Class Two. LA and EDM towards communication and collaboration
It is always confusing for me to differentiate Learning Analytics Knowledge and educational data mining. To me, both of them are bridging the computer science, education and psychology together to solve the problems in education. I guess these problems can be better solved: how to asses learning, how to increase teaching and learning efficiency etc.
However, how to differentiate learning analytics and educational data mining? The reading provided me a better view: Education Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of types of data that come from educational settings, and using those methods to better understand students, the settings which they learn in, while the definition for Learning Analytics is: the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
They both shared the same goal of improving the quality of analysis of large-scale educational data, to support both basic research and practice in education, the EDM research leverages human judgement in many cases does so to provide labels for classification, while learning analytics uses automated discovery in order to inform humans who make final decisions. In general, LA emphasizes that data is used to empower instructors and learners, while EDM focuses on automated adaption by the computer with no human in the loop.
The clip also inspires me in thinking about why measuring learning is so difficult. Firstly, we have to define what “measure” means. Usually we measure things because of conventional symbols that show different levels of outcome, but when in the education field, different culture context, environments, standards etc. all these elements affect our way to decide the results, so that makes it harder. The second thing I noticed is that, measurement is really about the construction. This construction contains the curriculum, environment and everything that it might affects the learning results. So when we considerate measurement, we still have take the whole learning and teaching process into account, this also enhances the difficulty in measuring learning.
However, as the emerging of Learning Analytics and Educational Data Mining, I think there should be more solutions to measure learning. For example, big data helps collect students’ learning characteristics and also helps plot the learning paths of students. Learning is magic, I have confidence that Educational Data Mining and Learning Analytics together will promote to better measure learning.
“Learning analytics, as a new form of assessment instrument, have potential to support current educational practices, or to challenge them and reshape education”, as I quoted from the theory into practice part, I do agree. But how can it influence education and even reshape it? In the article, there are three things to think about: 1. Marginalize learners and educators through the transformation of education into a technocratic system; 2. Limit what we talk about as “learning” to what we can create analytics for; 3). exclude alternative ways of engaging in activities to the detriment of learners.
It is important to know six questions before using learning analytics, what exactly are we measuring, how to measure, who is the assessment for, why knowledge important to us, where does the assessment happen and when does the assessment and feedback occur. These six questions are essential for me to understand the what, why and how can researchers use learning analytics. In simple words, we do have to know clearly what is the measurement target, what are we measuring, who should take part into the measurement and how to measure, and then we can use the learning analytics well and also know better what is the future development of the learning analytics. Since the technology gets more developed than before, more information educators can get from data mining and learning analytics. It still needs people those who make decisions to see the results of learning analytics and then find solutions for better learning and better teaching. Learning analytics is a good tool for educators, without further integrating the information we get from data mining, learning analytics can merely show data rather than intriguers movements to make learning and teaching process a better thing.
","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" \ No newline at end of file From f501bf66021b81bdadfd5f39dd9f6e423faae594 Mon Sep 17 00:00:00 2001 From: Yigu Liang <31895938+jessiiiiicatsu@users.noreply.github.com> Date: Thu, 5 Oct 2017 22:32:30 -0400 Subject: [PATCH 4/8] Yigu_Liang_Class Three_Zotero Hi Charles this is Yigu's reading notes for class three --- Yigu_Liang_Class Three_Zotero.csv | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 Yigu_Liang_Class Three_Zotero.csv diff --git a/Yigu_Liang_Class Three_Zotero.csv b/Yigu_Liang_Class Three_Zotero.csv new file mode 100644 index 0000000..38e2f63 --- /dev/null +++ b/Yigu_Liang_Class Three_Zotero.csv @@ -0,0 +1,2 @@ +"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" +"V5ZGLEAW","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 16:45:20","2017-09-14 16:45:20","","34-48","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","“Learning analytics, as a new form of assessment instrument, have potential to support current educational practices, or to challenge them and reshape education”, as I quoted from the theory into practice part, I do agree. But how can it influence education and even reshape it? In the article, there are three things to think about: 1. Marginalize learners and educators through the transformation of education into a technocratic system; 2. Limit what we talk about as “learning” to what we can create analytics for; 3). exclude alternative ways of engaging in activities to the detriment of learners.
It is important to know six questions before using learning analytics, what exactly are we measuring, how to measure, who is the assessment for, why knowledge important to us, where does the assessment happen and when does the assessment and feedback occur. These six questions are essential for me to understand the what, why and how can researchers use learning analytics. In simple words, we do have to know clearly what is the measurement target, what are we measuring, who should take part into the measurement and how to measure, and then we can use the learning analytics well and also know better what is the future development of the learning analytics. Since the technology gets more developed than before, more information educators can get from data mining and learning analytics. It still needs people those who make decisions to see the results of learning analytics and then find solutions for better learning and better teaching. Learning analytics is a good tool for educators, without further integrating the information we get from data mining, learning analytics can merely show data rather than intriguers movements to make learning and teaching process a better thing.
","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" \ No newline at end of file From fd02bf87fbe69720557776c8bbfb20dbd39d519f Mon Sep 17 00:00:00 2001 From: Yigu Liang <31895938+jessiiiiicatsu@users.noreply.github.com> Date: Thu, 5 Oct 2017 22:34:53 -0400 Subject: [PATCH 5/8] Yigu's Class 3 Reading Notes Yigu's Class 3 Reading Notes --- HUDK2017YIGU.csv | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 HUDK2017YIGU.csv diff --git a/HUDK2017YIGU.csv b/HUDK2017YIGU.csv new file mode 100644 index 0000000..748cf31 --- /dev/null +++ b/HUDK2017YIGU.csv @@ -0,0 +1,27 @@ +"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" +"64BXFXSX","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 16:45:18","2017-09-14 16:45:18","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"IMWQIPSA","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 16:45:18","2017-09-14 16:45:18","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"JJ238FHI","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 16:45:18","2017-09-14 16:45:18","2014-08-23 21:32:22","","","","","","","","","","","","","","","","","","","","","","","","http://chronicle.com/blogs/wiredcampus/why-students-should-own-their-educational-data/54329","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"BDNJTGH7","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 16:45:18","2017-09-14 16:45:18","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"YGP22TPY","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 16:45:18","2017-09-14 16:45:18","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"PKPYNM2G","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 16:45:18","2017-09-14 16:45:18","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"RHSHU69V","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 16:45:18","2017-09-14 16:45:18","2016-01-17 18:50:57","","","","","","","","","","","","","","","","","","","YouTube","","","Class Two. LA and EDM towards communication and collaboration
It is always confusing for me to differentiate Learning Analytics Knowledge and educational data mining. To me, both of them are bridging the computer science, education and psychology together to solve the problems in education. I guess these problems can be better solved: how to asses learning, how to increase teaching and learning efficiency etc.
However, how to differentiate learning analytics and educational data mining? The reading provided me a better view: Education Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of types of data that come from educational settings, and using those methods to better understand students, the settings which they learn in, while the definition for Learning Analytics is: the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
They both shared the same goal of improving the quality of analysis of large-scale educational data, to support both basic research and practice in education, the EDM research leverages human judgement in many cases does so to provide labels for classification, while learning analytics uses automated discovery in order to inform humans who make final decisions. In general, LA emphasizes that data is used to empower instructors and learners, while EDM focuses on automated adaption by the computer with no human in the loop.
The clip also inspires me in thinking about why measuring learning is so difficult. Firstly, we have to define what “measure” means. Usually we measure things because of conventional symbols that show different levels of outcome, but when in the education field, different culture context, environments, standards etc. all these elements affect our way to decide the results, so that makes it harder. The second thing I noticed is that, measurement is really about the construction. This construction contains the curriculum, environment and everything that it might affects the learning results. So when we considerate measurement, we still have take the whole learning and teaching process into account, this also enhances the difficulty in measuring learning.
However, as the emerging of Learning Analytics and Educational Data Mining, I think there should be more solutions to measure learning. For example, big data helps collect students’ learning characteristics and also helps plot the learning paths of students. Learning is magic, I have confidence that Educational Data Mining and Learning Analytics together will promote to better measure learning.
“Learning analytics, as a new form of assessment instrument, have potential to support current educational practices, or to challenge them and reshape education”, as I quoted from the theory into practice part, I do agree. But how can it influence education and even reshape it? In the article, there are three things to think about: 1. Marginalize learners and educators through the transformation of education into a technocratic system; 2. Limit what we talk about as “learning” to what we can create analytics for; 3). exclude alternative ways of engaging in activities to the detriment of learners.
It is important to know six questions before using learning analytics, what exactly are we measuring, how to measure, who is the assessment for, why knowledge important to us, where does the assessment happen and when does the assessment and feedback occur. These six questions are essential for me to understand the what, why and how can researchers use learning analytics. In simple words, we do have to know clearly what is the measurement target, what are we measuring, who should take part into the measurement and how to measure, and then we can use the learning analytics well and also know better what is the future development of the learning analytics. Since the technology gets more developed than before, more information educators can get from data mining and learning analytics. It still needs people those who make decisions to see the results of learning analytics and then find solutions for better learning and better teaching. Learning analytics is a good tool for educators, without further integrating the information we get from data mining, learning analytics can merely show data rather than intriguers movements to make learning and teaching process a better thing.
","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"HHPFXXS5","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 16:45:20","2017-09-14 16:45:20","","61-68","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"TLSIT2D9","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 16:45:20","2017-09-14 16:45:20","","49-57","","","","","","","","","","","Society for Learning Analytics Research","Vancouver, BC","EN","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"598KL5W2","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 16:45:20","2017-09-14 16:45:20","","69-76","","","","","","","","","","","Society for Learning Analytics Research","Vancouver, BC","EN","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"HCLXQYF8","journalArticle","2011","Gelman, A; Niemi, J","Statistical graphics: making information clear – and beautiful","Significance","","","","","","2011-09","2017-09-14 16:45:20","2017-09-14 16:45:20","","134-136","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"9EJJVUQH","journalArticle","1984","Wainer, H","How to display data badly","The American Statistician","","","","","","1984","2017-09-14 16:45:20","2017-09-14 16:45:20","","137-147","","2","38","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"I6N34JU4","journalArticle","2012","Gelman, A; Unwin, A","Infovis and Statistical Graphics: Different Goals, Different Looks (with discussion)","","","","","","","2012","2017-09-14 16:45:20","2017-09-14 16:45:20","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"H4P2D8KL","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 16:45:21","2017-09-14 16:45:21","","","","","","","","","","","","","","","","","Blog","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" \ No newline at end of file From 0dbf318a3e17a674be66d3cf1278b9322d80e11f Mon Sep 17 00:00:00 2001 From: Yigu Liang <31895938+jessiiiiicatsu@users.noreply.github.com> Date: Thu, 5 Oct 2017 23:57:26 -0400 Subject: [PATCH 6/8] Yigu's Class Notes (Till Class8) Yigu's Class Notes (Till Class8) --- Yigu_Liang_Notes to Class8.csv | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 Yigu_Liang_Notes to Class8.csv diff --git a/Yigu_Liang_Notes to Class8.csv b/Yigu_Liang_Notes to Class8.csv new file mode 100644 index 0000000..27a7772 --- /dev/null +++ b/Yigu_Liang_Notes to Class8.csv @@ -0,0 +1,27 @@ +"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" +"64BXFXSX","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 16:45:18","2017-09-14 16:45:18","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"IMWQIPSA","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 16:45:18","2017-09-14 16:45:18","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"JJ238FHI","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 16:45:18","2017-09-14 16:45:18","2014-08-23 21:32:22","","","","","","","","","","","","","","","","","","","","","","","","http://chronicle.com/blogs/wiredcampus/why-students-should-own-their-educational-data/54329","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"BDNJTGH7","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 16:45:18","2017-09-14 16:45:18","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"YGP22TPY","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 16:45:18","2017-09-14 16:45:18","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"PKPYNM2G","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 16:45:18","2017-09-14 16:45:18","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"RHSHU69V","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 16:45:18","2017-09-14 16:45:18","2016-01-17 18:50:57","","","","","","","","","","","","","","","","","","","YouTube","","","Class Two. LA and EDM towards communication and collaboration
It is always confusing for me to differentiate Learning Analytics Knowledge and educational data mining. To me, both of them are bridging the computer science, education and psychology together to solve the problems in education. I guess these problems can be better solved: how to asses learning, how to increase teaching and learning efficiency etc.
However, how to differentiate learning analytics and educational data mining? The reading provided me a better view: Education Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of types of data that come from educational settings, and using those methods to better understand students, the settings which they learn in, while the definition for Learning Analytics is: the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
They both shared the same goal of improving the quality of analysis of large-scale educational data, to support both basic research and practice in education, the EDM research leverages human judgement in many cases does so to provide labels for classification, while learning analytics uses automated discovery in order to inform humans who make final decisions. In general, LA emphasizes that data is used to empower instructors and learners, while EDM focuses on automated adaption by the computer with no human in the loop.
The clip also inspires me in thinking about why measuring learning is so difficult. Firstly, we have to define what “measure” means. Usually we measure things because of conventional symbols that show different levels of outcome, but when in the education field, different culture context, environments, standards etc. all these elements affect our way to decide the results, so that makes it harder. The second thing I noticed is that, measurement is really about the construction. This construction contains the curriculum, environment and everything that it might affects the learning results. So when we considerate measurement, we still have take the whole learning and teaching process into account, this also enhances the difficulty in measuring learning.
However, as the emerging of Learning Analytics and Educational Data Mining, I think there should be more solutions to measure learning. For example, big data helps collect students’ learning characteristics and also helps plot the learning paths of students. Learning is magic, I have confidence that Educational Data Mining and Learning Analytics together will promote to better measure learning.
Reading Data Wrangling cheat sheet provides me better view of what is it and how can I use it. When I saw this sheet for the first time, I still feel a little bit lost like what should I memorize and when should I use it.
It is interesting that when I learned HTML, my professor told us we did not have to memorize the code, however, we have to memorize the hierarchy of the code. Every time as I saw new languages, I was trying to find the relationship between it and languages I learned before. For example, the tidy data reminds me of tidy in HTML, it makes the code look cleaner.
I guess the best way to remember it is to use it again and again. The more practice will entitle me more proficiency.
","","http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"BZ4TWMGD","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 16:45:19","2017-09-14 16:45:19","","49–53","","","","","","","","","","","ACM","","","","","","","","","","This is a hard piece for me. Terms such as CAI, CMI and collaborative learning etc. are so strange to me. However, after reading I do have more understandin on the data wrangler. The Data Wranglers are a group of academics who analyze data about student learning and prepare reports with actionalble recommendations based on data. This role is to translate the theory described above into practice: to act as human sense-makers, facilitating action on feedback from learners, making better sense of what that feedback means and how the data can be improved. I guess Data Wranglers really helps to improve to better use learning analytics.
I do like the examples listed in the article, they really helps me better understand how does Data Wrangler work and how can it reflect students’ information. But I guess it is cost a lot to better use Data Wrangler. If it is nobody’s job to make sense of the data, the risk is that the data do not make sense but nobody realizes. After all, it is for educators and researchers’ better use. It is indeed to have necessary for people to analyze and then to decide, then it makes sense.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"DXZWD627","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 16:45:19","2017-09-14 16:45:19","2016-01-18 19:14:05","","","","","","","","","","","","","","","","","","","","","","","","https://theconversation.com/zuckerberg-is-ploughing-billions-into-personalised-learning-why-51940","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"TVH4294I","videoRecording","2015","Georgia Tech","Feature Selection","","","","","https://www.youtube.com/watch?v=8CpRLplmdqE","","2015-02-23","2017-09-14 16:45:19","2017-09-14 16:45:19","2016-01-18 19:18:06","","","","","","","","","","","","Youtube","","","","","","","","","","","","https://www.youtube.com/watch?v=8CpRLplmdqE","","","","","","","","","","","","","","","Udacity","","","","","","","","3:13","","","","","","","","","","","","","","","","","","","","","","","","","" +"4DLXXS3E","webpage","2014","Groelmund, Garrett","RStudio Cheat Sheets","RStudio","","","","https://www.rstudio.com/resources/cheatsheets/","","2014-08-01","2017-09-14 16:45:19","2017-09-14 16:45:19","2016-01-19 21:17:28","","","","","","","","","","","","","","","","","","","","","","","","http://shiny.rstudio.com/articles/rm-cheatsheet.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"M4L298G4","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 16:45:19","2017-09-14 16:45:19","2016-09-03 18:55:41","42-57","","3","15","","Journal of Educational Technology & Society","Translating Learning into Numbers","","","","","","","","","","","","JSTOR","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"TXZBMGUN","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 16:45:19","2017-09-14 16:45:19","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)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"PBW7KTUD","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 16:45:20","2017-09-14 16:45:20","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"CL8PG975","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 16:45:20","2017-09-14 16:45:20","2016-01-18 20:17:24","","","","","","","","","","","","","","","","","","","","","","","","http://faculty.ucr.edu/~hanneman/nettext/C1_Social_Network_Data.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"GG9YRR6F","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 16:45:20","2017-09-14 16:45:20","","","","","","","","","","","","","","Vancouver, BC, Canada","EN","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"V5ZGLEAW","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 16:45:20","2017-09-14 16:45:20","","34-48","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","“Learning analytics, as a new form of assessment instrument, have potential to support current educational practices, or to challenge them and reshape education”, as I quoted from the theory into practice part, I do agree. But how can it influence education and even reshape it? In the article, there are three things to think about: 1. Marginalize learners and educators through the transformation of education into a technocratic system; 2. Limit what we talk about as “learning” to what we can create analytics for; 3). exclude alternative ways of engaging in activities to the detriment of learners.
It is important to know six questions before using learning analytics, what exactly are we measuring, how to measure, who is the assessment for, why knowledge important to us, where does the assessment happen and when does the assessment and feedback occur. These six questions are essential for me to understand the what, why and how can researchers use learning analytics. In simple words, we do have to know clearly what is the measurement target, what are we measuring, who should take part into the measurement and how to measure, and then we can use the learning analytics well and also know better what is the future development of the learning analytics. Since the technology gets more developed than before, more information educators can get from data mining and learning analytics. It still needs people those who make decisions to see the results of learning analytics and then find solutions for better learning and better teaching. Learning analytics is a good tool for educators, without further integrating the information we get from data mining, learning analytics can merely show data rather than intriguers movements to make learning and teaching process a better thing.
","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"HHPFXXS5","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 16:45:20","2017-09-14 16:45:20","","61-68","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"TLSIT2D9","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 16:45:20","2017-09-14 16:45:20","","49-57","","","","","","","","","","","Society for Learning Analytics Research","Vancouver, BC","EN","","","","","","","","It is true that learning analytics is an emerging technology. However, without certain ethics and regulations, technology can also do harm to people because of the risks.
The first risk should be the protection of the data. As long as data expands, students’ data could be hacked. The development of the regulation by the government is has not kept pace, policies were not clear stated enough, which will all caused problems.
There are three principles which are easier to understand: 1. Learning analytics as moral practice that focusing not only on what is effective but on what is appropriate and morally necessary; 2. Students as agents to be engaged as collaborators and not as mere recipients of interventions and services; 3. Student identity and performance as temporal dynamic constructs-recognizing that analytics provides a snapshot view of a learner at a particular time and context;
However, I did not understand the one: student access as a complex, multidimensional phenomenon. Does this mean, students have to be given more choice of access to data?
In the future considerations part, I do agree that there should be increasing concern balancing optimism around AI, machine learning and big data. Yes, newest technology could bring much convenience for us, deeper data mining could provide educators more information. However, there could also be other problems. It is important to use the ethics framework as a tool to avoid the potential harm by these new technologies, and moreover, utilize them to protect and analyze data better.
","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"598KL5W2","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 16:45:20","2017-09-14 16:45:20","","69-76","","","","","","","","","","","Society for Learning Analytics Research","Vancouver, BC","EN","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"HCLXQYF8","journalArticle","2011","Gelman, A; Niemi, J","Statistical graphics: making information clear – and beautiful","Significance","","","","","","2011-09","2017-09-14 16:45:20","2017-09-14 16:45:20","","134-136","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"9EJJVUQH","journalArticle","1984","Wainer, H","How to display data badly","The American Statistician","","","","","","1984","2017-09-14 16:45:20","2017-09-14 16:45:20","","137-147","","2","38","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"I6N34JU4","journalArticle","2012","Gelman, A; Unwin, A","Infovis and Statistical Graphics: Different Goals, Different Looks (with discussion)","","","","","","","2012","2017-09-14 16:45:20","2017-09-14 16:45:20","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"H4P2D8KL","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 16:45:21","2017-09-14 16:45:21","","","","","","","","","","","","","","","","","Blog","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" \ No newline at end of file From 2fc60ddb62bb3d42651f23556fa763b2ea7662ba Mon Sep 17 00:00:00 2001 From: Yigu Liang <31895938+jessiiiiicatsu@users.noreply.github.com> Date: Fri, 22 Dec 2017 09:49:49 -0500 Subject: [PATCH 7/8] Yigu Liang Yigu's All Semester Notes --- Yigu_Liang_zotero.csv | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/Yigu_Liang_zotero.csv b/Yigu_Liang_zotero.csv index 29046b2..4c51e76 100644 --- a/Yigu_Liang_zotero.csv +++ b/Yigu_Liang_zotero.csv @@ -1 +1,25 @@ -"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" \ No newline at end of file +"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" +"64BXFXSX","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 16:45:18","2017-09-14 16:45:18","2014-09-24 19:31:29","","","7","15","","","Analyzing the Longitudinal K-12 Grading Histories of Entire Cohorts of Students","","","","","","","en","","","","","ERIC","","","It is amazing to see how to use data well for education. This article elaborates what are teacher-assigned grades as useful data in schools, how to visualize data, what method should be taken, how to do clustergrams and etc. The figure of hierarchical cluster analysis is amazing, but my only question would be that what data should we input to get these results? If situations are different (for example, we are researching for another topic), what necessary data or information should we input? How to choose?
","","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"IMWQIPSA","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 16:45:18","2017-09-14 16:45:18","2014-08-20 20:21:46","167-178","","2","13","","CBE Life Sci Educ","Understanding Classrooms through Social Network Analysis","","","","","","","en","","","","","www.lifescied.org","","","It is true that social relationships are a important that we need to research. The SNA provides the necessary tool kit, along with the methods of data mining, I do think that using SNA in classroom environments will allow we proceed more information. However, my question is that what kind of analysis can be proved that is validated and reliable?
","","http://www.lifescied.org/content/13/2/167","Week 2","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"BDNJTGH7","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 16:45:18","2017-09-14 16:45:18","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","","","I tried so many times but cannot view this article from the website.
","","","","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"YGP22TPY","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 16:45:18","2017-09-14 16:45:18","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","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"PKPYNM2G","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 16:45:18","2017-09-14 16:45:18","2015-12-15 18:26:39","","","","","","","","","","","","O'Reily Media","Sebastopol, CA","","","","","","","","","This book really helps me to know better about machine learning models' key concepts, from the machine learning workflow, different testing mechanisms, evaluation metrics, and terms explanation such as hyperparameters. The A/B test is also interesting.
Evaluating Machine Learning Models
","","http://www.oreilly.com/data/free/evaluating-machine-learning-models.csp?intcmp=il-data-free-lp-lgen_free_reports_page","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"RHSHU69V","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 16:45:18","2017-09-14 16:45:18","2016-01-17 18:50:57","","","","","","","","","","","","","","","","","","","YouTube","","","Class Two. LA and EDM towards communication and collaboration
It is always confusing for me to differentiate Learning Analytics Knowledge and educational data mining. To me, both of them are bridging the computer science, education and psychology together to solve the problems in education. I guess these problems can be better solved: how to asses learning, how to increase teaching and learning efficiency etc.
However, how to differentiate learning analytics and educational data mining? The reading provided me a better view: Education Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of types of data that come from educational settings, and using those methods to better understand students, the settings which they learn in, while the definition for Learning Analytics is: the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.
They both shared the same goal of improving the quality of analysis of large-scale educational data, to support both basic research and practice in education, the EDM research leverages human judgement in many cases does so to provide labels for classification, while learning analytics uses automated discovery in order to inform humans who make final decisions. In general, LA emphasizes that data is used to empower instructors and learners, while EDM focuses on automated adaption by the computer with no human in the loop.
The clip also inspires me in thinking about why measuring learning is so difficult. Firstly, we have to define what “measure” means. Usually we measure things because of conventional symbols that show different levels of outcome, but when in the education field, different culture context, environments, standards etc. all these elements affect our way to decide the results, so that makes it harder. The second thing I noticed is that, measurement is really about the construction. This construction contains the curriculum, environment and everything that it might affects the learning results. So when we considerate measurement, we still have take the whole learning and teaching process into account, this also enhances the difficulty in measuring learning.
However, as the emerging of Learning Analytics and Educational Data Mining, I think there should be more solutions to measure learning. For example, big data helps collect students’ learning characteristics and also helps plot the learning paths of students. Learning is magic, I have confidence that Educational Data Mining and Learning Analytics together will promote to better measure learning.
Reading Data Wrangling cheat sheet provides me better view of what is it and how can I use it. When I saw this sheet for the first time, I still feel a little bit lost like what should I memorize and when should I use it.
It is interesting that when I learned HTML, my professor told us we did not have to memorize the code, however, we have to memorize the hierarchy of the code. Every time as I saw new languages, I was trying to find the relationship between it and languages I learned before. For example, the tidy data reminds me of tidy in HTML, it makes the code look cleaner.
I guess the best way to remember it is to use it again and again. The more practice will entitle me more proficiency.
","","http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"BZ4TWMGD","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 16:45:19","2017-09-14 16:45:19","","49–53","","","","","","","","","","","ACM","","","","","","","","","","This is a hard piece for me. Terms such as CAI, CMI and collaborative learning etc. are so strange to me. However, after reading I do have more understandin on the data wrangler. The Data Wranglers are a group of academics who analyze data about student learning and prepare reports with actionalble recommendations based on data. This role is to translate the theory described above into practice: to act as human sense-makers, facilitating action on feedback from learners, making better sense of what that feedback means and how the data can be improved. I guess Data Wranglers really helps to improve to better use learning analytics.
I do like the examples listed in the article, they really helps me better understand how does Data Wrangler work and how can it reflect students’ information. But I guess it is cost a lot to better use Data Wrangler. If it is nobody’s job to make sense of the data, the risk is that the data do not make sense but nobody realizes. After all, it is for educators and researchers’ better use. It is indeed to have necessary for people to analyze and then to decide, then it makes sense.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"DXZWD627","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 16:45:19","2017-09-14 16:45:19","2016-01-18 19:14:05","","","","","","","","","","","","","","","","","","","","","","In Zuckerberg's definition, personalised learning means teachers ""working with students to customise instruction to meet the student's individual needs and interests."" I do agree with the dangers brought up by the author, that education has always been acquiring knowledge and skills relevant to a profession, but also the general knowledge, and there is no one learning way suitable for everyone, and students' preferences are not always fixed. And also, there will be a risk that students' data be misused. I think these problems could be solved in some day, but it just takes longer time and we have to have better technology.
","","https://theconversation.com/zuckerberg-is-ploughing-billions-into-personalised-learning-why-51940","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"TVH4294I","videoRecording","2015","Georgia Tech","Feature Selection","","","","","https://www.youtube.com/watch?v=8CpRLplmdqE","","2015-02-23","2017-09-14 16:45:19","2017-09-14 16:45:19","2016-01-18 19:18:06","","","","","","","","","","","","Youtube","","","","","","","","","","I always think that Machine Learning is so complicated. This video introduces key thoughts of it: interpret ability (in sight). There are so many features we are cared about, we have to decide most important parts from all these tons of features. The curse of dimensionality is that amount of data we made have n squire to two.
","","https://www.youtube.com/watch?v=8CpRLplmdqE","","","","","","","","","","","","","","","Udacity","","","","","","","","3:13","","","","","","","","","","","","","","","","","","","","","","","","","" +"4DLXXS3E","webpage","2014","Groelmund, Garrett","RStudio Cheat Sheets","RStudio","","","","https://www.rstudio.com/resources/cheatsheets/","","2014-08-01","2017-09-14 16:45:19","2017-09-14 16:45:19","2016-01-19 21:17:28","","","","","","","","","","","","","","","","","","","","","","R is so powerful. With R I have no doubt that we can process larger data set, but one thing is important that what is our train of thought look like, in other words, how does the structure of analyzing data be built? It is important to have a holistic view with clear objects, and then choosing effective instruments to get results. The cheat sheet is powerful that if someone has already known that what data he wants to get finally, what outcomes he would like to have.
","","http://shiny.rstudio.com/articles/rm-cheatsheet.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"M4L298G4","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 16:45:19","2017-09-14 16:45:19","2016-09-03 18:55:41","42-57","","3","15","","Journal of Educational Technology & Society","Translating Learning into Numbers","","","","","","","","","","","","JSTOR","","","I always think that learning is hard to translating. How to translate different learning stages into numbers? What algorithms should we use? This article answered me this question, that what analysis and what from deciding the dimensions, proposing design framework for learning analytics and choosing correct instruments.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"TXZBMGUN","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 16:45:19","2017-09-14 16:45:19","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)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"PBW7KTUD","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 16:45:20","2017-09-14 16:45:20","2016-09-03 20:48:57","","","","","","","Machine Beats Experts","","","","","International Educational Data Mining Society","","en","","","","","ERIC","","","I like the title that Machine beats experts, which remind me of AlphaGo. Yes, machine which trained by human beings can beat experts with enough data support. It is also true in the field of education, that data would support many actions。 Designing MOOCs, it is crucial to decide what skills should be learned and leveled them through its difficulty and importance. With the methods and formulas provided in this article, I do learn a lot.
","","http://eric.ed.gov/?id=ED560513","","data; Automation; Comparative Analysis; Correlation; Formative Evaluation; models; Online Courses; Skills","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"CL8PG975","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 16:45:20","2017-09-14 16:45:20","2016-01-18 20:17:24","","","","","","","","","","","","","","","","","","","","","","There is not anything about social network data that is all that unusual, and the data sets that social network analysts develop usually end up looking different from the conventional rectangular data away. How to apply statistics? That is one major question we do have to think about.
","","http://faculty.ucr.edu/~hanneman/nettext/C1_Social_Network_Data.html","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"GG9YRR6F","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 16:45:20","2017-09-14 16:45:20","","","","","","","","","","","","","","Vancouver, BC, Canada","EN","","","","","","","","One thing I am very interested is that how to distinguish learning analytics and educational data mining. I view them as that LA focuses more on the learning period while education data mining has a broader scope。
","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"V5ZGLEAW","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 16:45:20","2017-09-14 16:45:20","","34-48","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","“Learning analytics, as a new form of assessment instrument, have potential to support current educational practices, or to challenge them and reshape education”, as I quoted from the theory into practice part, I do agree. But how can it influence education and even reshape it? In the article, there are three things to think about: 1. Marginalize learners and educators through the transformation of education into a technocratic system; 2. Limit what we talk about as “learning” to what we can create analytics for; 3). exclude alternative ways of engaging in activities to the detriment of learners.
It is important to know six questions before using learning analytics, what exactly are we measuring, how to measure, who is the assessment for, why knowledge important to us, where does the assessment happen and when does the assessment and feedback occur. These six questions are essential for me to understand the what, why and how can researchers use learning analytics. In simple words, we do have to know clearly what is the measurement target, what are we measuring, who should take part into the measurement and how to measure, and then we can use the learning analytics well and also know better what is the future development of the learning analytics. Since the technology gets more developed than before, more information educators can get from data mining and learning analytics. It still needs people those who make decisions to see the results of learning analytics and then find solutions for better learning and better teaching. Learning analytics is a good tool for educators, without further integrating the information we get from data mining, learning analytics can merely show data rather than intriguers movements to make learning and teaching process a better thing.
","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"HHPFXXS5","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 16:45:20","2017-09-14 16:45:20","","61-68","","","","","","","","","","","Society for Learning Analytics Research (SoLAR)","Alberta, Canada","","","","","","","","","From this article, I learned that what are the process, practice and challenges of using predictive modelling in both learning and teaching. For researchers, there are still a lot of considerations they have to think about when decide to use predictive modelling. For example, what are we exactly measuring? Deciding what are we measuring, how are we measuring and then deciding what is our audience and who are we measuring for and then customized for them, and these are the process of thinking when measuring.
","","","","","Lang, Charles; Siemens, George; Wise, Alyssa Friend; Gaševic, Dragan","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"TLSIT2D9","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 16:45:20","2017-09-14 16:45:20","","49-57","","","","","","","","","","","Society for Learning Analytics Research","Vancouver, BC","EN","","","","","","","","It is true that learning analytics is an emerging technology. However, without certain ethics and regulations, technology can also do harm to people because of the risks.
The first risk should be the protection of the data. As long as data expands, students’ data could be hacked. The development of the regulation by the government is has not kept pace, policies were not clear stated enough, which will all caused problems.
There are three principles which are easier to understand: 1. Learning analytics as moral practice that focusing not only on what is effective but on what is appropriate and morally necessary; 2. Students as agents to be engaged as collaborators and not as mere recipients of interventions and services; 3. Student identity and performance as temporal dynamic constructs-recognizing that analytics provides a snapshot view of a learner at a particular time and context;
However, I did not understand the one: student access as a complex, multidimensional phenomenon. Does this mean, students have to be given more choice of access to data?
In the future considerations part, I do agree that there should be increasing concern balancing optimism around AI, machine learning and big data. Yes, newest technology could bring much convenience for us, deeper data mining could provide educators more information. However, there could also be other problems. It is important to use the ethics framework as a tool to avoid the potential harm by these new technologies, and moreover, utilize them to protect and analyze data better.
","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"598KL5W2","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 16:45:20","2017-09-14 16:45:20","","69-76","","","","","","","","","","","Society for Learning Analytics Research","Vancouver, BC","EN","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","1","","","","","","","","","","","","","","","","","","","","","","","","","","" +"HCLXQYF8","journalArticle","2011","Gelman, A; Niemi, J","Statistical graphics: making information clear – and beautiful","Significance","","","","","","2011-09","2017-09-14 16:45:20","2017-09-14 16:45:20","","134-136","","","","","","","","","","","","","","","","","","","","","It is interesting to read this article. I know that numbers and statistics just can make information more clear, but before reading this article, I was questioning, how? From this article, through the Zimbabwe vaccination coverage example, I see how does numbers get concluded in different levels. Data analysis is powerful that help we know better what does one phenomenon means. But I am questioning, how should we start our analysis when we just begin to process? How to decide what information is important that we should analyze first and then others?
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"9EJJVUQH","journalArticle","1984","Wainer, H","How to display data badly","The American Statistician","","","","","","1984","2017-09-14 16:45:20","2017-09-14 16:45:20","","137-147","","2","38","","","","","","","","","","","","","","","","","","What are good data and what are bad? I think the good and bad depends on how does the data present. Across the vast majority of educational data mining research, models are always evaluated based on the predictive accuracy. However, I am also curious about that how to build an explanatory models?
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"I6N34JU4","journalArticle","2012","Gelman, A; Unwin, A","Infovis and Statistical Graphics: Different Goals, Different Looks (with discussion)","","","","","","","2012","2017-09-14 16:45:20","2017-09-14 16:45:20","","","","","","","","","","","","","","","","","","","","","","","This is a hard reading for me. Though the examples do help me to understand, I still have so many questions and confusions.
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" +"H4P2D8KL","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 16:45:21","2017-09-14 16:45:21","","","","","","","","","","","","","","","","","Blog","","","","","","It is interesting that there are so many types in the trifecta charts. I still feel a little bit confuse between them. For example, what is the difference between Type QV and Type QD?
","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","" \ No newline at end of file From 74e3de6520e6bb87cc0d68037a83541351317b24 Mon Sep 17 00:00:00 2001 From: Yigu Liang <31895938+jessiiiiicatsu@users.noreply.github.com> Date: Fri, 22 Dec 2017 10:03:14 -0500 Subject: [PATCH 8/8] yigu final yigu final