Handout of my presentation on the student perspective of Learning Analytics. Most slides contain a few sentences in the speaker notes (in English) to describe the point I was making there.

1. This presentation has speaker notes, to make it easier to interpret the slides
afterwards when you weren't physically present at the time. I'm Sander Latour, active
in Learning Analytics in the Netherlands on a national level (community building, SIG),
a university level (University of Amsterdam, larger scale collaboration and stimulation
in learning analytics) and faculty level (pilot innovation projects to try things out in the
actual educational practice). Besides that I'm currently also a master student (Artificial
Intelligence) working on my thesis on finding out which educational resources proved
to be effective in order to select the right ones for a student. The presentation was
given at an introductory seminar on Learning Analytics, where I provided the student
perspective.

2. Some if not all teachers would ideally see education as being a delicate process in
which a precious organism gets the opportunity to grow to its full potential.

3. Sadly, from a student perspective, education is often a mass-production
manufacturing process where organisms need to grow at the same time and in the
same direction, or else...

4. What we want is to make education feel like somebody who helps you with the next
step of reaching your personal goal. Learning Analytics will be able to help with that.

5. So why do we want to change anything? Why should a student even be educated in
the first place?

6. Or perhaps the question should be, for what are students educated? And can that
define what education should bring to students.

7. The first typically mentioned is the job market. We need skilled workers to fill job
vacancies and contribute to the economy. What else?

8. An important second reason for education is that we need people to solve the world's
biggest problems of tomorrow. Whether it is global warming, the financial/economical
or political crisis, or a failing educational system...

9. Perhaps not that often mentioned as it should, one educates him or herself to fully
develop the mind and body in order to fulfill their potential. Whatever it may be that
triggers the passion in that person.

10. One thing that keeps coming back when we talk of the upcoming generations, is that
they need to have 21st century skills. Which includes critical thinking and problem
solving, creativity and innovation and communication and collaboration.

11. Another desired aspect of future generations that is slowly but surely getting attention
is that we are more in need of diversity than conformity. We need different people,
with different views on the world and different ways of looking at problems.

12. The society in which the upcoming generations will live has been truly transformed
compared to the times of the industrial revolution. The education system however
seems to not have experienced that transformation, at least not in its fundamentals.
And there is a reason. In order to organize an education system on a huge scale at so
many locations while maintaining some level of quality and comparability, it is not
idiotic to come up with the system that we've come to know as the traditional model of
formal education. And up till now we have not been able to overcome the
organisational and practical issues that have stopped us from changing it in the past.
Because of that, we've grown accustomed to the current ways and some of us
seemed to have stopped looking for improvements all together.

13. But, regardless of how difficult it may seem to be to overcome the organisational
issues. It does have the effect of the current mass-production process in which a
student seems to have been caught.

14. However, we have set our goal for this different experience, of achieving your own
personal goals. And there have been other technologies that promised to
revolutionize education before, such as the video recorder and tv. So why would
Learning Analytics make a difference now? The short answer is, because unlike
earlier technologies it can leverage both the scale and the learning experience itself.

15. Many different types of possibilities arise with learning analytics. Including even the
possibility for different educational concepts, by giving enough for the teacher to deal
with that. But there are two main categories of possibilities that I'll address in this talk.
One of them is tracking. What can we do with the fact that we can keep the data in
which the learning experience manifested itself in the past? How can that help a
student?

16. An obvious example would be that students are now able to reflect more truthfully on
their own learning experiences. At least of the portion of the experience that resulted
in digital data. How much did I work at all in the last period? What progress did I
make? What type of problems am I still struggling with and what is going well. Am I
more effective at some moments than at others? What type of learning material
seems to work for me?

17. Because we do not just gather the data of one student, a student can get insight in
how others are learning. How do I perform compared to the rest of the class? And to
students that are similar to me? Or in similar situations? What material seemed to
work for others to grasp certain concepts?

18. By examining the past one can attempt to predict the future, under some
assumptions. How does my learning activity so far compare to the activities of
students that have followed my path in the past? And did they succeed? Here the
path can refer to a course or a more general personal learning goal. Prediction is not
just occasionally telling students "the bad news". Prediction can reveal that a student
is well on course for a goal, or perhaps even ahead. Prediction allows for planning.
Planning a personalized education plan towards some goal. Beit a path with
additional help or additional material, or perhaps even alternative content if the
educational system would permit to do so.

19. That brings us to the second category of learning analytics possibilities:
personalization. The ability to alter the provided learning tools based on experience is
a gift that has only fairly recently been brought to the land of education, with careful
steps in Intelligent Tutoring Systems and Adaptive Hypermedia. Learning Analytics
combines this with the enormous amount of potential data that comes out of the large
scale of education, in order to create enough basis on which to build personalized
learning environments. There are several ways in which this can take form, I will
name a few.

20. Personalization has been introduced in many different fields before it came to
education. From personalized book recommendations to fully customized cars.
Learning material can be adapted to a student's preferences in terms of presentation,
e.g. visual or verbal, while taking into account if that helped other students that are
similar to you (in criteria that matter for that, not (just) per age group) and whether it
had helped that student in other occasions in that subject. It can also be adapted to
the students level, online learning systems that teach you language or mathematics
often include a mechanism to increase or decrease the difficulty of the material based
on their model of the student's competences.

21. Besides altering certain material, you could also alter which material is presented in
the first place. There are roughly two ways of doing that: adaptation and
recommendation. In adaption a system tries to automatically decide what material
should be presented to the student next when new material is requested. It might
even go as far as planning when to execute which pedagogical action like when to
ask a question to the student and which one. Examples of adaptive learning
environments are adaptive books that automatically write chapters on the go. In
recommendation you leave the decision to the student, you simply present a list of
several good options (based on some criteria) from which to choose. A possible
reason to go more for recommendation and not adaption is that we want to have
students that are capable of making those decisions and should actively and critically
take control of their own learning. But the optimal choice might lie in between
depending on the context and student.

22. Perhaps one of the biggest flaws in the current educational system is that every child
is expected to more or less understand the same things at the same moment. I'm
absolutely convinced that this is due to organizational constraints that come with
making a schedule. Learning should be able to take place until a student gets it, not
until a student is supposed to get it according to the schedule. Learning Analytics can
offer some help here by allowing an online system to continue the learning process
until fully completed (Khan Academy does this). Furthermore it allows teachers and
students to have more information on whether it is still going somewhere, without
disturbing the actual learning process. But in the end, in order to fully take advantage
of such a personalization the educational context in which it takes place must be
much more free and opened up than the current educational system.

23. Summarizing, there are some great benefits that a student could have from Learning
Analytics and many more I did not mention. In one sentence, it can provide more
insight on learning in the past and based on that improve learning in the future.

24. That all sounds great. Are there dangers along the way? Yes there are.

25. There are large open questions on data ownership, who decides what is allowed with
certain data? My personal belief is that we should go for a model where the person
whose data is gathered should be in control of that data. But there is many practical
situations to consider. Also legislation is on its way to make it slightly more clear what
you can and cannot do. For now we just have to ask ourselves what we find ethical to
do, and involve students in that decision.

26. When data is gathered and connected for a large part of your life, there is a risk that
the data of the past hunts you. In education this could also play some role. People
grow and weaknesses of the past need not exist anymore in the present or the future.
When we talk about connecting learning trails from primary school up to higher
education and beyond, we have to think about how to make sure that students can
still have a fresh start now and then.

27. A danger with a tool like Learning Analytics is that it is used for the exact opposite of
the personalization dream. You can use Learning Analytics as tool to see who falls
behind of the group and for example kick them out. You can use Learning Analytics
as the quality control mechanism in a factory, which doesn't personalize or allow for
more freedom. It allows for a larger scale of more of the same. It is up to us as
researchers, policy makers, teachers and students to decide to use it in other ways. If
we don't, it will still be beneficial to some but it won't improve upon the fundamental
structure of traditional education.

28. A good thing to keep in our heads at all time is the Learning Analytics cycle. In that
cycle it begins with learners, with education, that attempt to achieve some learning
goal. That leads to a certain pedagogical problem or situation with which we might
want to do something. That situation manifests itself, for a part, in data we can
capture. The data can be analysed in such a way that it can provide information that
might be helpful for our pedagogical situation. The resulting information can then be
used for a pedagogical intervention, via some route, rewarding or altering, that will
change the learner or the learning environment in which the learner operates. The
word pedagogical has been used a lot for a reason, since it helps to stay away from
many pitfalls if you keep in mind the goal: helping learning.

29. One might deduce some sort of checklist which can be used to score Learning
Analytics projects or tools. I did. It might be the case that a project does not score on
all points, which is not necessarily problematic at this stage. But it is good to make
clear the steps it has to make.

41. Like the learning analytics cycle, you could depict a learning analytics development
cycle. Which indicates the necessity for stakeholder involvement in many iterations to
come to a good result.