Credit
This is an open (and free) course offered by the Technology Enhanced
Knowledge Research Institute (TEKRI) at Athabasca University. The
course is not offered for-credit and is intended for professional
development and to raise awareness of the role that analytics can play
in education, learning and development, and in evaluating
organizational information
flows.
Course Description
The growth of data surpasses the ability of organizations or
individuals to make sense of it. This concern is particularly
pronounced in relation to knowledge, collaboration within an
organization, teaching, and learning. Learning
institutions and corporations make little use of the data learners
"throw off" in the process of accessing learning materials, interacting
with educators and peers, and creating 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 course and institutions levels.
Corporations likewise face pressure for increased competitiveness and
productivity, a challenge that requires important contributions in
organizational capacity building from work place and informal learning:
Learning analytics can play a role in highlighting the
development of employees through their learning activities.
In enterprise settings, information flow and learning/knowledge
networks can yield novel insights into organizational effectiveness and
capacity to address new challenges or adapt rapidly when unanticipated
event arise.
The expansion of learning and knowledge work beyond formal
institutional boundaries, myriad platforms in the cloud hosting the
activity of individuals will be providing/exchanging analytics.
Advances in knowledge modeling and representation, the semantic
web,
data mining, analytics, and open data form a foundation for new models
of knowledge development and analysis. The technical complexity of this
nascent field is paralleled by a transition within the full spectrum of
learning (education, work place learning, informal learning) to social,
networked learning. These technical, pedagogical, and social domains
must be brought into dialogue with each other to ensure that
interventions and organizational systems serve the needs of all
stakeholders. As a multi-disciplinary field, learning analytics
requires contributions from learning sciences, computer sciences,
statistics, information sciences, sociology, and psychology.
Learning and Knowledge Analytics 2011
is a conceptual and exploratory
introduction to the role of analytics in learning and knowledge
development. Most of the topics do not require advanced statistical
methods or technical skills. Topics covered during the six-week course
will introduce participants to a systemic and integrated view of
analytics in the following settings:
K-12
Higher Education
Corporate
Government
Organizational
The course will also lay a foundation for the upcoming 1st International Learning
Analytics and Knowledge Conference held in February, 2011 in
Banff,
Canada. More information on the conference is available here: https://tekri.athabascau.ca/analytics/
Course Tag
#LAK11 (for Diigo, Twitter, blogs, Delicious)
Audience
As an introductory course, faculty, administrators, grad students,
learning and development professionals, and organizational leaders will
benefit from the topics and concepts covered. While not a prerequisite,
participants will find it helpful to have some level of existing
familiarity with the
Internet, online social networks, and web-based communications -
particularly with synchronous and asynchronous technologies for
communicating, collaborating and sharing information. As an interactive
open course, participants will find that valuable learning occurs, and
social connections are formed, through offering contributions,
ideas, suggestions in course Moodle forums, live sessions, or on
their
blogs.
Course Outcomes
As a result of active participation throughout this course,
participants can expect to:
Define learning and knowledge analytics
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 future implications
of this transition.
Design a learning analytics implementation plan at a course
level.
Evaluate the potential impact of the semantic web and linked data
on learning resources and curriculum.
Detail various elements organizational leaders need to consider
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 develop models for their potential impact on teaching,
learning, and organizational knowledge.
Technologies Used
Various technologies will be used throughout the course - some for
interaction with other participants and others as analytic tools. A
partial list of the technologies we will use include:
Time Required
Depending on your familiarity with the concepts of analytics, this
course can take between 5-10 hours per week to complete course
readings, participate in discussions, complete activities, and attend
the live sessions. If you are not able to commit the time required, you
can select the level of participation that best meets your needs. Many
of the guest presentations, for example, can be treated as stand alone
topics. In order to gain a broad understanding of the role of knowledge
and learning analytics, sustained participation of the course is
warranted.
Weekly Activities
This course has synchronous and asynchronous components. The
asynchronous component includes discussion forums (Moodle) and/or
blogs/Twitter. The synchronous component involves a weekly guest
presenter (generally on Tuesdays) and a Friday follow up informal
discussion - both
to be held in Elluminate.
Recommended Weekly Activities
Recommended weekly activities are offered, but participants can
engage in the course at any level that their schedule or interests
permit.
Getting Started:
Set a few goals: what do you want to gain from the course? How
much
effort/time are you able/willing to commit? If you’re comfortable do
so, please share your goals in the introductory discussion forums.
Download VUE (http://vue.tufts.edu/)
or CMAP (http://cmap.ihmc.us/download/).
Update your course map on a weekly basis to add new concepts or ideas.
Knowledge (or concept) maps can be quite helpful in communicating to
others how you see the different elements of the course connecting.
Weekly blogging: reflect on how the discussions of the week can
translate into your work setting.
Each week, activities have been planned that will introduce you
to
different tools and methods for analytics. These activities may seem a
bit complex, but the benefit for engaging in them will be a
significantly enhanced understanding on analytics approaches.
How does this course work?
This
is an open course—no fees are required to join and participate.
The course is based on the Massive Open Online Course (MOOC) model that
George Siemens and Dave Cormier have run on various topics over the
last three years. We heavily
emphasize participant contributions and discussions as a means of
exploring the diversity of complex subject areas.
Your contributions will make the
course a success.
You can contribute in numerous areas: Moodle, live discussions,
Twitter, your blog, Second Life, or any other site that interests you.
If you feel that we, as course designers, have neglected a particular
feature that would help you learn better, then chances are that a few
others share your view. And we encourage you to rectify our oversight.
As a distributed, open course, we view our actions of pulling together
a syllabus and planning weekly topics, readings, and guest
presentations as a foundation or learning platform. You are encouraged
to build on that platform in whatever way helps you learn and share the
most.
Each week will start off with a link to a short summary of the topic,
links to relevant readings, and short podcasts/video interviews.
Daily emails will be sent to all course participants summarizing course
activity or highlighting important resources or contributions. To
receive daily emails, please join http://groups.google.com/group/LAK11.
If you prefer not to join Google Groups, all postings will be available
via RSS and public archive.
Two (sometimes three) live sessions will be held in Elluminate each
week. One session will involve a guest speaker addressing an important
topic in the course, the second will consist of a weekly discussion
with course facilitators.
Week 1: John Fritz: January 11, 1:00 pm, MST
Week 2: Ryan S.J.d. Baker: January 18, 1:00 pm, MST
Week 3: Dragan Gasevic: January 25, 1:00 pm, MST
Week 4: John Whitmer: February
3, 1:00 pm MST
Dave Snowden: February 4, Time TBD
Week 5: Linda Baer, February 8, Time TBD
Week 6: Simon Buckingham Shum, February 17, 1 pm MST
Course
Schedule
Week 1 (Jan
10-16): Introduction to learning and knowledge analytics
Topic Introduction
We produce an enormous quantity of data on a daily basis. Consider the
data trails you leave in your daily routine:
A quick login to Facebook/Twitter to see what’s happened in your
social network since you went to bed.
A few moments spent reading your email (gmail/yahoo/hotmail) with
your morning coffee...followed by signing in to a few accounts (with
Facebook Connect) to read/interact with your network
As you leave for work, you stop and fill your car with gas,
paying with a credit card and supplying some type of frequent flyer or
airmiles card. Perhaps, to hit your peak morning caffeine intake, you
stop by Starbucks and pay with your preloaded Starbucks card.
You checkin to foursquare while at Starbucks. You need to defend
your Mayor status.
You swipe your parking card as you enter the parkade at work.
You logon to your computer at work and start leaving work-related
data trails: emails, webinar activity, corporate database searches,
skype conversations, activity in Sharepoint, are all recorded - data
waiting to be analyzed to determine your productivity in relation to
others in the organization.
...and so on (Even the vegetables you pick up on the way home from work
are tracked by the small discount your grocery store offers when you
swipe your customer loyalty card).
We live in digital times. The conversations that used to evaporate
around the water cooler are now digitized, waiting for a clever
algorithm for analysis. The potential of analytics to increase employee
efficiency, match the right people to the right tasks, and to improve
access to help resources is tremendous. But significant privacy and
ethics concerns exist. Data silos protect individuals from
inappropriate use of *our* data. We don’t necessarily want our doctor,
insurance provider, or banker to know us fully. Cross-data silo access
products are far more accurate representations of who we are (and what
our interests are) than we might feel comfortable sharing.
When applied to learning - corporate, higher education, K-12 -
analytics raise similar concerns about the interplay between the value
between transparent data silos and privacy and ethics. This course will
explore learning and knowledge analytics, including analytics methods
and models, systemic application, potential data sources, the
“soft/human/non-quantifiable” aspect of learning, as well as privacy
and ethical considerations in deploying analytics.
In week one, we will focus mainly on building some familiarity with the
concepts (and language) of learning and knowledge analytics. We define
learning analytics as: “the measurement, collection, analysis and
reporting of data about learners and their contexts, for purposes of
understanding and optimising learning and the environments in which it
occurs” (Learning Analytics 2011 Conference site: https://tekri.athabascau.ca/analytics/).
Technologies used this week
Moodle
Elluminate
Blogs (if you decide to blog in addition to Moodle forum participation)
Netvibes
VUE (if you decide to use it to create your concept map)
Hunch
Activities
Participate in forums for Week 1: http://scope.bccampus.ca/mod/forum/view.php?id=8723
Create a Hunch Account: http://hunch.com/
. Go through the process of personalizing your account (i.e. answer the
Hunch questions).
Start searching/playing
What are your reactions? How can this model be used for
teaching/learning?
Share your views in the Moodle discussion forum for
Week 2 (Jan
17-23): Rise of “Big Data” and Data Scientists
Topic Introduction
Data
and data analysis is changing business, health care, society, and
education, largely necessitated by growth in abundance of data. As data
mining and analytics develop in technique and application, their
influence on decision makers will increased. "Big data" is challenging
long-established methods within science.
Data scientists (though some argue it's not a useful term - see Quora
readings this week) have enormous control over how we experience data
and
what is known about us by businesses and governments. How does big data
impact education? What roles do data scientists and practitioners play
in corporates, K-12 schools, and higher education? We'll tackle these
topics in greater detail this week.
New technologies used this week
SNAPP
Diigo/Delicious: tag resources with LAK11
Share your Twitter ID in the moodle discussion forum (http://scope.bccampus.ca/mod/forum/discuss.php?d=16361)
so you can connect with others in LAK11 who are also on Twitter.
Activities
Participate in forums for Week 2: http://scope.bccampus.ca/mod/forum/view.php?id=8724
Download SNAPP:
http://research.uow.edu.au/learningnetworks/seeing/snapp/index.html
Run SNAPP on moodle forums
In the discussion forum for the week (or on your blog) detail the value
of this tool for educators.
What is the benefit of SNAPP?
What additional functionality is required?
If you're using VUE or CMAP to develop your concept map, add new
concepts from this week and detail connections to previous concepts.
As the semantic web develops and knowledge is mapped, it can be linked
to existing analysis models in order to provide personalized and
adaptive content for learners. Personalized and adaptive learning has
been a dream of educators for decades. Developments with linked data
offer new promise in realizing a scalable system of learning. Through
readings, videos, and discussions this week, we will clarify frequently
misunderstood terms: semantic web, linked data, and the semantic web.
Activities:
Participate in forums for Week 3: http://scope.bccampus.ca/mod/forum/view.php?id=8725
If you're using VUE or CMAP to develop your concept map, add new
concepts from this week and detail connections to previous concepts.
To prepare for Week 4, start tagging (in delicious/diigo) resources and
tools for conducting analysis of data.
Review NodeXL: http://nodexl.codeplex.com/
Week 4 (Jan 31-Feb
6): Tools for, and examples of, analytics
Topic Introduction:
Analytics tools for learning are still developing, with limited
consensus to date on their role in organizations. Many analytics tools
adopt or extend functionality of innovations in
emerging technologies. While nascent, these tools provide an indication
of how
educator/learner/technology roles will be reshaped in the next decade.
This week will lay the foundation for discussion in Week 6, where we'll
consider what a data-driven world of education will look like...and
what we can do to ensure it doesn't become a nightmare.
Visualization of data is an important aspect of analytics. Once
patterns are discernible in data, we need to present that data in a way
that is clear, concise, and visually appealing. Resources and
discussion this week will explore visualization briefly. However,
visualization is a reasonably well-developed discipline and requires
greater study than is possible in a quick overview.
Additional Resources:
Wang, T. and Ren, Y. (2009). Research on Personalized Recommendation
Based on Web Usage Mining Using Collaborative Filtering Technique,
WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS 1(6).
Week 5 (Feb
7-Feb 13): Organizational Implementation of Learning and Knowledge
Analytics
Topic Introduction:
Analytics
can be deployed at individual classroom (course) levels. Greatest
impact, however, will be when analytics are integrated
(integrated Knowledge and Learning Analytics Model - iKLAM) and planned
at a systemic level. Analytics should also incorporate online/offline
(library, clickers in
classroom) data sources. Reducing barriers to information flow is
important for systemic-level analytics.
Activities this week:
Participate in forums for Week 5: http://scope.bccampus.ca/mod/forum/view.php?id=8727 If you're using VUE or CMAP to develop your concept map, add new
concepts (models of data collection, use, analysis, and
refinement (Campbell, Oblinger) as well as the organizational action
analytics model (Norris, Baer, et al)) from this week and detail
connections to previous concepts.
Week 6 (Feb
14-Feb 20): What’s next for Learning and Knowledge Analytics?
Topic Introduction:
Learning
and knowledge analytics are developing quickly, partly driven
by developments in peripheral fields such as business intelligence and
analytics in big data and online. Analytics models are fragmented, with
limited agreement on: a) how to
deploy analytics, b) their role in educational reform, c) success
metrics (i.e. patterns of success in learner data), and d) evaluation
models of analytics. Additionally, many of the technical components of
learning analytics are not yet developed. For example, what is the
technical infrastructure underlying learning analytics: identity,
tracking distributed activity, educational and knowledge protocols for
discovery, recommendation, and ethics? To some degree, existing tools
(such as Open ID) can be appropriated for learning and developed. Other
technical components of learning and knowledge analytics, however, need
to be developed.
Readings & Resources:
Mainly a discussion week - were are we going with analytics? What is
needed? What are opportunities? What are the ethical issues around
their use? (this topic will likely be a strand throughout the course,
so we'll review and extend the discussion this week). What type of
technical/conceptual infrastructure is needed for learning analytics
deployment?
