For designing your own project based learning, you can use Data Analytic Framework to frame your activity. For example, in below, 9 hours of activities were planned by 1. Defining the problem, 2 & 3. Consider and exploring data, 4 & 5. Drawing conclusions and making decisions.
Although there is no perfect way to teach DA, the following points are worth considering when you design your activities:
Task design – Real world problems are related to students’ interests and require real (multivariate) data to be explored through using various models and representations. Also comparing sets of data will be useful strategies to encourage statistical inferences in their data analytics learning processes.
Taking a project based approach – problems are complex, and ill-structured. Therefore students have to make sense of problem context, organise and model data so that they can manage and work with, and make decisions and inferences about data and their interpretations.
Teachers’ roles – teachers enrich, structure and scaffold students’ collaborative inquiry, give specific and planned guidance, be an organiser of the shared knowledge practices and support the dialogue of students to create the shared object.
Use of technology – use tools such as TinkerPlots or CODAP (Finzer, 2016) which can provide multiple and dynamic representations of data and models.
Students’ affection towards DA – it is necessary to provide safe and encouraging learning environments to reduce their anxiety levels.
For example, 9 hours of teaching activities for Y12 students were planned within the DA framework.
The students have studied basic statistical concepts, such as appropriate graphical representation involving discrete, continuous and grouped data; and appropriate measures of central tendency (mean, mode, median) and spread (range, consideration of outliers), scatterplots, etc.
The purpose of the teaching is to provide opportunities in a data analytics process using statistical measures and data visualisation tools, such as mean, median, mode, range, inter quartile range, standard deviation, histogram, box plot, stem and leaf and so on with a variety of IT tools, in particular CODAP and Excel.
1. Define the Problem
(a) Recognising the need for data to investigate the relationships between weather and airborne pollution, specifically particulate matter (PM10) and Nitrogen Dioxide (NO2);
(b) Generating specific questions that can be answered with data e.g. “Are there any relationships between air pollutions and weather?”
Possible Questions to be Explored
1. Drag and drop data from the table left to the graphs right, and explore temperatures etc. in 2015-17. For example, is there any relationship between daily temperature and daily radiation?;
2. ExploreHourly PM10 data for 2 local sites over a 3 year period;
3. Explore Monthly average NO2 data for around 30 sites over a 3 year period (2015-17)?;
4. Discuss what measurement such as mean, median etc. can be used to represent data? See the ‘spread’ of data by viewing box plots, IQR and standard deviation;
5. Summarise your findings by explicitly stating what evidence can be used to state your conclusions? Also what implications can you make based on your findings?
4. Draw Conclusions and 5. Make Decisions
The students were asked to write a report on the questions which were semi-structured: Q1) ‘Have PM10 levels reached a dangerous level?’, Q2) ‘Are PM10 levels rising over time?’, Q3) ‘What time of day are PM10 levels the highest?’ and Q4) ‘Is there any correlation between PM10 levels at the two sites?’.
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