Case Study - Learning Analytics to Inform Teaching Practice (Optional)

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Do curso por UNSW Sydney (The University of New South Wales)

Aprendendo a Lecionar Online

219 classificações

UNSW Sydney (The University of New South Wales)

Aprendendo a Lecionar Online

219 classificações

The Learning to Teach Online (LTTO) MOOC is based upon the multi award winning open educational resource of the same name, developed by Simon McIntyre and Karin Watson. We have built upon these resources in this MOOC, supporting them with a course structure that puts them into a supportive framework for you all to help you find and connect with each other to share your own experiences in the Discussion Forums. This course is not intended as a comprehensive manual for online teaching. Rather, it is intended as a starting point to help you develop your understanding of the principles involved; a place where you can get advice about strategies you might try; and a means from which you can begin to build your confidence and capacity to continue the learning journey in your own contexts and practice. On successful completion of the course you should be able to: - Identify the importance and use of a range of online technologies in learning, teaching, and course design in contemporary education - Evaluate different course designs involving digital technologies and online learning activities appropriate for a range of teaching contexts - Demonstrate independent enquiry and reflective practice On successful completion of the the two Assignments, you will should be able to: Achieve all of the above learning outcomes plus: - Develop an evidence-supported argument for selecting particular technologies to support an online component of your own course - Design an online component of your class considering your local context, your curriculum, and the benefits and risks of online technologies - Develop evaluation and engagement strategies to ensure your students are engaged with your online course components and how you plan to evaluate the effectiveness of your online course design.

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Engaging and Motivating Students + Evaluation Strategies

'Module 7: Engaging and Motivating Students' explores various strategies for engaging and motivating your students in a range of different online learning scenarios. It discusses the important roles that curriculum design, activity structure, the relevance of the chosen technology, and effective time management play in creating an enthusiasm for learning in your students. 'Module 8: Evaluation Strategies' examines the importance of evaluating your online teaching practice to ensure that it is effective, and to give you an opportunity to constantly improve. This is especially important when you are first developing an online teaching practice, or when you are trying something new for the first time. We will explore an evaluation strategy based upon four reflective angles: self reflection, peer observations, student feedback, and theory. The module also introduces the concept of learning analytics as an informative tool to enable up to the minute evaluation of your online class.

Key Concept - Evaluation Strategies3:27

Case Study - Learning Analytics to Inform Teaching Practice (Optional)7:44

Conheça os instrutores

Associate Professor Simon McIntyre
Associate Dean Education
UNSW Sydney | Art & Design
Dr Negin Mirriahi
Academic Developer, Learning and Teaching Unit (formerly)
Adjunct Lecturer, School of Education

[BLANK_AUDIO]

So what learning analytics means to me is the use of data

that you collect, about the events that occur in a learning environment.

And how you can you use that data to guide

your design decisions such that, the overall experience is improved.

Learning environments can both be online or

in class face to face traditional learning yes.

So the data that I am collecting is basically or it could be summarized as:

The interaction or all the interactions that occur in a learning environment.

And by interaction what I mean could be students with the

students, if they answer, each others' posts for a discussion forum.

It could be the students with the material, so

you detect when a student is using certain resource.

Students and tutors it could be when a, a tutor asks a question

in a forum and the students answer that or the other way around.

So any sort of interaction that occurs

in that environment, if it is technology mediated.

Then, you typically gain access to recordings of the events that occur.

So from my point of view, the way I see

it, is the first step that a teacher should consider for

using learning analytics is actually thinking, what kind of problem or aspect

do you want to detect and act on, your learning environment.

You need to have some sort of objectives.

So for example, suppose is I want to make

sure that my students don't drop out of my course.

Or I want to make sure that they

sustain their engagement all throughout the course.

or I want to make sure that they,

I don't know, they participate actively in team activities.

So when you tackle one specific aspect, then you start again working backwards and

trying to deduce first, what kind of

actions would be helping me to achieve that.

And what kind of data would give me

an insight if those actions are working or not.

So then, when you put everything together.

If you have this scheme in place.

You would have, data that is being collected.

You're looking at the data and see how does that relate to your objective and

then deciding what kind of actions or

adjustments you need to deploy in the environment.

Such that your objective or your outcome is achieved.

[BLANK_AUDIO]

So for example, sources of information that

you can get directly from the students.

You can ask them about the dedication they had, with some activities.

Was it too intense?

Was it not?

What kind of activities did they end up solving, or not solving?

Participating or not.

You can ask them also about when do they get these activities done.

How are they, how are they going about doing this?

suppose, for example, that you want to foster teamwork.

You can ask them and say, so do you do these alone?

Do you do these with your team?

You can even ask them directly.

Is your team working perfectly?

Would you change something on your team?

So that type of information already offers you a view or an insight

about what happens in terms of interactions and you can react on that.

So, the variety of data sources you can have is, is huge.

So you shouldn't be obsessed with

getting electronic data only from online platforms.

You again should be very creative and, and open-minded and say, what I

need is information, what I need is

insight about what happens in the environment.

And anyway I can see to get that information is correct.

Once you get that information then the delicate, part comes.

Which is the analysis or the sense making.

You have to make sense of that data you collected.

So, if you have the number of times that a student's logged into the platform.

Plus [SOUND] the number of times they post in the forum.

You have to make sense of that.

And you have to try to see if it gives

you an insight on the level of engagement or not.

And once you make sense out of that data, then, you, decide, what

kind of adjustments you would like to deploy on your, on your environment.

So for example suppose that, you get the data that, half of

the students barely connect to the,

platform or barely participate in the forum.

To give you a simple example, what kind of action would you, [SOUND] decide.

It could be something as simple as sending them an email

saying by the way, it's been two three weeks in the course.

I see that you haven't participated in the discussion forum.

It is important for our course, and

therefore I would like you to participate or

tell me what kind of issues, or

what kind of difficulties you're finding for participating.

And that type of action could produce an

effect in which you see either the student.

That begins to participate or do they come back

to you with some reasons by which the forum

is not actually working the way it should which

points you to another aspect you should go for improvement.

So I think the crucial part is not so much capturing the data, but

it is more like sense making and deciding what kind of action would you deploy.

So a concrete example that I'm using in terms of learning analytics is detecting.

So in my course the students are supposed to

use certain amount of tools to perform certain tasks.

Fairly procedural, but we give them those tools and some of the tools are optional.

But we want them to get used to that type of environment.

So one of the things that we are observing is the level of use

of those tools, so how often do you come in contact with those tools.

And what we have detected is a correlation between the set of tools

that are used more often out of three or four, and academic achievement.

So we see that certain students that do not

use a specific tool within the portfolio.

They correlate with low academic achievement.

So, the actions that we derive from there is, try to lower the barrier for

them to use that type of resource,

providing additional support, tutorials, hands-on type of guides.

Kind of like scaffold a little bit more.

That type of activity using those tools

such that they get exposed to that environment.

And hopefully, they will translate into better academic achievements.

Yes so another example is to provide the students with brief

questions about certain topics that you plan to cover in the class.

And you ask them to read before coming to class certain basic documentation.

And, what did is we embedded the questions like next to the document.

It's basically, you can, take, make no

difference between the document and the questions.

And the questions are grouped by topics.

And then what we know before going to class

is what kind of questions were answered more often.

And, which one of those were answered incorrectly more often.

And that informs us on how to approach the lecture.

We can go there and try to tackle certain

issues that we have identified previously that are difficult.

Or that are, or students are struggling with coming to

terms with that type of procedure or concept or topic.

And therefore, we emphasize a little bit more on the lecture based on that.

So my, my final comment about learning analytics

is that it is a very promising area.

But at the same time it comes with challenges.

The promise is that by knowing exactly when it happens in detail in a

learning environment, very likely we'll be in a much better position to improve it.

But when you try to deploy it in reality, it

requires a lot of multidisciplinary work a lot of people involved.

It's not only something you can do on your own, only in your class.

You will need support, technical support.

You probably need some strategic view of the level of the institution.

So it is a high potential, but also

tricky implementation type of tradeoff for learning analytics.

That's the way I see it.

[BLANK_AUDIO]

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