Category: Technology for the teaching of DA

The use of technological tools enrich experiences of the teaching and learning of Data analytics in schools, but which tools would be useful? Of course, it depends on the contexts, levels of contents, and so on. For example, R is a powerful tool to explore data, but its interface might not be so intuitive for some students? Amongst many, personally, I am interested in using CODAP.

CODAP is “a free web-based data tool designed as a platform for developers and as an application for students in grades 6–14” according to its website.

I think the exploration of the following data set would be a good starting point.

https://codap.concord.org/releases/latest/static/dg/en/cert/index.html?url=https://concord-consortium.github.io/codap-data/SampleDocs/Science/Biology/24cats/24cats.codap

What can we explore in the above? One might be a decision making (about cats in this case :)).

Decision making is one of the important aspects in data analytics, and this is now termed as informal statistical inference (Makar and Rubin, 2009). In particular, informal statistical inference defined as “decision-making in relation to a statistical question for a population based on evidence from a sample and acknowledging a degree of uncertainty in that decision” (Watson and English, 2018, p. 36) can become a foundation of formal/advanced statistical inference.

In particular, like Tinkerpltos it is possible for us to separate data into ‘male’ and ‘female’, and this makes data more accessible and visual, hopefully supporting students in their decision-making process? This kind of process is what I really want to see in the pilot studies in classrooms which hopefully will start in September 2018…

 

Reference

Makar, K., & Rubin, A. (2009). A framework for thinking about informal statistical inference. Statistics Education Research Journal, 8(1), 82–105

Watson, J. and English, L. (2018). Eye color and the practice of statistics in Grade 6: Comparing two groups, Journal of Mathematical Behavior, 49, 35-60.

One of the effective approaches might be the use of technological tools, in particular when students deal with complex or big data.  Tinkerplots (Konold and Miller, 2011) is one of such tools, which enables students to interact with data in intuitive and dynamic ways. Students can construct their own representations of data by ordering, stacking and separating data.  

Statstalk project led by Sibel Kazak investigated the issues around (1) young students’ conceptual understanding of statistical and probabilistic ideas within informal statistical inference, which seems to be neglected in the early grades of schooling, (2) mediating roles of technological tools and children’s talk, and (3) recommendations for practical educational contexts. The technology-based teaching can support students’ conceptual understanding of mathematics – Tinkerpots becomes scaffolding to develop their statistical inferences and decision making (Kazak et al. 2015a; Kazak et al. 2015b).   

 

But the use of the technology is not the final solution, and indeed we need to consider a lot of factors, such as task design, teacher, and educational context (Drijver, 2015)!

 

For example, while Tinkerplots can offer powerful learning opportunities for students, the roles of teachers should not be underestimated. For example, Watson and English (2018) who also used Tinkerplots found that: 

It was disappointing that some students did not score as well on Parts B or C of the Assessment that were based on TinkerPlots graphs. Although students had filled in their Workbooks during class individually, they worked in pairs using TinkerPlots. It may have happened that one of the pair took over the manipulation on the computer screen, with the other not paying attention, hence recalling less of the activity. (p. 18) 

This implies that teachers carefully monitor or manage the use of Tinkerplots during teaching. In fact, Parero and Aldon (2016) suggest that reported with technology-based learning environments teachers’ roles are very important.

 

In this project hopefully, we will be able to seek effective ways to teach data analytics with technological tools such as Tinkerplots!

 

References

Drijvers, P. (2015). Digital technology in mathematics education: Why it works (or doesn’t). In Selected regular lectures from the 12th international congress on mathematical education (pp. 135-151). Springer, Cham.

Kazak, S., Wegerif, R., & Fujita, T. (2015a). The importance of dialogic processes to conceptual development in mathematics. Educational Studies in Mathematics, 90(2), 105-120. 

Kazak, S., Wegerif, R., & Fujita, T. (2015b). Combining scaffolding for content and scaffolding for dialogue to support conceptual breakthroughs in understanding probability. ZDM, 47(7), 1269-1283. 

Panero, M., & Aldon, G. (2016). How teachers evolve their formative assessment practices when digital tools are involved in the classroom. Digital Experiences in Mathematics Education, 2(1), 70-86. 

Watson, J. and English, L. (2018). Eye color and the practice of statistics in Grade 6: Comparing two groups, Journal of Mathematical Behavior, 49, 35-60.