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Core Methods in Educational Data Mining: Syllabus

Introduction class - Yay! Best class eva!I agree！

Course Description

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.

Course Goals

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.

Assessment

In HUDK4050 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 HUDK 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
  • Notes on weekly readings
  • Complete Swirl course
  • Maintain documentation of work (Github, R Markdown)

One time only:

  • Ask one question on Stack Overflow
  • Attend office hours once
  • 8 short assignments (including one group assignment)
  • Group presentation of group assignment, 3-5 students each

Week-by-week

Unit 1: Introduction

Unit 2: Data Sources & Their Manipulation

Unit 3: Structure Discovery

Unit 4: Prediction

Unit 1: Introduction

Class 1 - Introduction (9/5/18)

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 (9/10/18)

Learning Objectives

  • Be familiar with the kinds of work done in the fields of LA and EDM

Tasks to be completed:

Read/watch:

Read chapter 1-3:

Due: Assignment 1 - Set up

Class 3 - Data Sources (9/12/18)

  • Be familiar with a range of data sources, formats and extraction processes
  • Be familiar with R & Github & markdown

Tasks to be completed:

Read:

Swirl:

  • Unit 1 - Introduction

Unit 2: Data Sources & their Manipulation

Class 4 - Data Wrangling (9/17/18)

Learning Objectives:

  • Understand the importance of workflow and recording workflow

Tasks to be completed:

Read:

Class 5 - Data Wrangling (9/19/18)

Learning Objectives:

  • Be able to perform a data tidying workflow

Tasks to be completed:

Read:

Class 6 - Data Wrangling (9/24/18)

Read:

Class 7 - Data Wrangling (9/26/18)

Learning Objectives:

  • Be familiar with a range of data manipulation commands

Tasks to be completed:

Read:

Swirl:

  • Unit 2 - Data Sources & Manipulation

Unit 3: Structure Discovery

Class 8 - Teachley Class Visit (10/1/18)

Class 9 - Start Social Networks (10/3/18)

Class 10 - Check-in Exam (10/8/18)

Learning Objectives:

  • Understand the place of data visualization in the data analysis cycle
  • Be familiar with a range of data simulation commands

Class 11 - Visualization (10/10/18)

Learning Objectives:

  • Be able to generate basic visualizations during on-the-fly analysis

Read:

Class 12 - Networks (10/15/18)

Learning Objectives:

  • Understand the basic premise of graph theory applied to social networks

Tasks to be completed:

Read:

Class 13 - Networks (10/17/18)

Learning Objectives:

  • Conceptualize a data structure suitable for network analysis, generate a network and produce basic summary metrics

Tasks to be completed:

Read:

Due: Assignment 2 - Social Network

Class 14 - Clustering (10/22/17)

Learning Objectives:

  • Understand the basic principle and algorithm behind cluster analysis

Tasks to be completed:

Read:

Class 15 - Clustering (10/24/17)

Learning Objectives:

  • Create a suitable data structure and perform clustering on a sample

Tasks to be completed:

Watch:

  • Chapter 7 in Baker, R. (2014). Big Data in Education: video 1, video 2

Class 16 - Principal Component Analysis (10/29/18)

Learning Objectives:

  • Be familiar with the basic ideas behind dimension reduction and the reasons for needing it
  • Understand the basic principles behind Principal Component Analysis

Tasks to be completed:

Read:

Class 17 - Principal Component Analysis (10/31/18)

Learning Objectives:

  • Perform principal component analysis

Tasks to be completed:

Watch:

Class 18 - Domain Structure Discovery (11/5/18)

Learning Objectives:

  • Be familiar with the range of strategies for mapping domains and skills

Tasks to be completed:

Read:

Due: Assignment 3 - Clustering

Class 19 - Domain Structure Discovery (11/7/18)

Learning Objectives:

  • Be familiar with the Q-matrix method

Tasks to be completed:

Watch:

  • Chapter 7 in Baker, R. (2014). Big Data in Education:video 6

Swirl:

  • Unit 3 - Structure Discovery

Unit 4: Prediction

Class 20 - Prediction (11/12/18)

Due: Assignment 4 - Principal Component Analysis

Learning Objectives:

  • Understand why prediction is desireable goal, the various meanings of the word and general strategies employed across statistics, machine learning and experimental psychology

Tasks to be completed:

Read:

Class 21 - Prediction (11/14/18)

Learning Objectives:

  • Employ a linear prediction model

Tasks to be completed:

Watch:

  • Chapter 1 in Baker, R. (2014). Big Data in Education: video 1

Class 22 - Classification (11/19/18)

Learning Objectives:

  • Understand the concept of classification and its relationship to modeling

Tasks to be completed:

Read:

Due: Assignment 5 - Prediction

Class 23 - Classification (11/21/18) - Thanksgiving No Class

Learning Objectives:

  • Implement a CART model

Tasks to be completed:

Watch:

  • Chapter 1 in Baker, R. (2014). Big Data in Education: video 3 & video 4

Class 24 - Diagnostic Metrics (11/26/18)

Learning Objectives:

  • Understand and apply the following diagnostic metrics to models: Kappa, A', correlation, RMSE, ROC

Tasks to be completed:

Read:

Watch:

Class 25 - Knowledge Tracing (11/28/18)

Vectr Class Visit

Learning Objectives:

  • Understand the concepts behind Bayesian Knowledge Tracing

Tasks to be completed:

Read: Corbett, A.T., Anderson, J.R. (1995) Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction, 4, 253-278.

Swirl:

  • Unit 4 - Prediction

Class 26 - Knowledge Tracing (12/3/18)

Learning Objectives:

  • Understand Bayesian Knowledge Tracing

Tasks to be completed:

Watch:

  • Chapter 4 in Baker, R. (2014). Big Data in Education: video 1

Due: Assignment 6 - CART Models

Class 27 - Work Session: Assignment 8, Group Project (12/5/18)

Class 28 - Work Session: Assignment 8, Group Project (12/10/18)

Due: Assignment 7 - Diagnostic Metrics

Class 29 - Rate video presentations (12/12/18)

Class 30 - Rate video presentations (12/17/18)

EVERYTHING DUE - 12/19/18

Fine Print

  1. All examinations, papers, and other graded work and assignments are to be completed in conformance with the [Academic Integrity Policy](http://www.tc.columbia.edu/administration/diversity/index.asp? Id=Civility+Resources+and+Policies&Info=Civility+Resources+and+Policies&Area=Studen t+Miscon duct+Policy). Students who intentionally submit work either not their own or without clear attribution to the original source, fabricate data or other information, engage in cheating, or misrepresentation of academic records may be subject to charges. Sanctions may include dismissal from the college for violation of the TC principles of academic and professional integrity fundamental to the purpose of the College.

  2. The College will make reasonable accommodations for persons with documented disabilities. Students are encouraged to contact the Office of Access and Services for Individuals with Disabilities for information about registration (166 Thorndike Hall). Services are available only to students who are registered and submit appropriate documentation. As your instructor, I am happy to discuss specific needs with you as well.

  3. The grade of Incomplete will be assigned only when the course attendance requirement has been met but, for reasons satisfactory to the instructor, the granting of a final grade has been postponed because certain course assignments are outstanding. If the outstanding assignments are completed within one calendar year from the date of the close of term in which the grade of Incomplete was received and a final grade submitted, the final grade will be recorded on the permanent transcript, replacing the grade of Incomplete, with a transcript notation indicating the date that the grade of Incomplete was replaced by a final grade. If the outstanding work is not completed within one calendar year from the date of the close of term in which the grade of Incomplete was received, the grade will remain as a permanent Incomplete on the transcript. In such instances, if the course is a required course or part of an approved program of study, students will be required to re-enroll in the course including repayment of all tuition and fee charges for the new registration and satisfactorily complete all course requirements. If the required course is not offered in subsequent terms, the student should speak with the faculty advisor or Program Coordinator about their options for fulfilling the degree requirement. Doctoral students with six or more credits with grades of Incomplete included on their program of study will not be allowed to sit for the certification exam.

  4. Teachers College students have the responsibility for activating the Columbia University Network ID (UNI) and a free TC Gmail account. As official communications from the College – e.g., information on graduation, announcements of closing due to severe storm, flu epidemic, transportation disruption, etc. -- will be sent to the student’s TC Gmail account, students are responsible for either reading email there, or, for utilizing the mail forwarding option to forward mail from their account to an email address which they will monitor.

  5. It is the policy of Teachers College to respect its members’ observance of their major religious holidays. Students should notify instructors at the beginning of the semester about their wishes to observe holidays on days when class sessions are scheduled. Where academic scheduling conflicts prove unavoidable, no student will be penalized for absence due to religious reasons, and alternative means will be sought for satisfying the academic requirements involved. If a suitable arrangement cannot be worked out between the student and the instructor, students and instructors should consult the appropriate department chair or director. If an additional appeal is needed, it may be taken to the Provost.