Archive for the ‘Learning Analytics’ Category
Delaying 5 Ways Data Science can Help Education Series
I’m delaying my series about 5 ways Data Science can help education… I actually didn’t mean to post Saturday’s intro to the series, but I often post things ahead of time… And I just forgot to stop it from publishing…. Also, this week there has been some activity about educational content standards that I’m involved with, that I hope to post about…
5 Ways Data Science and the Intelligent Web can help Schools and Education
This past week, I shared a simplified introduction to what is done with data science/machine learning/data mining/predictive analytics work, and the major tasks / roles. This coming week I’m going to share about how I think data science combined with the “intelligent web” (sometimes called Web 3.0 or above) can benefit human education and thus humanity. Some of these ideas can be done at the school level, others are probably better done by vendors, and yet others are best done by governmental organizations or associations. But each of them can make a big difference, if done well and ethically. And to not keep you too much in suspense, here are the ones I’ll be posting about this week:
Data Artistry: Using and Sharing the Knowledge in an Effective Manner
“Can you picture that?” – Dr. Teeth and The Electric Mayhem
The final stage of doing data science/machine learning/data mining/predictive analytics is to use the results, which generally involves some form of communication to one or more types of audiences. This, I will term “data artistry”. (This is not necessarily a common term used, but it does have some precedence in specific contexts)
Data Mining: Discovering Gold in your Data
“There’s gold in dem dere data!” – Adaptation of the original quote from M. F. Stephenson
After the data has been gathered and in a form that can be used, it can then have an appropriate algorithm used to accomplish the data mining/machine learning/predictive analytics. This is the stage that traditionally has been called “data mining” because it is the part that gets additional value from the data in the form of some type of knowledge (this is why early on, the process was sometimes called “knowledge discovery in data” (KDD).
Data Wrangling: Gathering the Data You Need in a Form You Can Use
“Data! Data! Data!’ I can’t make bricks without clay.” – Sherlock Holmes
Before data science/machine learning/data mining/predictive analytics can be done, you need to have the data you are going to use. This may see obvious, but in many cases there is more to this step than may first be assumed, and the whole process is what I will call “data wrangling”, although has other names like “data munging”.
Data Surfing: The Oft Forgotten First Stage of Discovery
“You got to drift in the breeze before you set your sails. It’s an occupation where the wind prevails. Before you set your sails drift in the breeze.” – Paul Simon
Many texts about data science (including machine learning, data mining, and predictive analytics) don’t include much about the very first step of the process, which is the step where you come up with what your goal is for your other steps. In traditional science, this might be called the step of making your hypothesis.
The Four Major Activities of Data Science / Machine Learning
Recently there was a post on LinkedIn by Erle Hall, lead for the Information and Communication Technologies (ICT) for the California Department of Education (CDE) with a diagram about machine learning. That diagram had 6 steps: Select Data, Model Data, Validate Model, Test Model, Use the Model, and Tune Model. Those 6 steps mostly encapsulate what traditionally has been called the “data mining” phase. But there are 3 other important phases, which I will call “data surfing”, “data wrangling” and “data artistry”. (These names were chosen to be easier to understand and more interesting for students, but also go by different names) I also personally prefer to use the term “algorithm” instead of “model”, because while traditionally in data science, statistical models were used, there are now often times methods like neural networks and other such algorithms that are less like a traditional statistical model. In the next few posts, I’ll dive into each of these 4 steps, and give a basic explanation of what each step does, and why the step is important.
SIS Review: Aspen – Great for Large School Systems
My first review of various SISes is that of Aspen by Follett. When Highlands Community Charter School recently was looking to switch to a new SIS, Aspen was in our top 3 choices, and only barely lost out to PowerSchool. During our review process, I had the chance to look at a sandbox system (demo) of their product for about a week, and we asked a lot of questions to their sales rep, Dylan Holcomb. As a matter of disclosure, I should note that Dylan was a friend from high school, but I think this review is fairly objective, as there are clearly things I don’t like about the product, along with many things I really like. I have written about Aspen previously also.
Dr. Felix Bankole has accepted me as a doctoral student
I am very excited this morning. This past year, my doctoral studies hit some snags, as I was having troubles “seeing eye to eye” with my supervising professor within the College of Education at UNISA. And while the Dean of the College of Education was very understanding, she couldn’t find anyone within the the College of Education who could supervise my research. Most of this has to do with the fact that my research is interdisciplinary, and heavily relies upon data mining methods. So, I started looking at the College of Science, Engineering and Technology, and wrote to Dr. Bankole, and today I received an email that he will take me on!
Thought of the Day: “Probability theory is telling us something about the way our own minds operate”
I have started to read the book Probability Theory: The Logic of Science, by the late E. T. Jaynes. From what I understand so far, I think there is a high plausibility that it will help me have a more unified and deeper understanding of probability (and hence statistics). In reading the preface, he makes some interesting observations about probability and human thinking, and it seems quite apropos, and relevant to the recent advances in the fields of artificial intelligence, such as the recent match of Go.
A quote from the book that particularly struck me was the following:
… it is clear that probability theory is telling us something about the way our own minds operate when we form intuitive judgments, of which we may not have been consciously aware. Some may feel uncomfortable at these revelations; others may see in them useful tools for psychological, sociological, or legal research.
