Session Format

Presentation Session (45 minutes)

Location

Room 2903

Abstract for the conference program

Three of the revised GAISE guidelines for statistics education are:

1a. Teach statistics as an investigative process of problem-solving and decision-making

2. Integrate real data with context and purpose.

5. Use technology to explore concepts and analyze data.

In the beginning, there was no technology (well, there were slide rules…); students were “forced” to add columns of data to compute means (data might be presented in sorted order to find a median). Calculating means and standard deviations literally “by hand” was time- and labor-intensive (and prone to error). This gave rise to “statistics” that are no longer in vogue (midrange and pseudo-standard deviation, anyone?) as well as “realistic” data that made these computations easier. Graphics were only what could be produced by pencil and paper. Along came calculators (that could even compute a linear regression!), computers, and statistical packages. Access was still an issue, however.

Today, practically everyone has a computer or smartphone, either of which have more computing power than mainframe computers of the past. Graphics have come a long way and “visualizations” are a current vogue. Web-scraping is possible as a source of “real” data. The internet is bursting with “big” data. How has the accessibility of technology changed how, what, and (especially) who we teach, in introductory statistics courses?

This talk will be a look back at the development of technology, courses of the past, a brief survey of where we are now, and some prognostications about the future.

[9-12 Teachers, High Ed faculty]

Proposal Track

Non-research Project

Start Date

3-4-2016 10:30 AM

End Date

3-4-2016 11:15 AM

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Mar 4th, 10:30 AM Mar 4th, 11:15 AM

Technology in Statistics Education: Where Have We Been? Where Are We? Where Are We Going?

Room 2903

Three of the revised GAISE guidelines for statistics education are:

1a. Teach statistics as an investigative process of problem-solving and decision-making

2. Integrate real data with context and purpose.

5. Use technology to explore concepts and analyze data.

In the beginning, there was no technology (well, there were slide rules…); students were “forced” to add columns of data to compute means (data might be presented in sorted order to find a median). Calculating means and standard deviations literally “by hand” was time- and labor-intensive (and prone to error). This gave rise to “statistics” that are no longer in vogue (midrange and pseudo-standard deviation, anyone?) as well as “realistic” data that made these computations easier. Graphics were only what could be produced by pencil and paper. Along came calculators (that could even compute a linear regression!), computers, and statistical packages. Access was still an issue, however.

Today, practically everyone has a computer or smartphone, either of which have more computing power than mainframe computers of the past. Graphics have come a long way and “visualizations” are a current vogue. Web-scraping is possible as a source of “real” data. The internet is bursting with “big” data. How has the accessibility of technology changed how, what, and (especially) who we teach, in introductory statistics courses?

This talk will be a look back at the development of technology, courses of the past, a brief survey of where we are now, and some prognostications about the future.

[9-12 Teachers, High Ed faculty]