یادگیری تجزیه و تحلیل برای اطلاع از تمرین

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گزارش خرابی

[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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