In this project we are seeking a better way to teach Data analytics and insights from research in the teaching of statistics are very useful. For example, the Guidelines for Assessment and Instruction in Statistics Education (Franklin et al., 2007) proposed the following components as a framework for the teaching and learning of statistics in schools:

- Formulate questions, anticipating variability;
- Collect data, acknowledging variability;
- Analyze data, taking account of variability;
- Interpret results, allowing for variability.

Garfield and Ben-Zvi (2009) describe how to implement a statistics course designed to develop students’ statistical inference/reasoning at the introductory secondary or tertiary level. The advocated teaching approach is different than traditional lectures, e.g. “teaching as telling” approach (p. 73). It is based on constructivist principles of learning. In this approach, the learning environment involves “combination of text materials, class activities and culture, discussion, technology, teaching approach and assessment” (p. 73). This approach is guided by Cobb and McClain’s (2004) six principles of instructional design:

- Focus on developing central statistical ideas rather than on presenting set of tools and procedures.
- Use real and motivating data sets to engage studentsin making and testing conjectures.
- Use classroom activities to support the development of students’ reasoning.
- Integrate the use of appropriate technological tools that allow students to test their conjectures, explore and analyse data, and develop their statistical reasoning.
- Promote classroom discourse that includes statistical argument and sustained exchanges that focus on significant statistical ideas.
- Use assessment to learn what students know and to monitor the development of their statistical learning, as well as to evaluate instructional plans and progress. (Garfield & Ben-Zvi, 2009, p. 73)

According to these principles, it is important for students to develop deep understanding of key statistical ideas, such as data, distribution, center and variability, correlation etc.. It is also emphasized that students need to experience various methods of collecting and producing data and understand how these methods affect the quality of data and appropriate types of analyses. Data sets need to be interesting enough to motivate students to make conjectures and test them.

Two different models of class activities are described: 1) engaging students in making about a statistical problem or data set, 2) group work for solving a problem. The use of technology allows students to spend more time on learning how to select appropriate analysis and how to interpret data instead of focusing on complicated calculations. Technology tools also help students visualize statistical concepts and understand abstract ideas through simulations…

References

Franklin, C., Kader, G., Mewborn, D., Moreno, J., Peck, R., Perry, M., et al. (2007). Guidelines for assessment and instruction in statistics education (GAISE) report: A preK–12 curriculum frameworkAlexandria, VA: American Statistical Association. Retrieved from http://www.amstat.org/education/gaise/.

Garfield, J., & Ben-Zvi, D. (2009). Helping Students Develop Statistical Reasoning: Implementing a Statistical Reasoning Learning Environment. Teaching Statistics, 31(3), 72-77.