Data Analytics Cycle

We consider Data analytics in schools as a process of ‘engaging creatively in exploring data, including big data, to understand our world better, to draw conclusions, to make decisions and predictions, and to critically evaluate present/future courses of actions’.

Hence at the heart of our conceptual framework for DA in schools (Figure 2.2) is the cyclic process of acts that we expect when students engage in DA. It is called ‘Data Analytics Cycle’ This investigative cycle is particularly concerned with solving real world problems.

The DA cycle is complemented with various competence areas that are in line with the existing frameworks such as “Framework for 21st Century Learning” by the Partnership for the 21st Century Learning (P21), or The Royal Society’s (2016) report (in partnership with the Royal Statistical Society) on “the need for data analytics skills”, or ProCivitStats framework (Engagement and Action, Knowledge, Enabling Processes).

Data Analytics Framework

Please click on the dots below, or the text is written out below this illustration.

1. Define the Problem

(a) Recognising the need for data;

(b) Generating specific questions that can be answered with data

2. Consider Data

(a) Deciding what individuals or entities to obtain data on, what to measure and how to collect data;

(b) Collecting and tidying/organising data;

3. Explore Data

Analysing data using data visualisation tools (i.e. tables, graphs), appropriate calculations (i.e. mean, median, standard deviation, quartiles, p-value etc.) and statistical models (i.e. probability distributions)

4. Draw Conclusions

(a) Using data as evidence for generalisations beyond describing the given data;

(b) Expressing an articulation of uncertainty ̶ for example, a qualitative way of expressing uncertainty “an observed result is “surprising” or “unlikely” (Makar and Rubin, 2018, p. 276);

(c) Communicating what has been learned.

5. Make Decisions

Making predictions and decisions based on data with acknowledgment of uncertainty.

6. Evaluate Courses of Actions

Evaluating courses of actions in connection with the problem defined earlier ̶ what actions need to be taken? (e.g., collect more data, do more analyses, ask experts and so on).

Statistical Literacy

(a) Understanding basic statistical concepts, vocabulary, procedures and techniques;

(b) Interpreting and evaluating statistical information or data-based claims where they are contextualised;

(c) Communicating opinions about the statistical information and concerns about the soundness of statistical arguments (Gal, 2002; Garfield, del Mas and Chance, 2003).

ICT Literacy

Using technology or computing capabilities to understand and solve problems, to visualise, model, code and organise data, and to communicate statistical information.

Ethics and Social Impact

(a) Taking responsibility to both act and refrain from certain actions with the interests of society at large in mind; (b) Demonstrating consciousness about the challenges in the digital age.

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Collaboration and Communication

(a) Using available tools and language effectively in articulating thoughts/ideas in the problem context;

(b) Working with others effectively in groups.

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Critical Thinking

(a) Reasoning effectively;

(b) Analysing and evaluating databased evidence and arguments;

(c) Interpreting and making conclusions based on the best analysis;

(d) Reflecting critically on processes in solving problems.

Creativity

(a) Using different approaches/techniques in a particular task;

(b) Generating new ideas and methods;

(c) Being open to new diverse perspectives