User Scenario: Remote/Online Use of Equipment as a Technique for Gathering Student Data
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.
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 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.
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
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
Back to the User Scenarios page.