Real science.

The advantage of portable computing devices is that they encourage “real science” activities out in the world – look at Sayid’s “Pushing up daisies” quadrat activity, for example. To have a spreadsheet available at the same time as fishing around in a ditch for tadpoles, or recording estimated speeds and accelerations of aircraft lifting from a runway, or exploring a lemonade bottling plant, brings the analysis of data vividly to life as part and parcel of the phenomena being observed. When it comes to sharing the excitement with others, though, these devices have their shortcomings.

Generally speaking, a pupil with hand held computer has to store field data in a spreadsheet or database, write notes in a word processor; return to school or home; upload both to a PC or Mac; and only then start to merge them or share them with peers.

With the trial set of Asus netbooks, I was able to take groups of students out and make the computing a seamless part of the fieldwork. There are several levels to this.

Most basic level: sneakernet.

This applies in most field contexts. Here, the pupil enters his or her own data and makes his or her own notes, as in the usual handheld setup. However, a single USB flash drive is circulated continually around the group, each pupil backing up their work to it as it reaches them and then copying a complete set of files back to their own machine. It’s necessary to name the files logically (Jesh_Kaur.doc, Jesh_Kaur.xls; John_Smith.doc, John_Smith.xls; and so on) and to avoid overwriting and keep individual work distinct, but once that habit is established it means that every member of the group has both multiple recent backups her or his own work (on both the USB drive and the computers of other members of the group) and also reference access to near current copies of everyone else’s.

The next level: WAN to go.

This was amazingly easy to set up and use, though not suitable for all settings. All that is required is a wireless router, a power supply, and a relatively small study area. When in a museum, that lemonade bottling plant, or many other visit sites, a temporary wifi zone can (with site permission) be set up in an area such as the café or visitor centre. No internet access is available, but work sharing becomes immediate. If a wifi hard disk is attached to the router, so much the better – all shared work is then available to anyone within the coverage area, regardless of whether its author is within reach. If an adaptor is carried for running the router and disk from a vehicle’s cigarette lighter, good use can also be made of time on the minibus home afterwards.

Continuity at school and at home.

If each pupil is made an author on a shared blog, with restricted readership (to avoid predation risks, but also to provide the group with privacy from nonparticipant peers) and the teacher as administrator, subsequent write ups and analysis can be pooled. By copying and pasting material from the word processor or spreadsheet such blog entries are quickly and easily generated, then can be edited and developed in place. The blog takes care of permissions – each member of a group can red everyone’s material but only change his/her own. A small portable computer continually in the same pupil’s hands, allowing work to be done when that pupil feels like it (at home or at school), able to access the blog whenever and wherever wifi access is accessible, a great incentive to participate.

Team science

All in all, my trial period with these “netbooks” has been the best opportunity yet to develop in pupils a genuine constructivist experience of working in a real community of team science. The pupils working on this pilot responded magnificently, simultaneously nourishing and feeding from each other, exchanging ideas and critiques, competing to be the best contributors to shared success.

All I have to do now is get funding to buy a full class set for long term use!

[contributed by KateQ]

The first problem we have encountered is an apparent dearth of devices or software which will listen to a note and read out its frequency. There are plenty of them (aimed at instrument tuning) which will do it the other way round, reading out a note name (C, F#, G, etc), but not a frequency. And although we did work out an alternative approach based on these guitar tuners, the interference from a building full of computing equipment, hearing aid loop generators, WiFi networks, several hundred cellphones etc, swamped them and made them useless.

A microphone attached to an oscilloscope is too unwieldy for our purpose: first introduce the oscilloscope, then explain the setting of time bases, learn to disregard noise … a one hour session would be over before anything useful had even stared. It will be useful and interesting further in, but not at the beginning.

Plan C involves auditory comparison of a tone generator signal to played keyboard and guitar string notes, by tweaking the frequency specified in the generator and deciding by consensus when a played note has been matched. This looks initially promising. We have started with NCH’s tone generator, which works well; the synthesiser at National Taiwan Normal University’s physics department also looks promising:

An alternative, offering sequential playing of different frequencies will be needed for subsequent work; a purpose made interface for preference, though it could be done using a mathematics package or even BASIC at a pinch. Ivor has written one as a Java Applet, but security measures in the browser environment where it will be used are raising barriers which have still to be resolved.

More as the idea progresses…

[contributed by Ivor McGillivray and Felix Grant]

Sometimes, with a small group, I will replace both card and sock with a frisbee in the centre of which a circular 113mm hole (to match the area of the 100mm square) has been cut.Throwing things around in the open air is always preferable, on a sunny day, to being indoors. We usually take a picnic along, and a set of palmtop computers, so we can conduct the subsequent analysis of our daisy data in relaxation amongst the daisies themselves. This approach pays dividends: I get a lot of good natured work out of children who would get bored and impatient if we did academically equivalent work indoors.

This week, instead of the palmtops, my year fours (age 8-9) took a laptop with InspireData (reviewed here). Instead of writing their results on the card, and collating them later in a spreadsheet, the pupils brought each count back to the laptop and typed it into InspireData’s data entry “questionnaire”. Each observation was identified by the child’s name, and a photograph of a daisy was imported to replace the standard marker, so as the session proceeded we watched a growing histogram of labeled daisies gradually assemble on screen.

The class kept on gathering data much longer than usual, keen to see their name on screen as often as possible. Result: a much larger results database than usual, and more pupil involvement in the analysis phase.

I plan to follow up, at the end of this week, with botany and geography lessons based on the results using the InspireData histogram as a reference point for analogy with quantitative methods in both of those fields.

“Pushing up the daisies” is a good educational activity, offering a number of painless entry points to maths and science topics. InspireData adds immeasurably to it.

[contributed by Sayid]

When you first switch from the worksheet to visualisation, you will find your data points scattered randomly all over the desktop. I found that this works well with introductory sorting exercises with found objects or record cards - especially if you start by applying a Venn diagram.

I say “applying” a Venn diagram, not “drawing” one, deliberately. Everything you (or the student) do here assembles itself before your eyes, each data point moving across the screen from its random initial position to the appropriate place in the graphic. Click the on screen Venn diagram button twice, to create two set loops; click each loop in turn and define them as “male” or “female”. Assuming that you have entered the name and gender of each pupil as your data, the points will travel quickly (but not too quickly) across the screen and cluster in the appropriate loop segments. Now switch on data point labels with another click, choosing “name”, and each point will show which pupil it represents. Now each member of the class can watch her or his own personal avatar move about in subsequent work.

Now click the stack diagram button. The Venn loops disappear, the points move again, and when everything comes to rest your pupils are stacked up in two bars above “male” and “female” markers, graphically showing the gender balance of the class.

Everything works the same way. If you entered the heights of your class members in centimetres, along with their genders, click the variable used for that stack chart and select “height”. More visual rearrangement, as the names shift around to align with the height bands which appear across the x-axis to replace the gender labels, for a schematic histogram. Select colouring, and the point beside each name changes hue to reflect gender - blue for girls, red for boys, perhaps. The way height is distributed by gender is immediately there for discussion. You can, if you wish, take the colouring back into a Venn diagram but this time define the loops as (for example) “height more than 120cm” and “height less than 150cm”, then discuss the way genders divide across the three set segments.

Pie charts work the same way. Leave the gender colouring in place, and define the sectors of the pie to reflect height bands - maybe start with the same three, then add more to increase the resolution as discussion develops. With each change, the names will shuffle about the screen to adopt their correct positions.

This needn’t seem to have anything to do with maths, so it’s a wonderful way to painlessly develop categorisation and quantitative vision alongside science as fun - possibly in an apparently nonscience context. I spent a session with a ten year old soccer team, feeding in their own choice of vital statistics for their personal heroes (club, field position, age, height, weight, number of goals last season…; for Beckham, Gerard, Rooney…).

Though I didn’t use it here, there is the facility to use custom icons (either across a whole variable or case by case), so a small photograph of each player would have been a valuable addition. Discussing the patterns which InspireData threw up, they generated their own questions, hypotheses, lines of enquiry. One of them had read a rule of thumb for ideal relation of height to weight - and InspireData moved the players (colour coded by performance) into a scattergram. Then, two hours in, one lad said: “could we use this for maths?”Getting the information into the worksheet is simplicity itself. There is a simple data entry form, called “Questionnaire”, into which each student can individually type their chosen information without having to navigate the worksheet at all. You can, if you wish, add helpful comments to each field (such as “how many goals did your player score last season?”). The user types into clearly laid out boxes, edits until they are happy, then a click commits the result to a row in the sheet.

For its purpose, and its level, I can’t praise this program highly enough. If you do any kind of data handling, in any subject, at any level where your learners are new to data analysis and would benefit from a visual approach, buy it.

[contributed by Felix Grant]