"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"
"FE3N9L2N","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 16:02:46","2017-09-14 16:02:46","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"I5W8CF3K","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 16:02:46","2017-09-14 16:02:46","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"ZEA39J24","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 16:02:47","2017-09-14 16:02:47","2014-08-23 21:32:22","","","","","","","","","","","","","","","","","","","","","","<p>Big data can work for the population but does not always work for the individual. Sometimes study can start with <strong>individual cases</strong> and then go to the population level keeping all its complexity. Deal with data with caution!</p> <p>It would be good if each student has her/his own profile containing info not only about the weakness but also the strength, for example good at comprehension by graphs. Then instructors know how to modify and cater to students' taste.</p> <p><span style=""text-decoration: underline;"">Doubt:</span> Whether it is appropriate to compare students to pilots? Though students differ in various aspects but most of them linger around the average I believe? But it is still justifiable to design learning materials for the minorities.</p> <p>Besides, the lecturer is talking about higher edu? In elementary or high school level, it can be hard to set standard to evaluate students' learning if there are difference in learning materials. Possible solutions might be to gauge individual learning experiences and not set students to standardized tests.</p>; <p>Enlightening moments:</p> <blockquote> <p>“jagged profile” <span style=""color: #333333; font-family: Heuristica,'Times New Roman',Times,serif; font-size: 18px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;""><br /></span></p> </blockquote> <p>Nobody is about average. Examples from pilots, we know that sometimes mean or median is not the population because everyone is individualized and are jagged. Jagged profile reveals the fact that the normal people is representing nobody.</p> <blockquote> <p>“Because our science textbook assumes every kid is reading on grade level, we’re in trouble,” he said in the talk. “For her, science class is first and foremost a reading test, and it’s doubtful that we will ever see what she’s truly capable of.”</p> </blockquote> <p> </p> <p> </p> <blockquote>It turns out your personality and your learning varies across contexts.</blockquote> <p> Context is very important in most conditions. Our goal is to explore the full potentials of every student. Therefore, context should be the major consideration as opposed to generalization.</p>","","http://chronicle.com/blogs/wiredcampus/why-students-should-own-their-educational-data/54329","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"SZXC6NA3","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 16:02:47","2017-09-14 16:02:47","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"E9Q2WKSR","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 16:02:47","2017-09-14 16:02:47","2015-01-16 03:15:55","252–254","","","","","","Learning Analytics and Educational Data Mining","","","","","ACM","New York, NY, USA","","","","","","ACM Digital Library","","","<p>Difference &amp; Similarities</p> <p>EDM (Educational Data Mining)</p> <p>&amp; LAK (Learning Analytics and Knowledge)</p> <p> </p> <p>EDM  system/model-oriented; Simplizing the system and reaching a stage withou human intervention. <span style=""background-color: #33cccc;"">automated discovery.</span></p> <p>LAK   human based; understanding the whole system; <span style=""background-color: #33cccc;"">leveraging human judgement.</span></p> <p><span style=""background-color: #33cccc;"">**Form <br /></span></p>; <p>There is no right or wrong between LAK and EDM. It is the difference between the origins and method to realize the same goal-- better education for students.</p> <p> </p> <p>Although the focus and concentration is not the same, they are complimentary.</p>","","","","Collaboration; educational data mining; learning analytics and knowledge","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"IKXFB8T6","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 16:02:47","2017-09-14 16:02:47","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"LQDJBGQN","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 16:02:47","2017-09-14 16:02:47","2016-01-17 18:50:57","","","","","","","","","","","","","","","","","","","YouTube","","","<p>Learning is a magic!</p> <p>Learning is multi-dimentional, broad.q</p> <p>data --simplified learning process</p> <p>learning is context-based(social culture)</p> <p>measure of learning/competence</p> <p><span style=""color: #000000; background-color: #33cccc;"">proxy indicators</span></p> <p><span style=""color: #000000;"">students competent beforehand</span></p>","","","","Assessment; Education; educational assessment; EDUCAUSE; Higher Education; learners; Learning; Teaching and learning","","","","","","","","","","","","","","","","","","","","","470 seconds","","","","","","","","","","","","","","","","","","","","","","","","",""
"YHUQC4BI","webpage","2016","Weinersmith, Zach","Saturday Morning Breakfast Cereal","","","","","http://www.smbc-comics.com/index.php?id=3978","","2016-01-05","2017-09-14 16:02:48","2017-09-14 16:02:48","2016-01-18 18:17:09","","","","","","","","","","","","","","","","","","","","","","<p>Evaluation in learning?</p> <p>I read it twice and I found education is an interesting field to practice some goals or methods. Skills can be enhanced and put to place because skills are skills to itself. We can train workers to assemble a phone a thousand times in order that they are more skilled and faster doing the work. In contrast, education is not. We can to impress students and more <span style=""background-color: #ccffff;"">importantly</span> to <span style=""background-color: #ccffff;"">inspire</span> them. Reading fast is correlated with accumulated reading comprehension but merely training students to read fast clearly drive off-track. In the same tune, mere data or causal relationship do not work when detached from its context.</p> <p> </p> <p>Practitioners and researchers are, more often than not, work seperately, however knowledge should be overlapping. It creates a gap between the original aim and the real action.</p>","","http://www.smbc-comics.com/index.php?id=3978","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"NIHB8GRP","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 16:02:48","2017-09-14 16:02:48","2016-01-18 18:42:27","","","","","","","","","","","","","","","","","","","","","","<p><strong>IMPORTANT SYNTAX:</strong></p> <p><strong>Gather:</strong></p> <p>Gather(data = mydata</p> <p>            key= "" varikey""</p> <p>            value = ""varivalue"",</p> <p>            var1:var5/-var6)</p> <p>Transform a wide form into a long one. Put var1:var5 into a key--varikey, and the corresponding value appears under ""varivalue"".</p> <p><strong>Spread:</strong></p> <p><strong>Spread </strong>(data = mydata,</p> <p>            key= ""varikey"",</p> <p>           value= ""varivalue"" <strong>         )</strong></p> <p> Transform a long form into a wide form. Put the key into a long list and the values appearing in varivalue as the corresponding value below.</p> <p> </p> <p><strong>Separate:</strong></p> <p>Separate one variable into two or more.</p> <p>seperate (data, C(""A"",""B"",""C""), SEP = ""_"")</p> <p> </p> <p> %&gt;%</p> <p>works as a pipe and get inserted into the function as the first argument.  Streamline the working logic.</p> <p> </p> <p> </p>; <p>Links for more info:</p> <p><a href=""http://genomicsclass.github.io/book/pages/dplyr_tutorial.html"">http://genomicsclass.github.io/book/pages/dplyr_tutorial.html</a></p> <p><a href=""https://rpubs.com/justmarkham/dplyr-tutorial"">https://rpubs.com/justmarkham/dplyr-tutorial</a></p> <p><a href=""https://blog.rstudio.com/2016/06/27/dplyr-0-5-0/"">https://blog.rstudio.com/2016/06/27/dplyr-0-5-0/</a></p> <p> </p>","","http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HWVN84MZ","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 16:02:48","2017-09-14 16:02:48","","49–53","","","","","","","","","","","ACM","","","","","","","","","","<p><strong>Data Wranglers</strong></p> <p>Data Wrangler is a activator among database and teachers. Before the program, only few teachers make good use of database while data wranglers plays a role in between to deploy data and design special format relevant to each teacher, whereby teachers are getting familiar with the database and are able to get connected to the data (both quan and quali).</p> <p> </p> <blockquote> <p><span style=""background-color: #33cccc;"">Bottom-up, grounded approach</span> is necessary for sense-making.</p> </blockquote> <p>Schools are subject to colonization if there are always up-bottom approach, which is only based on experience. Bottom-up approach is triggered by needs-- students' behavior, students' needs.</p> <p>Sometimes there are paradoxes and grounded research are vital to further investigation.</p> <p>Analysis do not have to be statistics. Simple graph can also show hints and insights. (e.g. evaluation, enjoyment in studying)</p>; <p>Enlightening moments:</p> <div title=""Page 3""> <div> <div> <blockquote> <p>It is important to note that all the data are available to academics directly, via various dashboards and online facilities. The role of the Data Wrangler is not only to analyse the data, but to increase the familiarity of academics with the data sources, to build learning analytics capacity as part of a Community of Practice.</p> </blockquote> <p>Just like online courses and resources, they are not being made good use of. It is in the</p> <p> </p> <div title=""Page 6""> <div> <div> <blockquote> <p>This is as expected in a capacity- building exercise: the process must start from people’s existing expertise, and if capacity building is required, this expertise will of necessity be lacking.</p> </blockquote> </div> </div> </div> </div> </div> </div>","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Z23D4N6G","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 16:02:48","2017-09-14 16:02:48","2016-01-18 19:14:05","","","","","","","","","","","","","","","","","","","","","","","","https://theconversation.com/zuckerberg-is-ploughing-billions-into-personalised-learning-why-51940","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FBMWGK9D","videoRecording","2015","Georgia Tech","Feature Selection","","","","","https://www.youtube.com/watch?v=8CpRLplmdqE","","2015-02-23","2017-09-14 16:02:48","2017-09-14 16:02:48","2016-01-18 19:18:06","","","","","","","","","","","","Youtube","","","","","","","","","","","","https://www.youtube.com/watch?v=8CpRLplmdqE","","","","","","","","","","","","","","","Udacity","","","","","","","","3:13","","","","","","","","","","","","","","","","","","","","","","","","",""
"W2QQRF9Q","webpage","2014","Groelmund, Garrett","RStudio Cheat Sheets","RStudio","","","","https://www.rstudio.com/resources/cheatsheets/","","2014-08-01","2017-09-14 16:02:48","2017-09-14 16:02:48","2016-01-19 21:17:28","","","","","","","","","","","","","","","","","","","","","","<p>Markdown: opposite to Markup, used in R markdown to make the text more readable.</p> <p>heading: #</p> <p>code:  ```   ```</p> <p> </p>","","http://shiny.rstudio.com/articles/rm-cheatsheet.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"BWF49Q5P","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 16:02:49","2017-09-14 16:02:49","2016-09-03 18:55:41","42-57","","3","15","","Journal of Educational Technology & Society","Translating Learning into Numbers","","","","","","","","","","","","JSTOR","","","<p>Algorithm ---efficient but wait</p> <p>Learning Analytic researches, containing human learning process and various human-related elements, is an complex and intricate , which calls for patience and caution. It is destined to come with double sides. More data, more opportunities with more vulnerabilities.--privacy</p> <p>Therefore it is vital to prepare a well-rounded template for reference.</p> <p>Number should not be just numbers. Numeric data contains vast info but also is a simplified version, stripped of background and other qualitative data.</p> <p>Despite flaws, researches are still valuable in providing precious information. Technology does not stop wheeling on in spite of crowds of fear. So should education learning analytic.</p> <p> </p>; <p>Six dimensions of the proposed LA frame work:</p> <p><span style=""background-color: #33cccc;"">Stakeholders (data clients &amp; data subjectives)</span></p> <p> </p> <p> </p> <p><span style=""background-color: #33cccc;"">Objective (reflection)</span></p> <p> </p> <p> </p> <p><span style=""background-color: #33cccc;"">Data (protected database; relevant indicators; time scale)</span></p> <p> </p> <p> </p> <p><span style=""background-color: #33cccc;"">Instruments (Pedagogic theory; Technology; Presentation)</span></p> <p> </p> <p> </p> <p><span style=""background-color: #33cccc;"">External Limitations (Conventions: Privacy &amp; Ethics)</span></p> <p> </p> <p> </p> <p><span style=""background-color: #33cccc;"">Internal Limitations (Required competences: Interpretation &amp; Critical Thinking)</span></p>","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"MKC5VHUQ","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 16:02:49","2017-09-14 16:02:49","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)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"AZIL8YWE","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 16:02:49","2017-09-14 16:02:49","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4VB4MQ5D","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 16:02:49","2017-09-14 16:02:49","2016-01-18 20:17:24","","","","","","","","","","","","","","","","","","","","","","","","http://faculty.ucr.edu/~hanneman/nettext/C1_Social_Network_Data.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"Z3ASTW7A","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 16:02:49","2017-09-14 16:02:49","","","","","","","","","","","","","","Vancouver, BC, Canada","EN","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","",""
"FM6PS8LS","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 16:02:49","2017-09-14 16:02:49","","34-48","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","; <p>ITR: Item Response Theory</p> <p>modelling individual person-item interactions</p> <p>purpose: to describe the items by item parameters and the examinees by examinee parameters in such a way that we can predict probabilitstically the response of any examinee to any item, even if similar examinees have never taken similar items before.</p>","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","",""
"KN47IKCH","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 16:02:50","2017-09-14 16:02:50","","61-68","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","",""
"EWSXKF6I","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 16:02:50","2017-09-14 16:02:50","","49-57","","","","","","","","","","","Society for Learning Analytics Research","Vancouver, BC","EN","","","","","","","","<p>Privacy</p> <p>The debate surrounding privacy and surveillance of students information is heated. There is no agreement on to what extent should  privacy be protected. Personally speaking, I believe that more power, more responsibilities. Data is weapon, with which man with kindness can harness wild grass while man with selfishness can make a profit. I personally believe that data analyst should stick to these moral code. Endowed with power and access, restrictions and responsibilities come with the territory. However, moral is not a solid means to guard against fortune-seekers. But laws and contracts are.</p> <p>On the other hand, laws and contracts should not only set limits on the practitioners but also should set the line between the public and the practitioners so that data wranglers wanting to explore data and make good use data can ""freely"" apply what they have learned to schools and families rather than hesitate.</p> <p> </p>","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","",""
"HLQ99P36","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 16:02:50","2017-09-14 16:02:50","","69-76","","","","","","","","","","","Society for Learning Analytics Research","Vancouver, BC","EN","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YL3Q69MB","journalArticle","2011","Gelman, A; Niemi, J","Statistical graphics: making information clear – and beautiful","Significance","","","","","","2011-09","2017-09-14 16:02:50","2017-09-14 16:02:50","","134-136","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"YWQ2LQL6","journalArticle","1984","Wainer, H","How to display data badly","The American Statistician","","","","","","1984","2017-09-14 16:02:50","2017-09-14 16:02:50","","137-147","","2","38","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"M5HTLMJE","journalArticle","2012","Gelman, A; Unwin, A","Infovis and Statistical Graphics: Different Goals, Different Looks (with discussion)","","","","","","","2012","2017-09-14 16:02:50","2017-09-14 16:02:50","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"4Z3DUFLE","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 16:02:50","2017-09-14 16:02:50","","","","","","","","","","","","","","","","","Blog","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""