COMP.5870 Computer Science in Secondary Education, Fall 2017

Prof. Fred Martin, fred_martin@uml.edu

Catalog Description

The goal of this course is to introduce teachers to using collaborative data visualization in their STEM instruction. Teachers will learn how to use a state-of-the-art collaborative data visualization system called iSENSE. They will conduct practical experiments in the areas of physical science, mathematics, statistics, engineering, biology, and earth and space science content areas, and use iSENSE to share, visualize, and make sense of their data. Teachers will also design their own data-centric collaboration and visualization activities, connecting with their instructional goals, and bring these activities to their students.


This course was co-created with Samantha Michalka.


The course is offered through UMass Lowell's Division of Online and Continuing Education and is hosted in Blackboard.

This web page provides a “hub-and-spoke” index to the course materials. The links below, under Course Materials provide links to the entire course content.

To enrolled students in Blackboard, the course looks like this:

Course Materials

Overview, schedule, and syllabus

Weeks 1 and 2

Introduction to iSENSE; measurement labs with data entry using contributor keys; creating projects on iSENSE; grouping data by categories; Probability reading and reflection

Willis, M. B., Hay, S., Martin, F. G., Scribner-MacLean, M., & Rudnicki, I. (2015). Probability with Collaborative Data Visualization Software. Mathematics Teacher, 109(3), 194–199.

Weeks 3 and 4

Assignment review; hurricane, baseball, water quality tutorials; setting default visualization; Data Walk reading assignment; Classroom lesson assignment

Cogger, S. (2015). Doing the Data Walk. The Science Teacher, 82(2), 43–45.

Weeks 5 and 6

iSENSE skills; data walk reading, discussion, and activity; presentation of three classroom projects; prep for classroom work

Philip, T. M., Schuler-Brown, S., & Way, W. (2013). A framework for learning about big data with mobile technologies for democratic participation: Possibilities, limitations, and unanticipated obstacles. Technology, Knowledge and Learning, 18(3), 103–120.

Weeks 7 and 8

Discussion of big data and democracy paper, geospatial projects, teacher presentations

Doering, A., & Veletsianos, G. (2008). An investigation of the use of real-time, authentic geospatial data in the K–12 classroom. Journal of Geography, 106(6), 217–225.

Week 9 (Saturday half-day meeting)

Creating data-intensive apps using MIT App Inventor and Appvis for iSENSE

Weeks 10 and 11

Time-series data; Comprehending and Learning from ‘Visualizations’ paper; Assignments 11 and 12; final project assignment

Uttal, D. H., & Doherty, K. O. (2008). Comprehending and learning from ‘visualizations’: A developmental perspective. Visualization: Theory and practice in science education, 53–72.

Weeks 12 and 13

Discussion of Visualizations paper; small-group conversations about final projects; iSENSE skills checkoff and Assignment 13 (final project presentation slides) assigned

Week 14

Final meeting—in-class presentations and discussion