Gathering Student Data with Remote Equipment

Keith Andrew—Eastern Illinois University
Cameron Dorey - University of Central Arkansas
Ahmed Elgamal—University of California, San Diego

Scenario 1

Use of expensive or unique instrument by students at remote schools for individual or collaborative investigation (pedagogy or research)

Motivation

Solve real problems for students who do not have the necessary equipment, in lieu of a simulated exercise (or no exercise at all)

Example

NMR (nuclear magnetic resonance) or SEM (scanning electron microscope), both instruments would cost $200K or more

Procedure

Student submits sample (after s/he has done all possible sample prep at her/his school). Instrument time is scheduled at both schools

Possible modes of data acquisition are

• Virtual interaction
• Operator interaction (remote observation)
• Data messaging

Data is transmitted back to student, possibly sample Data archived at instrument site for subsequent use/review Student uses data back at her/his site

Example

Shaker table for structural analysis controlled over internet, an instrument which is not found in many high school educational settings, but can demonstrate important structural engineering principles

Procedure

• Student submits model of structure to be tested, technician at site sets up experiment
• Student controls experiment remotely with video feedback
• Major interest to 7-12 grade students, college structural dynamics classes

Scenario 2 Unsupervised remote collection of data in the field over a long time frame

Motivation

Time/logistical constraints prohibit real-time on-site monitoring by experimenter

Example

Monitoring of riverbed erosion

Procedure

• Monitoring would take place over an extended time period
• Robust camera/recording equipment is needed, with the capability of unmonitored time-lapse photography
• Experimenter retrieves collected data or it is transmitted automatically from site at intervals
• Student analyzes data on campus, data is compared to local simulation

Scenario 3: Multiple groups comparing data

Motivation

Gather data from large-scale spatial region

Motivation

Logistical constraints prevent one student/group from conducting all experiments

Example

• Weather-related data collection over the length of a river
• Monitoring takes place simultaneously at several sites
• Data shared between sites, analyses of data compared between sites

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