>> This is an introduction to the data and analytic cycle, now this obviously vary
in different context including access to data within an organization and the kinds of tools
that you have available and even just organizational processes.
But this is roughly the types of things that you'll be aware of that you'll be engaging
in when you're working with educational data.
So first of all, when we're dealing with educational data,
you have a range of data source options, now these sources are growing in quantity regularly
as there's no approaches for data collection, as there are different types
of integration opportunities between datasets, which means you can get some level
of additional insight when you bring in multiple datasets but these sources can include anything
from student information systems to student -- or to the institution LMS data,
it might also be used through some mining of social media, use of swipe cards
within a university or within a school system so there's a range of things
where we use our basic data sources with.
Now I haven't seen a lot of systems that have done a great job
with putting together an integrated repository, which brings these different datasets together
and partly because it's not a very simple process to do
but the general idea is you have a range of datasets and then you have a range
of questions that you're asking of that data.
Now in some cases, you're looking at things such as how does being connected socially
within a course impact students' success and are there ways
that we can help foster better social connections if there's actually a relationship
between social positioning in a network and the performance
of that student in an academic setting.
It may also be around trying to identify or sensitizing models early on based
on student profiles so if a student comes from a particular type of a background,
first in family in terms of degree completion, there's a possibility
that that information can be helpful for a university in terms of tailoring resources
that the student might need or at least in sensitizing the analysis work that they do
so that certain behavior from a student who's deemed to be at risk due to a variety
of factors are addressed in advance and the behavior of that individual would be different
from a student that's perhaps not deemed to be at risk so try that again,
they could both be exhibiting similar behavior but the system would treat them differently
in terms of how they're assessing or evaluating that student.
Now this comes out in two perspectives and there's academic analytics,
which is really using data and analytics to improve organizational performance.
There's learning analytics, which we're obviously focused on in this course
where we're targeting what the student is actually doing, what the faculty is doing
and ways to improve both the teaching and the learning aspect of it.
So the analytics model that we're looking at looks something like this, as I mentioned,
it can change for a variety of context and a variety of reasons so I'll go through each
of these elements individual fairly -- individually fairly quickly.
So the first part obviously involves getting a hold of the data and this can come from a range
of options, both institutional or outside the institutional.
There may be factors such as you're looking at getting data to --
for the organization to improve its marketing or promotion to a particular type of student
or student profile or it may be you're trying to build a particular model of pedagogy
or assess a pedagogical model based on certain attributes of that student or based
on certain practices of the educator.
So once you've really sort of defined or looked at what is it that we're trying to do
with this particular analytics activity, then you can define how you get a hold of
and how you make sense of the data that you're collecting.
From there, there can be questions that relate to storage.
In many cases, the data is automatically going to be stored with the native application
that we're going to use to do analytics work with, meaning that the storage
of LMS data isn't an issue that you necessarily need to look at, it's getting a hold
of that data that's more consequential.
On the other hand, if you're going to use some data that is being generated
through social media or that is being generated
through other organizational data collection practices, then issues of storage and security
of storage and moving it out of that initial database into a data format and a database
that you can use for analysis work is an important consideration.
In some instances, you're going to have reasonably clean data or data at least that's
in an analytics friendly state and that's typically if you have, as mentioned already,
LMS data, which is institutionally configured against certain variables, often that's related
at some level at least to a student information system data
so that's in a reasonably good shape.
On the other hand, if you have data that comes from social media or that comes from a variety
of different systems that aren't necessarily connected meaningfully, then you likely do have
to go through a process of both cleaning and then ultimately integrating those datasets.
From there, it's the type of analysis work that you're doing,
now obviously this is a question actually that's decided early on.
It's not -- you're -- you don't start thinking about analysis at the point of having all
of your data together; quite often, you're going to be looking at specific questions
and the process of collection and acquisition of data is going to be related to the types
of questions that you're asking institutionally or that you're asking
at a particular class level, whether that's a specific analytics technique
or whether you have broader goals as a system in terms of being able to get a sense
of which students are at risk of dropping out or what are the best course sequences
that a student should take through a program
that produces the greatest possibility for success.
Now as mentioned in the previous video, there are important considerations to be dealt
with around how you present that data.
It certainly isn't sufficient to just take a --
the analytics output of work that you've been doing and just present it
to someone in a CSV or in a similar format.
You need to present a visualization that ideally is interactive, which means that the individual
that is viewing the data or working with the data has the ability to change variables
or to begin to look at well, what happens if we change this here or ask specific questions
of that data; that's why the tool we're using in week 2 is tableau enables a good explanation
or a good overview of what that process is and how that actually works.
And then finally, which we aren't going to look at in this course but it's something
to be aware of, once you've gone through this experience or this entire cycle
of collecting your data and cleaning it and analyzing it and representing it to learners,
faculty, teachers or administrators, it's about what you do now and that's not something
that we're looking at in this course but that's an important consideration that should feed
into the subsequent process of additional data collection.