Proceedings of the 2nd International Conference on Learning Analytics and Knowledge最新文献_第3页

Using computational methods to discover student science conceptions in interview data 利用计算方法在访谈数据中发现学生的科学概念

Proceedings of the 2nd International Conference on Learning Analytics and Knowledge Pub Date : 2012-04-29 DOI: 10.1145/2330601.2330649

B. Sherin

{"title":"Using computational methods to discover student science conceptions in interview data","authors":"B. Sherin","doi":"10.1145/2330601.2330649","DOIUrl":"https://doi.org/10.1145/2330601.2330649","url":null,"abstract":"A large body of research in the learning sciences has focused on students' commonsense science knowledge---the everyday knowledge of the natural world that is gained outside of formal instruction. Although researchers studying commonsense science have employed a variety of methods, one-on-one clinical interviews have played a unique and central role. The data that result from these interviews take the form of video recordings, which in turn are often compiled into written transcripts, and coded by human analysts. In my team's work on learning analytics, we draw on this same type of data, but we attempt to automate its analysis. In this paper, I describe the success we have had using extremely simple methods from computational linguistics---methods that are based on rudimentary vector space models and simple clustering algorithms. These automated analyses are employed in an exploratory mode, as a way to discover student conceptions in the data. The aims of this paper are primarily methodological in nature; I will attempt to show that it is possible to use techniques from computational linguistics to analyze data from commonsense science interviews. As a test bed, I draw on transcripts of a corpus of interviews in which 54 middle school students were asked to explain the seasons.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"188 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134012250","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: 14

Investigating the core group effect in usage of resources with analytics 用分析法调查资源使用中的核心群体效应

Proceedings of the 2nd International Conference on Learning Analytics and Knowledge Pub Date : 2012-04-29 DOI: 10.1145/2330601.2330656

A. Merceron

引用次数: 2

Networked individualism: how the personalized internet, ubiquitous connectivity, and the turn to social networks can affect learning analytics 网络个人主义:个性化互联网、无处不在的连接以及转向社交网络如何影响学习分析

Proceedings of the 2nd International Conference on Learning Analytics and Knowledge Pub Date : 2012-04-29 DOI: 10.1145/2330601.2330603

B. Wellman

引用次数: 4

Using agglomerative hierarchical clustering to model learner participation profiles in online discussion forums 利用聚类分层聚类方法模拟在线论坛中学习者的参与概况

Proceedings of the 2nd International Conference on Learning Analytics and Knowledge Pub Date : 2012-04-29 DOI: 10.1145/2330601.2330660

Germán Cobo, David García-Solórzano, J. A. Morán, Eugènia Santamaria, Carlos Monzo, J. Melenchón

引用次数: 23

Course signals at Purdue: using learning analytics to increase student success 普渡大学的课程信号:使用学习分析来提高学生的成功

Proceedings of the 2nd International Conference on Learning Analytics and Knowledge Pub Date : 2012-04-29 DOI: 10.1145/2330601.2330666

Kimberly E. Arnold, M. Pistilli

引用次数: 931

1st International Workshop on Learning Analytics and Linked Data 第一届学习分析与关联数据国际研讨会

Proceedings of the 2nd International Conference on Learning Analytics and Knowledge Pub Date : 2012-04-29 DOI: 10.1145/2330601.2330607

H. Drachsler, S. Dietze, W. Greller, M. d’Aquin, J. Jovanović, A. Pardo, Wolfgang Reinhardt, K. Verbert

引用次数: 6

Deriving group profiles from social media to facilitate the design of simulated environments for learning 从社交媒体中获取群体资料，以促进模拟学习环境的设计

Proceedings of the 2nd International Conference on Learning Analytics and Knowledge Pub Date : 2012-04-29 DOI: 10.1145/2330601.2330650

A. Ammari, L. Lau, V. Dimitrova

{"title":"Deriving group profiles from social media to facilitate the design of simulated environments for learning","authors":"A. Ammari, L. Lau, V. Dimitrova","doi":"10.1145/2330601.2330650","DOIUrl":"https://doi.org/10.1145/2330601.2330650","url":null,"abstract":"Simulated environments for learning are becoming increasingly popular to support experiential learning in complex domains. A key challenge when designing simulated learning environments is how to align the experience in the simulated world with real world experiences. Social media resources provide user-generated content that is rich in digital traces of real world experiences. People comments, tweets, and blog posts in social spaces can reveal interesting aspects of real world situations or can show what particular group of users is interested in or aware of. This paper examines a systematic way to analyze user-generated content in social media resources to provide useful information for learning simulator design. A hybrid framework exploiting Machine Learning and Semantics for social group profiling is presented. The framework has five stages: (1) Retrieval of user-generated content from the social resource (2) Content noise filtration, removing spam, abuse, and content irrelevant to the learning domain; (3) Deriving individual social profiles for the content authors; (4) Clustering of individuals into groups of similar authors; and (5) Deriving group profiles, where interesting concepts suitable for the use in simulated learning systems are extracted from the aggregated content authored by each group. The framework is applied to derive group profiles by mining user comments on YouTube videos. The application is evaluated in an experimental study within the context of learning interpersonal skills in job interviews. The paper discusses how the YouTube-based group profiles can be used to facilitate the design of a job interview skills learning simulator, considering: (1) identifying learning needs based on digital traces of real world experiences; and (2) augmenting learner models in simulators based on group characteristics derived from social media.","PeriodicalId":311750,"journal":{"name":"Proceedings of the 2nd International Conference on Learning Analytics and Knowledge","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130102357","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: 12

Probability estimation and a competence model for rule based e-tutoring systems 基于规则的网络辅导系统的概率估计与胜任力模型

Proceedings of the 2nd International Conference on Learning Analytics and Knowledge Pub Date : 2012-04-29 DOI: 10.1145/2330601.2330663

Diederik M. Roijers, J. Jeuring, A. Feelders

引用次数: 6

Connecting levels and methods of analysis in networked learning communities 网络学习社区中的连接层次和分析方法

Proceedings of the 2nd International Conference on Learning Analytics and Knowledge Pub Date : 2012-04-29 DOI: 10.1145/2330601.2330608

D. Suthers, H. Hoppe, M. Laat, S. B. Shum

引用次数: 7

Applying quantified self approaches to support reflective learning 运用量化的自我方法来支持反思性学习

Proceedings of the 2nd International Conference on Learning Analytics and Knowledge Pub Date : 2012-04-29 DOI: 10.1145/2330601.2330631

Verónica Rivera-Pelayo, V. Zacharias, Lars Müller, Simone Braun

引用次数: 108