Tutorial To Be Presented in the 11th Asian Technology Conference in Mathematics
December 12-16, 2006, Hong Kong SAR, China

Data-Streaming: Demonstration of the Technique and Discussion of the Pedagogy

GT Springer

Robin McLeod
Saltire Software Inc


The use of real-world data as motivation to study mathematical topics has gained popularity throughout the world. There are even entire courses built around this notion. In most cases, the data is collected by the students themselves, using graphing calculators, probes, data loggers, and data logging software. The data logging approach requires the student to know beforehand how many samples to collect and at what rate to collect them. In practice, the student ends up running the experiment multiple times as he or she tinkers with the equipment. In the end, the student learns more about the probes and equipment than they do about the data and the mathematics.

In this presentation, we will demonstrate the data streaming approach. Under this approach, the student simply plugs in the probe and watches the results streaming in graphically. The only decision the student makes before starting is the time duration represented by the width of the entire display. The data is displayed on the device in as continuous a stream as possible. When the students see what they want on the screen, they can choose to capture that set of data. Since so much data is collected, the student is assured that the desired subset is available within the captured set. The student then works with the data, selecting and filtering until the desired subset is revealed. The final subset is then used as the data set for the typical graphing calculator explorations. This approach has the advantages of not only being simpler and less costly, but also of being more aligned with the goals of giving students experience working with numerical data with an eye to making inferences and drawing conclusions.

We will use the HP 39GS graphing calculator and the Saltire data streamer to demonstrate the data streaming technique and expose the differences in the steps and decisions the student faces using this approach versus traditional data logging.

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