This term I created three data visualisation. They were about Daily Learning Process, Weekly Teaching Methods Used in a Lesson, and A School’s Data Collection Types and Frequency. The part I had difficulty with was differentiating the learning and the teaching parts because they are really connected with each other. In each visualisation, I focused on different areas. In the first two visualisations, I tried to show how we can see the pattern in repeated data.
With different applications, patterns can be found between the data, and with help of this those applications can make predictions (Knox, Williamson, Bayne 2020). This is connected with learning, teaching, and governing with data. In the learning part patterns and predictions help learners to use them for personalized learning. AI systems might be examples of this. The systems are advising some activities according to their results. For example, if a student has more problems with a specific topic, the application can direct the student about this according to the student’s level. In the teaching part, the data can help teachers to shape their lessons (Brown 2020). Teachers can check their students’ levels and according to the pattern can have some ideas about the learning level. In a classroom environment, there are different applications to do that instantly. On the other hand, students’ data are also collected by different companies or governments (Williamson, Bayne, and Shay 2020). This data might be collected to create some new applications or governmental politics. There are some new educational technologies developed by companies for students’ or teachers’ needs. Also, the patterns and the data might be used for curriculum design, the number of schools in a city and etc.
In the teaching part, I focused on and created a visualisation of teaching methods used by a teacher for a lesson. On the other data analysis is used for teaching without a teacher. This might have some negative and positive sides for students. As Williamson, Bayne, and Shay mentioned these applications don’t have the same pedagogical communication as teachers (Williamson, Bayne, and Shay 2020). For example, while learning the only important point is not just exercises or readings. When I am thinking of younger students one of the important points is motivation. It is not easy for computers to understand and provide motivation.
Learning, teaching, and governing with data is not just about students, teachers, companies, or governments. There is also the parents’ part. Parents are using the data for their children. This might be about the selection of schools, getting information from schools, and understanding students’ levels, and attendance. Parents are using the data about schools for their children’s school decisions (Anagnostopoulos, Rutledge & Jacobsen 2013).
Creating and trying data visualisation was enjoyable for me. I saw that with hands-on drawing it is not easy to visualise long-term data. Besides a student’s individual data or a class’, it might be hard to analyse and visualise governmental data. As Anagnostopoulos, Rutledge & Jacobsen says there are some technological tools helping to analyse the data that are more complicated Anagnostopoulos, Rutledge & Jacobsen 2013).
That’s why thinking of a visualisation area for the last block was the hardest point for me. This area contains more complicated data. Of course, even for only one student, we can create a complicated data visualisation but there are some ways to simplify that by combining similar headlines.
As a result, I tried to mention different uses of data in different areas called learning, teaching and governing. With my drawings, I could not show a long-term visualisation but I explained some parts about what happens if we use these visualisations for a long period of time in my block posts.
References:
Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. 2013. Introduction: Mapping the Information Infrastructure of Accountability. In, Anagnostopoulos, D., Rutledge, S.A. & Jacobsen, R. (Eds.) The Infrastructure of Accountability: Data use and the transformation of American education.
Knox, J., Williamson, B., & Bayne, S. (2020). Machine behaviourism: Future visions of ‘learnification’and ‘datafication’across humans and digital technologies. Learning, Media and Technology, 45(1), 31-45.
Brown, M. (2020). Seeing students at scale: How faculty in large lecture courses act upon learning analytics dashboard data. Teaching in Higher Education, 25(4), 384-400.
Williamson, B., Bayne, S., & Shay, S. (2020). The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education, 25(4), 351-365.