Computer Science (COMP) 683
Introduction to Learning and Knowledge Analytics (Revision 1)
Delivery Mode: Grouped Study Online
Area of Study: IS Elective
Faculty: Faculty of Science and Technology
Instructor: Dr. Vive Kumar
The growth of data overwhelms those who try to make sense of it. This concern is particularly evident in complex knowledge-intensive organizations. Learning institutions and corporations often don’t pay attention to the data trails that learners create in the process of accessing learning materials, interacting with educators and peers, and creating new content. In an age where institutions are under growing pressure to adjust and adapt rapidly, learning and knowledge analytics hold opportunities for improved decision-making and planning at institutions levels.
Advances in knowledge modeling and representation, the semantic web, data mining, analytics, and open data form a foundation for new approaches of knowledge development and analysis. The technical complexity of this field is paralleled by a transition within schools and organizations to consider the full spectrum of learning (education, work place learning, informal learning) while transitioning to social and networked learning models. These technical, pedagogical, and social domains are amplified when they are considered in relation to one another – a foundational philosophy of this course.
Introduction to Learning and Knowledge Analytics 2011 is an overview course detailing the role of analytics in learning and knowledge development. Most of the topics do not require advanced statistical methods or technical skills.
After completing this course, the student should be able to:
- Define learning and knowledge analytics and detail how these differ from educational data mining
- Map the developments of technologies and practices that influence learning and knowledge analytics as well as developments and trends peripheral to the field.
- Evaluate prominent analytics methods and tools and determine appropriate contexts where the methods would be most effective.
- Describe how “big data” and data-driven decision making differ from traditional decision making and the potential implications of this transition in education, training, and general organizational functioning.
- Evaluate “intelligent curriculum” as a basis for future content development and its connection to analytics.
- Design and implement a model deploying learning analytics relating to a course or specific area of study
- Evaluate the potential impact of the semantic web and linked data on the development of learning resources and curriculum.
- Detail various principles that organizational leaders need to consider in order to roll out an integrated knowledge and learning analytics model in an organizational setting.
- Describe and evaluate developing trends in learning and knowledge analytics and determine their potential impact on teaching, learning, and organizational knowledge.
This course will introduce learners to how information quantity alters its qualitative attributes. In the early 1970’s, PW Anderson stated that “more is different”. Throughout this course, discussion will centre on how abundance of information requires new technologies and analysis methods in order to decide and act meaningfully. Concepts of wayfinding and sensemaking in complex settings will be addressed through emerging technologies, social networks, and analysis methods. Additionally, learners will be asked to consider organizational and cultural barriers that hamper analytics-based decision-making in companies.
To receive credits for COMP 683 toward the Master of Science in Information Systems Program, you must achieve a cumulative course grade of at least C+ (66 percent), including an average grade of 60 percent on the assignments and achieve a grade of at least 60 percent on the Final Examination.
To receive credit for COMP 683 toward the Post-Baccalaureate Certificate in Data Analytics, you must achieve a cumulative course grade of at least B- (70 percent), including an average grade of 60 percent on the assignments and achieve a grade of at least 60 percent on the Final Examination.
To receive credit for COMP 683 as a non-program student, you must achieve a cumulative course grade of at least C+ (66 percent), including an average grade of 60 percent for each required learning outcome it is intended to address.
The weighting of the composite grade is as follows:
|Participation in Weekly Discussions (threaded, blogs)||20%|
|Analytics Project (TME 1)||40%|
|Concept Map (TME 2)||20%|
All course resources will be open and online, utilizing the conference proceedings from the Learning and Knowledge Analytics and Educational Data Mining conferences.
Optional readings are provided, some of which may require access through AU Library databases.
Video recordings of presentations from LAK11
Additional analytics resources will be posted here: http://www.diigo.com/user/gsiemens/analyticsas well as the course tag within the Landing.
Course Materials - Other
The remainder of the learning materials for COMP 683 are delivered through Athabasca University's learning management system (LMS), Moodle. Online course materials include discussion forums, learning materials, and assignments. Assignments will be submitted online.
- Computer Science 683Study Guide
- Detailed descriptions of the requirements for the individual tutor-marked assignments
- A course evaluation form
This course schedule is based on working approximately 15 hours per week, so this would best translate into (per week):
Readings (12 hrs) / Synthesis and/or exercises (3 hrs)
Special Course Features
Computer Science 683 is offered by computer mediated communications (CMC) mode, and can be completed at the student's workplace or home.
Students registered in this course will NOT be allowed to apply for a course extension due to the nature of the course activities.
Athabasca University reserves the right to amend course outlines occasionally and without notice. Courses offered by other delivery methods may vary from their individualized-study counterparts.
Opened in Revision 1, July 14, 2011.