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Overview: How to use these course materials


MaGE Training Course Guide


Overview

The MaGE (Megas and Gigas Educate) program is designed to accomplish three main objectives:

  1. increase enrollment capacity in introductory classes
  2. increase enrollment and retention of women and other underrepresented groups in STEM fields
  3. to train students to educate, mentor, and support others in inclusive ways.

This document describes the course developed to accomplish this third objective.


The MaGE Training Course is designed to prepare students for the task of being a GEM (Giga Education Mentor) within the Computer Science Department at Mount Holyoke College. They are exposed to many new concepts, and grow and develop myriad skills.


Based on the eventual duties of a GEM, we chose to focus that growth and development on these key competencies:

  1. Written feedback

  2. In-person feedback

  3. Technical feedback

  4. Active learning

  5. In-person support

  6. Instruction/discussion leadership


The course is laid out over seven three-hour sessions (one session per week), each with a different focus, but filled with activities that primarily serve one or more of these competencies. In the following sections you will find a description of the MaGE Training course as taught at Mount Holyoke College, as well as recommendations and suggestions to adapt the course to your institution and field of study.



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Course Layout


Each of the seven sessions has a topic related to pedagogy and useful in peer mentorship. 

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Each session has a complete lesson plan available which lays out lots of useful information, including: topic description and vocabulary, prerequisite readings, discussion topics and prompts, 
in-class activities, and homework.


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The sessions may be completed out of order. Those sessions at the top must be completed first, with session dependency indicated by an arrow. Think of these as “paths” through the course. For example, Session 7 on
Active Learning Modules requires both sessions Introduction to Peer Mentorship, and Active Learning. However, you would not need to complete the session on Emotional Intelligence, for instance.

Note, both the session Self-Regulation, Self-Efficacy, Growth Mindset, and Goal Orientation and the session Emotional Intelligence should be completed prior to completing the session Climate, Diversity, Inclusion, and Feedback.


Course Assessment


The training course is assessed based on attendance, participation and engagement in class activities and discussion, and the completion of a final portfolio. The portfolio contains:
  1. One active learning module lesson plan (in pairs)
  2. Two written code reviews
  3. Three short written reflections
  4. One MaGE Training video

Successful completion of the MaGE Training Course requires completing all four parts of the portfolio. There will time in class to begin working on most of the parts, but time must be spent outside of class to finish them.


Active Learning Module Lesson Plan
This assignment consists of creating a lesson plan for a 30-40 minutes activity designed to engage introductory students. More information can be found in the lesson plan for Session 5.

Code Reviews (2)
Students will complete two separate code reviews on sample assignments. Code Review 1 occurs before Session 4 and Code Review 2 occurs before Session 5.

Written Reflections (4)
Each a few paragraphs in length, these reflections help students to digest the topics from the reading and class discussions. Prompts are given in the lesson plan in which the reflections are assigned as homework (Sessions 2, 3, 4 and 5).

MaGE Training Video
Students make a one minute video pertaining to some (assigned) topic from the course. The video should describe the topic briefly, and share one concrete thing about the topic. For instance, why is it relevant to computer science?





Recommendations for Adaptation


Use Cases

There probably exist many ways to adapt the material from this course to your particular situation. We will highlight a few common use cases.


Non-CS STEM field or CS without technical feedback

Given the need for diverse and inclusive environments in all STEM fields, the majority of the program remains the same, with the exception of code review,

To adapt this program to another STEM field, there are two options. First, is to strip out the technical feedback portions of the course that focus on code review. This will also work for computer science programs that do not plan on requiring their peer mentors to do code review. To accomplish this remove the code review activities from Sessions 2, 3, and 4. These are discussion heavy sessions and it is unlikely that, despite the removal of these activities, these session could be pared down to two. We recommend maintaining the three topic sequence.

The other option is to replace the code review with some field-specific technical portion. Perhaps your peer mentors will be required to grade problem sets and this time could be used to guide them in proper methodology.

The Active Learning component may also be removed, depending on the STEM field and the eventual duties of your peer mentors.


One-day bootcamp for teaching assistants (any STEM field)

Teaching assistants (students who hold drop in hours for other students to come ask questions and get help on assignments) should create an inclusive environment, serve as an effective mirror or coach, and provide effective in-person feedback.

This type of bootcamp should combine small group discussions and activities to help students quickly absorb the concepts. A reflection, drawing on the reflection prompts from the training course, could also be given after the bootcamp to aid in retention and critical thinking about the topics. A suggested sequence, broken down by topic (topics in red can be removed for additional time):

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Managing the Discussions

This a discussion-based course, and depending on class size and disposition this can be a challenge for instructors - especially in STEM fields where this type of course may be unfamiliar to the instructor. Here are some helpful tips for managing these discussions.

Class size

Depending on the size of your class, frequent all-class discussions may be both time-prohibitive and disenfranchising to students uncomfortable speaking out in larger groups. Consider these alternative approaches (or use a combination of them depending on your goals for that discussion):
  • Small group discussion first (groups of 2-3), then entire group discussion
    • Each group reports back what they discussed
  • Individual reflections, then group discussion
    • Each student takes 3-5 minutes to write on a notecard (not to be collected)
    • [Optional] Small group discussion
    • Entire group discussion

The choice of these, or an entire group discussion, will depend on the size of your group, the number of instructors/experienced mentors, and the amount of time available.

Staying on track

When using small group or individual discussions, consider providing the students with all the prompts at the beginning so they can work through them at their pace. 

When using entire group discussions, consider providing one prompt at a time to allow the students to drive the discussion forward. Try not to rush through the prompts, while being mindful of time. Often students will naturally come around to discussing the next prompt without your intervention. If they do get sidetracked, or have spent too much time on a single topic, then prompt them to move on to the next topic.


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Activities by Competency

All activities from all seven sessions are listed here by the competency they fulfill. Given the specific outcomes your require in your peer mentorship program, you may wish to target just some of these competencies.

Note: Some activities are listed under multiple competencies.

Written Feedback

GEMs provide written feedback to their mentees as part of a code review process.

  • Session 2: Introduction to Code Review: Overview of types of feedback and tools
  • Session 3: Discussion: Emotional Intelligence and Self-Efficacy
  • Session 4 (completed prior): Practice Code Review
  • Session 4: Activity: Compare code reviews in pairs
  • Session 4: Discussion: Code review experience




In-person Feedback

GEMs meet with their mentees in person once per week for 10 minutes.

  • Session 2: Introduction to Code Review: Overview of types of feedback and tools 
  • Session 2: Activity: Peer Mentoring Roles and Effective Feedback
  • Session 3: Discussion: Why is Emotional Intelligence relevant to mentoring?
  • Session 4: Discussion: Code review experience
  • Session 4: Discussion: Diversity in Computer Science
  • Session 4: Discussion: Inclusion and climate in educational settings and mentor relationships
  • Session 5: Discussion: Initial reactions to mock 1-on-1 experience
  • Session 5: Activity: Inclusion and climate 
  • Session 6: Viewing Mock 1-on-1 Video Clips




Active Learning

GEMs deliver active learning modules during part of the laboratory session. Each module has been created by a GEM in the training course.

  • Session 5: Active Learning Module parameters and How to Structure a Pitch 
  • Session 5: Activity: ALM brainstorming
  • Session 5: ALM Pitches and Voting
  • Session 6 and 7: Active Learning Module Run-throughs




In-person Support

GEMs are present during the laboratory sessions to assist with student questions.

  • Session 2: Discussion: Self-Efficacy, Motivation, and Learning Styles
  • Session 3: Activity: Emotional Intelligence Scenarios
  • Session 3: Discussion: Why is Emotional Intelligence relevant to mentoring?
  • Session 3: Discussion: Emotional Intelligence and Self-Efficacy
  • Session 4: Activity: Assumptions scenarios
  • Session 4: Discussion: Diversity in Computer Science 
  • Session 4: Discussion: Inclusion and climate in educational settings and mentor relationships
  • Session 5: Activity: Inclusion and climate





Instruction/Discussion Leadership

GEMs must be comfortable leading discussion on technical topics, as when deliver Active Learning Modules.

  • Session 1: Discussion: What does it mean to be a peer mentor?
  • Session 2: Discussion: Self-Efficacy, Motivation, and Learning Styles
  • Session 3: Discussion: Why is Emotional Intelligence relevant to mentoring?
  • Session 3: Discussion: Emotional Intelligence and Self-Efficacy
  • Session 4: Activity: Assumptions scenarios
  • Session 4: Discussion: Diversity in Computer Science 
  • Session 4: Discussion: Inclusion and climate in educational settings and mentor relationships
  • Session 5: Activity: Inclusion and climate 
  • Session 5: ALM Pitches and Voting
  • Session 6 and 7: Active Learning Module Run-throughs



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