1. ECI 519: An Introduction to Learning Analytics
Course Overview: As the use of digital resources continues expand in education, an unprecedented
amount of new data is available to educators. However, many schools and organizations lack the
knowledge and skills needed to leverage these new data sources. This introductory course to
Learning Analytics (LA) is designed to prepare educational leaders and practitioners to more
effectively and ethically use this data. This course will provide an overview of learning analytics,
examples of its use in educational contexts, and applied experience with tools and techniques related
to the field. As participants gain experience in the collection, analysis, and reporting of data
throughout the course, they will be better prepared help educational organizations understand and
improve both online and blended learning environments.
Number of Credits: 3
Course Prerequisites/Co-requisites: This course requires graduate-level standing.
Meeting Time: This distance education course is predominantly asynchronous. Online tools are
utilized throughout the course for communication and interaction. In addition, we may use Google
Hangouts, Twitter, and/or Collaborate for real-time web conferencing, virtual office hours, or whole
class discussions.
Virtual Class Locations: This course will be taught online through NC State's Moodle course
management platform. Access http://wolfware.ncsu.edu/ and log-in with your Unity ID and
password. After logging-in, locate and click on the ECI 519 section 602 to access the course site.
Instructor Information
Name: Dr. Shaun Kellogg
Email: shaun.kellogg@ncsu.edu
Skype: sbkellogg
Office location: Friday Institute for Educational Innovation
Office phone: (919) 513-8563
Office Hours: any weekday by appointment
Required Course Texts: None
Required and Recommended Software: Students must have Internet access and access to a Web
browser (e.g., Safari, Firefox, chrome) to participate in this course. The Moodle course site and
Web-based software required for completing course projects may only be accessed online. It is
strongly recommended that students have high-speed Internet access (e.g., cable modem). Finally,
this course requires, at no cost, several software applications (linked below) that will be used to
provide hands-on experience with the concepts and skills addressed in course readings. While
tutorials will be provided for application of these tools, only basic instructions will be provided for
their installation and troubleshooting. Students should feel comfortable installing new software
programs and navigating unfamiliar graphical user interfaces. It is also recommended that students in
this class have some background knowledge of online learning environments (e.g. LMS, MOOCs,
etc.) and a understanding of basic descriptive statistics (e.g. distribution, mean, variability, etc.).

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Required Software:
1. Tableau (Mac and PC)
a. About: http://www.tableausoftware.com/products/desktop
b. Download: http://www.tableausoftware.com/tft/activation
c. Student Key: TDHZ-9773-B290-AACD-2CC4
2. Microsoft Excel (Mac and PC)
a. Download: http://software.ncsu.edu
3. NodeXL (PC Only)
a. About: http://nodexl.codeplex.com
b. Download: http://nodexl.codeplex.com/downloads/get/806203
4. Topic Modeling Tool
a. About: https://code.google.com/p/topic-modeling-tool/
b. Download: http://topic-modeling-tool.googlecode.com/files/TopicModelingTool.jar
5. LightSide (Mac and PC)
a. About: http://lightsidelabs.com/what/research/
b. Download: http://ankara.lti.cs.cmu.edu/side/download.html
6. Rapid Miner 5.3 (Mac and PC)
a. About: https://rapidminer.com/products/studio/
b. Download: http://sourceforge.net/projects/rapidminer/
Recommended:
1. Parallels (for Mac Users)
a. About: http://www.parallels.com/cross-platform-solutions/
b. Note: The only non-Mac tool is NodeXL, Gephi is provided as an alternative, but
lacks features and ease of use compared to NodeXL. Also, I’ve run into some Java
issues with LightSide and RapidMiner, and find both tend to run more smoothly on
a PC.
Optional:
1. Gephi (PC and Mac)
a. About: http://gephi.github.io/features/
b. Download: http://gephi.github.io/users/download/
Server Space: NC State is a Google Apps for Education institution. Your Google Drive provides
"an infinitely large, ultra-secure, and entirely free bookbag for the 21st century." This space may be
useful for your project work, or you may use a third party Internet service provider to place your
data files and projects online (e.g., Dropbox). In addition, Moodle provides space for storing private
files.
Course Outcomes: The primary goal of this course is to provide students with a deeper
understanding of Learning Analytics and practical experience with the tools and techniques of the
field. By the conclusion of this course, students will be able to:
1. Define LA and describe educational problems/questions/issues associated with the field;
2. Identify relevant data sources and measures and explore their association with student learning;
3. Evaluate and apply a range of LA tools and techniques using open-source and proprietary
software to address these problems/questions;
4. Design and implement an analytics cycle that provides actionable insight into a learning
environment;
5. Recommend guidelines for educational organizations for integrating a learning analytics model.

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Course Structure & Schedule: This course is divided into 4 four-week units focused on common
techniques in the field of learning analytics. Each unit consist of two sections. Section 1 of each unit
introduces basic terminology, core concepts, and applications of an analytical approach through
readings and course videos. In Section 2 of each unit, students develop skills in explore are provided
a video walk-through of the software features using educational data as an example will design and
implement an investigation that addresses the main components of the data analytics cycle.
Course Orientation (Week 1; Jan 6-12)
• Introductions, syllabus review, and completed Learner Agreement.
Unit 1: An Introduction to Learning Analytics and Data Dashboards (Weeks 2-5; Jan 13-Feb 2)
1. Students are introduced to the field of Learning Analytics, including general approaches and
applications, with an initial focus on visualization of learner data.
2. Students develop basic skills with Tableau, a tool for data access and visualization, and
create a data product that addresses a question of interest.
Unit 2: Learner Interaction and Social Network Analysis (Weeks 6-9; Feb 3-Mar 8)
1. Students are introduced to concepts and measures of SNA and the relationship between
network measures and student learning.
3. Students develop basic skills with NodeXL, a tool for network analyses and visualization, ,
and create a data product that addresses an learner question of interest.
Unit 3: Discourse Analysis and Text Mining (Weeks 10-13; Mar 16-Apr 6)
1. Students are introduced to approaches for analysis text-based data such as online forum
postings and student writing products.
2. Students will explore Topic Modeling Tool and LightSide, tools for text mining and
machine learning, and create a data product that addresses a question of interest.
Unit 4: Predictive Modeling and Student Learning (Weeks 14-17; Apr 7-May 4)
1. Students are introduced to predictive modeling with student trace data.
2. Students will explore RapidMiner, a free tool for advanced analytics, and create a data
product that addresses a question of interest.
Major Assignments/Projects: A major component of this course is active and productive
participation in the course learning community through substantive contributions and constructive
peer feedback. Students will complete four small data analysis projects, or data products,
corresponding with the four major units that comprise the course. Students are permitted to work in
small teams on data products, with size of team varying depending on depth of proposed product.
Cumulative grades can be tracked during the semester by clicking the "Grades" link on the Moodle
menu. All course assignments are submitted online.
Assessment: Community engagement and unit data products will be graded using criteria provided
in the corresponding rubric. Full credit is not awarded for late work; however, assignments
submitted by the due date may be revised and resubmitted for a higher grade by the following week.
Grading Scale: The grading scale is based on 100 points:
A+ (97-100), A (94-96), A- (90-93), B+ (87-89), B (84-86), B- (80-83)
C+ (77-79), C (74-76), C- (70-73), D+ (67-69), D (64-66), D- (60-63), F (59 or less)
Course Feedback Expectations: Please contact your instructor via email (shaun.kellogg@ncsu.edu)
with any questions about the course project or other assignments. Your instructor will strive to
answer any emails within 24 hours (M-F) and 48 hours on the weekend, and grade submitted
assignments within 3-5 days of the due date. In addition, students will be provided ongoing
opportunities, and are strongly encourage, to provide course feedback for to help improve the design
of current and future course implementations.

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Academic Integrity: Students are bound by the academic integrity policy as stated in the code of
student conduct. Therefore, students are required to uphold the university pledge of honor and
exercise honesty in completing any assignment. See the website for a full explanation:
http://www.ncsu.edu/policies/student_services/student_discipline/POL11.35.1.php
University Non-Discrimination Policies: It is the policy of the State of North Carolina to provide
equality of opportunity in education and employment for all students and employees. Accordingly,
the university does not practice or condone unlawful discrimination in any form against students,
employees or applicants on the grounds of race, color, religion, creed, sex, national origin, age,
disability, or veteran status. In addition, North Carolina State University regards discrimination on
the basis of sexual orientation to be inconsistent with its goal of providing a welcoming environment
in which all its students, faculty, and staff may learn and work up to their full potential.
Reasonable accommodations will be made for students with verifiable disabilities. In order to take
advantage of available accommodations, students must register with Disability Services for Students
at 1900 Student Health Center, Campus Box 7509, 515-7653.
http://www.ncsu.edu/provost/offices/affirm_action/dss/
For more information on NC State's policy on working with students with disabilities, please see
http://www.ncsu.edu/policies/academic_affairs/courses_undergrad/REG02.20.1.php