Overview
Learning and knowledge analytics is a relatively new field that is becoming more and more important due to the growing amounts of data gathered by diverse information systems and the huge potential those data have to support informed decision-making. Corporations face pressure for increased competitiveness and productivity. Making use of, and benefiting from, the data customers "throw off" in the process of accessing a corporation’s website and other information systems can lead to significant cost reductions, as well as personalization and customization for customers. Similarly, educational institutions can enormously benefit from analysing data, such as how learners access course materials, interact with educators and peers, and create new content.
In an age where educational institutions are under growing pressure to reduce costs and increase efficiency, analytics promises to be an important lens through which to view and plan for change at both course and institutional levels. In addition, increasing the competitiveness and productivity of a business is a challenge that requires important contributions in organizational capacity building from workplace and informal learning. Learning analytics can play a role in highlighting the development of employees through their learning activities.
In this course, you will be introduced to areas like big data and data science, which are closely related to learning and knowledge analytics. You will also learn about models, procedures, methods, tools and technologies to use for analytics, whether for educational data or any other type of data. In addition, this course will introduce you to areas that either contribute to or benefit from learning and knowledge analytics, such as the Sematic Web, open data, linked data, the Internet of Things, and intelligent/personalized systems. Moreover, practical applications of learning and knowledge analytics will be explored. Furthermore, models of adopting analytics-based cultures within organizations and institutions will be introduced and the role of privacy and security will be discussed.
While you will have to use or learn how to use certain technologies to analyse data, this course is not technical in nature. It is intended to serve as an introduction to learning and knowledge analytics for people who are interested in how to benefit from the huge amounts of data around us. For learners wishing to pursue more technical courses, AU’s School of Computing and Information Systems (SCIS) offers courses on data mining, artificial intelligence and computational intelligence. You may choose to register for them after this course to get a strong technical foundation that can then be applied to a learning and knowledge analytics topic (e.g., in an MSc essay, project or thesis).
Outline
Unit 1: Introduction to Learning and Knowledge Analytics
Unit 2: Big Data and Data Science
Unit 3: Models, Methods, and Tools of Analytics
Unit 4: Fundamental Concepts Related to Learning and Knowledge Analytics
Unit 5: Practical Applications of Learning and Knowledge Analytics
Unit 6: Introducing Analytics in an Organization/Institution
Unit 7: Privacy and Security in Learning and Knowledge Analytics
Learning outcomes
Upon successful completion of this course, you should be able to
- define learning and knowledge analytics and detail how it differs from (educational) data mining.
- define the terms big data, data science, and data-driven decision making. Explain how they change traditional decision making and describe potential implications this may have in education, training, and general organizational functioning.
- evaluate prominent analytics models, methods and tools, and determine appropriate contexts in which they would be most effective.
- use analytics models, methods and tools to analyse and draw conclusions from data.
- discuss areas and concepts that contribute to, and benefit from, learning and knowledge analytics and evaluate their potential impact on learning and knowledge analytics.
- explore practical applications of learning and knowledge analytics.
- detail the principles that organizational leaders need to consider to roll out an integrated learning and/or knowledge analytics model in an organizational setting.
- discuss potential concerns regarding privacy and security in learning and knowledge analytics and ways to ensure data privacy and data security is achieved.
Evaluation
To receive credit for COMP 683, you must achieve a course composite grade of at least B– (70 percent) and an average grade of at least 50 percent on the assignments and 50 percent on participation.
The weighting of the composite grade is as follows:
| Activity | Weight |
| Assignment 1: Technique and Tool Matrix | 15% |
| Assignment 2: Participation Analysis | 25% |
| Assignment 3: Concept Map | 15% |
| Assignment 4: Analytics Project | 35% |
| Participation in Discussions | 10% |
| Total | 100% |
Materials
All course materials are found online.