From d6bcb393f82ccc0b93cd3c89a137591f47b31ebf Mon Sep 17 00:00:00 2001 From: chenszheng <31739635+chenszheng@users.noreply.github.com> Date: Tue, 3 Oct 2017 21:54:37 -0400 Subject: [PATCH] Zotero notes Uploaded Zotero notes. --- Chen_Zheng_zotero.csv | 59 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 59 insertions(+) create mode 100644 Chen_Zheng_zotero.csv diff --git a/Chen_Zheng_zotero.csv b/Chen_Zheng_zotero.csv new file mode 100644 index 0000000..1bc8f68 --- /dev/null +++ b/Chen_Zheng_zotero.csv @@ -0,0 +1,59 @@ +"Key","Item Type","Publication Year","Author","Title","Publication Title","ISBN","ISSN","DOI","Url","Abstract Note","Date","Date Added","Date Modified","Access Date","Pages","Num Pages","Issue","Volume","Number Of Volumes","Journal Abbreviation","Short Title","Series","Series Number","Series Text","Series Title","Publisher","Place","Language","Rights","Type","Archive","Archive Location","Library Catalog","Call Number","Extra","Notes","File Attachments","Link Attachments","Manual Tags","Automatic Tags","Editor","Series Editor","Translator","Contributor","Attorney Agent","Book Author","Cast Member","Commenter","Composer","Cosponsor","Counsel","Interviewer","Producer","Recipient","Reviewed Author","Scriptwriter","Words By","Guest","Number","Edition","Running Time","Scale","Medium","Artwork Size","Filing Date","Application Number","Assignee","Issuing Authority","Country","Meeting Name","Conference Name","Court","References","Reporter","Legal Status","Priority Numbers","Programming Language","Version","System","Code","Code Number","Section","Session","Committee","History","Legislative Body" +"I794FRN4","journalArticle","2014","Shermis, M.D.","State-of-the-art automated essay scoring: Competition, results, and future directions from a United States demonstration","Assessing Writing","","","","http://www.sciencedirect.com.ezp-prod1.hul.harvard.edu/science/article/pii/S1075293513000196","","2014-04","2017-09-08 13:39:59","2017-09-08 13:39:59","2014-08-20 23:48:38","53-76","","","20","","","","","","","","","","","","","","","","","","","","http://www.sciencedirect.com.ezp-prod1.hul.harvard.edu/science/article/pii/S1075293513000196","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"9MUUZDA7","webpage","2014","Honan, Mat","I Liked Everything I Saw on Facebook for Two Days. Here’s What It Did to Me | Gadget Lab","WIRED","","","","http://www.wired.com/2014/08/i-liked-everything-i-saw-on-facebook-for-two-days-heres-what-it-did-to-me/","I like everything. Or at least I did, for 48 hours. Literally everything Facebook sent my way, I liked---even if I hated it.","2014-08-11","2017-09-08 13:39:59","2017-09-08 13:39:59","2014-08-12 14:01:43","","","","","","","","","","","","","","","","","","","","","","","","http://www.wired.com/2014/08/i-liked-everything-i-saw-on-facebook-for-two-days-heres-what-it-did-to-me/","","Facebook; like buttons; News Feed","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"WKUBEND9","journalArticle","2014","Grunspan, Daniel Z.; Wiggins, Benjamin L.; Goodreau, Steven M.","Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research","CBE-Life Sciences Education","",", 1931-7913","10.1187/cbe.13-08-0162","http://www.lifescied.org/content/13/2/167","Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA) provides the necessary tool kit for investigating questions involving relational data. We introduce basic concepts in SNA, along with methods for data collection, data processing, and data analysis, using a previously collected example study on an undergraduate biology classroom as a tutorial. We conduct descriptive analyses of the structure of the network of costudying relationships. We explore generative processes that create observed study networks between students and also test for an association between network position and success on exams. We also cover practical issues, such as the unique aspects of human subjects review for network studies. Our aims are to convince readers that using SNA in classroom environments allows rich and informative analyses to take place and to provide some initial tools for doing so, in the process inspiring future educational studies incorporating relational data.","2014-06-20","2017-09-08 13:40:00","2017-09-08 13:40:00","2014-08-20 20:21:46","167-178","","2","13","","CBE Life Sci Educ","Understanding Classrooms through Social Network Analysis","","","","","","","en","","","","","www.lifescied.org","","","","","http://www.lifescied.org/content/13/2/167","Week 2","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"BBW8FGK2","blogPost","2014","Young, Jeffrey R.","Why Students Should Own Their Educational Data","The Chronicle of Higher Education Blogs: Wired Campus","","","","http://chronicle.com/blogs/wiredcampus/why-students-should-own-their-educational-data/54329","","2014-08-21","2017-09-08 13:40:00","2017-09-08 13:40:00","2014-08-23 21:32:22","","","","","","","","","","","","","","","","","","","","","","","","http://chronicle.com/blogs/wiredcampus/why-students-should-own-their-educational-data/54329","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"NLT5HIWR","conferencePaper","2013","Lee, Victor R.; Drake, Joel","Quantified Recess: Design of an Activity for Elementary Students Involving Analyses of Their Own Movement Data","","978-1-4503-1918-8","","10.1145/2485760.2485822","http://doi.acm.org.ezp-prod1.hul.harvard.edu/10.1145/2485760.2485822","Recess is often a time for children in school to engage recreationally in physically demanding and highly interactive activities with their peers. This paper describes a design effort to encourage fifth-grade students to examine sensitivities associated with different measures of center by having them analyze activities during recess using over the course of a week using Fitbit activity trackers and TinkerPlots data visualization software. We describe the activity structure some observed student behaviors during the activity. We also provide a descriptive account, based on video records and transcripts, of two students who engaged thoughtfully with their recess data and developed a more sophisticated understanding of when and how outliers affect means and medians.","2013","2017-09-08 13:40:00","2017-09-08 13:40:00","2014-02-27 05:07:35","273–276","","","","","","Quantified Recess","","","","","ACM","New York, NY, USA","","","","","","ACM Digital Library","","","","","","","activity trackers; Collaboration; competition; elementary students; fitbit; physical activity; Quantified Self; TinkerPlots","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"CPL3RLID","conferencePaper","2012","Siemens, George; Baker, Ryan S. J. d.","Learning Analytics and Educational Data Mining: Towards Communication and Collaboration","Proceedings of the 2Nd International Conference on Learning Analytics and Knowledge","978-1-4503-1111-3","","10.1145/2330601.2330661","http://doi.acm.org/10.1145/2330601.2330661","Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research into the models, methods, technologies, and impact of analytics. Two research communities -- Educational Data Mining (EDM) and Learning Analytics and Knowledge (LAK) have developed separately to address this need. This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.","2012","2017-09-08 13:40:01","2017-09-08 13:40:01","2015-01-16 03:15:55","252–254","","","","","","Learning Analytics and Educational Data Mining","","","","","ACM","New York, NY, USA","","","","","","ACM Digital Library","","","","","","","Collaboration; educational data mining; learning analytics and knowledge","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"FYMXW9S8","blogPost","2015","Manai, J","The Learning Analytics Landscape: Tension Between Student Privacy and the Process of Data Mining","Carnegie Foundation for the Advancement of Teaching","","","","http://www.carnegiefoundation.org/blog/the-learning-analytics-landscape-tension-between-student-privacy-and-the-process-of-data-mining/","Data mining is a powerful tool being used by educational institutions to support student success, but often students do not know what data are being","2015-11-06","2017-09-08 13:40:01","2017-09-08 13:40:01","2016-01-06 22:44:02","","","","","","","The Learning Analytics Landscape","","","","","","","","","","","","","","","","","http://www.carnegiefoundation.org/blog/the-learning-analytics-landscape-tension-between-student-privacy-and-the-process-of-data-mining/","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"Y9DRW7BZ","book","2015","Zheng, Alice","Evaluating Machine Learning Models","","","","","http://www.oreilly.com/data/free/evaluating-machine-learning-models.csp?intcmp=il-data-free-lp-lgen_free_reports_page","Data science today is a lot like the Wild West: there’s endless opportunity and excitement, but also a lot of chaos and confusion. If you’re new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming...","2015-09","2017-09-08 13:40:01","2017-09-08 13:40:01","2015-12-15 18:26:39","","","","","","","","","","","","O'Reily Media","Sebastopol, CA","","","","","","","","","","","http://www.oreilly.com/data/free/evaluating-machine-learning-models.csp?intcmp=il-data-free-lp-lgen_free_reports_page","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"SHDUQ4CV","journalArticle","2015","Krueger, Keith R.; Moore, Bob","New technology “clouds” student data privacy","Phi Delta Kappan","","0031-7217, 1940-6487","10.1177/0031721715569464","http://pdk.sagepub.com/content/96/5/19","As technology has leaped forward to provide valuable learning tools, parents and policy makers have begun raising concerns about the privacy of student data that schools and systems have. Federal laws are intended to protect students and their families but they have not and will never be able to keep up with rapidly evolving technology. School systems can help themselves and their students by following a list of guidelines, the authors say.","2015-02-01","2017-09-08 13:40:01","2017-09-08 13:40:01","2015-12-17 16:16:17","19-24","","5","96","","Phi Delta Kappan","","","","","","","","en","","","","","pdk.sagepub.com","","","","","http://pdk.sagepub.com/content/96/5/19","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"CBN87MWA","blogPost","2015","Leong, B; Polonetsky, J","Why Opting Out of Student Data Collection Isn’t the Solution","EdSurge","","","","https://www.edsurge.com/news/2015-03-16-why-opting-out-of-student-data-collection-isn-t-the-solution","In every privacy debate across every industry, the same questions arise about the rights of individuals to “opt-out” of their data being collected or used. So it should come as no surprise that the “when” and “how” of parent and student opt-outs of education data collection or use has become a robust","2015-03-16","2017-09-08 13:40:01","2017-09-08 13:40:01","2016-01-16 16:31:25","","","","","","","","","","","","","","","","","","","","","","","","https://www.edsurge.com/news/2015-03-16-why-opting-out-of-student-data-collection-isn-t-the-solution","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"LGX4Q85Y","videoRecording","2013","Poulson, Barton","Up and Running with R","","","","","http://www.lynda.com/R-tutorials/Up-Running-R/120612-2.html?org=nyu.edu","Introduces the R statistical processing language, including how to install R, read data from SPSS and spreadsheets, analyze data, and create charts and plots.","2013-04-04","2017-09-08 13:40:02","2017-09-08 13:40:02","2016-01-17 18:03:03","","","","","","","","","","","","Lynda.com","","","","","","","","","","","","http://www.lynda.com/R-tutorials/Up-Running-R/120612-2.html?org=nyu.edu","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"LU88EZKV","videoRecording","2015","Educause","Why Is Measuring Learning So Difficult?","","","","","https://www.youtube.com/watch?v=_iv8A1pHNYA","Several higher education learning and assessment professionals discuss the difficulties of measuring learning.","2015-08-17","2017-09-08 13:40:02","2017-09-08 13:40:02","2016-01-17 18:50:57","","","","","","","","","","","","","","","","","","","YouTube","","","","","","","Assessment; Education; educational assessment; EDUCAUSE; Higher Education; learners; Learning; Teaching and learning","","","","","","","","","","","","","","","","","","","","","470 seconds","","","","","","","","","","","","","","","","","","","","","","","","","" +"VXJ3FYEI","webpage","2016","Weinersmith, Zach","Saturday Morning Breakfast Cereal","","","","","http://www.smbc-comics.com/index.php?id=3978","","2016-01-05","2017-09-08 13:40:02","2017-09-08 13:40:02","2016-01-18 18:17:09","","","","","","","","","","","","","","","","","","","","","","","","http://www.smbc-comics.com/index.php?id=3978","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"B2M5GPI9","webpage","2015","RStudio","The Data Wrangling Cheatsheet","","","","","http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf","","2015-01","2017-09-08 13:40:02","2017-09-08 13:40:02","2016-01-18 18:42:27","","","","","","","","","","","","","","","","","","","","","","","","http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"89KX8N97","conferencePaper","2014","Clow, Doug","Data wranglers: human interpreters to help close the feedback loop","Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","","","","","","2014","2017-09-08 13:40:02","2017-09-08 13:40:02","","49–53","","","","","","","","","","","ACM","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"HQJI2638","magazineArticle","2015","Kucirkova, Natalia; FitzGerald, Elizabeth","Zuckerberg is ploughing billions into 'personalised learning' – why?","The Conversation","","","","http://theconversation.com/zuckerberg-is-ploughing-billions-into-personalised-learning-why-51940","Zuckerburg wants to plough billions into personalised learning, but his way may not be the right way.","2015-12-09","2017-09-08 13:40:03","2017-09-08 13:40:03","2016-01-18 19:14:05","","","","","","","","","","","","","","","","","","","","","","","","https://theconversation.com/zuckerberg-is-ploughing-billions-into-personalised-learning-why-51940","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"93S3E6PQ","videoRecording","2015","Georgia Tech","Feature Selection","","","","","https://www.youtube.com/watch?v=8CpRLplmdqE","","2015-02-23","2017-09-08 13:40:03","2017-09-08 13:40:03","2016-01-18 19:18:06","","","","","","","","","","","","Youtube","","","","","","","","","","","","https://www.youtube.com/watch?v=8CpRLplmdqE","","","","","","","","","","","","","","","Udacity","","","","","","","","3:13","","","","","","","","","","","","","","","","","","","","","","","","","" +"G4MVCXYK","blogPost","2013","Perez-Riverol, Yasset","Introduction to Feature selection for bioinformaticians using R, correlation matrix filters, PCA & backward selection","R-bloggers","","","","http://www.r-bloggers.com/introduction-to-feature-selection-for-bioinformaticians-using-r-correlation-matrix-filters-pca-backward-selection/","Bioinformatics is becoming more and more a Data Mining field. Every passing day, Genomics and Proteomics yield bucketloads of multivariate data (genes, proteins, DNA, identified peptides, structures), and every one of these biological data units are described by a number of features: length, physicochemical properties, scores, etc. Careful consideration of which features to select when trying to reduce the dimensionality of a specific dataset is, therefore, critical if one wishes to analyze and understand their impact on a model, or to identify what attributes produce a specific biological effect. For instance, considering a predictive model C1A1 + C2A2 + C3A3 … CnAn = S, where Ci are constants, Ai are features or attributes and S is the predictor output (retention time, toxicity, score, etc). It is essential to identify which of those features (A1, A2 and A3…An) are most relevant to the model and to understand how they correlate with S, as working with such a subset will enable the researcher to discard a lot of irrelevant and redundant information. There are two main approaches to this selection process: Filter approaches: you select the features first, then you use this subset to execute classification or clustering algorithms, etc; Embedded or Wrapper approaches a classification algorithm is applied to the raw dataset in order to identify the most relevant features. One of the simplest and most powerful filter approaches is the use of  correlation matrix filters. In regression and data mining problems, variables may be highly correlated with one another or ""redundant"". For example in cheminformatics, aromatic rings, bond counts and carbon atom counts can be very tightly correlated. If so, any one of these variables could be used as a proxy for all the others. It is best to choose the feature which is most likely to be the direct cause of toxicity, absorption or a specific response distribution.  Correlation Matrix :R Example: Removing features with more than 0.70 of Correlation.import java.util.List;library(corrplot)#corrplot: the library to compute correlation matrix.datMy <- read.table(""data.csv"", header = TRUE)#read the tab file using the read table function.datMy.scale<- scale(datMy[2:ncol(datMy)],center=TRUE,scale=TRUE);#scale all the features (from feature 2 bacause feature 1 is the predictor output)corMatMy <- cor(datMy.scale)#compute the correlation matrixcorrplot(corMatMy, order = ""hclust"")#visualize the matrix, clustering features by correlation index. Resulting Output: Highly Correlate Matrix for 400 features. After inspecting the matrix, we set the correlation threshold at 0.70.highlyCor <- findCorrelation(corMatMy, 0.70)#Apply correlation filter at 0.70,#then we remove all the variable correlated with more 0.7.datMyFiltered.scale <- datMy.scale[,-highlyCor]corMatMy <- cor(datMyFiltered.scale)corrplot(corMatMy, order = ""hclust"") Resulting Output: Correlation matrix after filter. Now it is possible to filter out “redundant” features by examining in detail the correlation matrix. Remember that the closer the correlation between two variables is to 1, the more related their behavior and the more redundant one is with respect to the other. Using PCA A relatively sophisticated way to do the correlation matrix analysis would be to perform a Principal Components Analysis (PCA). Feature extraction approaches transform data in high-dimensional space to a space of fewer dimensions. Principal component analysis, the most important linear technique for reducing dimensionality, performs a linear mapping of the data to a lower dimensional space in such a way that the variance of the data in the low-dimensional representation is maximized. In other words, PCA analysis builds a set of features by selecting those axes which maximize data variance. Principal Component Analysis (PCA) is a multivariate technique that summarizes systematic patterns of variation in the data. From a data analysis standpoint, PCA is used for studying one table of observations and variables with the idea of transforming the observed variables into a set of new variables, the principal components, which are uncorrelated and explain the variation of the data. Therefore, PCA can be used to bring down a “complex” data set to a lower dimensionality, in order to reveal the structures or the dominant types of variations in both the observations and the variables. PCA in R In R, several functions from a number of different packages can be used to perform PCA. My suggestion is FactoMineR whose typical PCA output consists of a set of eigenvalues, a table with the scores or Principal Components (PCs), and a table of loadings (or correlations between variables and PCs).R Example: PCA function using FactoMineR for 400 features & 5 PCs require(FactoMineR) # PCA with function PCAdatMy <- read.table(""data.csv"", header = TRUE)#read the tab file using the read table function.pca <- PCA(datMy, scale.unit=TRUE, ncp=5, graph=T)#scale all the features,  ncp: number of dimensions kept in the results (by default 5)dimdesc(pca)#This line of code will sort the variables the most linked to each PC. It is very useful when you have many variables. Here, you can find an excellent tutorial on FactoMineR and Principal Component analysis in R: Wrapper Approaches with Backwards Selection""Wrapper"" approaches can be viewed as built-in functions to optimize the number of predictors in the optimization or regression problem.  Many feature selection routines use a ""wrapper"" approach to find appropriate variables such that an algorithm searching through feature space repeatedly fits the model with different predictor sets. The best predictor set is determined by some measure of performance (correlation R^2, root-mean-square deviation). An example of one search routine is backwards selection (a.k.a. recursive feature elimination). In many cases, using these models with built-in feature selection will be more efficient than algorithms where the search routine for the right predictors is external to the model. Built-in feature selection typically couples the predictor search algorithm with parameter estimation, and is usually optimized with a single objective function (e.g. error rates or likelihood). Using Built-in Backward SelectionThe algorithm fits the model to all predictors. Each predictor is ranked according to relevance to the model. With each iteration of feature selection, the Ci top-ranked predictors are retained, the model is refit and performance is re-assessed. Built-in backward selection is being used for at least three purposes: predictor selection, model fitting and performance evaluation. Unless the number of samples is large, especially in relation to the number of variables, one static training set may not be able to fulfill these needs. The ""crantastic"" package caret contains functions for training and plotting classification and regression models. In this case, the rfe function is used to obtain the potential selection. It has several arguments: x, a matrix or data frame of predictor variables y, a vector (numeric or factor) of outcomes sizes, an integer vector for the specific subset sizes that should be tested (which must not include ncol(x)) rfeControl, a list of options that can be used to specify the model and the methods for prediction, ranking etc. For a specific model, a set of functions must be specified in rfeControl$functions. There are a number of pre-defined sets of functions for several models, including: linear regression (in the object lmFuncs), random forests (rfFuncs), naive Bayes (nbFuncs), bagged trees (treebagFuncs) and functions that can be used with caret's train function (caretFuncs). R example: Selecting features using backward selection and the caret package library(caret);#load caret librarydata_features<-as.matrix(read.table(""data-features.csv"",sep=""\t"", header=TRUE));#load data featuresdata_class<-as.matrix(read.table('data.csv', header=TRUE));#load data classesdata_features<- scale(data_features, center=TRUE, scale=TRUE);#scale data featuresinTrain <- createDataPartition(data_class, p = 3/4, list = FALSE); #Divide the dataset in train and test sets#Create the Training Dataset for Descriptors trainDescr <- data_features[inTrain,];# Create the Testing dataset for DescriptorstestDescr <- data_features[-inTrain,];trainClass <- data_class[inTrain];testClass <- data_class[-inTrain];descrCorr <- cor(trainDescr);highCorr <- findCorrelation(descrCorr, 0.70);trainDescr <- trainDescr[, -highCorr];testDescr <- testDescr[, -highCorr];# Here, we can included a correlation matrix analysis to remove the redundant features before the backwards selection svmProfile <- rfe(x=trainDescr, y = trainClass, sizes = c(1:5), rfeControl= rfeControl(functions = caretFuncs,number = 2),method = ""svmRadial"",fit = FALSE);#caret function: the rfe is the backwards selection, c is the possible sizes of the features sets, and method the optimization method is a support vector machine. Finally I would like to recommned an excellent Review about Feature Selection in Bioinformatics.","2013-10-17","2017-09-08 13:40:03","2017-09-08 13:40:03","2016-01-18 19:42:18","","","","","","","","","","","","","","","","","","","","","","","","http://www.r-bloggers.com/introduction-to-feature-selection-for-bioinformaticians-using-r-correlation-matrix-filters-pca-backward-selection/","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"DNV5J2VS","blogPost","2014","Sharma, Aseem","Why open data matters in education","Opensource.com","","","","https://opensource.com/education/14/10/why-open-data-matters-education","Aseem Sharma writes that improving the state of education and making children better learners is a human endeavor. It requires understanding the behavior of children, what motivates them, and what demotivates them. Technology solutions based on open data can strengthen and fuel that human endeavor on which much of our future depends.","2014-10-13","2017-09-08 13:40:03","2017-09-08 13:40:03","2016-01-18 20:10:08","","","","","","","","","","","","","","","","","","","","","","","","https://opensource.com/education/14/10/why-open-data-matters-education","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"64L254U3","bookSection","2016","Hanneman, R.A.; Riddle, M.","Chapter 1: Social Network Data","Introduction to Social Network Methods","","","","http://faculty.ucr.edu/~hanneman/nettext/C1_Social_Network_Data.html","","2016-01-18","2017-09-08 13:40:04","2017-09-08 13:40:04","2016-01-18 20:17:24","","","","","","","","","","","","","","","","","","","","","","","","http://faculty.ucr.edu/~hanneman/nettext/C1_Social_Network_Data.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"LHV82HP4","blogPost","2014","Farr, Christina","Microsoft and Knewton partner up to bring adaptive learning to publishers & schools","VentureBeat","","","","http://venturebeat.com/2014/03/13/microsoft-and-knewton-partner-up-to-bring-adaptive-learning-to-publishers-schools/","Knewton provides an open API for ""adaptive learning,"" an insidery term for computing that helps students learn at their own pace.","2014-03-13","2017-09-08 13:40:04","2017-09-08 13:40:04","2016-01-19 14:56:06","","","","","","","","","","","","","","","","","","","","","","","","http://venturebeat.com/2014/03/13/microsoft-and-knewton-partner-up-to-bring-adaptive-learning-to-publishers-schools/","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"LGFYFL58","journalArticle","2007","Wu, Xindong; Kumar, Vipin; Quinlan, J. Ross; Ghosh, Joydeep; Yang, Qiang; Motoda, Hiroshi; McLachlan, Geoffrey J.; Ng, Angus; Liu, Bing; Yu, Philip S.; Zhou, Zhi-Hua; Steinbach, Michael; Hand, David J.; Steinberg, Dan","Top 10 algorithms in data mining","Knowledge and Information Systems","","0219-1377, 0219-3116","10.1007/s10115-007-0114-2","http://link.springer.com.ezp-prod1.hul.harvard.edu/article/10.1007/s10115-007-0114-2","This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.","2007-12-04","2017-09-08 13:40:04","2017-09-08 13:40:04","2016-01-19 15:40:59","1-37","","1","14","","Knowl Inf Syst","","","","","","","","en","","","","","link.springer.com.ezp-prod1.hul.harvard.edu","","","","","http://link.springer.com.ezp-prod1.hul.harvard.edu/article/10.1007%2Fs10115-007-0114-2","","Business Information Systems; Information Systems and Communication Service","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"BVD8TX55","journalArticle","2011","Nadkarni, Prakash M; Ohno-Machado, Lucila; Chapman, Wendy W","Natural language processing: an introduction","Journal of the American Medical Informatics Association : JAMIA","","1067-5027","10.1136/amiajnl-2011-000464","http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168328/","Objectives To provide an overview and tutorial of natural language processing (NLP) and modern NLP-system design. Target audience This tutorial targets the medical informatics generalist who has limited acquaintance with the principles behind NLP and/or limited knowledge of the current state of the art. Scope We describe the historical evolution of NLP, and summarize common NLP sub-problems in this extensive field. We then provide a synopsis of selected highlights of medical NLP efforts. After providing a brief description of common machine-learning approaches that are being used for diverse NLP sub-problems, we discuss how modern NLP architectures are designed, with a summary of the Apache Foundation's Unstructured Information Management Architecture. We finally consider possible future directions for NLP, and reflect on the possible impact of IBM Watson on the medical field.","2011","2017-09-08 13:40:04","2017-09-08 13:40:04","2016-01-19 16:19:41","544-551","","5","18","","J Am Med Inform Assoc","Natural language processing","","","","","","","","","","","","PubMed Central","","","","","http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168328/","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"6Q7WVI95","journalArticle","2014","Crawford, K; Schultz, J","Big Data and Due Process: Toward a Framework to Redress Predictive Privacy Harms - Boston College Law Review","Boston College Law Review","","","","http://bclawreview.org/review/55_1/03_crawford_schultz/","","2014","2017-09-08 13:40:05","2017-09-08 13:40:05","2016-01-19 16:33:49","","","1","LV","","","Big Data and Due Process","","","","","","","","","","","","","","","","","http://bclawreview.org/review/55_1/03_crawford_schultz/","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"GEGRGR4L","book","2015","Thompson, J.","Text Mining, Big Data, Unstructured Data","","","","","http://www.statsoft.com/Textbook/Text-Mining#overview","","2015-05-08","2017-09-08 13:40:05","2017-09-08 13:40:05","2016-01-19 16:41:08","","","","","","","","","","","","Dell Computing","","","","","","","","","","","","http://www.statsoft.com/Textbook/Text-Mining#overview","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"5CYHH64W","blogPost","2015","Kamenetz, Anya","The Quantified Student: An App That Predicts GPA","NPR","","","","http://www.npr.org/sections/ed/2015/06/02/409780423/the-quantified-student-an-app-that-predicts-gpa","Researchers found that a phone's activity tracker can automatically predict students' school performance.","2015-06-03","2017-09-08 13:40:05","2017-09-08 13:40:05","2016-01-19 20:00:19","","","","","","","The Quantified Student","","","","","","","","","","","","","","","","","http://www.npr.org/sections/ed/2015/06/02/409780423/the-quantified-student-an-app-that-predicts-gpa","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"V9MNMKUZ","videoRecording","2015","Datacamp","The ggvis R package - How to Work With The Grammar of Graphics - YouTube","","","","","https://www.youtube.com/watch?v=rf55oB6xX3w","Share your videos with friends, family, and the world","2015-12-05","2017-09-08 13:40:05","2017-09-08 13:40:05","2016-01-19 20:37:17","","","","","","","","","","","","Youtube","","","","","","","","","","","","https://www.youtube.com/watch?v=rf55oB6xX3w","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"X793K72W","bookSection","2008","Friendly, Michael","A Brief History of Data Visualization","Handbook of Data Visualization","978-3-540-33036-3 978-3-540-33037-0","","","http://link.springer.com.ezp-prod1.hul.harvard.edu/chapter/10.1007/978-3-540-33037-0_2","It is common to think of statistical graphics and data visualization as relatively modern developments in statistics. In fact, the graphic representation of quantitative information has deep roots. These roots reach into the histories of the earliestmap making and visual depiction, and later into thematic cartography, statistics and statistical graphics, medicine and other fields. Along the way, developments in technologies (printing, reproduction), mathematical theory and practice, and empirical observation and recording enabled the wider use of graphics and new advances in form and content.","2008","2017-09-08 13:40:06","2017-09-08 13:40:06","2016-01-19 20:53:21","15-56","","","","","","","","","","","Springer Berlin Heidelberg","","en","©2008 Springer-Verlag","","","","link.springer.com.ezp-prod1.hul.harvard.edu","","","","","http://link.springer.com.ezp-prod1.hul.harvard.edu/chapter/10.1007/978-3-540-33037-0_2","","Computational Biology/Bioinformatics; Computer Applications; Computer Imaging, Vision, Pattern Recognition and Graphics; Statistical Theory and Methods; Statistics and Computing/Statistics Programs","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"K743L5Y2","webpage","2014","Groelmund, Garrett","The R Markdown Cheat sheet","RStudio","","","","http://shiny.rstudio.com/articles/rm-cheatsheet.html","","2014-08-01","2017-09-08 13:40:06","2017-09-08 13:40:06","2016-01-19 21:17:28","","","","","","","","","","","","","","","","","","","","","","","","http://shiny.rstudio.com/articles/rm-cheatsheet.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"S7X2HBMP","blogPost","2016","Leong, B; Polonetsky, J","Passing the Privacy Test as Student Data Laws Take Effect","EdSurge","","","","https://www.edsurge.com/news/2016-01-12-passing-the-privacy-test-as-student-data-laws-take-effect","On January 1, 2016, “ SOPIPA”—the recently passed California student data privacy law that defines how edtech companies can use student data became effective. About 25 other states have passed similar laws that are already in effect, or will become effective. At the same time, more than 200 sc","2016-01-12","2017-09-08 13:40:06","2017-09-08 13:40:06","2016-01-19 23:17:28","","","","","","","","","","","","","","","","","","","","","","","","https://www.edsurge.com/news/2016-01-12-passing-the-privacy-test-as-student-data-laws-take-effect?utm_content=bufferc0042&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"83YBFDT9","conferencePaper","2013","Ridgway, Jim; Smith, Alan","Open data, official statistics and statistics education: threats, and opportunities for collaboration","Proceedings of the Joint IASEIAOS Satellite Conference “Statistics Education for Progress”, Macao, China","","","","http://iase-web.org/documents/papers/sat2013/IASE_IAOS_2013_Paper_K3_Ridgway_Smith.pdf","","2013","2017-09-08 13:40:06","2017-09-08 13:40:06","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"KCFAJP3H","conferencePaper","2013","san Pedro, Maria Ofelia; Baker, Ryan; Bowers, Alex; Heffernan, Neil","Predicting college enrollment from student interaction with an intelligent tutoring system in middle school","Educational Data Mining 2013","","","","","","2013","2017-09-08 13:40:07","2017-09-08 13:40:07","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"NINWPELE","journalArticle","2010","Bowers, Alex J.","Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students: Grades, Data Driven Decision Making, Dropping out and Hierarchical Cluster Analysis","Practical Assessment, Research & Evaluation","","1531-7714","","","School personnel currently lack an effective method to pattern and visually interpret disaggregated achievement data collected on students as a means to help inform decision making. This study, through the examination of longitudinal K-12 teacher assigned grading histories for entire cohorts of students from a school district (n=188), demonstrates a novel application of hierarchical cluster analysis and pattern visualization in which all data points collected on every student in a cohort can be patterned, visualized and interpreted to aid in data driven decision making by teachers and administrators. Additionally, as a proof-of-concept study, overall schooling outcomes, such as student dropout or taking a college entrance exam, are identified from the data patterns and compared to past methods of dropout identification as one example of the usefulness of the method. Hierarchical cluster analysis correctly identified over 80% of the students who dropped out using the entire student grade history patterns from either K-12 or K-8. (Contains 5 figures.)","2010-05","2017-09-14 15:44:51","2017-09-14 15:44:51","2014-09-24 19:31:29","","","7","15","","","Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students","","","","","","","en","","","","","ERIC","","","","","http://eric.ed.gov/?id=EJ933686","","data; data analysis; Decision Making; Dropouts; Elementary School Students; Grades (Scholastic); Identification; MULTIVARIATE analysis; School Districts; Secondary School Students","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"EA45Z4E9","journalArticle","2014","Grunspan, Daniel Z.; Wiggins, Benjamin L.; Goodreau, Steven M.","Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research","CBE-Life Sciences Education","",", 1931-7913","10.1187/cbe.13-08-0162","http://www.lifescied.org/content/13/2/167","Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA) provides the necessary tool kit for investigating questions involving relational data. We introduce basic concepts in SNA, along with methods for data collection, data processing, and data analysis, using a previously collected example study on an undergraduate biology classroom as a tutorial. We conduct descriptive analyses of the structure of the network of costudying relationships. We explore generative processes that create observed study networks between students and also test for an association between network position and success on exams. We also cover practical issues, such as the unique aspects of human subjects review for network studies. Our aims are to convince readers that using SNA in classroom environments allows rich and informative analyses to take place and to provide some initial tools for doing so, in the process inspiring future educational studies incorporating relational data.","2014-06-20","2017-09-14 15:44:52","2017-09-14 15:44:52","2014-08-20 20:21:46","167-178","","2","13","","CBE Life Sci Educ","Understanding Classrooms through Social Network Analysis","","","","","","","en","","","","","www.lifescied.org","","","","","http://www.lifescied.org/content/13/2/167","Week 2","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"XW3E9H4F","blogPost","2014","Young, Jeffrey R.","Why Students Should Own Their Educational Data","The Chronicle of Higher Education Blogs: Wired Campus","","","","http://chronicle.com/blogs/wiredcampus/why-students-should-own-their-educational-data/54329","","2014-08-21","2017-09-14 15:44:52","2017-09-14 15:44:52","2014-08-23 21:32:22","","","","","","","","","","","","","","","","","","","","","","

Main ideas:

- Population studies does not tell you anything about any individual in that group.

- Most learners have a ""jagged profile"" (not average) of traits when it comes to learning, highly contextualized. Technology may help, by giving educators detailed data on students and the ability to customize teaching materials per individual.

- User should own their data.

","","http://chronicle.com/blogs/wiredcampus/why-students-should-own-their-educational-data/54329","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"X2E2Q8JL","journalArticle","1994","Corbett, Albert T.; Anderson, John R.","Knowledge tracing: Modeling the acquisition of procedural knowledge","User Modeling and User-Adapted Interaction","","0924-1868, 1573-1391","10.1007/BF01099821","http://link.springer.com.ezp-prod1.hul.harvard.edu/article/10.1007/BF01099821","This paper describes an effort to model students' changing knowledge state during skill acquisition. Students in this research are learning to write short programs with the ACT Programming Tutor (APT). APT is constructed around a production rule cognitive model of programming knowledge, called theideal student model. This model allows the tutor to solve exercises along with the student and provide assistance as necessary. As the student works, the tutor also maintains an estimate of the probability that the student has learned each of the rules in the ideal model, in a process calledknowledge tracing. The tutor presents an individualized sequence of exercises to the student based on these probability estimates until the student has ‘mastered’ each rule. The programming tutor, cognitive model and learning and performance assumptions are described. A series of studies is reviewed that examine the empirical validity of knowledge tracing and has led to modifications in the process. Currently the model is quite successful in predicting test performance. Further modifications in the modeling process are discussed that may improve performance levels.","1994-12-01","2017-09-14 15:44:52","2017-09-14 15:44:52","2013-04-21 21:21:19","253-278","","4","4","","User Model User-Adap Inter","Knowledge tracing","","","","","","","en","","","","","link.springer.com.ezp-prod1.hul.harvard.edu","","","","","","","Learning; Education (general); empirical validity; individual differences; intelligent tutoring systems; Management of Computing and Information Systems; mastery learning; Multimedia Information Systems; procedural knowledge; Psychology, general; student modeling; User Interfaces and Human Computer Interaction","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"KAW7IFD4","conferencePaper","2012","Siemens, George; Baker, Ryan S. J. d.","Learning Analytics and Educational Data Mining: Towards Communication and Collaboration","Proceedings of the 2Nd International Conference on Learning Analytics and Knowledge","978-1-4503-1111-3","","10.1145/2330601.2330661","http://doi.acm.org/10.1145/2330601.2330661","Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research into the models, methods, technologies, and impact of analytics. Two research communities -- Educational Data Mining (EDM) and Learning Analytics and Knowledge (LAK) have developed separately to address this need. This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.","2012","2017-09-14 15:44:53","2017-09-14 15:44:53","2015-01-16 03:15:55","252–254","","","","","","Learning Analytics and Educational Data Mining","","","","","ACM","New York, NY, USA","","","","","","ACM Digital Library","","","

EDM vs. LAK

EDM: focuses on automated discovery; models often used as the basis of automated adaptation, conducted by a computer system; reduces phenomena to components and analyze individual components and their relationships.

LAK: leverages human judgement; models often inform and empower instructors and learners; emphasize on attempting to understand systems as wholes in their full complexity.

 

","","","","Collaboration; educational data mining; learning analytics and knowledge","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"SETG24MJ","book","2015","Zheng, Alice","Evaluating Machine Learning Models","","","","","http://www.oreilly.com/data/free/evaluating-machine-learning-models.csp?intcmp=il-data-free-lp-lgen_free_reports_page","Data science today is a lot like the Wild West: there’s endless opportunity and excitement, but also a lot of chaos and confusion. If you’re new to data science and applied machine learning, evaluating a machine-learning model can seem pretty overwhelming...","2015-09","2017-09-14 15:44:53","2017-09-14 15:44:53","2015-12-15 18:26:39","","","","","","","","","","","","O'Reily Media","Sebastopol, CA","","","","","","","","","","","http://www.oreilly.com/data/free/evaluating-machine-learning-models.csp?intcmp=il-data-free-lp-lgen_free_reports_page","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"GIHL47GN","videoRecording","2015","Educause","Why Is Measuring Learning So Difficult?","","","","","https://www.youtube.com/watch?v=_iv8A1pHNYA","Several higher education learning and assessment professionals discuss the difficulties of measuring learning.","2015-08-17","2017-09-14 15:44:53","2017-09-14 15:44:53","2016-01-17 18:50:57","","","","","","","","","","","","","","","","","","","YouTube","","","

Key words/Main points:

Learning is multidimensional/too broad (social, cultural, individual elements)/contextual/idiosyncratic/personal/private

It's hard to simplify or to find proxy indicators/observable behaviorally only.

Lack tools to measure and to measure at different time points including the learners' competency prior to learning.

Measurement shouldn't be diagnostic, but suggesting what could be learned.

 

 

 

","","","","Assessment; Education; educational assessment; EDUCAUSE; Higher Education; learners; Learning; Teaching and learning","","","","","","","","","","","","","","","","","","","","","470 seconds","","","","","","","","","","","","","","","","","","","","","","","","","" +"YEDG9Z4G","webpage","2016","Weinersmith, Zach","Saturday Morning Breakfast Cereal","","","","","http://www.smbc-comics.com/index.php?id=3978","","2016-01-05","2017-09-14 15:44:53","2017-09-14 15:44:53","2016-01-18 18:17:09","","","","","","","","","","","","","","","","","","","","","","

Direction of causality. Over-generalization of research results.

","","http://www.smbc-comics.com/index.php?id=3978","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"M7C9JM9K","webpage","2015","RStudio","The Data Wrangling Cheatsheet","","","","","http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf","","2015-01","2017-09-14 15:44:54","2017-09-14 15:44:54","2016-01-18 18:42:27","","","","","","","","","","","","","","","","","","","","","","

Reshaping, subset, summarize, group, combine, create new.

","","http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"J39DIRA7","conferencePaper","2014","Clow, Doug","Data wranglers: human interpreters to help close the feedback loop","Proceedings of the Fourth International Conference on Learning Analytics And Knowledge","","","","","","2014","2017-09-14 15:44:54","2017-09-14 15:44:54","","49–53","","","","","","","","","","","ACM","","","","","","","","","","

Key words:

- Feedback loop: where actionable intelligence is produced from data about learners and their contexts, and interventions are made with the aim of improving learning.

- Gap: between data and academics who need to act

- Data Wranglers: deployed to engage in sense-making activity with learning analytics data, to produce reports with actionable recommendations and increase academics' familiarity with data sources.

","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"3Y2JPRVC","magazineArticle","2015","Kucirkova, Natalia; FitzGerald, Elizabeth","Zuckerberg is ploughing billions into 'personalised learning' – why?","The Conversation","","","","http://theconversation.com/zuckerberg-is-ploughing-billions-into-personalised-learning-why-51940","Zuckerburg wants to plough billions into personalised learning, but his way may not be the right way.","2015-12-09","2017-09-14 15:44:54","2017-09-14 15:44:54","2016-01-18 19:14:05","","","","","","","","","","","","","","","","","","","","","","","","https://theconversation.com/zuckerberg-is-ploughing-billions-into-personalised-learning-why-51940","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"QQHUGSWD","videoRecording","2015","Georgia Tech","Feature Selection","","","","","https://www.youtube.com/watch?v=8CpRLplmdqE","","2015-02-23","2017-09-14 15:44:54","2017-09-14 15:44:54","2016-01-18 19:18:06","","","","","","","","","","","","Youtube","","","","","","","","","","","","https://www.youtube.com/watch?v=8CpRLplmdqE","","","","","","","","","","","","","","","Udacity","","","","","","","","3:13","","","","","","","","","","","","","","","","","","","","","","","","","" +"JKKWFRY6","webpage","2014","Groelmund, Garrett","RStudio Cheat Sheets","RStudio","","","","https://www.rstudio.com/resources/cheatsheets/","","2014-08-01","2017-09-14 15:44:54","2017-09-14 15:44:54","2016-01-19 21:17:28","","","","","","","","","","","","","","","","","","","","","","

The R Markdown cheat sheet:

""R markdown is an authoring format that makes it easy to write resuable reports with R. You combine your R code with narration written in markdown (plain text) and then export the results as an html, pdf, or Word file.

You can even use R markdown to build interactive documents and slideshows.""

","","http://shiny.rstudio.com/articles/rm-cheatsheet.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"KSL84PKW","journalArticle","2012","Greller, Wolfgang; Drachsler, Hendrik","Translating Learning into Numbers: A Generic Framework for Learning Analytics","Journal of Educational Technology & Society","","1176-3647","","http://www.jstor.org/stable/jeductechsoci.15.3.42","ABSTRACT With the increase in available educational data, it is expected that Learning Analytics will become a powerful means to inform and support learners, teachers and their institutions in better understanding and predicting personal learning needs and performance. However, the processes and requirements behind the beneficial application of Learning and Knowledge Analytics as well as the consequences for learning and teaching are still far from being understood. In this paper, we explore the key dimensions of Learning Analytics (LA), the critical problem zones, and some potential dangers to the beneficial exploitation of educational data. We propose and discuss a generic design framework that can act as a useful guide for setting up Learning Analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency. Furthermore, the presented article intends to inform about soft barriers and limitations of Learning Analytics. We identify the required skills and competences that make meaningful use of Learning Analytics data possible to overcome gaps in interpretation literacy among educational stakeholders. We also discuss privacy and ethical issues and suggest ways in which these issues can be addressed through policy guidelines and best practice examples.","2012","2017-09-14 15:44:55","2017-09-14 15:44:55","2016-09-03 18:55:41","42-57","","3","15","","Journal of Educational Technology & Society","Translating Learning into Numbers","","","","","","","","","","","","JSTOR","","","

Two structures:

- Dimensions of learning analytics; stakeholders; objectives; data; instruments; internal limitations; external constraints.

- LA and pedagogy; Pedagogic behavior -> LA -> Pedagogic consequences -> Pedagogic behavior.

","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"MU5ZTQ32","journalArticle","2015","Konstan, Joseph A.; Walker, J. D.; Brooks, D. Christopher; Brown, Keith; Ekstrand, Michael D.","Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC","ACM Trans. Comput.-Hum. Interact.","","1073-0516","10.1145/2728171","http://doi.acm.org/10.1145/2728171","","2015-04","2017-09-14 15:44:55","2017-09-14 15:44:55","2016-09-03 20:38:02","10:1–10:23","","2","22","","","Teaching Recommender Systems at Large Scale","","","","","","","","","","","","ACM Digital Library","","","","","","","learning assessment; Massively Online Open Course (MOOC)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"PMF7PUNV","book","2015","Matsuda, Noboru; Furukawa, Tadanobu; Bier, Norman; Faloutsos, Christos","Machine Beats Experts: Automatic Discovery of Skill Models for Data-Driven Online Course Refinement","","","","","http://eric.ed.gov/?id=ED560513","How can we automatically determine which skills must be mastered for the successful completion of an online course? Large-scale online courses (e.g., MOOCs) often contain a broad range of contents frequently intended to be a semester's worth of materials; this breadth often makes it difficult to articulate an accurate set of skills and knowledge (i.e., a skill model, or the QMatrix). We have developed an innovative method to discover skill models from the data of online courses. Our method assumes that online courses have a pre-defined skill map for which skills are associated with formative assessment items embedded throughout the online course. Our method carefully exploits correlations between various parts of student performance, as well as in the text of assessment items, to build a superior statistical model that even outperforms human experts. To evaluate our method, we compare our method with existing methods (LFA) and human engineered skill models on three Open Learning Initiative (OLI) courses at Carnegie Mellon University. The results show that (1) our method outperforms human-engineered skill models, (2) skill models discovered by our method are interpretable, and (3) our method is remarkably faster than existing methods. These results suggest that our method provides a significant contribution to the evidence-based, iterative refinement of online courses with a promising scalability. [For complete proceedings, see ED560503.]","2015-06","2017-09-14 15:44:55","2017-09-14 15:44:55","2016-09-03 20:48:57","","","","","","","Machine Beats Experts","","","","","International Educational Data Mining Society","","en","","","","","ERIC","","","","","http://eric.ed.gov/?id=ED560513","","data; Automation; Comparative Analysis; Correlation; Formative Evaluation; models; Online Courses; Skills","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"M6S7QT3J","bookSection","2016","Hanneman, R.A.; Riddle, M.","Chapter 1: Social Network Data","Introduction to Social Network Methods","","","","http://faculty.ucr.edu/~hanneman/nettext/C1_Social_Network_Data.html","","2016-01-18","2017-09-14 15:44:55","2017-09-14 15:44:55","2016-01-18 20:17:24","","","","","","","","","","","","","","","","","","","","","","","","http://faculty.ucr.edu/~hanneman/nettext/C1_Social_Network_Data.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"UWS83NRT","bookSection","2017","Klerkx, Joris; Verbert, Katrien; Duval, Erik","Learning Analytics Dashboards","The Handbook of Learning Analytics","978-0-9952408-0-3","","","www.solaresearch.org","","2017","2017-09-14 15:44:55","2017-09-14 15:44:55","","","","","","","","","","","","","","Vancouver, BC, Canada","EN","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"WDUED3JA","bookSection","2017","Bergner, Yoav","Measurement and its Uses in Learning Analytics","The Handbook of Learning Analytics","978-0-9952408-0-3","","","http://solaresearch.org/hla-17/hla17-chapter1","Psychological measurement is a process for making warranted claims about states of mind. As such, it typically comprises the following: de ning a construct; specifying a measurement model and (developing) a reliable instrument; analyzing and accounting for various sources of error (including operator error); and framing a valid argument for particular uses of the outcome. Measurement of latent variables is, after all, a noisy endeavor that can neverthe- less have high-stakes consequences for individuals and groups. This chapter is intended to serve as an introduction to educational and psychological measurement for practitioners in learning analytics and educational data mining. It is organized thematically rather than historically, from more conceptual material about constructs, instruments, and sources of measurement error toward increasing technical detail about particular measurement models and their uses. Some of the philosophical differences between explanatory and predictive modelling are explored toward the end.","2017","2017-09-14 15:44:56","2017-09-14 15:44:56","","34-48","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","

Use this article as a checklist to prep for Cert.

","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"RS9G8SQJ","bookSection","2017","Brooks, Christopher; Thompson, Craig","Predictive Modelling in Teaching and Learning","The Handbook of Learning Analytics","978-0-9952408-0-3","","","http://solaresearch.org/hla-17/hla17-chapter1","This article describes the process, practice, and challenges of using predictive modelling in teaching and learning. In both the elds of educational data mining (EDM) and learning analytics (LA) predictive modelling has become a core practice of researchers, largely with a focus on predicting student success as operationalized by academic achievement. In this chapter, we provide a general overview of considerations when using predictive modelling, the steps that an educational data scientist must consider when engaging in the process, and a brief overview of the most popular techniques in the eld.","2017","2017-09-14 15:44:56","2017-09-14 15:44:56","","61-68","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"M8QIMQ7E","bookSection","2017","Prinsloo, P; Slade, S","Ethics and Learning Analytics: Charting the (Un)Charted","The Handbook of Learning Analytics","978-0-9952408-0-3","","","https://solaresearch.org/hla-17/hla17-chapter4/","","2017-03","2017-09-14 15:44:56","2017-09-14 15:44:56","","49-57","","","","","","","","","","","Society for Learning Analytics Research","Vancouver, BC","EN","","","","","","","","

Development of learning analytics.

Concerns: Surveillance, algorithms.

Ethics and privacy.

","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"2MJ9XCWN","bookSection","2017","Liu, R; Koedinger, K","Going Beyond Better Data Prediction to Create Explanatory Models of Educational Data","The Handbook of Learning Analytics","978-0-9952408-0-3","","","https://solaresearch.org/hla-17/hla17-chapter6/","","2017-03","2017-09-14 15:44:56","2017-09-14 15:44:56","","69-76","","","","","","","","","","","Society for Learning Analytics Research","Vancouver, BC","EN","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"NR8WSZK7","journalArticle","2011","Gelman, A; Niemi, J","Statistical graphics: making information clear – and beautiful","Significance","","","","","","2011-09","2017-09-14 15:44:56","2017-09-14 15:44:56","","134-136","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"2KIYRM2E","journalArticle","1984","Wainer, H","How to display data badly","The American Statistician","","","","","","1984","2017-09-14 15:44:56","2017-09-14 15:44:56","","137-147","","2","38","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"A8HJB7YW","journalArticle","2012","Gelman, A; Unwin, A","Infovis and Statistical Graphics: Different Goals, Different Looks (with discussion)","","","","","","","2012","2017-09-14 15:44:56","2017-09-14 15:44:56","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"YYDD87GG","blogPost","2014","Fung, K","Junkcharts Trifecta Checkup: The Definitive Guide","Junkcharts","","","","http://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html","","2014","2017-09-14 15:44:56","2017-09-14 15:44:56","","","","","","","","","","","","","","","","","Blog","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" \ No newline at end of file