Data literacy and use for teachers (Agile EDU MOOC webinar) - Barbara Wasson and Cecilie Hansen

Summary

In a recent European Schoolnet Academy webinar, Barbara Wasson and Cecilie Hansen from the University of Bergen discussed the significance of data literacy and teacher inquiry in education. They highlighted projects like the Dolly, aimed at enhancing data literacy through playful learning resources, and shared insights on the TISL method for systematic teacher inquiry. Techniques such as the use of games and digital courses like Data Journey are examples of innovative methods to boost understanding and engagement with data literacy. The importance of critical thinking, ethical handling of data, and collaborative learning among teachers are emphasized as essential elements in enhancing educational practices.

Highlights

• Barbara Wasson and Cecilie Hansen emphasize the need for data literacy in education! 👩‍🏫
• Introduction of playful methods like games to enhance learning about data! 🎮
• The Dolly project focuses on adult data literacy through gaming pedagogy! 🧩
• TISL framework assists teachers in systematically improving educational practices! ✍️
• The innovative Data Journey course is open to anyone eager to learn about data! 💻

Key Takeaways

• Data literacy is a crucial skill for modern educators, allowing them to make informed decisions! 📊
• Games can effectively teach complex topics like data literacy through playful engagement! 🎲
• The TISL (Teacher Inquiry into Student Learning) framework helps teachers systematically improve their practice! 📚
• Projects like Dolly provide accessible resources for data literacy across different age groups! 🌍
• Critical thinking about data involves understanding its biases and making ethical decisions! 🤔

Overview

In an engaging webinar, experts Barbara Wasson and Cecilie Hansen from the University of Bergen discussed the vital role of data literacy in modern education. They presented innovative projects, such as Dolly, which aim to enhance data literacy through a playful learning approach. From adults to seniors, the program provides accessible resources to foster a better understanding of data among diverse age groups. The use of games, both digital and physical, is a primary strategy highlighting how fun can be an effective learning tool.

The speakers also introduced the Teacher Inquiry into Student Learning (TISL) framework, a systematic method for educators to investigate and improve their teaching practices. This process encourages teachers to delve into understanding their assumptions and actively engage in collaborative inquiry. Such practices not only foster professional growth but also lead to improved educational outcomes for students.

Furthermore, the Data Journey course was showcased, representing an open-access platform for anyone interested in understanding data and its implications. Encouraging critical thinking and ethical use of data, these projects and approaches underscore the importance of equipping educators and learners with the skills necessary to navigate the increasingly data-driven world, thus enhancing overall educational experiences.

Chapters

• 00:00 - 01:30: Introduction and Webinar Overview The chapter titled "Introduction and Webinar Overview" discusses the beginning of the webinar focusing on teacher data literacy and teacher inquiry. It notes the presence of Barbara Vason and Cecilia Hansen from the University of Bergen who will present their work on the Delhi project and TISL. The host expresses excitement about the ongoing project and the participation of the presenters.
• 01:30 - 03:00: Introduction by Barbara Wasson In the introduction by Barbara Wasson, the focus is on setting the stage for the activities and discussions that will follow. There's an emphasis on managing time effectively by encouraging participants to ask questions via chat or Q&A functions, which will be addressed towards the end of the session. Barbara aims to keep the introduction concise in order to maximize the time available for interaction and engagement during the session.
• 03:00 - 05:00: SLATE and Learning Analytics The chapter introduces Barbara Wson, a professor at the University of Bergen, and her colleague Cecilia, who is pursuing her PhD in the department of education. They have collaborated for over 20 years. The focus of the discussion is on data literacy and its application.
• 05:00 - 08:30: Project Dolly Overview The chapter 'Project Dolly Overview' provides an introduction to the center for the science of learning and technology. It highlights the center's role in investigating the various aspects related to learning analytics and artificial intelligence in education, with funding from the ministry of education and the University of Bergen since 2016. The areas of investigation include technological, pedagogical, interpretive, cultural, ethical, and legal perspectives.
• 08:30 - 12:00: Dolly Data Literacy Framework The chapter discusses the promotion of responsible technology use in education and introduces three projects. The first project mentioned is 'Dolly,' an Erasmus Plus initiative. The discussion also includes 'daughter' and 'tissle method' projects.
• 12:00 - 15:30: Dolly Playful Toolkit The chapter titled 'Dolly Playful Toolkit' revolves around a project named 'Dolly' aimed at empowering adults towards responsible citizenship and civic engagement, particularly through data literacy. Running from 2021 to 2023, this project was led by coordinators in Norway with five partner organizations, including universities in Spain. The main focus was on acquiring and developing key competences related to data literacy.
• 15:30 - 22:00: Data Journey Course The chapter discusses the Data Journey Course with an emphasis on training adult learners in data literacy within non-formal contexts. It highlights initiatives in Germany (FA) and the UK (Coventry), where innovative pedagogical strategies are co-created, piloted, and evaluated. These strategies include a toolkit featuring games and playful learning resources, which were effectively utilized to enhance the learning experience.
• 22:00 - 27:00: Introduction to Teacher Inquiry This chapter provides an introduction to the project focused on teacher inquiry through game design. It highlights the approach of using both analog and digital games to develop educational resources. The target audience is segmented into young adults (18-29), adults (30-64), and seniors (65+), with the program designed to cater to these age groups individually as well as intergenerationally.
• 27:00 - 39:00: Teacher Inquiry Steps and Projects The chapter discusses the Dolly project which had five main deliverables, but focuses on three significant ones. These include the Dolly data literacy framework, a innovative approach in the educational field. Other deliverables mentioned are the learning approach, the playful learning toolkit, framework for implementation, and policy recommendations. The specifics about the Dolly framework outline its role in enhancing data literacy through a playful and comprehensive learning environment. This approach aims at transforming traditional teaching methodologies.
• 39:00 - 59:00: Questions and Discussion In the chapter titled 'Questions and Discussion', a systematic literature review is discussed. The team conducted a Delphi study which involved experts and led to the definition of personas for different stakeholders. These personas are used to identify the target audience for a game. Additionally, the Dolly data literacy framework, which is a product of their research, is highlighted. This framework is available in English, Spanish, German, and Norwegian on their website.

Data literacy and use for teachers (Agile EDU MOOC webinar) - Barbara Wasson and Cecilie Hansen Transcription

• 00:00 - 00:30 Good afternoon everyone. It's uh it's great to see already many joining us. Well, I I've had my eyes on the the Delhi project and the TISL for a while while we were working on on this uh MOO because this MOO is about teacher uh data literacy and teacher inquiry. So, I'm very happy to have Barbara Vason and Cecilia Hansen to present us about their work. uh they're both from the University of Bergen and they will talk about Delhi
• 00:30 - 01:00 Tisl and maybe some some other activities they do around. Uh I will keep it short so that we make the most of the time we have and perhaps if uh participants have questions you can use the chat or the Q&A function and uh we'll keep an eye on that towards the end to see if we can address some questions. Uh without any yeah keeping any longer Baba and Cecilia the floor is
• 01:00 - 01:30 yours. Hi. Uh my name is Barbara Wson and I'm a professor at the University of Bergen in the center for the science of learning and technology and uh I have with me uh my colleague Cecilia who I've worked with probably for over 20 years I guess we figured and uh although currently she's in the department of education doing her PhD. Um I'm going to talk we're going to talk today about data literacy and use
• 01:30 - 02:00 for teachers. And first I wanted to tell you a little bit we're from the center from the science of learning and technology which is a national center for learning analytics and AI and education and it's funded by the ministry of education and the university of Bergen since 2016. And at Slate we investigate the technological, pedagogical, interpretive, cultural, ethical and legal aspects of learning analytics and artificial intelligence in education.
• 02:00 - 02:30 and we promote the responsible use of technology and education. And you can visit us at slate.uib.no. Today we're going to talk about three different projects that we've been involved with over the years. The first one is the Rasmus Plus project Dolly. The second one is a project called daughter and the third one is to talk a little bit about the tissle method. To start with Dolly, as I mentioned, it was an Arasmus plus project that ran
• 02:30 - 03:00 from 20 2021 to 2023. And the main goal of Dolly was to empower adults for responsible citizenship civic engagement in terms of data by acquiring and developing key competences d related to data literacy. The project had five partners in addition to us in Norway who were the coordinators. We had univers dearcia and univers delay ille bellar in Spain and
• 03:00 - 03:30 then we had fa in Germany and coventry in the UK. Dolly addressed the basic training of data literacy for adult learners in nonformal contexts through the co-creation, piloting and evaluation of pedagogical strategies and the provision of a toolkit of games and playful learning resources. So we we did and they we basically used a gaming uh
• 03:30 - 04:00 pedagogy. So they're making games and as you'll see some analog and some digital. And this is the way that we decided to develop these resources. The project was focused on adults. So we have the European definition of young adults being those 18 to 29, adults from 30 to 64 and seniors 65 plus. So everything we did was aimed at these three different groups or at two of them sort of intergenerational groups
• 04:00 - 04:30 etc. In Dolly we had five deliverables. the conceptual Dolly framework, the learning approach which wasworked and playful, the Dolly playful learning toolkit, the framework for implementation and some policy recommendations and guidelines, but today I'll only tell you about three of them um briefly. The first is the Dolly data literacy framework and here we carried
• 04:30 - 05:00 out a systematic literature review. We did a deli study with expert and we defined persona def uh personas from the different stakeholders. So we talked to the stakeholders and we generated these um different kinds of uh p personas that helped us define who the game should be for. The dolly data literacy framework is available on our website and it's available in uh four languages English, Spanish, German and Norwegian. And to go
• 05:00 - 05:30 a little bit deeper into this, our definition of data literacy is like about data literacy is about the competences people need to engage with and use data encountered in everyday life. It implies finding ways to make datainformed decisions both in everyday life and in a v various contexts according to personal or collective goals. Data literacy also includes understanding what data is and having
• 05:30 - 06:00 awareness and attitude towards non-neutrality the bias aspects of data. It implies having the skills to collect select store preserve and manage data to analyze evaluate interpret critique apply use and work with data and also to represent visualize and communicate stories from data. It also encompasses having the competence to ask and answer questions from data sets through an inquiry process. Furthermore, data literacy
• 06:00 - 06:30 means having the knowledge to critically make judgments and interrogate the claims accompanying data, including ethical and legal aspects that affect ones and others people's rights. It also includes the ability to use data as part of a design process to solve problems and to take decisions. And this is captured in our framework which includes the four categories. Understanding data, acting
• 06:30 - 07:00 on data, engaging through data and ethics and privacy. And each of these has a um under categories. So when you go into the framework you can go in and see different aspects related to these uh categories of data literacy. So for example on understanding data we have knowledge awareness and critical thinking. So for for an example of critical thinking it's the relationship between humans and data. The use of
• 07:00 - 07:30 automated processes versus human actions. Who is making decisions the human or the algorithm? How data tools work? data being used for targeted advertisements, the misrepresentation of data, how data is monetized, for which purposes is it being collected. Those are things that you can think about if you're going to make a course or some materials around understanding data and critical thinking. The same for acting on data, for example, collecting data,
• 07:30 - 08:00 how to configure privacy settings, revoke access, request to your da d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d data is being erased. Use collected data to change your own behavior. For example, in a health app. Make informed decisions when interacting with data collecting actors. For example, mobile apps, internet portals, and employers. So, how do you say yes or no to cookies, for example, could be something there. And then engaging through data. This is where you are being a data activist or you're using data to make decisions.
• 08:00 - 08:30 you're advocating your own uh policy and uh um regulation like the regulations around data you actively involved in that. So for as an activist you could use data as a basis for activis activism. You can put data rights your data rights into practice or you can self-regulate your own data footprint. So you can learn things about these aspects of it and wrapped around that you have the ethics in uh Dolly uh playful toolkit.
• 08:30 - 09:00 So what we did after we had the um framework is that we made these different games that address different aspects of the uh framework and we have a toolkit that's available online. So here's the toolkit. It's toolkit.dalcitizens.eu and you can come in here and get lots of information about the project. You can get the framework I just talked about. you can
• 09:00 - 09:30 get different publications and resources that we've uh shared with you and most importantly you can get access to our games. So we've made 17 different games that are aimed at different age groups or cross age groups that are aimed at different aspects of the um uh framework and five of them are digital. The rest of them are actually physical games. So here are the uh ones that are and you can search for them and go through them.
• 09:30 - 10:00 You can pick different what you want it to talk about. If you want the games for understanding data or if you want games about acting on data or engaging through data, you can select you can choose which uh type of game if you want digital or card game or board game etc. So you can filter through the different games that we have. So I'm going to show you three or four of the games that we have. So we have games of phones and this is a very popular game that's used quite a bit still and in this game it's
• 10:00 - 10:30 a card game and it's about sharing and communicating uh data sets that already exist under ethical conditions. You what you do is everybody sits in a circle and you give out the cards and you ask people to pick a card. There's one person who's um uh the the judge each round and that person reads the card and they have to browse the web quickly to find the result that best fits the description on the card. So it might be
• 10:30 - 11:00 your favorite flower and people go off and search on their phones for websites that show their flower for instance and then they share it in their group that they're playing with and then somebody decides which is the best uh answer for that particular question and it gets much more complicated of course and uh this one has been uh developed further and I realized I forgot to put in a picture of how we've developed this for all schools high schools in Norway. This is part of a game set that they have
• 11:00 - 11:30 gotten. Um, and then another one is the delicacious the week. And here what you're working on and thinking about is creating, editing, and storing data in simple formats. For example, as a text and the main goal is to achieve your ideal week by efficiently organizing your schedule and engaging activities related to energy, rest, exercise, and love. And the person who reaches their ideal week first is the winner. So it's
• 11:30 - 12:00 a game that uh talks about formats of data that you can have. Deli Life is actually a real board game with movable pieces and it's about uh being aware and knowing about the existence of data and it's an educational game mixing event and action cards to race to the finish line. So you start down here on the start and you go around the board to the finish. And the winner of the game is the first person to reach the finish. So there's a variety. That's just to show a variety
• 12:00 - 12:30 of the different kinds of games we have. All these games are available in print and play. So you as a teacher can go into our website and you can download and you get all the materials to print. For instance, you would get directions about how to play the game. You would get the scorecards that you would print and you would get the different cards that you use to play it. And so this has been done in all the languages and you can just download and use these games and we have lots of people downloading
• 12:30 - 13:00 them and as s uh mentioned you can actually get it in the different languages. The framework for implementation this provides the people who want to use our games with different ideas about how you can use them. And uh so it goes through um and gives you uh you can go to this page and get different facilitator guides and get
• 13:00 - 13:30 links to the games as well. But here you get a handbook that tells you about the game and the number of players you need, what the goal of the game is, the page in the manual where you can find the different directions about playing the game. So this is basically about the directions for the games and tells you if it's a card game or a board game, if it's digital or not. So you can get a lot of information about these particular games and there's a facilitator's guide
• 13:30 - 14:00 as well that tells a little bit more about the ideas behind it relates the games to the um framework that we developed. So we have used these games with teachers in workplaces in university courses in libraries and in fact in Norway they like it in prisons and that one surprised us but they said that they can use analog games with people who are not allowed on the internet and they can learn about data literacy and aspects
• 14:00 - 14:30 around it that they normally wouldn't be able to have aspects to. We've actually written a paper on using analog games to have access to uh knowledge about being digital. Here's in Spain, our colleague using this in a teacher workshop. She's run many many of these. We've used them in Norway as well with workshops. And here on the left is the in a course that we have had where we had a um a day where we played the dolly games. Here they're playing something called iceberg, the iceberg game. on the
• 14:30 - 15:00 right we had a games day at the library where people could come and play the games. So this is a project that has ended up being uh really uh having a lot of very good outputs and they're still being used and we get lots of uh questions about using these materials still and as I said you can get them for free on the web. So here I can just leave this for a second if you want to take a picture of the uh code. So first one the fir f first QR code is for the project itself
• 15:00 - 15:30 and the second one is for the toolkit but if you come into the project you also can get the toolkit directly. Yeah. Okay. So then we're going to move on to how we have also built another resource based on how to have awareness about about understanding data and about awareness and this is called data ra in Norwegian or the data journey course and uh this course is uh
• 15:30 - 16:00 developed through a project that we had funded by competence Norway which is part of the directorate for higher education and we had many people involved. So we had my academic team here at Bergen. We had uh worked with digital Norway which is a conglomerate of u it's an NGO but it's an organization owned by many companies in Norway about helping make digital resources learning to helping them to uh become more digital for example right now very much focus on AI etc. And then
• 16:00 - 16:30 we worked with some text writers in a company called Newslab. And then the course which you can go on and see and you'll see in a moment more about it. It was developed by a company called Feed and Oslo. And this is the welcome to the course. It's available in both English and Norwegian. So I've brought some English slides with here. So it's an online course available for anybody. So any of you can go into this and if you know
• 16:30 - 17:00 about the Finnish um University of Helsinki called elements of AI, they have an online course which is quite well known. This is inspired by them. So it's open to anybody. Even my grandmother could take the course and learn something. We've also had people from that are computer scientists take the course and they also tell us they learn something. So it's organized in five chapters. Um and the first chapter you go in and you click on the chapter why is data worth its gold and why is it worth
• 17:00 - 17:30 protecting and it has different sections in each chapter. And if we look at the overall structure of the course as I said we have the five um sections or chapters. So why is data worth its weight and gold technology and beyond how do we get a hold of data making data useful and putting data to use. So those chapters each with subchapters. The other thing is that this material is actually used in some
• 17:30 - 18:00 courses at the University of Bergen where you can get um credits. So we have six weeks courses which I'll tell a little bit more about in a moment that use the first three chapters and we have another course that uses the last two chapters and those are available in a moment. I'll tell you who they're available to. But if you take the online course, the dot raisin, you can get a certificate that you finished it at the end. So here you see an example of what it looks like in one chapter. So here's
• 18:00 - 18:30 one section in in a chapter. Why should I care about data? So you'll have a text and then you see the pink are usually examples of some sort that you can click on and get more information about it. So you basically read the course here. You signal that you finished reading. And if you do that while you take the course, this little blob at the bottom is collecting your data, how you interact with this course, and it will grow with your interaction with it. And at the end of the course, if you take all five
• 18:30 - 19:00 chapters, you can get a certificate of finishing the course. And this blob, your individual blob will be on your course certificate. And in each chapter, there's quizzes. So this is all about awareness and understanding on the very low level about what data is, how we should collect it, about the fact that you have rights about data, about how data can be your voice, how data can be a sensor in a tunnel in a parking lot,
• 19:00 - 19:30 different kinds of data. So it's really fundamental which I believe you need to know in order to understand AI and what algorithms do and how they use data. So this is for everybody. So I recommend you can go in. So in these courses that we have in Bergen, we have a set of digital courses at the university that are non um credit courses for your program of study, but they are extra credits you can take. And the students seem to think that they're very good to give them some um courses that they need
• 19:30 - 20:00 when they go into working life. So they see it as sort of training for the future. So we have this one on fantastic data which is mine and then we have some AI we have a GDPR course we have a cyber security course um a course uh very intro programming um data and democracy we also teach in that course etc. So this is something the university has decided to do in a competence pack digital competence package. So we have a set of courses. So the first one digi
• 20:00 - 20:30 110 fantastic data is available for UIB campus and staff. So what we found was that the staff was registering as students to be able to take the course the first semester we have it. So this has about 250 to 300 students and staff every semester and we've had it now for six semesters. We then got money at the university to change this set of digital competence courses into continuing education courses. So my course 610, it's now run
• 20:30 - 21:00 two two times. So we run it once a year and we just finished it about a month ago with over uh close to 900 in the course. And not everybody finishes it, but we had over uh half of them finishing the course and getting the exam. And this was available to all uh companies in western Norway where we live. And we had like 45 from a bank taking the course and we've had people between 19 and 71 years taking the
• 21:00 - 21:30 course. We get feedback saying everybody should have this course and they keep coming back and what has happened after we took this course the seafood industry asked us if we could make a particular course for the seafood industry. So we've chain now made we're making that now and giving the thing that will be different the same pensome will be used the daughter that I've told you about um but it will have examples in the assignments and things for the seafood industry we've been asked to do it for teachers so this will also be available
• 21:30 - 22:00 in the fall and we've also been asked to make it available for un students at the charm university network which is the European university network that UIB is with so from the one course that we started started with uh two and a half years ago, we've ended up now with five versions and in fact both finance and the naval academy are asking us to give courses for them. So the naval academy would be cadetses I think about 250 of them each semester coming into it. So this course has obviously been useful
• 22:00 - 22:30 for some people. And so what happens is we go from the the framework of Dolly telling us what we need to teach people about to developing the data journey program and then using it in these UIB data literacy courses. And here's an example here. We also use something that we developed in Dolly which is a we are data website which is the first assign. We use that as the first assignment in the course even though it was developed for Dolly where people have to visualize some
• 22:30 - 23:00 version of um data in their life in their city in their country. So for instance up here the number of rainy days in Oslo and Bergen being compared. You see Bergen's very rainy um and they can handdraw them or they can you know just visualize in some way. And then the second assignment is one of the games that we made in data literacy. So it's about aware the kinds of data that you develop. Some of some data is visible, some's invisible etc. So they play this game that we've developed in data. So
• 23:00 - 23:30 these three things together play off of each other which has been very fruitful. Okay. And now we'll move on to teacher inquiry. Should should we uh Yeah, maybe we switch. So she has Yes. Hello. So, uh now you listen to Barbara
• 23:30 - 24:00 and now you oh learned a little bit about what the data is and uh I will tell you about teacher inquiry and how to use data. Uh teacher inquiry it's a method uh systematic method um and we have a definition also. So generally teacher inquiry is the systematic intentional
• 24:00 - 24:30 self-critical planned investigation into one's own teaching practice. um and it aims to contribute to the development of teacher professionalism and school improvement through a focus on teacher centered practice-based evidence oriented activity. So um and uh if we look at this definition, it's more the school approach to teacher
• 24:30 - 25:00 inquiry. While for teachers, teacher inquiry is a systematic, intentional, self-critical, planned investigation of own teaching practice. Um and there are seven steps in this method. uh and it starts with a kickoff. Is there something you would like to know about your class, about your students? Uh what are students
• 25:00 - 25:30 learning needs or what are your own stu your own learning needs? Uh and then there's this second step and often we have assumptions about something. Uh so you have to state your assumptions uh to formulate and explain your first thoughts from the kickoff and then you formulate a research question something you want to
• 25:30 - 26:00 inquire. Develop this inquiry question and formulate and reformulate. often you tend to have a very broad question and then you should sort of try to narrow it. Uh because teacher inquiry is to it's not to just do one time the seven step it's to sort of work in this way uh with small narrow
• 26:00 - 26:30 projects. Um and then it's the method. So you should find a method. How will you f find and collect the answers? And often you could just you use tools to collect you for instance you have assessments um you have maybe digital tools and they give some kind of data. So it sort of gives you a an opportunity to use that data that you already have
• 26:30 - 27:00 and use that to follow these seven steps and then you should make some changes to try to collect the data from the teaching. Uh and that's based on your research question. How you organize that changing of teaching and assessment. And then the sixth step is to analyze what's the result of the changed practice. What's the learning
• 27:00 - 27:30 outcome? And then a very important step that's the feedback and sharing sharing with your colleagues what you found. And um what we done uh so we done this project with many many teachers and um everybody works in teams in their school. So it's so you of course you can do these seven steps alone but what we found is that they go together they
• 27:30 - 28:00 group together uh find some common question uh and work together and that's obviously more fun also uh and I I like to show you um some project that teachers actually done. So this is a project uh a presentation that uh a team of teachers done um in primary school and they
• 28:00 - 28:30 shared this presentation with me uh and they had this presentation with their uh shared it with their findings in their school. Uh and then they had this question, how can interdicciplinary work with coding lead to increased understanding in mathematics? And they have this assumption that we believe coding lead to increased use of mathematical
• 28:30 - 29:00 concepts and enhanced understanding. And the inquirer question is therefore when struggling student do coding do they also gain increased understanding when we work interdisciplinary and they have this method. So we listen and observe. We can also test the students in coding. Can I create a board using mathematical and English concepts? And then they did this change.
• 29:00 - 29:30 So we started by showing a salabi that's a mathematic program um that they use in Norwegian schools is very popular. So can we use this salaby or coding in schools to the students and together we can we have codeed through various levels and we learn concepts through a function of repetitive functions and then we have an activity
• 29:30 - 30:00 center visiting us and we have connected it to learning rhythm sequences in music. So that's the interdisciplinary project that they had and we had also linked the coding to art and craft focusing on coordinate systems in mathematics and we have utilized a language students were familiar with from gaming in everyday life. So if children use especially English in their language when gaming and then it
• 30:00 - 30:30 collected data. So since our students are too young to use questionnaires, we have chosen to collect data through conversations and observations. During learning conversations, the students have reflected on the use of coding in everyday life. They have independently connected coding to subjects we have not focused on. So these are um um something that the students have told when they
• 30:30 - 31:00 doing this project. So the students told them that we are double coding when we are gaming. First we code our brain to perform an operation like pressing a button and then the button further codes an operation that tells the character in the game what to do. Very clever. So also we code our brain to form words when we put letters together and we code ourselves when we move.
• 31:00 - 31:30 So the result we see that students connect knowledge from coding to other subjects and everyday tasks. However, since they are still quite young, we we will continue to use coding intentionally in the future future to maintain their understanding. And the next tissel project, we wish to continue our research by linking it to both physical education activities and other types of mathematical tasks, especially
• 31:30 - 32:00 with a coordinate system and graphs. So that was coding. And we have another project here on writing. And um their kickoff in this group was writing by hand versus writing on a computer with and without audio support is the question kickoff and assumptions they had. For some students,
• 32:00 - 32:30 there are too many processes to deal with when writing. spelling, letter formation, formal features, sentence structure, content, and remembering what they have written. And the research question they end up with, can doing written work on a computer lead to student producing longer text, more text, and with better spelling than when writing by hand? And is there a difference with if they have audio
• 32:30 - 33:00 support? and a method students group will write about a picture in Google Docs in three different sessions. One writing by hand, two writing on computer without audio support and three writing on computer with audio support. And then they did this in their classrooms using the group texts. We conducted the number of words in different text and recorded the number of spelling mistakes. We entered the result in a table and the
• 33:00 - 33:30 result was that we see the same trend among students with age appropriate writing skills. They produce significantly more text when using computers without audio support and the number of spelling mistakes decreases. When writing with audio support into words, we see few spelling mistakes but the amount of text produced decreases significantly. and then they shared the result in a presentation with their
• 33:30 - 34:00 colleagues. So how to sort of implement this work into your school? So what we have done in the schools um together with teachers. So the teachers have a workshop and uh they plan in the work this workshop the kick off and what they're supposed to to inquire and then they collaborate on this conduct the
• 34:00 - 34:30 plan and also have guidance throughout this when they have conducted a plan and then they collaborate again about their findings and then they share. So this is sort of the implementation of tissle and what we have done we found out that using this padlet is a very good tool to work together in teams in groups in the school and I'll just show
• 34:30 - 35:00 uh one of these padlets it's in Norwegian though but this is from a school just an example and one of the group the pink group. Um they had the kickoff in uh the first row here and then they have the assumptions they put in. And doing this you see for instance in method that's the fourth row here they even put in a picture of the tool that supposed to gather data from and
• 35:00 - 35:30 investigate. And there is this uh white group and the yellow group and the lilac group. Uh so then using this Padlet they can see each other's groups what they have been interested in to investigate and inquire. So they share everything and then they can support each other in in the work.
• 35:30 - 36:00 This is the same. We used the Padlet in many schools. This is another school. Um they done the same. But as you see, I I took a picture of this before they finished the all the different steps. So it's still empty in uh in the third in the three last steps. Uh so of course there are some
• 36:00 - 36:30 recommendations if you if you like to do this. Uh and these tal workshops can be organized as remote sessions with presentations and group work uh integrated with breakout rooms for instance. uh we've done that uh we've been attending schools but we also helped uh digitally and the introduction can start with a theme the school is focusing on at the moment. Uh maybe they
• 36:30 - 37:00 can need some they need some knowledge and background to set the stage. Uh that's that's a recommendation. uh before the the workshop it's nice to have a discussion with the principal or recctor on ICT um after workshop informant about the result of the workshop. Uh so everybody feel an ownership of this. Um and also the teachers who have
• 37:00 - 37:30 conducted project find teacher inquiry very useful and will continue to follow the method in order to use data to improve own practice. But also we have this finding that you you should just try to find small questions to investigate to inquire. You're not supposed to sort of solve the whole world problems. small because you're going to have you're not
• 37:30 - 38:00 writing a master thesis again. This is something that you're supposed to be able to do over and over again. And it's your data. It's in your classroom. So, it's not maybe applicable to others classrooms and findings in other classrooms. Um so let's go through this. So the we have the inquiry phase. The teachers
• 38:00 - 38:30 work together with a group and use their plan. The teachers will then conduct the project in accordance to the plan and the collaboration decide implement and make a presentation on the project and finding and uh you have the group work. The teachers are divided in divided into groups and they identify an inquiry question and make a plan for the inquiry pro uh processes. Um and then of course the
• 38:30 - 39:00 sharing session that's very important. The group will reflect on findings and present their project to the other groups and this can be done in different formats or a longer session. And then this is an iterative process. Teacher can start a new project when they have presented the project being inspired by their community. Often you see that groups see other groups what
• 39:00 - 39:30 they've been doing and then they like to try to do the same thing. So it ends up with switching the projects in in the schools. Yes. What's the time? Yeah. I think we're okay. Yeah, I think it's 10 minutes 10 minutes left for questions. I already see a few questions and
• 39:30 - 40:00 thoughts in the chat. I don't know if anyone would like to take the floor or um we can also just uh read them. Yeah, I think we can read them. All right. Um well there were some challenging questions. There's one about uh asking about whether there shouldn't be a baseline. I think it's referring to the first example you shared Siccilia uh about the coding. So of course you implemented they implemented this new
• 40:00 - 40:30 method and then with observations um you looked at the impact. So I think uh Joanna was asking whether we shouldn't first know how their math skills were before. Uh I can imagine that's a difficult thing to implement in a school context. But uh yeah maybe you have some thoughts or you thought about it but often that's a that's based on the assumptions often because you
• 40:30 - 41:00 already have some data that's why you start this. So of course then you already have that data and then you make a change and then you can compare if you need to compare. But often we see that uh some uh teams like to just um make interviews with their students how they experienced the changes. uh because it's not
• 41:00 - 41:30 um we we maybe we tend to think about this as um um very natural science uh aspect that we we have these assumptions. It's like this it's very hypothetic dedicative method but it's not like that. We're not supposed to have a baseline that we're comparing the results with necessarily. You can have
• 41:30 - 42:00 that because you have already data and then you can make some changes and then you see the difference. You can already have a baseline but you don't need that. Yeah. So it's not like a master's project. No, it's not a master's project. Yeah. That's that's the issue. The the main problem we find in schools is teachers don't have time. So this is something to help them that they find they can manage to do. They do them in
• 42:00 - 42:30 teams like the math group at the school or something like that and they go through the process together to help them find out something that they're interested in knowing a little bit more about. But it probably could not be published as a as an academic paper or something. But that's where the difference goes because we find in practice they don't a lot of them have masters already but they don't have the time you know you don't have the time to go through that large of a process but this gives them some insights that help them and it's not a research project.
• 42:30 - 43:00 Yes. It's an inquiry project. Yeah. That that's that's a good point to emphasize and it applies to their context anyway and they already come in with the assumptions maybe from their own observations or even maybe maybe research done in the past. Yeah. And we get them to write down the assumptions because they're really important because they are going probably into this wanting to show one way or the other. And maybe it makes
• 43:00 - 43:30 them reflect a little bit to say get it down what they think their assumption is and then maybe put that to a side and then do something that can help them um you know be more precise. And it and the difference is really because we all we we tend to act on assumptions. Yeah. Yeah. So, so these these assumptions, it's something we have uh often it's uh actually when when we see teachers write down the kickoff and
• 43:30 - 44:00 then try to find an assumption, it's very hard. Oh, they can't figure out what's their assumptions. But when they start to talk, they often have assumptions because they have already been thinking about this. Yeah, I believe it's like this because of this. And then let's go into and investigate that a little bit more. So it's the assumption step. It's it's hard, but it's it's very important
• 44:00 - 44:30 just to reflect. Yeah. And I see another question that is maybe again referring to a scientific method, maybe a um what do you call it? a confirmatory bias. Um because I think Clauddio, yeah, it was Clauddio saying yes. Is there a risk of polluting the data while talking about the results with the teachers and steer it
• 44:30 - 45:00 towards the desired answers? It says uh but I think that's the that's sort of the difference between research and inquiry. So what's the risk then if you pollute the data? It's this is not a research project. It's nothing that you're going to prove uh that it's like this.
• 45:00 - 45:30 uh you just this is an inquiry project that will sort of make you be able to think a little bit different about your practice and maybe make your practice better. So, so it's a difference uh it's not it's inquiring your practice. It's not research about practice. It's not uh so we are always biased anyway.
• 45:30 - 46:00 I think even in in natural sciences we have already the ideas about what we're supposed to find or but it's not polluting data here. That's uh not an sort of issue. Data is pollute pollut polluted anyway. Yeah. Yeah. Someone asked if do you do the teachers share their results with
• 46:00 - 46:30 students as well as each other? They have. Yes, some have. And the students have found it very nice actually. Yes. even in primary schools they showed because uh even in primary schools um and I think that's important uh that some teachers have focused on this uh and that's that when the teachers do this activity with their um students
• 46:30 - 47:00 they tell about that we are doing this project and we're going to inquire this and this instead of why should you sort of do it in the silence? Uh and then that you find that the students are with on that project together you do something together. You inquire together. Yeah. Someone else asked which is interesting. Should the assumption come
• 47:00 - 47:30 first instead of the kickoff question? That's interesting. Yeah. Yes. I like that. Yeah. I I I I can't say that there is something that should it's not uh it's not like these seven steps are uh written in stone that you should do this. This is just a possibility for you to sort of follow the steps. These are the steps that are
• 47:30 - 48:00 most common to follow. But you should think yourself if you find that you're I I guess that some of the kickoff already comes from assumptions. Yeah, I think it's to narrow down what it is you're going to work on. And if you look at assumptions comes before the research question and that actually got changed while we were going through developing this I think because the question they're going to ask they couldn't start with that because they
• 48:00 - 48:30 didn't like the I mean this started out very academic the way we think about doing research but it doesn't work it didn't work with the teachers it has to you know with the um you know the requirements on doing formal research it just doesn't work in a school situation. So we had to change the name of all the steps over the you know the the project when we were developing this. So we ended up talking about the kickoff instead of about
• 48:30 - 49:00 having it's not a research project. Yeah. No. Yeah. So, this went through many iterations to come with the names for the steps and to get them to think about it because it's usually they're, you know, they often get a new tool in school and they try it out and they're wondering how it actually works and they start talking with their colleagues and then when you get them to think about something that they're doing that they have access to collect some data about, then you get them talk and they talk about it and they said, "Okay, what about this uh newbie tool? Let's could
• 49:00 - 49:30 we do something around that?" And it starts that way more than a sort of more formal question. And when you get down after they've talked about it, think about their assumptions, they're able then to be more formal about, okay, what are we actually going to inquire here? That was from our experience. Yeah. Just looking here. Is there still time? Yeah. For a few questions. It's up to you.
• 49:30 - 50:00 if it's up to me. I'm holding holding on my questions as well. But uh yeah, I'm checking if there's any from the floor. I remember in the course we had asked if they have questions in advance. Um yeah there was one question about collecting real- time data and I can say I can say that as a learning an during the last 10 years of looking at learning analytics it's
• 50:00 - 50:30 really hard to collect data it's not hard to collect data real time most tools are collecting data when you use them but it's really hard to process data real time and give feedback in the classroom as the data is being collected that is really a challenge and I I think even in the whole field of learning analytics have realized that that's really problematic. Usually the infrastructure is not there. The teachers don't really have time to look at the data during the class. So collecting the data and then looking at
• 50:30 - 51:00 it later, but being aware that you're collecting the data and what kind of data do you have access to? And there are lots of teachers that maybe could take say log files out of some tools that students are using and analyze them. But even researchers have trouble doing that, getting it in a format they can use it, etc. So most of the teachers aren't looking at that kind of data. They're maybe looking at a pre-EST and a post- test or looking at the content of something someone has made or these
• 51:00 - 51:30 kinds of things that are easier to deal with than sort of real-time data. Someone is asking about the library day. Yeah. Sarah, yeah, if you could tell more about it. Yeah, so our local library actually has a room where they have different games and they have different activities. So we were invited to come there and have a day for for these data literacy games. So maybe you
• 51:30 - 52:00 could go and get our game, get me a game from there because we have these physical games which is for a for a digital group. I can show you. So, we went over there and we set up and we had um about I think we had five or six of our games with us and uh the librarians were very supportive and they've always liked this particular project. Like you see here, we have like actual physical games with the you can open and they
• 52:00 - 52:30 have the different game parts and things in there. So we had these with us and we were set up I think if I remember correctly it was sort of midafter afternoon to e evening and people came by and could play the different games and the one game they all love if you're looking for something it's called game of the game of phones. This is a very simple card game that you can download and in fact the western part of Norway um was liked this game so much that we
• 52:30 - 53:00 have made it into a package that has been the the I guess it's the municipality. It's bigger than a municipality. They have made this kit bright screaming pink kit if you can see on my picture that contains two games. It has this game of phones in we made it into a a version for schools and then the data um iceberg game and they get this package and they are using it at the beginning of the school year. So for the game of phones, they find it's a good
• 53:00 - 53:30 way for the new students in high school to get to meet each other because they're always a bit shy when they start a new school and new class and they play the game and we have a we have a set of materials with that go with this one that um help the teacher talk with them. So for instance, after you've played this game on your phone and you're looking for different things, you say, "Okay, did you use a net browser? Did you use an app? Did you use um Instagram? Where did you use? What were
• 53:30 - 54:00 you using? Get make them aware of where they're collecting the data. What data have you left behind? What data have you given to these websites? You know, you're building up what your search uh thing is on your thing. So, you we have some uh uh materials for the uh Yeah, you can show up materials for the teachers to help them go into a dialogue with the students with this game. And I've never like Yeah. cards. Bring it a bit closer. Yeah. So, the thing is is that we've
• 54:00 - 54:30 never had this game of phone games played anywhere with any age group that within two minutes they're not laughing and having a great time. So, it's really a really nice game that can very easily teach something about data literacy. Yeah. Then you data ice iceberg. Yeah. Yeah. Yeah. So this was sent to 45 high schools in uh Norway and we have asked we've requested all the time for it from other school districts but uh it's quite
• 54:30 - 55:00 a lot of work and we need to have a dedicated person to working on it I think if we're going to do it that way. So otherwise we send them to the website where you can also go and download. Yeah. Great. Yeah. And if anyone tries the.no that we mentioned it would be nice to hear from you. You can find us on Slate and send us a message because people really like that particular course as well. I haven't been able to do all of it, but I started looking at it and I really
• 55:00 - 55:30 like it. It it's very clearly explained. Uh yeah, we're about to um update it and that actually came about because from we had uh we had the core we had a course that was um uh you know with very much academic way of presenting things and we gave our slides to this company that managed to write it into a way more accessible way for everybody. So the conceptual knowledge is still correct, the terminology is correct and
• 55:30 - 56:00 everything, but it's just written in a really nice way that people like to read it. And I think that's why it's so popular is because it's really understandable. And our vice director read it. She took the she did the course and she's a computer scientist and she wrote me and said, "Oh, I learned lots of things I didn't know about." Yeah. But we will update it now um during the next half year. Um but the course will still be available as is but we update the text and go through it again. And it became in it was done originally in
• 56:00 - 56:30 Norwegian and Ecuador which is the big oil company conglomerate here in Norway that has offices around the world. They wanted to use it for all their employees. So they paid for the translation to English. So it was not written in English to begin with. So that's been quite nice too. And what I really like about the the Delhi uh toolkit, when I was going through it, I realized that some of the games are adaptations of of classical
• 56:30 - 57:00 board games as well, like the data and that's really nice. And we tried the game of phones also. Uh it also works well as an icebreaker. Yeah. And the thing is with the that's part of the methodology that so we were uh Coventry um Sebastian Arnob he's a game researcher and he has this methodology of how you do start with games that you know because you have the game mechanics there and everything that helps it's really quite complicated
• 57:00 - 57:30 actually to develop a game it as we can say it was about a year and a half of work almost each game and having it tested and we tested at different phases and then in the end and uh It was, that project was a lot of work, I have to admit, to get everything into the right formats, even just, we have a company in Spain that printed the games for us because we needed the physical games, but they also did the print and play for us. Um, so yeah, it was a lot lot of work, but it's probably one of the my most favorite projects ever. Um, and
• 57:30 - 58:00 we've gotten a lot out of it. So, um, we're very happy with that project. the the print and play works definitely well because I tried it with the the delicious week and if you really print a paper uh well two-sided from the printer, you can just cut it and you get the the cards with the Yeah. double sides. Yeah. And that's because we had these professional people do the final version for us. So everything is was
• 58:00 - 58:30 very strict on exactly how things had to be placed on the pages and everything. So it was worth it in the end. Mhm. Um I don't see new questions but I have a question if if you still have a moment because earlier you mentioned the uh fantastic data for teachers uh and the G600 course. So I was wondering what how did you adapt the content to teachers in
• 58:30 - 59:00 that case? Ma basically we don't change daughter dot race and that stays the same and of the assignments that they have in the course there's there's uh six quizzes and five and five assignments that they do over the six weeks and it's a fully digital course um and the course where they have to read something and write some summaries and stuff. We pick courses that are relevant for teachers and we're actually updating
• 59:00 - 59:30 a couple of the uh quizzes as well to make them relevant. So that's the only thing we're changing not too much but I've told them that you know really the course is relevant for anybody but they want their own. So we will do that. And the other thing is the teachers like the the municipality we're working in, they like to have one physical day even though it's a online course fully digital and uh so we make it in that particular for the teachers only that they can come and and then we played
• 59:30 - 60:00 games actually and that pink work kit came out of the first time we ran the course because they got really excited and they wanted these games and they went to the municipality and they ended up paying for us to put together that package. So that came out of having the physical day that we had the teachers playing the games. Yeah. All right. Um we could maybe have some closing words. Uh maybe to answer the question
• 60:00 - 60:30 where does a teacher start if they don't have any much time? If they don't have much knowledge about data data literacy what do they do for what's a simple thing to do to start with inquiry or data literacy I think they should take.no Tenno take that course. It will give you lots of insights into what data is in fact on a low level also give you ideas about you
• 60:30 - 61:00 know your rights about data being critical thinking around data and then for inquiry what how do you think they should start? Well, I think that of course they could start just by looking at what kind of data do they have and what do they wonder about in the classroom? What would I like to learn more about? But of course, teacher inquiry, I would also try to ask if it's
• 61:00 - 61:30 possible to do this for a planning day or something like that. Actually, that's uh most schools have have planning days and have started the teacher inquiry process and implementing and having the workshop in such a day. Yeah, that's perfect.
• 61:30 - 62:00 Well, thank you very much. Yeah. Um, this course has an inquiry plan template like it's it's the assignment uh for the MOO and the the template is inspired by by the TISLE framework, the hearts model as well. So I hope that this will encourage uh well if there are participants among us already thinking about it they will it will encourage them further to look it up.
• 62:00 - 62:30 Um so thank thank you for your time and uh yeah I wish everyone a good day. Yeah thank you thank you thank you for nice questions. Thank you thank you for the all the insights.