We share a potential model for online recitation sessions for MIT residential courses based on our experiences running similar sessions for courses in the MITx MicroMasters Program in Statistics and Data Science.

1. Online Recitation Sessions
A Model for MIT Residential Courses
Based on Support for
MITx MicroMasters Courses
in Statistics and Data Science
Glenda Stump, Brandon Muramatsu, Andrés Salazar
MIT Abdul Latif Jameel World Education Lab
and MIT Open Learning Projects
Copyright 2020, Massachusetts Institute of Technology
Unless otherwise expressly stated, this work is licensed under a Creative Commons Attribution 4.0 International License.
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2. 2Glenda S Stump, PhD
A Model for Online Recitation Sessions
Project Context
 Wrap around online “facilitated
sessions” for a university using
MITx MicroMasters in Statistics and
Data Science Courses
 Third party approach
 Team approach
 Up to 30 learners per session with
breakouts, 2 facilitators
 Working professionals meeting
before or after work
 Non-native English speakers
MIT Residential Courses
 Direct applicability to many online
recitation sessions for engineering,
physical science, & math courses (lots
of equations, code)
 Direct instructional link better!
 Team approach is better, solo is ok
 20 or so learners is ideal, have a
colleague lend a hand to get started
 Scale activities to fit timing

3. Session Description &
Pedagogical Approach
3Glenda S Stump, PhD

4. Training Agenda
 Segment 1
 Facilitated session components
 Facilitator expectations/role
 Segment 2
 Student engagement
 Active learning & ICAP Framework
 Segment 3
 Instructional strategies for facilitated sessions
 Planning a facilitated session
 Conducting a facilitated session
4Glenda S Stump, PhD

5. 5Glenda S Stump, PhD
THE SESSION & THE FACILITATOR
Segment 1
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6. Intended Learning Outcomes
 By the end of this segment, you should be able to:
 Describe the basic structure of a facilitated session
 Describe the facilitator’s role and responsibilities for
managing facilitated sessions
6Glenda S Stump, PhD

7. Facilitated sessions
 Conducted online in a synchronous session
 Led by MIT facilitators
 Held once per week throughout the course
 Last 1.5 hours
 Follow a lesson plan prepared in advance
 Intended Learning Outcomes (ILOs), selected concepts or skills associated with
the material for the week
 Contain concept (poll) questions and worked examples
 Clarify key concepts emphasized in edX course
 Conducted in English
7Glenda S Stump, PhD

8. Facilitator Expectations/Role
 Learn features of the delivery platform, i.e., breakout rooms, chat, poll
questions, etc.
 Interact with learners in the LMS discussion forum to answer questions
between sessions
 Prepare material before every session—lesson plan and related slides or
code
 Interact with other course facilitators to develop and revise materials
 As course progresses, adjustments to facilitated sessions may be required
 Facilitate 1 session per week
8Glenda S Stump, PhD

9. 9Glenda S Stump, PhD
STUDENT ENGAGEMENT / ACTIVE LEARNING
USING THE ICAP FRAMEWORK
Segment 2
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10. Intended Learning Outcomes
 By the end of this segment, you should be able to:
 Describe strategies that promote student engagement
 Describe modes of student engagement as identified
in the ICAP Framework
10Glenda S Stump, PhD

11. What promotes student engagement in online learning
experiences?
 Problem-centric learning
 Active learning supported by timely feedback
 Course resources that allow for diverse learning
needs or preferences
 Instructor attributes (enthusiasm, humor)
 Peer interaction
 Instructor accessibility
11Glenda S Stump, PhD (Hew, 2018)

12. Active Learning
 Engages students in the process of learning through
activities and/or discussion in class, as opposed to
passively listening to an expert
 Emphasizes higher-order thinking and often involves
group work
12Glenda S Stump, PhD (Freeman et al, 2014)

13. Active learning: What are you asking students to do?
1. Sit quietly and listen?
2. Manipulate the material in some way? e.g., use formula
to solve problem, follow lab instructions, remember rule
or law, match concept with its definition
3. Make an inference or construct a product that goes
beyond the information you give them?
4. Interact with each other to make inferences or construct
products that go beyond the information you give them?
13Glenda S Stump, PhD

14. The ICAP Framework
I
• Interactive – generating
together
C
• Constructive -
generating
A • Active -
manipulating
P • Passive -
listening
(Chi, 2009; Chi & Wylie, 2014) 14Glenda S Stump, PhD

15. The ICAP Hypothesis – I>C>A>P
• Interactive mode produces better learning than
Constructive (I > C)
• Constructive mode produces better learning than Active
(C > A)
• Active mode produces better learning than Passive
(A > P)
Constructive & Interactive modes of engagement usually
result in deeper learning than Active & Passive modes
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16. Check your understanding of Active Learning
 Using the ICAP framework, what mode of
engagement should the following activities produce?
16Glenda S Stump, PhD

17. Example
After giving information about a topic:
 Group students into fours
 Ask them to take five minutes to decide on the one
question they think is crucial for you to answer right
now
17Glenda S Stump, PhD

18. Example
For the experiment of flipping a coin, determine
whether we have a legitimate sample space for the
statement below.
 Ω = {Heads and it is raining, Heads and it is not
raining, Tails}
State your answer and explain the rationale for your
choice.
18Glenda S Stump, PhD

19. Example
True or false: The chart below supports that increased
glyphosate use over time has led to an increase in the
number of cases of autism among children aged 6-21
years.
What additional evidence is needed to support this
claim?
19Glenda S Stump, PhD

20. Example
After an experience/activity in class, ask students to
reflect upon and write:
 “what” they learned
 “so what” (why is it important and what are the
implications)
 “now what” (how to apply it or do things differently)
20Glenda S Stump, PhD

21. Example
We want to estimate a function
for daily cigarette consumption.
To perform this, we will use a
database which contains
information about daily
consumption of cigarettes and
other variables for a random
sample of smoking single adults
from the United States for the
year 2000.
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22. Choose all the correct statements related to the R
output (the coefficients table):
 The expected value of the daily cigarette consumption decreases
0.37685 units for each additional year of school education.
 Each additional unit of the variable restaurn reduces the average
cigarettes smoked per day by 2.93576.
 The model doesn’t explain too much about the total variability of
the explained variable (daily consumption of cigarettes), but overall
it is significant when considering all the variables jointly.
 The model is good at explaining the total variability of the explained
variable (daily consumption of cigarettes), but overall, it is not
significant considering all the variables jointly.
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23. One vs. Many
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24. One vs. Many
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I think...
My idea
is..
That’s not
right….
How do
you
know?

25. 25Glenda S Stump, PhD
INSTRUCTIONAL STRATEGIES/
PLANNING & CONDUCTING FACILITATED
SESSIONS
Segment 3
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26. Intended Learning Outcomes
 By the end of this segment, you should be able to:
 Describe rationale/instructor responsibilities for
concept questions, worked examples, multiple modes
of representation
 Develop plan for facilitated session/recitation
26Glenda S Stump, PhD

27. Strategy 1: Concept Questions
 A blood platelet drifts along with the flow of blood through an
artery that is partially blocked by deposits.
As the platelet moves from the narrow region to the wider region,
its speed
 Increases
 Remains the same
 Decreases
27Glenda S Stump, PhD (Mazur, 1997)

28. Concept Questions
 Purpose
 Learning and self-evaluation (Student)
 Formative assessment (Instructor)
 What? Questions that…
 focus on a single concept
 are not solvable by relying on equations
 have multiple-choice answers
 are unambiguously worded
 are of medium difficulty
28Glenda S Stump, PhD (Mazur, 1997)

29. Concept Questions
 How?
1. Instructor poses a question 1 minute
2. Students think about the question 1-2 minutes
3. Students answer the question individually
4. Students discuss question with their group 10-15 minutes
5. Students record revised answers individually
6. Students explain rationale for their choices 10-15 minutes
7. Instructor explains correct answer if needed 2 + minutes
29Glenda S Stump, PhD (Adapted from Mazur, 1997)

30. Professor Eric Mazur
Physics Course – Harvard
30Glenda S Stump, PhD
Eric Mazur shows interactive teaching
https://youtu.be/wont2v_LZ1E

31. Strategy 2: Worked Examples
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32. Worked Examples
 Purpose
 Decrease cognitive load for students when learning
complex problem-solving
 What?
 Step-by-step solutions to solved problems
 Written or oral
 Requires deep thinking (e.g., self-explanation) to be
effective
32Glenda S Stump, PhD

33. Worked Examples
When?
 As concept is introduced in class
 For complex problems – Backward fading
 Consider learner experience
 Good for novice learners – helps them build schema for
working the problem
 Not good for experienced learners – once schema is
established, better to solidify schema by exercising it
33Glenda S Stump, PhD

34. Model
Transitioning from Worked Examples to Practice Problems
= Worked in Lesson
= Worked by the Learner
Worked Example
Steps 1, 2, 3
worked
Completion
Example 1
Steps 1, 2 worked
Completion
Example 2
Step 1 worked
Practice Problem
No Steps worked
Glenda S Stump, PhD 34(From Clark, Nygen, & Sweller, 2006)

35. Strategy 3: Multiple Representations
Symbolic representation
mẍ + bẋ + kx = 0
Text and / or Verbal representation
“This equation describes a body undergoing damped vibration. The amplitude of the function
decreases with time”
Visual representation
Physical representation
35Glenda S Stump, PhD

36. Planning a Session: The Backward Design Process
36Glenda S Stump, PhD
Determine
Acceptable
Evidence
Intended
Learning
Outcomes
Learning
Experiences &
Instruction
What’s important? How do you help them
get it?
How do you know if
they get it?
(Wiggins & McTighe, Understanding by Design, p. 18)

37. Conducting a Facilitated Session
 Review Intended Learning Outcomes
 Concept questions (poll)
 Present question and possible responses
 Everyone responds to question individually
 Method 1 – Break out into small groups
 Each person tells how they responded to question and why
 Group leader helps group come to consensus about correct response
 Ask everyone to respond to the question individually again after discussion
 Ask for volunteers to explain the response they chose. Ask for correct & incorrect responses
 Ask who agrees or disagrees with the explanations
 Method 2 – All learners as one group
 Ask for volunteers to explain the response they chose. Ask for correct & incorrect responses
 Ask who agrees or disagrees with the explanations
 Ask everyone to respond to the question individually again after discussion
37Glenda S Stump, PhD

38. Conducting a Facilitated Session
 Worked example
 Discuss difficult concepts
 Allow for student questions
 Review Intended Learning Outcomes again
 ‘MUD’ Card (feedback for session)
38Glenda S Stump, PhD

39. Summary
 The facilitated session
 Roles/responsibilities of facilitator
 Student engagement
 Active learning – planning activities using ICAP
 Instructional strategies (3)
 Conducting a session
39Glenda S Stump, PhD

40. Questions?
Glenda Stump
Education Research Scientist
gsstump@mit.edu
Brandon Muramatsu
Associate Director, Projects
mura@mit.edu
40Glenda S Stump, PhD

41. References
Chi, M. T. H. (2009). Active‐constructive‐interactive: A conceptual framework for differentiating learning
activities. Topics in Cognitive Science, 1(1), 73-105.
Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning
outcomes. Educational psychologist, 49(4), 219-243.
Clark, R. C., Nguyen, F., & Sweller, J. (2011). Efficiency in learning: Evidence-based guidelines to manage
cognitive load. John Wiley & Sons.
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P.
(2014). Active learning increases student performance in science, engineering, and
mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410-8415.
Hew, K. F. (2018). Unpacking the strategies of ten highly rated MOOCs: Implications for engaging
students in large online courses. Teachers College Record, 120(1), n1.
Mazur, E. (1997) Peer instruction. Prentice Hall: Upper Saddle River, NJ.
Wiggins, G., Wiggins, G. P., & McTighe, J. (2005). Understanding by design. Ascd.
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