 5 years of data consisting of 1 million users and their 1.9 million projects. Examination of interactional patterns among highly active members of the community uncovered a markedly temporal dimension to participation. As membership of the Scratch online community grew over time, interest-based subcultures started to emerge. This pattern was uncovered even when clustering was based solely on social network of members. This process, which closely resembles urbanism or the growth of physically populated areas, allowed new members to combine their interests with programming.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72614364","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}
MOOCs offer valuable learning experiences to students from all around the world. In addition to providing filmed lectures, readings, and problem sets, many MOOCs allow students to ask and answer questions about course materials with each other through interactive user forums. However, in current MOOCs, only 3 to 5 percent of those students interact in the user forum (Breslow 2013, Rosé et al. 2014) and more than 90 percent of students stop attending the course altogether (Jordan 2014). According to prior studies, this low level of social engagement in MOOCs may lead to student attrition and low performance (Ren et al. 2007). Hence, a natural question that arises then is, how can we promote interaction among students in MOOC discussion forums in order to reduce students' attrition and raise their performance? In this paper, we conduct a field experiment on the edX platform to identify factors that promote student engagement in MOOC discussion forums. Researchers have discovered that the number of people interacting in one online location (e.g. group, community or virtual classroom size) is a key characteristic mediating user engagement (Butler et. al 2014), and most prior works have shown that users in a smaller size group participate more per person. However, contrary to prior research, our results show that the students in larger size cohorts interact more per person and that this greater interaction in turn increases student retention and performance.
mooc为来自世界各地的学生提供了宝贵的学习经验。除了提供视频讲座、阅读材料和问题集外，许多mooc还允许学生通过交互式用户论坛相互提问和回答有关课程材料的问题。然而，在目前的mooc中，只有3%到5%的学生在用户论坛上进行互动(Breslow 2013, ros et al. 2014)，超过90%的学生完全停止参加课程(Jordan 2014)。根据先前的研究，mooc中这种低水平的社会参与可能导致学生流失和低绩效(Ren et al. 2007)。因此，一个自然出现的问题是，我们如何在MOOC论坛中促进学生之间的互动，以减少学生的流失，提高他们的表现?在本文中，我们在edX平台上进行了实地实验，以确定促进学生参与MOOC论坛的因素。研究人员发现，在一个在线位置(例如群体、社区或虚拟教室规模)互动的人数是调节用户参与度的关键特征(Butler et. al . 2014)，大多数先前的研究表明，规模较小的群体中的用户人均参与度更高。然而，与之前的研究相反，我们的研究结果表明，在更大的群体中，学生的人均互动更多，而这种更大的互动反过来又增加了学生的留存率和表现。
{"title":"Promoting Student Engagement in MOOCs","authors":"Jiye Baek, Jesse Shore","doi":"10.1145/2876034.2893437","DOIUrl":"https://doi.org/10.1145/2876034.2893437","url":null,"abstract":"MOOCs offer valuable learning experiences to students from all around the world. In addition to providing filmed lectures, readings, and problem sets, many MOOCs allow students to ask and answer questions about course materials with each other through interactive user forums. However, in current MOOCs, only 3 to 5 percent of those students interact in the user forum (Breslow 2013, Rosé et al. 2014) and more than 90 percent of students stop attending the course altogether (Jordan 2014). According to prior studies, this low level of social engagement in MOOCs may lead to student attrition and low performance (Ren et al. 2007). Hence, a natural question that arises then is, how can we promote interaction among students in MOOC discussion forums in order to reduce students' attrition and raise their performance? In this paper, we conduct a field experiment on the edX platform to identify factors that promote student engagement in MOOC discussion forums. Researchers have discovered that the number of people interacting in one online location (e.g. group, community or virtual classroom size) is a key characteristic mediating user engagement (Butler et. al 2014), and most prior works have shown that users in a smaller size group participate more per person. However, contrary to prior research, our results show that the students in larger size cohorts interact more per person and that this greater interaction in turn increases student retention and performance.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79020512","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 large datasets produced by learning at scale, and the need for ways of dealing with high learner/educator ratios, mean that MOOCs and related environments are frequently used for the deployment and development of learning analytics. Despite the current proliferation of analytics, there is as yet relatively little hard evidence of their effectiveness. The Evidence Hub developed by the Learning Analytics Community Exchange (LACE) provides a way of collating and filtering the available evidence in order to support the use of analytics and to target future studies to fill the gaps in our knowledge.
{"title":"Learning at Scale: Using an Evidence Hub To Make Sense of What We Know","authors":"Rebecca Ferguson","doi":"10.1145/2876034.2893419","DOIUrl":"https://doi.org/10.1145/2876034.2893419","url":null,"abstract":"The large datasets produced by learning at scale, and the need for ways of dealing with high learner/educator ratios, mean that MOOCs and related environments are frequently used for the deployment and development of learning analytics. Despite the current proliferation of analytics, there is as yet relatively little hard evidence of their effectiveness. The Evidence Hub developed by the Learning Analytics Community Exchange (LACE) provides a way of collating and filtering the available evidence in order to support the use of analytics and to target future studies to fill the gaps in our knowledge.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78144732","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}
J. Uchidiuno, A. Ogan, K. Koedinger, Evelyn Yarzebinski, Jessica Hammer
Open access and low cost make Massively Open Online Courses (MOOCs) an attractive learning platform for students all over the world. However, the majority of MOOCs are deployed in English, which can pose an accessibility problem for students with English as a Second Language (ESL). In order to design appropriate interventions for ESL speakers, it is important to correctly identify these students using a method that is scalable to the high number of MOOC enrollees. Our findings suggest that a new metric, browser language preference, may be better than the commonly-used IP address for inferring whether or not a student is ESL.
{"title":"Browser Language Preferences as a Metric for Identifying ESL Speakers in MOOCs","authors":"J. Uchidiuno, A. Ogan, K. Koedinger, Evelyn Yarzebinski, Jessica Hammer","doi":"10.1145/2876034.2893433","DOIUrl":"https://doi.org/10.1145/2876034.2893433","url":null,"abstract":"Open access and low cost make Massively Open Online Courses (MOOCs) an attractive learning platform for students all over the world. However, the majority of MOOCs are deployed in English, which can pose an accessibility problem for students with English as a Second Language (ESL). In order to design appropriate interventions for ESL speakers, it is important to correctly identify these students using a method that is scalable to the high number of MOOC enrollees. Our findings suggest that a new metric, browser language preference, may be better than the commonly-used IP address for inferring whether or not a student is ESL.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81191015","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}
Faculty development in the area of emerging technologies is demanding and resource intensive. This increases when aiming to qualify instructors to support their teaching virtually, e.g. in blended- and distance learning environments. Most elements of instructional design, delivery, and assessment require rethinking for technology integration. It is also a challenge to develop a sound instructional design model and corresponding teaching materials for courses aimed at developing the necessary skills and competences among staff. With "e-Tutor" a corresponding certificate course was developed at Ankara University, Turkey, a country for which, due to its geographical size and population, e-Learning is now highly popular. Under a project funded by the Swiss National Science Foundation, the course was translated into English, Russian, and Ukrainian, and then made accessible as an Open Educational Resource under Creative Commons Licence. Delivered in Turkish since 2011 with 350 participants, the course has also been successfully conducted with 300 participants from 11 countries in English, and with 320 participants in Ukrainian. This paper will briefly introduce the course design and its resources, before addressing to what extent it allows for scaling effects in staff development.
{"title":"e-Tutor: Scaling Staff Development in the Area of e-Learning Competences","authors":"Christian Rapp, Yasemin Gülbahar","doi":"10.1145/2876034.2893401","DOIUrl":"https://doi.org/10.1145/2876034.2893401","url":null,"abstract":"Faculty development in the area of emerging technologies is demanding and resource intensive. This increases when aiming to qualify instructors to support their teaching virtually, e.g. in blended- and distance learning environments. Most elements of instructional design, delivery, and assessment require rethinking for technology integration. It is also a challenge to develop a sound instructional design model and corresponding teaching materials for courses aimed at developing the necessary skills and competences among staff. With \"e-Tutor\" a corresponding certificate course was developed at Ankara University, Turkey, a country for which, due to its geographical size and population, e-Learning is now highly popular. Under a project funded by the Swiss National Science Foundation, the course was translated into English, Russian, and Ukrainian, and then made accessible as an Open Educational Resource under Creative Commons Licence. Delivered in Turkish since 2011 with 350 participants, the course has also been successfully conducted with 300 participants from 11 countries in English, and with 320 participants in Ukrainian. This paper will briefly introduce the course design and its resources, before addressing to what extent it allows for scaling effects in staff development.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90522916","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}
In October 2014, one-time MOOC developer Udacity completed its transition from primarily producing massive, open online courses to producing job-focused, project-based microcredentials called "Nanodegree" programs. With this transition came a challenge: whereas MOOCs focus on automated assessment and peer-to-peer grading, project-based microcredentials would only be feasible with expert evaluation. With dreams of enrolling tens of thousands of students at a time, the major obstacle became project evaluation. To address this, Udacity developed a system for hiring external experts as project reviewers. A year later, this system has supported project evaluation on a massive scale: 61,000 projects have been evaluated in 12 months, with 50% evaluated within 2.5 hours (and 88% within 24 hours) of submission. More importantly, students rate the feedback they receive very highly at 4.8/5.0. In this paper, we discuss the structure of the project review system, including the nature of the projects, the structure of the feedback, and the data described above.
{"title":"Expert Evaluation of 300 Projects per Day","authors":"David A. Joyner","doi":"10.1145/2876034.2893384","DOIUrl":"https://doi.org/10.1145/2876034.2893384","url":null,"abstract":"In October 2014, one-time MOOC developer Udacity completed its transition from primarily producing massive, open online courses to producing job-focused, project-based microcredentials called \"Nanodegree\" programs. With this transition came a challenge: whereas MOOCs focus on automated assessment and peer-to-peer grading, project-based microcredentials would only be feasible with expert evaluation. With dreams of enrolling tens of thousands of students at a time, the major obstacle became project evaluation. To address this, Udacity developed a system for hiring external experts as project reviewers. A year later, this system has supported project evaluation on a massive scale: 61,000 projects have been evaluated in 12 months, with 50% evaluated within 2.5 hours (and 88% within 24 hours) of submission. More importantly, students rate the feedback they receive very highly at 4.8/5.0. In this paper, we discuss the structure of the project review system, including the nature of the projects, the structure of the feedback, and the data described above.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89760685","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}
Miguel Sánchez-Santillán, M. Paule-Ruíz, Rebeca Cerezo, J. C. Núñez
One of the Educational Data Mining (EDM) main aims is to predict the final student's performance, analyzing their behavior in the Learning Management Systems (LMSs). Many studies make use of different classifiers to reach this goal, using the total interaction of the students. In this work we study if it is possible to build more accurate classification models in order to predict the output, analyzing the interaction in an incremental way. We study the data gathered for two years with three kinds of classifying algorithms and we compare the total interaction models with the incremental interaction models.
{"title":"Predicting Students' Performance: Incremental Interaction Classifiers","authors":"Miguel Sánchez-Santillán, M. Paule-Ruíz, Rebeca Cerezo, J. C. Núñez","doi":"10.1145/2876034.2893418","DOIUrl":"https://doi.org/10.1145/2876034.2893418","url":null,"abstract":"One of the Educational Data Mining (EDM) main aims is to predict the final student's performance, analyzing their behavior in the Learning Management Systems (LMSs). Many studies make use of different classifiers to reach this goal, using the total interaction of the students. In this work we study if it is possible to build more accurate classification models in order to predict the output, analyzing the interaction in an incremental way. We study the data gathered for two years with three kinds of classifying algorithms and we compare the total interaction models with the incremental interaction models.","PeriodicalId":20739,"journal":{"name":"Proceedings of the Third (2016) ACM Conference on Learning @ Scale","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89182487","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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