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List of miniprojects for Grumps

• Richard Thomas has some specific hypotheses about how to explain keystroke time distributions. He plans to test them on the level1 DCS lab data to be collected starting Oct 2002.
• Quintin and Steve: feeding back to individual students some stats on their own data. The software to do this was created in an IT project, and so already exists. Adding or changing the stats displayed should be fairly easy. There are two motives for this miniproject a) to return something to the students for agreeing to participate in giving data; b) develop their possible use in promoting "reflection" by the students on their own activity, and learning on the course.
• Steve and Quintin: collecting data from the PRS lecture theatre handsets across uses, and developing their use. Part of this will be collecting and presenting records of use. Part will be linking the use in level 1 lectures to other data on the same set of students.
• Rebecca Black's student project will be looking at various variables in the level 1 DCS class, and testing whether they predict exam performance. This is not using Grumps data collection (mostly) BUT is an important example of a REDDI: fishing in a big pool of data.
• Gregor is interested in looking for misconceptions, and in the hypothesised cumulative nature of the course material, where once a student has fallen off the bandwagon they don't recover.
• Gregor is interested in relating type of motivation or "goal" (basically, for interest vs. to get the qualification) and persistence in programming tasks.
• Karen Renaud is interested in studying interruptions in the level 1 lab.
• Dag at Simula in Norway has expressed strong interest and may use Grumps in a future experiment there.
• We plan to select a single Bioinformatics tool with a substantial user community, implemented in Java. We hope to develop further both a generic "bytecode" tool for extracting data, and a particular implementation for the selected tool; and then collect data from its user community. The "client" may be the developer/maintainer of the tool, although it would be feasible to act as clients ourselves, collecting data for rather generic user tool design purposes i.e. measuring what parts are used, relative command frequencies, common sequences of commands.
• It would be good for grumps if we persuaded computing services to act as clients, and use grumps to collect usage data in CSCE clusters with a view to informing students about current and predicted usage in the clusters.
• Cara McNish from UWA may be interested in using Grumps in connection with her software for automatic submission of assignments.

Range of investigation types

One type of variation is in the sense of "data mining": whether an investigation defines a set of data and goes "fishing" by induction: seeking patterns that just emerge, versus coming with a specific hypothesis about what pattern to look for and testing for that. Induction is likely to require more statistical expertise.

Another type of variation is in the number of kinds of data combined. We may be able to illustrate this, and furthermore its importance in some cases for getting meaning out of the data. Grumps itself just gives software usage or user input data. We may also use: PRS handset data, stills from CCTV, human observation to produce hypotheses which are then tested (as Kate Gilmore did), deliberate artificial testing of users to produce reference or calibration data, other databases on the same users (e.g. class exam records).

Another type of variation is in the bottlenecks. One bottleneck can be statistics: finding the right way to express a question in terms of (statistical) functions of the data.

Another bottleneck can be artificial testing or calibration. Whether designing the extraction tool, or designing a data filter, or finding what indicators in the data correspond to a particular meaning (such as the user switching tasks), a key method is likely to be active experimentation where someone imitates a user to generate data of known meaning, while the resulting data is watched (preferably in real time) to establish correspondences, test data cleaning validity, etc.

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