It has been an interesting, thought-provoking, and frustrating year for many of us in education. This statement includes everyone from administrators to teachers to staff to students. At least where I teach and live, there has been a huge learning curve for many as we learn new technologies, new strategies for learning, new motivational techniques to encourage students to stay focused on learning, and even new strategies on how we can engage parents more in the learning process.
Over the past few weeks, I have found some interesting information about learning analytics. The basic concepts seem to be very familiar as they are something I use every day in trying to determine student success, predict failure, and what I may be able to do in my own lesson planning to influence either objective. In my readings, I found this interesting exchange between two of our reading’s authors, George Siemens and Mike Sharkey. Very generally, the discussion forum was focused on the variable definition of learning analytics and how whatever your chosen definition could be applied. I have included almost all the discussion between Sharkey and Siemen, only editing out what I determined (if I am allowed to do so for this exercise) to be irrelevant at this point.
The reason I have used a large part of their discussion was that I wanted a record in the same place of the context of what Sharkey and Siemen were talking about. I found it so applicable to how myself and others in my field approach academics and how we define success or failure in the classroom. This is a struggle I deal with throughout each school year as I move with the ebb and flow of students’ accomplishments during their assignments and assessments.
The following discussion took place in Learning Analytics Google Group Discussion, in August of 2010 (the exchange below is conveyed verbatim and has not been edited in terms of grammar, syntax or emoticon use):
I wanted to add a dimension to the discussion, specifically around
defining success. In the descriptions of learning analytics we talk
about using data to “predict success”. I’ve struggled with that as I
pore over our databases. I’ve come to realize there are different
views/levels of success:
In its simplest form, academic success means getting a good/passing
grade. That works for a 15-week course since you can use the first
few weeks of data to predict the remainder of the course. However, I
work in an environment where courses are 5, 6, or 9 weeks long (we
teach courses one- or two-at-a-time in serial). That prevents me from
using data within a course to predict the outcome for that student.
There’s a second part to this argument about whether good grades =
success. That’s a discussion we need to have over a beer so I’ll pass
Another academic metric is learning outcomes. Look at assessment
data and use mastery of outcomes as a gauge of success. If the
institution does a good job measuring learning outcomes, this is a
If we can’t measure success within a course, we might look at it
across the student’s program. From an academic standpoint, that means
GPA. That will just lead us to the same discussion about whether or
not grades are a good measure of success. From a practical
standpoint, success may mean “is the student still attending”. Are
they progressing through the program in a timely fashion? This isn’t
a good qualitative measure, but the argument can be made that if the
student is still attending, there’s a better chance they will succeed
in the program (especially when you compare that to students who have
stopped attending and have zero chance of graduating).
“Are they attending” is aligned with engagement. Is the student
actively engaged in the course? We can measure this by attendance
(did they show up) or by some alternate engagement metric (e.g. number
of actions in the course LMS). We can even get more detailed on the
progression metric and look at two dimensions:
– Persistence (when is the last time we heard from the student)
– Density (over the last x weeks, what percent of the time has the
student been engaged)
I have started to model metrics and I haven’t come to any solid
conclusions yet. It really boils down to who you are and how you
define success. Different parts of the institution will have
I hope to chat more with you at the conference in February.
Director of Academic Analytics
University of Phoenix
Hi Mike – thanks for contribution. Last year, I met someone from U of Phoenix (can’t remember how it was!) and they mentioned some of the current – and planned future – use of analytics at UoP. It was quite advanced from what I’ve seen at other institutions. Analytics require explication. Online courses, programs, and institutions are uniquely placed to be early trail-blazers of analytics.
Good question about success. Success has come up a few times already and, as you note, will be different in different situations and institutions. Or learners, for that matter. For some learners, simply passing a course could be defined as success. For others, only top grades would be seen as success.
Your points about persistence and density form part of the research that needs to be done around analytics. What learners characteristics contribute to success (however it is defined)? Which signals or deviation from those characteristics can we observe early enough through analytics to intervene to ensure success? Some great areas of research and exploration!
Mike (and others from the perspective of their institutions) – would you mind sharing a bit more about how you use analytics at UoP? What is working well? How are learners responding? What technology are you using for data collection and analytics? What role does visualization play?
In conclusion, sort of, when Siemens mentioned the types of analytics being discussed would work well for online course, etc., it reminded me of the evaluations we completed in the Course Design for Digital Environments course at the University of Edinburgh just last Fall. We had to consider various analytical frameworks to create operable and meaningful learning outcomes for the courses we designed. Of course, these outcomes were both dependent and determinant of the curriculum and activities we included in the course structure. It is very easy to see, from my perspective, how difficult it is to create and implement a solid strand of outcomes yet try to address as many of the different facets of learning and teaching that each teacher and student face each day
I came across the above article somewhat by accident. It reminded me of the replicator used in the Star Trek shows that dispensed food and drink to crew members. This article details how, using electronic signals and sensors, the taste and color of lemonade can be transmitted from its source to a glass of water. I understand this may not be a direct application of AI to which we have become familiar, but it does connect in the sense that sensations such as taste and vision are being replicated and transmitted within an algorithmic framework that mimics real human sensations. This is just another facet of real humanity being replicated into artificial humanity.
A simple form of sensory illusion has been in place at Disneyland, for example, for years. On certain rides the smell of old buildings and musty odors are commonly sprayed around for sensory effect. On one ride at California Adventure, when the carriage flies around California orchards, the smell of oranges and other citrus are present in the subtle vapors sprayed above the heads of the passengers. But these features are the results of the simple process of chemical sprays and mists. The technology detailed in the article cited below is a step further into mimicking the electronic signals used by the human body to transmit sensory signals to other parts of the body, or across space.
If this technology ever gets perfected I wonder how far we can take it? What of the classroom? Could we use such tools to bring past historical events alive to students? Events such as the Battle of Gettysburg: could we mimic the smell of gunpowder or the stench of a field hospital? In studies of the Middle Ages could we bring to life again the smells and colors of the roadhouse where travelers ate and rested on weary journeys? Could we taste what food may have tasted like 100, 200, or 500 years ago? And what of medicine? Could we use the smells of medications, diseases and the real colors of tissue to train our medical personnel more effectively? Of course, the medical value would be substantial in helping people with sensory deprivations to enhance what may have been lost through disease or injury. I have posted a couple of things on this blog related to the regeneration of tissue, drawing “inspiration” from Frankenstein’s Monster. Using Frankenstein again, can we couple this technology of sight and taste with the potential re-animation of tissue thus restoring senses lost? Or, in terms of post-human development, using these advances to create a new form of human, a cyborg for lack of a better term, equipped with all the sensations a “normal” human would possess. Coupled with what we have discussed already about AI, the potential for the next step in human evolution could be somewhat frightening and/or exciting to contemplate.
And what of our schools? How much technology is too much? How far can, or should, we go in providing students the means to complete assignments, understand calculations, contemplate the subjective context of paintings and philosophy? I am reminded of the scientists in Jurassic Park who cloned dinosaurs but had no understanding of the basics of the genetic dispositions of the animals they thought were so beautiful and majestic. As Dr. Malcom told them, they simply built on the work of scientists who had gone before yet did not try and understand the actual work those prior minds had completed. Is that what we are doing to our students with all the advanced technology we now place at their fingertips? They can accomplish great things now, but do students understand HOW things work in the first place? What if they can put humans on Mars, yet when the power goes out cannot complete simple arithmetic on an abacus or slide rule or do long division? Perhaps the issue remains, as Dr. Malcom put it (and I paraphrase), in terms of how far we push ourselves into the post-human world, it is not a matter of if we can but rather if we should.
To re-state perhaps a little, the possibilities of this technology could be limitless. Yet, with any application that further stretches the edge of the evolutionary envelope from human to post-human, we must consider the ramifications of it. How far can we go? How far should we go? What is the positive potential as opposed to the negative? Is there the possibility of abuse and if so, what is it and how great is the danger?
My own opinion is that sometimes I believe our technological demands and accomplishments are proceeding much faster than the ethics and morals of the technology that need to be considered. One must ask, for any technological advance in question, what is the rush? Is there such a dire need for this specific technology that consideration of the ethical impact of it must wait?
I don’t have the answers to many of these questions.
For the next little while, I am going to be looking into the concepts and theories of “Posthumanism”. Basically, what I am looking into is the mergence of humans and machines. This goes beyond using apps or other tools to improve learning or behavior. I am specifically wanting to explore the actual merging of humans and machines; something akin to the creation of what we would call cyborgs, or cybernetic organisms.
We have seen in Hollywood productions examples of this mergence in films such as Robocop and The Terminator. I would like my focus to go beyond that and look into how this new technology can not only influence our lives but how we can use new and better technology to improve our educational systems from kindergarten to post-graduate.
I have included here a short video about Posthumanism. Just try and watch it without judgement, although I think each of us already comes into this arena of the future with certain biases that transcend morality, ethics and even religious attitudes and paradigms.
Let’s just see where this road may be taking us and see what happens.