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MathSciDataEdfall15

91.587 Computer Science Education in Secondary School, Fall 2015
Prof. Fred Martin, (http://www.cs.uml.edu/~fredm), fredm@cs.uml.edu

Office hours: tba

Dr. Samantha Michalka (http://samanthamichalka.com), samantha@machinescience.org

Log in to the course at http://uml.umassonline.net

Meeting Times

The course will meet at Methuen High School:

  • 12 Mondays, 3p to 6p, Sept 14, 21, 28, Oct 5, 19, 26, Nov 2, 9, 16, 23, 30, Dec 7
  • 1 Saturday, 8a to 2p, Oct 17
  • 1 Saturday with Dr. Kwong, 8a to 12p, Dec 12

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.

Materials

The course will make extensive use of the isenseproject.org online collaborative data visualization platform. Teachers should have ready access to the internet in their classroom to carry out activities with students. iPads and Chromebooks may be used for most activities.

One instructional module will make use of Vernier sensors. Chromebooks or regular Mac/Windows computers are required for this.

Work Products and Grading

  • 3 lesson plans 15%
  • 3 implementation of lesson plans
  • 3 reflections on implementations 30%
  • 8-12 readings/writings/discussion 15%
  • 8-10 assignments and/or mini-lesson designs (e.g., create a curated data set and describe how you would use it with your students) 15%
  • ~30 peer assessments 15%
  • overall class participation 10%
  • 1 pre- and 1 post-assessment (requirement, but not graded)

Policies and Standards

The course is based on participation in a series of in-class exercises that develop course concepts in the specific STEM domains, and associated homeworks to extend the in-class work. Grading is based on classroom work, homework, and subsequent write-ups that describe work done and learning accomplished. There will be approximately ten weekly assignments, and a final project assignment and writeup.

Participants will be expected to notify the instructor of absences before class meets, and arrange to make up missed work by being in touch with other participants.

Grades will be assigned per graduate grading policies at http://www.uml.edu/Catalog/Graduate/Policies/Grading-Policies.aspx, per the key below. Written and classroom work will be graded with letter grades.:

  • A Superior Work: Highest Quality 4.0
  • A- High Honors Quality 3.7
  • B+ High Quality 3.3
  • B Basic Honors Quality 3.0
  • B- Below Honors Quality 2.7
  • C+ Above Satisfactory Quality 2.3
  • C Satisfactory 2.0
  • C- Below Satisfactory Quality 1.7
  • D+ Above Minimum Passing 1.3
  • D Minimum Passing 1.0
  • F Failed 0.0

Learning Objectives in S.M.A.R.T. Format

  1. By the 4th week of the course, teachers will be able to contribute data to existing projects using the iSENSE data visualization system.
  2. By the 8th week of the course, teachers will be able to create their own projects and describe how data should be contributed to them.
  3. By the end of the course, teachers will be able to select appropriate visualization methods (including time series plots, scatter plots, bar plots, histogram plots, and map-based visualizations) and describe underlying relationships embedded in data.
  4. By the end of the course, teachers will be able to design an original collaboration- and visualization-centric activity for use in their STEM instruction, and carry out this activity with their students.
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Page last modified on September 04, 2015, at 04:21 PM