eezzee · eezzee · Sep 11, 2018 · Sep 11, 2018 · Sep 11, 2018 · Sep 11, 2018
diff --git a/Code of Conduct.pdf b/Code of Conduct.pdf
diff --git a/README.md b/README.md
@@ -1,11 +1,12 @@
 # Core Methods in Educational Data Mining: Syllabus 
 
-Introducation class
+Introduction class - Yay! Best class eva!
 
 * **Course:** [HUDK 4050, Teachers College, Columbia](http://www.columbia.edu/~rsb2162/EDM2015/index.html)
-* **Instructor:** Charles Lang [charles.lang@tc.columbia.edu](lang2@tc.columbia.edu), @learng00d
-* **Day/Time:** Tuesdays and Thursdays / 5:10pm - 6:50pm  
-* **Location:** GDH 363
+* **Instructor:** Charles Lang, [charles.lang@tc.columbia.edu](lang2@tc.columbia.edu), Twitter: @learng00d
+* **Course Assistants:** Anna Lizarov, [al38684@tc.columbia.edu](al3868@tc.columbia.edu), Aidi Bian, [ab4499@tc.columbia.edu](ab4499@tc.columbia.edu)
+* **Day/Time:** Tuesdays/Thursdays, 5:10pm - 6:50pm  
+* **Location:** TH 136
 * **Instructor Office Hours:** Thursdays, 3:00pm - 5:00pm in GDH 454 - **[Please make an appointment to attend office hours here](https://calendar.google.com/calendar/selfsched?sstoken=UUNxY1RIY01kNmJZfGRlZmF1bHR8M2U5ODgxZmNiOWQ0NDc2N2VmNWQ0NThiM2JmMGRmZmQ)** 
 **(If no appointments are available or you cannot attend those that are please send an email to charles.lang@tc.columbia.edu and CC amy@x.ai)**   
 
@@ -35,13 +36,13 @@ Tasks that need to be completed during the semester:
 Weekly:
  * Attend class
  * Weekly readings
+ * Notes on weekly readings
  * Complete Swirl course
- * Maintain documentation of work (Github, R Markdown, Zotero)
- * Ask or answer questions on Vectr (about an article)
+ * Maintain documentation of work (Github, R Markdown)
 
 One time only:
  * Ask one question on Stack Overflow
- * In person meeting with instructor
+ * Attend office hours once
  * 8 short assignments (including one group assignment)
  * Group presentation of group assignment, 3-5 students each
 
@@ -58,14 +59,14 @@ One time only:
 
 # <A NAME="unit1:">Unit 1: Introduction
 
-## Class 1 - Introduction (9/6/18)
+## Class 1 - Introduction (9/5/19)
 ### Learning Objectives
 
 * Be familiar with course philosophy, logic & structure
 * Install and be familiar with the software to be used in the course
 * Appreciate the importance of tightly defining educational goals
 
-## Class 2 - LA, EDM and the Learning Sciences (9/11/18)
+## Class 2 - LA, EDM and the Learning Sciences (9/10/18)
 
 ### Learning Objectives
 
@@ -74,28 +75,30 @@ One time only:
 ### Tasks to be completed:
 
 Read/watch:
-  * [Siemens, G. and Baker, R.S.J. d. 2012. Learning Analytics and Educational Data Mining: Towards Communication and Collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (New York, NY, USA, 2012), 252–254.](http://users.wpi.edu/~rsbaker/LAKs%20reformatting%20v2.pdf)
-  * [Educause 2015. Why Is Measuring Learning So Difficult?](http://er.educause.edu/multimedia/2015/8/why-is-measuring-learning-so-difficult-v)
+  * [Siemens, George. and Baker, Ryan S.J. d. 2012. Learning Analytics and Educational Data Mining: Towards Communication and Collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (New York, NY, USA, 2012), 252–254.](http://www.upenn.edu/learninganalytics/ryanbaker/LAKs%20reformatting%20v2.pdf)
+
+Read chapter 1-3:
+  * [Grolemund, Garrett. 2014. Hands-On Programming with R](https://d1b10bmlvqabco.cloudfront.net/attach/ighbo26t3ua52t/igp9099yy4v10/igz7vp4w5su9/OReilly_HandsOn_Programming_with_R_2014.pdf)
 
 #### Due: Assignment 1 - Set up
 
-## Class 3 - Data Sources (9/13/18)
+## Class 3 - Data Sources (9/12/19)
 
 * Be familiar with a range of data sources, formats and extraction processes
 * Be familiar with R & Github & markdown
 
 ### Tasks to be completed:
 
 Read:
-* [Bergner, Y. (2017). Measurement and its Uses in Learning Analytics. In C. Lang, G. Siemens, A. F. Wise, & D. Gaševic (Eds.), The Handbook of Learning Analytics (1st ed., pp. 34–48). Vamcouver, BC: Society for Learning Analytics Research.](http://solaresearch.org/hla-17/hla17-chapter1)
+* [Bergner, Yoav. (2017). Measurement and its Uses in Learning Analytics. In C. Lang, G. Siemens, A. F. Wise, & D. Gaševic (Eds.), The Handbook of Learning Analytics (1st ed., pp. 34–48). Vancouver, BC: Society for Learning Analytics Research.](http://solaresearch.org/hla-17/hla17-chapter1)
 * [The R Markdown Cheat sheet: 2014.](http://shiny.rstudio.com/articles/rm-cheatsheet.html)
 
 Swirl:
 * Unit 1 - Introduction
 
 # <A NAME="unit2">Unit 2: Data Sources & their Manipulation
 
-## Class 4 - Data Wrangling (9/18/18)
+## Class 4 - Data Wrangling (9/17/19)
 
 ### Learning Objectives:
 
@@ -104,10 +107,10 @@ Swirl:
 ### Tasks to be completed:
 
 Read:
-* [Prinsloo, P., & Slade, S. (2017). Ethics and Learning Analytics: Charting the (Un)Charted. In C. Lang, G. Siemens, A. F. Wise, & D. Gaševic (Eds.), The Handbook of Learning Analytics (1st ed., pp. 49–57). Vancouver, BC: Society for Learning Analytics Research.](https://solaresearch.org/hla-17/hla17-chapter4/)
-* [Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Journal of Educational Technology & Society, 15(3), 42–57.](https://www.jstor.org/stable/jeductechsoci.15.3.42?seq=1#page_scan_tab_contents)
+* [Prinsloo, Paul, & Slade, Sharon (2017). Ethics and Learning Analytics: Charting the (Un)Charted. In C. Lang, G. Siemens, A. F. Wise, & D. Gaševic (Eds.), The Handbook of Learning Analytics (1st ed., pp. 49–57). Vancouver, BC: Society for Learning Analytics Research.](https://solaresearch.org/hla-17/hla17-chapter4/)
+* [Greller, Wendy, & Drachsler, Hendrik. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Journal of Educational Technology & Society, 15(3), 42–57.](https://www.jstor.org/stable/jeductechsoci.15.3.42?seq=1#page_scan_tab_contents)
 
-## Class 5 - Data Wrangling (9/20/18)
+## Class 5 - Data Wrangling (9/19/19)
 
 ### Learning Objectives:
 
@@ -120,13 +123,13 @@ Read:
 
 * [Data Wrangling Cheatsheet: 2015.](http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf)
 
-## Class 6 - Data Wrangling (9/25/18)
+## Class 6 - Data Wrangling (9/24/19)
 
 Read:
-* [Clow, D. 2014. Data wranglers: human interpreters to help close the feedback loop. Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (2014), 49–53.](http://oro.open.ac.uk/40608/2/Clow-DataWranglers-final.pdf)
-* [Young, J.R. 2014. 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)
+* [Clow, Doug. 2014. Data wranglers: human interpreters to help close the feedback loop. Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (2014), 49–53.](http://oro.open.ac.uk/40608/2/Clow-DataWranglers-final.pdf)
+* [Young, Jeffrey R. 2014. 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)
 
-## Class 7 - Data Wrangling (9/27/18)
+## Class 7 - Data Wrangling (9/26/19)
 
 ### Learning Objectives:
 
@@ -137,31 +140,29 @@ Read:
 Read:
 * [Data Wrangling Cheatsheet: 2015.](http://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf)
 
+Watch:
+* [Getting Started with RMarkdown: 2016](https://youtu.be/MIlzQpXlJNk)
+
 Swirl:
 * Unit 2 - Data Sources & Manipulation  
 
 # <A NAME="unit3">Unit 3: Structure Discovery
 
-## Class 8 - Ed Pioneers Class Visit (10/2/18)
+## Class 8 - Teachley Class Visit (10/1/19)
 
-## Class 9 - Check-in Exam (10/4/18)
+## Class 9 - Start Social Networks (10/3/19)
 
-## Class 10 - Visualization (10/9/18)
+* [Network Analysis and Visualization with R and igraph: 2016](https://kateto.net/netscix2016.html)(Start at Section 3)
+* [iGraph Documentation](https://igraph.org/r/doc/)
+
+## Class 10 - Check-in Exam (10/8/19)
 
 ### Learning Objectives:
 
  * Understand the place of data visualization in the data analysis cycle
  * Be familiar with a range of data simulation commands
 
-### Tasks to be completed:
-
-Read:
-* [Klerkx, J., Verbert, K., & Duval, E. (2017). Learning Analytics Dashboards. In C. Lang, G. Siemens, A. F. Wise, & D. Gaševic (Eds.),The Handbook of Learning Analytics (1st ed., pp. 143–150). Vancouver, BC: Society for Learning Analytics Research.](https://solaresearch.org/hla-17/hla17-chapter12/)
-* [Gelman, A., & Niemi, J. (2011). Statistical graphics: making information clear – and beautiful, *Significance*, September, 134-136](http://www.stat.columbia.edu/~gelman/research/published/niemi.pdf)  
-* [Wainer, H. (1984). How to display data badly, *The American Statistician*, 38(2), 137-147](http://rci.rutgers.edu/%7Eroos/Courses/grstat502/wainer.pdf)  
-
-
-## Class 11 - Visualization (10/11/18)
+## Class 11 - Visualization (10/10/19)
 
 ### Learning Objectives:
 
@@ -171,7 +172,7 @@ Read:
 * [Gelman, A., & Unwin, A. (2012). Infovis and Statistical Graphics: Different Goals, Different Looks (with discussion)](http://www.stat.columbia.edu/~gelman/research/published/vis14.pdf)  
 * [Fung, K. (2014). Junkcharts Trifecta Checkup: The Definitive Guide](http://junkcharts.typepad.com/junk_charts/junk-charts-trifecta-checkup-the-definitive-guide.html)  
 
-## Class 12 - Networks (10/16/18)
+## Class 12 - Networks (10/15/19)
 
 ### Learning Objectives:
 
@@ -182,7 +183,7 @@ Read:
 Read:
 * [Grunspan, D. Z., Wiggins, B. L., & Goodreau, S. M. (2014). Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research. CBE-Life Sciences Education, 13(2), 167–178.](http://www.lifescied.org/content/13/2/167.full.pdf)
 
-## Class 13 - Networks (10/18/18)
+## Class 13 - Networks (10/17/19)
 
 ### Learning Objectives:
 
@@ -196,8 +197,7 @@ Read:
 
 #### Due: Assignment 2 - Social Network
 
-## Class 14 - Clustering (10/23/18)
-
+## Class 14 - Clustering (10/22/19)
 ### Learning Objectives:
 
  * Understand the basic principle and algorithm behind cluster analysis
@@ -207,9 +207,7 @@ Read:
 Read:
 * [Bowers, A.J. (2010) 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 (PARE), 15(7), 1-18.](http://pareonline.net/pdf/v15n7.pdf)
 
-
-## Class 15 - Clustering (10/25/18)
-
+## Class 15 - Clustering (10/24/19)
 
 ### Learning Objectives:
 
@@ -220,9 +218,7 @@ Read:
 Watch:
 * Chapter 7 in Baker, R. (2014). Big Data in Education: [video 1](https://youtu.be/mgXm3AwLxP8), [video 2](https://youtu.be/B9dvJYwBfmk)
 
-#### Due: Assignment 3 - Clustering
-
-## Class 16 - Principal Component Analysis (10/30/18)
+## Class 16 - Principal Component Analysis (10/29/19)
 
 ### Learning Objectives:
 
@@ -232,9 +228,10 @@ Watch:
 ### Tasks to be completed:
 
 Read:
+* [Visually Explained](http://setosa.io/ev/principal-component-analysis/)
 * [Konstan, J. A., Walker, J. D., Brooks, D. C., Brown, K., & Ekstrand, M. D. (2015). Teaching Recommender Systems at Large Scale: Evaluation and Lessons Learned from a Hybrid MOOC. ACM Trans. Comput.-Hum. Interact., 22(2), 10:1–10:23.](https://dl.acm.org/citation.cfm?id=2728171)
 
-## Class 17 - Principal Component Analysis (11/1/18)
+## Class 17 - Principal Component Analysis (10/31/19)
 
 ### Learning Objectives:
 
@@ -245,9 +242,7 @@ Read:
 Watch:
 * [Georgia Tech 2015. Feature Selection. Youtube.](https://www.youtube.com/watch?v=8CpRLplmdqE)
 
-##### Due: Assignment 4 - Principal Component Analysis
-
-## Class 18 - Domain Structure Discovery (11/6/18)
+## Class 18 - Domain Structure Discovery (11/5/19)
 
 ### Learning Objectives:
 
@@ -258,7 +253,9 @@ Watch:
 Read:
 * [Matsuda, N., Furukawa, T., Bier, N., & Faloutsos, C. (2015). Machine Beats Experts: Automatic Discovery of Skill Models for Data-Driven Online Course Refinement. International Educational Data Mining Society.](http://eric.ed.gov/?id=ED560513) 
 
-## Class 19 - Domain Structure Discovery (11/8/18)
+#### Due: Assignment 3 - Clustering
+
+## Class 19 - Domain Structure Discovery (11/7/19)
 
 ### Learning Objectives:
 
@@ -274,7 +271,9 @@ Swirl:
 
 # <A NAME="unit4">Unit 4: Prediction
 
-## Class 20 - Prediction (11/13/18)
+## Class 20 - Prediction (11/12/19)
+
+##### Due: Assignment 4 - Principal Component Analysis
 
 ### Learning Objectives:
 
@@ -284,9 +283,9 @@ Swirl:
 
 Read:
 * [Kucirkova, N. and FitzGerald, E. 2015. Zuckerberg is Ploughing Billions into “Personalised Learning” – Why? The Conversation.](https://theconversation.com/zuckerberg-is-ploughing-billions-into-personalised-learning-why-51940)
-* [Brooks, C., & Thompson, C. (2017). Predictive Modelling in Teaching and Learning. In The Handbookf of Learning Analytics (1st ed., pp. 61–68). Vancouver, BC: Society for Learning Analytics Research.](https://solaresearch.org/hla-17/hla17-chapter5/)
+* [Brooks, C., & Thompson, C. (2017). Predictive Modelling in Teaching and Learning. In The Handbook of Learning Analytics (1st ed., pp. 61–68). Vancouver, BC: Society for Learning Analytics Research.](https://solaresearch.org/hla-17/hla17-chapter5/)
 
-## Class 21 - Prediction (11/15/18)
+## Class 21 - Prediction (11/14/19)
 
 ### Learning Objectives:
 
@@ -297,9 +296,7 @@ Read:
 Watch:
 * Chapter 1 in Baker, R. (2014). Big Data in Education: [video 1](https://youtu.be/dc5Nx3tyR8g)
 
-#### Due: Assignment 5 - Prediction
-
-## Class 22 - Classification (11/20/18)
+## Class 22 - Classification (11/19/19)
 
 ### Learning Objectives:
 
@@ -311,8 +308,9 @@ Read:
 
 * [Liu, R., & Koedinger, K. (2017). Going Beyond Better Data Prediction to Create Explanatory Models of Educational Data. In The Handbook of Learning Analytics (1st ed., pp. 69–76). Vancouver, BC: Society for Learning Analytics Research.](https://solaresearch.org/hla-17/hla17-chapter6/)
 
+#### Due: Assignment 5 - Prediction
 
-## Class 23 - Classification (11/22/18)
+## Class 23 - Classification (11/21/19) - Thanksgiving No Class
 
 ### Learning Objectives:
 
@@ -323,7 +321,7 @@ Read:
 Watch:
 * Chapter 1 in Baker, R. (2014). Big Data in Education: [video 3](https://youtu.be/k9Z4ibzH-1s) & [video 4](https://youtu.be/8X0UlMShss4)
 
-## Class 24 - Diagnostic Metrics (11/27/18)
+## Class 24 - Diagnostic Metrics (11/26/19)
 
 ### Learning Objectives:
 
@@ -339,9 +337,9 @@ Watch:
 * Chapter 2 in Baker, R. (2014). Big Data in Education: [video 5](https://youtu.be/1P34cxpEdKA)
 * [Georgia Tech 2015. Cross Validation. Youtube.](https://youtu.be/sFO2ff-gTh0)
 
-#### Due: Assignment 6 - CART Models
+## Class 25 - Knowledge Tracing (11/28/19)
 
-## Class 25 - Knowledge Tracing (11/29/18)
+### Vectr Class Visit
 
 ### Learning Objectives:
 
@@ -356,7 +354,7 @@ Swirl:
 * Unit 4 - Prediction
 
 
-## Class 26 - Knowledge Tracing (12/4/18)
+## Class 26 - Knowledge Tracing (12/3/19)
 
 ### Learning Objectives:
 
@@ -367,19 +365,19 @@ Swirl:
 Watch:
 * Chapter 4 in Baker, R. (2014). Big Data in Education: [video 1](https://youtu.be/_7CtthPZJ70)
 
-##### Due: Assignment 7 - Diagnostic Metrics
+#### Due: Assignment 6 - CART Models
 
-## Class 27 - Work Session: Assignment 8, Group Project (12/6/18)
+## Class 27 - Work Session: Assignment 8, Group Project (12/5/19)
 
-## Class 28 - Work Session: Assignment 8, Group Project (12/11/18)
+## Class 28 - Work Session: Assignment 8, Group Project (12/10/19)
 
-#### Due: Assignment 8 - Quantified Student
+##### Due: Assignment 7 - Diagnostic Metrics
 
-## Class 29 - Rate video presentations (12/13/18)
+## Class 29 - Rate video presentations (12/12/19)
 
-## Class 30 - Rate video presentations (12/18/18)
+## Class 30 - Rate video presentations (12/17/19)
 
-## EVERYTHING DUE - 12/20/18
+## EVERYTHING DUE - 12/19/19
 
 ----------------------------------------------------