Podcast 
More and more data is being collected on learning and teaching. New technologies and digital platforms are making it possible to use it more effectively. What are the future prospects for using learning analytics and data? Marjo Keckman from Satakunta University of Applied Sciences and Tomi Rautaoja from the University of Turku discuss this topic in the podcast. The host of the podcast is Programme Director Hanna Nordlund.
The language of the podcast is Finnish.
Listen to the episode here:
Transcription of the episode
Hanna Nordlund:
Welcome to the podcast of Digivisio 2030 project, where we talk about what kind of future of learning we are currently building in Finland. I am Hanna Nordlund, and there are different specialist guests sharing the microphone with me. This is where Oppimisen Seuraava Luku [the next chapter of learning] begins.
[Music]
Hanna Nordlund:
In the fourth episode of Oppimisen Seuraava Luku podcast, we are talking about utilising data in learning. Why and how is information collected and how will data be used here and in the world in the future. Here with me to discuss this and much more related to data are Marjo Keckman, the lector of Satakunta University of Applied Sciences and the student counsellor of the service business area of expertise, as well as Tomi Rautaoja, the research coordinator of the Research Institute for Learning Analytics in University of Turku. It’s great to have the real professionals of the subject as my guests. Welcome.
Tomi Rautaoja:
Thank you.
Marjo Keckman:
Thank you.
Hanna Nordlund:
Tomi, you do research on learning analytics. What originally inspired you to work with this subject?
Tomi Rautaoja:
I have always been very interested in how people learn and what drives the learning forward and what supports it. That was maybe the reason why I started doing this. After my master’s studies, I stayed at the university to become a doctoral thesis researcher. Back then my subject was learning a foreign language, so the theme of learning has been with me for a long time. Learning analytics is an interesting subject because it visible the process of studying and what happens there, what kind of things support it, what maybe challenges it. So in that sense I am in a very interesting position in this current job to watch the process of learning.
Hanna Nordlund:
Marjo, you work in Satakunta University of Applied Sciences as a student counsellor. Do you think we can currently utilise learning analytics sufficiently or is it more of a thing of the future?
Marjo Keckman:
I feel like today it’s a hot topic. We have begun to notice it and it’s a subject of interest for us. Then again, in the future it will be a part of our daily life and it will help us in the durability and guiding durability and learning.
Hanna Nordlund:
We already said the phrase learning analytics multiple times. Let’s go back a little bit to the basic question of what learning analytics actually means and why we are collecting data related to learning. Tomi, you can start.
Tomi Rautaoja:
Yes. Thank you. If we start with a commonly used definition, learning analytics is the examination, analysis and measurement of the study process, where the goal usually is to better understand learning and the process of learning and we could also somehow develop it. I would look at it from the perspective that it’s of course a branch of science. Learning analytics is studied a lot in higher education institutes nowadays. People are also developing different solutions for it from the ICT perspective. But in addition to that and maybe the most important thing is that it’s the daily activity that happens in the educational institutions. The teachers are gathering information about how their students learn and about that process, what kind of challenges there are and what kind of procedures can be done to solve these challenges.
Hanna Nordlund:
Marjo, do you want to continue? Tomi already started, but can you expand on what kind of data we collect from our students and learning in higher education institutes nowadays? What kind of data do we already have?
Marjo Keckman:
I think we already have a lot of data that we use and collect, but the question is if we can utilise it properly. If we first think of the basics about learning so grades, course feedback and exercise feedback. Then we have data from the AVOP answers after the studies and career monitoring surveys. We have a huge amount of data, but we still have to continue to examine how it is being utilised in developing people’s own learning or teaching or guidance.
Hanna Nordlund:
It sounds like right now we are at a turning point. We talk about data so much at the moment, and data economy is one hot topic right now. You just mentioned that we have a lot of data and whether we can utilise that. Marjo, you said that maybe in the future it will be a part of our daily lives but not yet. What kind of change are we actually going through right now, and what kind of things should higher education institutes pay attention to right now?
Tomi Rautaoja:
I think we have had a good start in utilising learning analytics. Different educational institutes are using more and more learning analytics and the utilisation is broader every year. But I think one of the turning points will be from my perspective that there are a lot of differences between educational institutes about how much they utilise the analytics, and I’m sure there are also differences within the educational institutes of how different departments utilise it. There are benefits for everybody, but I’m not sure if everybody can utilise it yet in a way where they would get everything out of it. Then another turning point I see will happen in the future is related directly to the analytics. Most of the analytics we are currently using in educational institutes at the moment are so-called descriptive analytics. That means they reflect backwards on the learning. For example, what has happened on my course so far. But there are many other types of analytics. We can talk about more sophisticated forms of analytics, such as explanatory analytics, which aims to describe why these things have happened on my course. There is also predictive analytics, which instead of reflecting back on the situation, it aims to predict what will happen on my course. Then there is also prescriptive analytics which aims to guide the teacher or learner or act as their friend about what kind of things they should do, so that they can reach the best end result during the course.
Hanna Nordlund:
So in a way instead of looking in the rear-view mirror, we should look forward and find support to how we can also change things.
Tomi Rautaoja:
Yes, because I think that will open up new possibilities in how we can, for example, even earlier recognise the need for support in our groups.
Hanna Nordlund:
Tomi, you already brought up the benefits, and both of you talked about whether we know how to utilise it. Let’s go back and talk a bit more about the benefits and how the learner benefits from learning analytics. Marjo, can you start?
Marjo Keckman:
Yes. I think that it should be a tool for the students and learners in the future. It would be sort of life design. You can design your life in a more precisely. We use life design in counselling, but in addition to increasing the know-how, wellbeing and career counselling are related to learning analytics and everything else. Even if it’s just one course, you think about how that would serve the career you want to have, or if there are things related to wellbeing, you think about how you would physically manage that job you are aiming for. So all of these things together. Analytics would be a tool. If we think about physical condition, you can have a pedometer. You have some sort of an indication of what your physical condition is. You can also monitor your pulse. It’s the same with this. It’s a tool that will show you an indication, but doesn’t tell you everything. You also need self-knowledge.
Hanna Nordlund:
Can learning analytics help with improving self-knowledge?
Marjo Keckman:
Yes, definitely. I think that sometimes students are also a bit lost. They don’t know what the direction they want to go in is and what they should do. Then it would be very good to have these means of getting a direction where you could go. That sounds interesting. I will try that out, and then maybe decide what I will do. Yes. It works.
Tomi Rautaoja:
Yes, and I think there is huge potential here that we haven’t quite opened up yet. Analytics have been developed a lot during the years from the perspective of the teacher and the administration. The perspective of a learner is something that will be brought up significantly more in the future. You described nicely, what kind of possibilities there are that the learner can get out of analytics.
Hanna Nordlund:
This is an interesting perspective in the sense that in Digivisio, we have chosen learner orientation as a key foundation. You described well how the learner can actually get a lot of support for themselves through learning analytics. What about the teachers? Tomi, do you want to clarity what could be beneficial from the perspective of a teacher if learning analytics are used?
Tomi Rautaoja:
Yes. There are a huge amount of different benefits. I think one of the key benefits, which I already mentioned, is that we can more easily find things to develop. We can recognise need for support and react to it in an earlier stage than without these tools. But then we also talk a lot about things like diversifying evaluation. If we have more information that we can utilise in evaluation, the evaluation would hopefully better reflect the real situation. With the help of analytics, the teacher can also understand factors that affect learning, such as matters related to student’s wellbeing, motivational factors and so on. The teacher can keep up with them better and answer to them as well when they know about it more.
Marjo Keckman:
I could also add to that that the teacher can also recognise good learners and give them a boost. So not only those who are at a risk of dropping out or falling behind, but that you also find people whose goals might be higher and they would like to proceed faster. As a teacher, you can bring it up. I am also partly speaking about tutors who have their own group they are teaching, and finding people there who have high aims. The students don’t always dare to bring it up themselves, so you as a teacher have a tool for that.
Hanna Nordlund:
This is actually a good bridge to the next subject I would like to discuss with you. You said that the focus has been more on the dropouts and finding challenges, and that leads me to my question that can the learner also get a bit of a big brother is watching you vibe from the learning analytics? What kind of risks are related to the use of learning analytics or things that we should be aware of when we start utilising it?
Marjo Keckman:
I think that the risk is that you as a learner start only looking at the results. You don’t understand what your end goal is. Or if we take the data too far, it can start guiding you a certain direction. So if you are this kind of a person, these and these courses and professions match you and so forth. So it guides you too far, and maybe the student doesn’t want to do that but they don’t recognise that. This requires enough information for the students so that they can look at the amount of information and know when it’s a good direction for them but also know when it’s a risk. That they, I can do something else than what the data is telling me as well.
Tomi Rautaoja:
I think we have failed a bit if the student feels like a big brother is watching them. The most important thing in utilising analytics is that the process is transparent so that the learner can also keep up with what information is collected and why. They also get the feeling that OK, it isn’t a dystopia where the information is gathered for monitoring, but that we want to help them forward and that they can also get benefits from the data for their learning. You brought up well what I also think is possibly the biggest risk in utilising analytics, which is that if analytics is utilised in a very one-sided way and without criticism, it is easy to end up in false conclusions about the whole subject. That is why analytics and these kinds of machines should never be given the status of a decision maker, but the decisions and interpretations need to always be made by the teacher who is a professional in their field and know their learners the best. When there is a person like this interpreting the analytics, I don’t think we are going down the wrong paths. But of course it requires the teacher to have the skills to interpret it. They have to understand what kind of strengths and weaknesses analytics has. After all, analytics always only tells about the data that has been fed into it. It cannot take external things into account. That is also important for the teacher to be aware of. We are talking precisely about having the skills in the organisation to utilise analytics in the right way.
Hanna Nordlund:
Yes. We arrived quite naturally to the theme of counselling. So analytics can also bring a lot of added value to that. What I got from what you just said is that it is still important that there is a personal relationship and that the teacher or counsellor can walk beside the learner. Then we could get support for that from analytics. But how do you see we could use learning analytics in an even better way in counselling?
Marjo Keckman:
In the Satakunta University of Applied Sciences, we have utilised Power BI as a method or tool for it. I get to see all of the data of the student at the same time, and then I can also see who are at a risk of dropping out or have challenges with a specific course. Maybe they are getting stuck or have failed a course many times, and what we can do about that. But at the same time, I can also see the courses that are on offer next that they could take again. So at one glance, I can see all of the possibilities and that makes it faster since I don’t have to click a lot and find stuff from the computer otherwise. Now I can ask more questions from the student and use time to do that. If needed if the student is not present, I can send them a quick email depending on what the student needs. I feel like it helps. It makes my work faster, and takes away from the not so important work and gives more time for the encounter.
Tomi Rautaoja:
Yes. Marjo already described the benefits well here. I also think that analytics is a good tool for planning counselling. At one point we were thinking about making analysis where we looked more broadly at the course selection and see how the students proceed and behave there. Then we could find tendencies of what courses sort of go together and how courses are chosen, but then also more challenging combos. So for example if some courses affect each other negatively in a certain order or something like this. Then we can also look at the learning path from a step higher and plan the bigger paths through that.
Hanna Nordlund:
Tomi has again given me a great bridge to the next subject. We already talked about what benefits learning analytics bring from the perspective of a teacher and a learner. What about from the perspective of the administration and leadership of the higher education institute? Now we are coming to themes of knowledge-based management. In Digiviso, we have actually shifted to talk about knowledge-based activity so that we would remember all the possible target groups. But if we talk more about the knowledge-based management, what benefits are there?
Tomi Rautaoja:
I think learning analytics offer possibilities in the same way as for the other target groups. I think knowledge-based management has been a hot topic for the last few years, and learning analytics offers the world of data acquisition and analysis to it. In the end, I think that learning analytics is a method or tool of data acquisition. If we want to do knowledge-based management or like you well said, activity, then there also needs to be data that the conclusions are based on. In the context of school, that often comes through learning analytics. In the last few years, we have concentrated in Finland a lot more on the fact that learning analytics can benefit the administration as well. Often questions that the administration deals with are such that they actually have to or it would be good that they are based on data. If we talk about wellbeing questions or things like these, a gut feeling is not the best thing to go forward on, but you need data to support the decision making.
Marjo Keckman:
To add to what Tomi said, resource matters is one thing. When the administration is told which courses are challenging and they are shown with numbers, after that we can think about if we should target more resources for them. Should we change how it’s executed, agree on the possible pedagogy with the teacher or what should we do. Right now our students are constantly changing. So whether it’s international students, or students who come from the vocational schools that might have learning disabilities or challenges with learning. How do we react to those. Are we using more time and more resources on the first courses, so that they can reach a certain level and can handle the next three and a half, four or six years forward on their study path.
Tomi Rautaoja:
Yes. They talk about the same thing in primary and secondary school when I discussed with principals about the topic. It’s exactly the questions of resources and support and how they can target the scarce resources they have to get the best benefit out of them. This is a very essential question at this point.
Hanna Nordlund:
I think this discussion has brought up well the fact that we are talking about an emerging theme where there is quite a lot of work to do still that we could manage this in higher education institutes. What kind of capabilities does this require of higher education institutes and how should they be developed?
Marjo Keckman:
Well. That is a good question. Capabilities. I think it requires excitement and curiosity. I have been listening to Olli-Pekka Heinonen, and he talks about how we don’t know what good is coming. Things are good now, but that won’t be enough in the future. We have to be ready to develop and be curious and excited and sometimes persistent in finding new things. New things cannot scare you. You have to be curious.
Tomi Rautaoja:
Yes. That is very well said. If the subject scares people or makes them nervous, it’s going to hinder implementing it in higher education institutes. I think one capability that is required is a certain type of literacy of analytics, so you understand how this kind of analytics is interpreted and what the pitfalls are in the interpreting stage. How do we come to the conclusions that actually tell us about the real-life situation. Let’s take for example take into account all of the surrounding information about the group and the learning situation when we are making decisions. Then how to do this in practice, I think that in some higher education institutes they have already started making learning analytics policy work. For example, we at University of Turku have a learning analytics programme that was published a few years ago and it gives good policies about what goals we have for learning analytics from the perspective of the higher education institute, what kind of principles guide its use, what it is utilised for and what it is not utilised for. It also takes well into account how we keep the learner in on the whole process and how their rights are taken into account there.
Hanna Nordlund:
How do you see that the development of know-how is related to managing this theme in higher education institutes?
Tomi Rautaoja:
It specifically requires development of know-how. This is a new subject for many people. It might not be scary, but it might feel foreign to many. The best way to answer to that is through training and joint discussions, and through that, the know-how accumulates in time into the departments and higher education institutes.
Marjo Keckman:
I think that know-how is accumulated when you dare to start doing things. You are going to make mistakes and things happen. It’s like Emil of Lönneberga. You try things out and then see what the result is. But there is constantly so much new information that you cannot always be on top of it all and then start doing things, but you have to dare to try things.
Hanna Nordlund:
And then if we look a bit outside of Finland, what kind of exciting international examples do you know and would like to bring up here that we could maybe draw inspiration from and learn from?
Tomi Rautaoja:
There are new applications coming up all the time. This is an active field in that sense. Maybe I would look at it from the perspective that every time these new things come up, we think about how they work with our teaching. Where I need additional information. What kind of questions do I want to answer with analytics. Then also what has some kind of proof of is operability or effectivity. That’s also a good perspective to have. But there is one interesting example that I ran into a while ago. We were talking earlier about learner-centric analytics and a good example of that is an analytics tool for study skills and self-regulations skills that was made for higher education institutes. It’s a three-part system. In the first part, you looked at the course you were working on at the moment and took all of that work or goals of the course and disassembled it into partial goals and scheduled the work. So basically you planned how you would complete the course. Then the other tool for it was a monitoring tool for the student themselves. They wrote down what they actually had done for the course, which probably gives an interesting contrast to the previously-made plan. Then the third tool was a learning diary which is more common while working on a course. Then the student could reflect their learning process in it. I think this is a very interesting example of how we can plan these tools in a learner-centric way.
Marjo Keckman:
I feel like we constantly have different new methods and tools, so we don’t know yet which of them will live on and which have some proof they work. I think in some ways the development in Finland is on quite a high level. That is the feeling I have at the moment. I think we can proudly carry that knowledge, and then pick out some good ideas from the world and develop them further. I can’t name any one international idea that we should take note of here in Finland.
Hanna Nordlund:
Let me ask you this way. What examples do you have from Finland that we should maybe bring up and take out into the world?
Marjo Keckman:
I think in terms of the methods we are currently developing related to learning and counselling, we have a lot that I could recommend we take out in the world to show what we are about. But I can’t name any specific ones in this case either. Nationally we have lots of different things here, but we have to think about how those things would work internationally with different educational systems. They might be different to ours, so we have to be careful because what works with us doesn’t necessarily work with other countries.
Tomi Rautaoja:
Yes. That is true. We always have to think about our own context and if it fits the need we thought it would. But Marjo, I liked that thought that in a sense we are very advanced at analytics in Finland. Maybe we could think about it in the sense of what we could give internationally.
Marjo Keckman:
I have to add that there is a small worry internationally that some people still use a pen and a paper, if that. The gap is getting really big. We need to find solutions for that as well.
Tomi Rautaoja:
Indeed. I think we also have a certain responsibility as a forerunner to take this world further. Of course I think that pen and paper also have their place in education. I hope that electronic solutions won’t replace these fully. I think we usually get the best end result by versatilely combining different teaching methods and tools.
Hanna Nordlund
Yes. These were good words to end on. So we are looking for a balance and thinking how these means of analytics are brought as a support to the existing means and tools that we are already using. I think you nicely brought up in this discussion that we have a good chance here to take things into the direction that we learn to take a more future-oriented perspective and not only to look back with the help of analytics. We need to remember that this benefits a lot of different actors in higher education institutes. At least from what I got from Tomi was that we have to remember the learner and the learner-centric development of analytics. We can give the learners a lot of support and maybe self-knowledge through that, and in that way support ant make the work of counsellors easier. We are taking on learning analytics with an enthusiastic and curious but at the same time a persistent attitude. But it also requires the higher education institutes to make policies of how analytics is used. I think it’s important what Tomi about also explaining what it’s not used for so that trust is maintained. We should also remember to toot our own horn in Finland and realise that we are quite advanced in these things. We should remember to be proud of our own know-how and how far we have already come, and then continue doing good work in the future as well. This was a very interesting and inspiring discussion. Thank you both for being a part of it. You can continue the discussion about these subjects on social media with the hashtag Digivisio2030. I am Hanna Nordlund, and this was Oppimisen Seuraava Luku.