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Instructor: Charles Lang, [email protected], @learng00d
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Day/Time: Tuesdays/Thursdays, 5:10pm - 6:50pm
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Location: GDH 285
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Instructor Office Hours: Thursdays, 3:00pm - 5:00pm in GDH 454 - Please make an appointment to attend office hours here (If no appointments are available or you cannot attend those that are please send an email to [email protected] and CC [email protected])
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Prerequisite: HUDM 5122 or HUDM 5126 or approved statistics/computer science data mining course.as you said
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Credits: 3
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Required Technology: Laptop with RStudio installed, Phone with the Sensor Kinetics Pro app installed
The Internet and mobile computing are changing our relationship to data. Data can be collected from more people, across longer periods of time, and a greater number of variables, at a lower cost and with less effort than ever before. This has brought opportunities and challenges to many domains, but the full impact on education is only beginning to be felt. Core Methods in Educational Data Mining provides an overview of the use of new data sources in education with the aim of developing students’ ability to perform analyses and critically evaluate their application in this emerging field. It covers methods and technologies associated with Data Science, Educational Data Mining and Learning Analytics, as well as discusses the opportunities for education that these methods present and the problems that they may create.
The overarching goal of this course is for students to acquire the knowledge and skills to be intelligent producers and consumers of data mining in education. By the end of the course students should:
- Systematically develop a line of inquiry utilizing data to make an argument about learning
- Be able to evaluate the implications of data science for educational research, policy, and practice
This necessarily means that students become comfortable with the educational applications of three domain areas: computer science, statistics and the context surrounding data use. There is no expectation for students to become experts in any one of these areas but rather the course will aim to: enhance student competency in identifying issues at the level of data acquisition, data analysis and application of analyses to the educational enterprise.
In EDCT-GE 2550 students will be attempting several data science projects, however, unlike most courses, students will not be asssessed based on how successful they are in completing these projects. Rather students will be assessed on two key components that will contribute to their future sucess in the field: contribution and organization. Contribution reflects the extent to which students participate in the course, whether or not students complete assignments and quizzes, attend class, etc. Organization reflects how well students document their process and maintain data and software resources. For example, maintaining a well organized bibliographic library with notes, maintaining a well organized Github account and maintaining organized data sets that are labelled appropriately. To do well in HUDM 4050 requires that students finish the course with the resources to sucessfully use data science in education in the future. Do the work and stay organized and all will be well!
Tasks that need to be completed during the semester:
Weekly:
- Attend class
- Weekly readings
- Compete Swirl course
- Maintain documentation of work (Github, R Markdown, Zotero)
One time only:
- Ask one question on Stack Overflow
- In person meeting with instructor
- 8 short assignments (including one group assignment)
- Group presentation of group assignment, 3-5 students each
- Submit Zotero file with semester's notes
Unit 2: Data Sources & Their Manipulation
- 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
- Be familiar with the kinds of work done in the fields of LA and EDM
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.
- Educause 2015. Why Is Measuring Learning So Difficult?
- Be familiar with a range of data sources, formats and extraction processes
- Be familiar with R & Github & markdown
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.
- The R Markdown Cheat sheet: 2014.
Swirl:
- Unit 1 - Introduction
- Understand the importance of workflow and recording workflow
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.
- Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Journal of Educational Technology & Society, 15(3), 42–57.
- Be able to perform a data tidying workflow
Read:
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.
- Young, J.R. 2014. Why Students Should Own Their Educational Data. The Chronicle of Higher Education Blogs: Wired Campus.
- Be familiar with a range of data manipulation commands
Read:
Swirl:
- Unit 2 - Data Sources & Manipulation
- Understand the place of data visualization in the data analysis cycle
- Be familiar with a range of data simulation commands
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.
- Gelman, A., & Niemi, J. (2011). Statistical graphics: making information clear – and beautiful, Significance, September, 134-136
- Wainer, H. (1984). How to display data badly, The American Statistician, 38(2), 137-147
- Be able to generate basic visualizations during on-the-fly analysis
Read:
- Gelman, A., & Unwin, A. (2012). Infovis and Statistical Graphics: Different Goals, Different Looks (with discussion)
- Fung, K. (2014). Junkcharts Trifecta Checkup: The Definitive Guide
- Understand the basic premise of graph theory applied to social networks
Read:
- Conceptualize a data structure suitable for network analysis, generate a network and produce basic summary metrics
Read:
- Understand the basic principle and algorithm behind cluster analysis
Read:
- Create a suitable data structure and perform clustering on a sample
Watch:
- Be familiar with the basic ideas behind dimension reduction and the reasons for needing it
- Understand the basic principles behind Principal Component Analysis
Read:
- Perform principal component analysis
Watch:
- Be familiar with the range of strategies for mapping domains and skills
Read:
- Be familiar with the Q-matrix method
Watch:
- Chapter 7 in Baker, R. (2014). Big Data in Education:video 6
Swirl:
- Unit 3 - Structure Discovery
- Understand why prediction is desireable goal, the various meanings of the word and general strategies employed across statistics, machine learning and experimental psychology
Read:
- Kucirkova, N. and FitzGerald, E. 2015. Zuckerberg is Ploughing Billions into “Personalised Learning” – Why? The Conversation.
- 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.
- Employ a linear prediction model
Watch:
- Chapter 1 in Baker, R. (2014). Big Data in Education: video 1
- Understand the concept of classification and its relationship to modeling
Read:
- Understand and apply the following diagnostic metrics to models: Kappa, A', correlation, RMSE, ROC
Read:
Watch:
- Chapter 2 in Baker, R. (2014). Big Data in Education: video 2, video 3 and video 4
- Chapter 2 in Baker, R. (2014). Big Data in Education: video 5
- Georgia Tech 2015. Cross Validation. Youtube.
- Understand the concepts behind Bayesian Knowledge Tracing
Swirl:
- Unit 4 - Prediction
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