lamp : a framework for l arge-scale a ddressing of m uddy p oints
DESCRIPTION
LAMP : A Framework for L arge-scale A ddressing of M uddy P oints . 1 - 2 - 3 - 4 - 5 - 6. Mechanism to solicit and respond to student queries in a large class. Rwitajit M ajumdar Sridhar Iyer. CS101 Spring 2013 IIT Bombay . I already know most of what prof is saying. - PowerPoint PPT PresentationTRANSCRIPT
LAMP: A Framework for Large-scale Addressing of Muddy Points
Mechanism to solicit and respond to student queries in a large class
Rwitajit MajumdarSridhar Iyer
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Batch of 2013
I already know most of what prof is saying
CS101 Spring 2013 IIT Bombay
Will two threads that receive the same event,
execute simultaneously?
What is a loop control variable?
How are threads scheduled in multi-core processors?
How to use a C++ string class variable in
a printf statement?
Is P=NP?
Can we make our own blocks in Scratch?
I already know most of what prof is saying
What are the problems to tackle in this scenario?
What are the intuitive solution?
Students vary in • pre-exposure to subject knowledge• learning styles• cultural background
various active learning strategies• Peer instruction • Just-in-Time-Teaching • Inverted Classroom
Classification of muddy points Technology assisted mechanism for scaling
Prio
r res
earc
h
Framework - a basic structure underlying a system, concept, or text.
Muddy Points (MPs)- instructor typically asks the question ‘what was least clear to you?’ at the
end of a class. A student’s response to this question is called a muddy
point.
LAMP: A Framework for Large-scale Addressing of Muddy Points
Graesser and Person
(a) degree of specification (b) content (c) question-generation mechanism
18 categories
Moodle 15 modules facilitating 7 different teaching learning activities
Costa et. al.
mechanisms for soliciting and addressing
muddy points, efficiently and effectively
in large classroom scenarios.
G A P
Our Framework
Queries to instructor outside class
Queries raised in class
Queries posted on Moodle
Systematic collection of muddy points
Diffe
rent
mod
es o
f col
lecti
on
Categories of muddy points
Clarification Core Deep AdvancedTechnical skill
Off-Topic
Mechanism to address muddy points
Will two threads that receive the same event,
execute simultaneously?
What is a loop control variable?
How are threads scheduled in multi-core processors?
How to use a C++ string class variable in
a printf statement?
Is P=NP?
6 categories
Can we make our own blocks in Scratch?
Categories of muddy points Mechanism to address muddy points
highlighting important MPs or recurring MPs to the whole class
reflecting
• The pilot runCS101 Spring 2013 IIT Bombay
• Course format• Integration of the LAMP framework• Example queries
What did we study in the pilot run?
Research Questions (RQ)
RQ1: How effective is the LAMP framework?a. For collection of muddy point from students.b. To address the muddy points of students.
RQ2: How does the pattern of muddy change as the semester progresses?
How did we study?
Methodology
instrument
sample
Analysis
RQ1a RQ1bRQ2
Log Analysis
1. perception of effectiveness of the collection 2. perception of effectiveness of the addressal3. preference of mode for MPs
} Likert scale
------------Rank order (1st – 4th )
Tracked logs on Moodle forum
Online survey
450 students 340 responses
343 queries
195 b.m.+ 148 b.e. 274 complete
Combined Likert scale to 3 group (agreed – neutral – disagreed)Checked distributions of responses% agreed
χ2 test to check whether one perception influenced other
% compositionof categories
Results
N=274
(χ2 = 77.26, dof=4, P<0.001) shows that
the perception of getting a satisfactory answer depends on
the perception of whether they had enough opportunities to ask a query
N=276 Raise Q. in class
Muddy point chit
Post on Moodle
Discuss with instructor
1st 80 85 38 73
2nd 51 77 72 76
3rd 43 59 109 65
4th 102 55 57 62
Rank distribution of each mode of asking MPs
Stratified Attribute Tracking (SAT) Diagram
¾ agreed - agreed
Raise Q. in class Muddy point chit Post on Moodle Discuss with instructor
34% 27% 12% 27%
b.m.before mid-term
b.e.before endterm
Trends in nature of questioning based on MP categories
Ta
ke
a
wa
y68% of students agree that the LAMP provided them with satisfactory means to pose their MPs.
57% of students agree that their MPs were answered satisfactorily either in class or on forum.
44% Clarification (~1.5x) 71%
21% Deep (~0.3x) 7%
4 modes in LAMP integrates the advantages of face to face interaction, anonymous muddy point slips, and online forum,
to elicit and address muddy points in a large class.
Ta
ke
a
wa
y68% of students agree that the LAMP provided them with satisfactory means to pose their MPs.
57% of students agree that their MPs were answered satisfactorily either in class or on forum.
44% Clarification (~1.5x) 71%
21% Deep (~0.3x) 7%
4 modes in LAMP integrates the advantages of face to face interaction, anonymous muddy point slips, and online forum,
to elicit and address muddy points in a large class.
Rwitajit [email protected] [email protected]
www.et.iitb.ac.in/~rwito
Sridhar [email protected]
www.it.iitb.ac.in/~sri
LAMP: A Fram
ework for Large-scale Addressing of M
uddy Points
Thank you