Program: Learning Analytics

The goal of the Graduate Certificate in Learning Analytics (LA) is to increase the capacity of education researchers and practitioners to understand and improve learning and the contexts in which it occurs through new data sources and analytical approaches. Students will both deepen their disciplinary knowledge of LA methodologies, applications and ethical issues and develop technical proficiency with up-to-date analytic tools, such as Tableau and R, used to apply computational analysis techniques to real-world educational datasets.

Guided by the North Carolina State University motto and mantra, Think and Do, the graduate certificate program will help students to:

1. Think deeply about learning analytics as an emerging discipline for understanding and improving learning; and, 
2. Do something impactful with the technical skills and knowledge gained.

The overarching learning objectives for the Graduate Certificate in Learning Analytics are twofold:

1. Disciplinary Knowledge. Students will deepen their understanding of learning analytics as an emerging research and teaching field that aims to understand and improve learning and the educational contexts in which learning occurs. 
2. Technical Skills. Students will develop proficiency with the processes, tools and techniques necessary to help educational organizations and institutions apply learning analytics both ethically and effectively. 

The following learning outcomes are the cumulative product of completing the Graduate Certificate in Learning Analytics program and are embedded in each course.

Throughout the program, students will learn:

• Conceptual Foundations: Describe learning analytics as a discipline (e.g. history, concepts, theories, methodologies, stakeholders, legal and ethical issues) and how it has been applied to important problems, questions and issues in education;
• Data Sources and Measures: Identify and appropriately use educational data sources (e.g. learning management systems) and associated measures;
• Tool Proficiency: Efficiently and effectively apply up-to-date software and tools (i.e. R, Tableau, Gephi, etc.) to implement LA workflows for preparing, analyzing and sharing data;
• Processes and Techniques: Understand and apply analytic processes and computational techniques (i.e. data visualization, text mining, machine learning and network analysis) in order to understand and improve learning and the contexts in which learning occurs; and, 
• Communication: Clearly communicate methods, analyses, findings and recommendations that provide actionable insight into learning contexts for a range of education stakeholders.

The Graduate Certificate in Learning Analytics (GCLA) is composed of 12 credit hours of online, graduate-level coursework. The certificate may be completed entirely online from anywhere in the world. No transfer credits from other institutions or from NC State courses are allowed as substitutes. The expected time to finish the certificate is one to two years (two courses offered every fall semester, two courses offered every spring semester).

The traditional course sequence is ECI 586 and ECI 587 in fall, and 588 and ECI 589 in spring. It is ideal for students to start the certificate with ECI 586 Introduction to Learning Analytics, which introduces some core skills with R coding that are relevant to other courses. However, if students wish to start the certificate in spring, they can catch up by taking some supplemental R modules, or by working primarily in the non-programming track.

The required core courses include the following:

• ECI 586 (fall): Introduction to Learning Analytics is designed to provide students an overview of the field of Learning Analytics and prepare students to wrangle and explore data from educational contexts. Students will also learn principles of good data visualization and how to communicate education data effectively.
• ECI 587 (fall): Machine Learning in Education will introduce students to applications of Machine Learning in educational settings. Students will learn to conceptualize educational problems, build and evaluate models, and work with a wide range of algorithms and methods (e.g. decision trees and clustering) to address those problems.
• ECI 588 (spring): Text Mining in Education provides students with an introduction to text mining concepts, applications in educational contexts, and applied experience with widely adopted tools and techniques. Students will learn to analyze a large collection (or corpus) of documents using methods such as tf-idf analysis, sentiment lexicons, topic modeling and classification.
• ECI 589 (spring): Analyzing Learning Networks will introduce students to social network theory and how network analysis can be applied in online and blended learning environments. Students will learn to calculate network statistics, visualize network properties and use modeling to discover underlying structures and factors impacting their development.

Tuition for these certificate courses is based on the “graduate – standard DE rate” set by the university for a 3-credit hour course (residential or nonresidential).

The expected timeline for completion is one year; however, students are allowed up to three years to finish all courses. As the technology in this field is emerging and rapidly changing, students should seek to complete the certificate in one to two years, if possible.

An application for acceptance into the Graduate Certificate in Learning Analytics is required for all new students. Applicants must complete the Graduate School application.

Requirements for admission to the certificate program include: 

1. A bachelor’s degree with a GPA of 3.0 or greater on a 4.0 scale, a 3.0 derived from the last 60 credits of undergraduate study, or a graduate degree from an accredited college or university;
2. A resumé that identifies educational preparation and professional employment and experiences; 
3. A professional goals statement indicating how the GCLA will enhance job performance or career development, and 
4. A projected timeline for completing certificate requirements. 

Applicants with less than a 3.0 GPA at the undergraduate level may be admitted provisionally if professional experiences and goals are deemed high quality, with provisional admits required to earn a grade of B or better to remain enrolled.

Those applicants who are currently enrolled in an NC State graduate degree program and in good standing need only to submit the Graduate Student Certificate Plan Data Entry Form to the program coordinator.

This online graduate certificate program will increase education practitioners’ understanding of the learning analytics field and develop their repertoire of tools and techniques for improving learning and the contexts in which learning occurs. The Society for Learning Analytics Research maintains an active job board to find positions and qualifications related to learning analytics in the United States and abroad. Many learning analytics jobs are also posted on LinkedIn as examples of the employment opportunities available to graduates possessing the advertised skills.

This certificate serves both education practitioners and current graduate students who want to develop expertise in learning analytics. For certificate students who desire to move on to a full master’s or doctoral degree program after finishing the certificate, this opportunity is available for well-qualified graduates who apply to and are accepted into the Learning Design and Technology master’s or doctoral programs. The 12-credits earned in the certificate will transfer into these programs and count toward these degrees; however, earning the certificate does not guarantee admission into these degree programs. The student must still meet all qualifications for admission into these degree programs.

The Graduate Certificate in Learning Analytics is a collaborative venture between the Friday Institute for Educational Innovation and the Learning Design and Technology Program within the NC State College of Education.

Courses are taught by the following faculty members:

Shiyan Jiang, Ph.D.
Assistant Professor of Learning Design and Technology
sjiang24@ncsu.edu

Shaun Kellogg, Ph.D.
Director of the Friday Institute Research and Evaluation Team
shaun.kellogg@ncsu.edu