CompSci 249, Spring 2017: CS Education Research Seminar

Schedule

Session 1 (1/11): Introduction to Peer Mentoring & Teaching

• Introductions & Goals
• Qualities of Good UTAs
• Course Syllabus and Details
• Readings
  • Paper on My Digital Hand
  • NCWIT Tips for Effective Feedback
  • Negotiating Peer Mentoring Roles in Undergraduate Research Lab Settings (Packard et al., 2014)

Session 2 (1/18): Helper Hours: Self-Regulation, Self-Efficacy, Growth Mindset and Goal Orientation

• Administrative:
  • Discussion assignments
  • Helper hours
  • Helper hours czar
  • Git/assignment maven?
• Discussion Prep:
  • Eclipse issues
  • Where to get help with Java?
  • WOTO - How to lead groupwork?
  • CirclesCountry
• Tool: ASCEND remote collaboration
• Reflection: Qualities of a good UTA/peer mentor?
  • Have you had a peer mentor? What was positive or negative about that experience?
  • Have you ever been a peer mentor? What made you successful or unsuccessful?
  • What qualities should a peer mentor have?
  • What might make peer mentoring difficult? Does it depend on the mentor? The mentee? The environment?

Session 3 (1/25): Helper Hours: Emotional Intelligence

• Assessment: Learning Self-Inventory
• Reflection:
  • Learning & Motivation: What type of mindset do you have? How will that affect your role as a UTA?
    • Growth mindset
    • Self-regulated learning
    • Self-efficacy
    • Goal orientation
  • Feedback on Code
• Administrative:
  • Piazza
  • MDH Feedback
• Assignment:
  • Create five (5) JUnit tests for Markov

Session 4 (2/1): Emotional Intelligence and Feedback

• Tools:
  • Viewing student code on Git
  • Sakai Gradebook for entering discussion scores
  • Gradescope for entering assignment scores
  • My Digital Hand for managing helper hours
    • Issues
      • High wait times
      • Long interactions
      • Fairness
        • Unequal distribution of instruction time
        • Starvation
      • Data collection
    • Interaction model
      • Students: be ready.
      • UTAs: keep it focused and brief.
      • UTAs: : suggest next steps.
      • Matching mechanism: encourage rapport.
• Assessment: Emotional Intelligence Self-Assessment
  For each of the following scenarios consider these prompts:
  • What factor might be contributing to this situation?
  • How does this situation make you feel?
  • What might be the best way to react?
  Sample emotional intelligence scenarios:
  
  • A student shows up late to an appointment. They seem flustered and unfocused. When you begin discussing some of the weak points in their work, they begin to cry.
  • A student gets increasingly frustrated when listening to your feedback on his recent work. Eventually he has a hostile outburst, blaming your teaching skills for his difficulty with the assignments.
  • A student appears to be less motivated and not taking the work as seriously. When you meet with her, she makes it clear that she does not feel challenged and can finish her work much faster than her classmates.
  Conclusions:
  • How are emotional intelligence and self-efficacy related?
  • Even when students complete programming assignments successfully, students may still perceive their own competence negatively. Why is emotional intelligence important in this context?
  • In what ways might you encourage emotional intelligence and self-reflection in your students?
• Discussion Prep: Discussion 3 draft

Session 5 (2/8): GPS Syndrome, Helper hours, and Discussion Sections

Session 6 (2/15): Evaluating Student Work

Session 7 (2/22): Mock 1-on-1 Reviews

Session 8 (3/1): Active Learning Modules

• Percolation
• Discussion Reflection

Session 9 (3/8): No class

Session 10 (3/22): Active Learning

• Reading
  • The Mentor's Dilemma: Providing. Critical Feedback Across the Racial Divide. Geoffrey L. Cohen. Claude M. Steele. Lee D. Ross. Stanford University.
  • "Active Learning and Active-Learning-Based Teaching Model", Guide to Teaching Computer Science (ch.2) (Hazzan et al., 2004)"Active Learning and Active-Learning-Based Teaching Model", Guide to Teaching Computer Science (ch.2) (Hazzan et al., 2004)
• Active Learning Material from Mt. Holyoke:
  • Active Learning Overview
  • Prezi on Active Learning by Sara Rut
  • Creating an Active Learning Module
• Assignments:
  • Topic selection for:
    • Active Learning Modules
      • Java Basics: Objects & Classes
      • Java Basics: Collections & Arrays
      • Java Basics: Functions & Parameters
      • Java Basics: Static vs. Dynamic
      • Debugging strategies
      • Union-Find algorithms
      • Linked lists
      • Recursion
      • Recognizing recurrences
      • Code tracing
      • Big-Oh from code
      • Binary Trees
      • Others?
    • Projects
      • Create an assignment for CompSci 201
      • Create a tool to support automation of an activity for CompSci 201
      • Create a proposal for a new topic of study OR a new way to study a current course topic in a different way
      • Other?
• Reflection: Discussion Reflection

Session 11 (3/29): Evaluating teaching

Session 12 (4/5): ALMs

• Reflection: Discussion Reflection
• Classwork:
  • ALMs
    • Criteria
    • 5-minute demos
    • Refinement
  • Project elevator pitches
    • Need: What problem have you observed in CompSci 201?
    • Solution: How do you solve this problem?
    • Impact: How will this impact students or UTAs in the course?
    • Plan: How will you make it work?
• Administrative:
  • Autocomplete
  • MDH reminder
  • Test grading and scanning

Session 13 (4/12): Active Learning Modules

Technically sound rubrics are:

1. Continuous: The change in quality from score point to score point must be "equal": the degree of difference between a 5 and 4 should be the same as between a 2 and 1.
2. Parallel: Similar language should be used to describe each level of performance (e.g., low skill level, moderate skill level, and high skill level), as opposed to non-parallel constructions (e.g., low skill level, understands how to perform some of the task, excellent performance).
3. Coherent: The rubric must focus on the same achievement target throughout, although each level of the rubric will specify different degrees of attainment of that target. For example, if the purpose of the performance assessment is to measure organization in writing, then each point on the rubric must be related to different degrees of organization, not factual accuracy or creativity.
4. Highly Descriptive: Highly descriptive evaluative language ("excellent," "poor,") and comparative language ("better than," "worse than") should be used to clarify each level of performance in order to help teachers and raters recognize the salient and distinctive features of each level. It also communicates performance expectations to students, parents, and other stakeholders.
5. Valid: The rubric permits valid inferences about performance to the degree that what is scored is what is central to performance, not what is merely easy to see or score, or based on factors other than the achievements being measured. The proposed differences in levels of performance should a) reflect the key components of student performance, b) describe qualitative, not quantitative differences in performance, and c) not confuse merely correlative behaviors with authentic indicators of achievement (e.g., clarity and quality of information presented should be a criterion in judging speaking effectiveness, not whether the speaker used note cards while speaking). Valid rubrics reduce the likelihood of biased judgments of students' work by focusing raters' attention on factors other than students' gender, race, age, appearance, ethnic heritage, or prior academic record.
6. Reliable: In traditional assessments, such as multiple choice tests, where a student selects a response from among several options, the reliability of the score has to do primarily with the stability of the test score from one testing occasion to another in the absence of intervening growth or instruction. Establishing the reliability of a rubric for a performance assessment, however, is more complex. A reliable performance assessment rubric enables:

Session 14 (4/19): Project Flash Talks

Session 15 (4/26): LDOC

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