techniques for data-driven curriculum analysis
DESCRIPTION
Five techniques to understand the data that could help to re-design CurriculumTRANSCRIPT
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Techniques for Data-Driven Curriculum Analysis
Gonzalo Mendez, Xavier Ochoa & Katherine Chiluiza
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Siemens, George, and Phil Long. "Penetrating the fog: Analytics in learning and education." Educause Review 46.5 (2011): 30-32.
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Siemens, George, and Phil Long. "Penetrating the fog: Analytics in learning and education." Educause Review 46.5 (2011): 30-32.
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Which are the hardest/more difficult courses?
What lead our students to success/failure?
How courses are related?
Are there courses that could be eliminated?
Is the work-load adequate for our students? ??
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How can Learning Analytics help?
Which tools could it provide to curriculum-designers?
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Our goals
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Use readily available data
Grades are always collected and historically stored
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Create discussion starters
Metrics for evaluation are evil, butmetrics for insight could be useful
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Easy to apply and understand
Could be integrated into a Learning Analytics toolbox
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Eat your own dog-food
Apply them to our own data to obtain insight
(12-year historical data on CS program)
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Let’s start
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(1) Difficulty Estimation
How difficult a course is, not how good the students are
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Technique
Difficulty metrics
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Two estimation metrics
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GPA - Course grade
Course grade > GPA
Course grade < GPA
0
Course grade = GPA
Three scenarios:
Differences betweenGPA and course grade
> 0< 0
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Real examples
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But…
They are not normal!
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Three Two estimation metrics
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Difficult Classes (Top 10)
Perceived
Estimated (first 5)Algorithms Analysis
Operating Systems
Physics A
Differential Equations
Linear Algebra
Programming Fundamentals
Object-Oriented Programming
Differential Calculus
Data Structures
Statistics
Operating Systems
Statistics
Differential Equations
Linear Algebra
Programming Languages
Electrical Networks I
Artificial Intelligence
Programming Fundamentals
Data Structures
Hardware Architecture and Organization
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Perception != Estimation
What makes a course difficult then?
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(2) Dependance Estimation
How well I do a student does in a course affects how well he/she does
in another
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CORE - CS CURRICULUMBasic Physics
Integral Calculus
Multivariate Calculus
Electrical Networks
Digital Systems I
Hardware Architectures
Operative Systems
General Chemistry
ProgrammingFundamentals
Object-orientedProgramming
Data Structures
ProgrammingLanguages
Database Systems I
Software Engineering I
Software Engineering II
Oral and WrittenCommunication Techniques
Computing and Society
Discrete Mathematics
Algorithms Analysis
Human-computerInteraction
Differential Calculus
Linear Algebra
Differential Equations
Ecology andEnvironmental Education
Statistics
Economic Engineering I
Artificial Intelligence
PROFESSIONAL TRAINING HUMANITIES BASIC SCIENCE
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Technique
Pearson product-moment correlation coefficient
(A lot of it)
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DEPENDANCE ESTIMATIONProgrammingFundamentals
Data Structures(0.321)
Object Oriented Programming
(0.309)
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DEPENDANCE ESTIMATION
Computingand Society
Operating Systems(0.582)
Discrete Mathematics(0.614)
Human-Computer Interaction(0.6226)
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Maybe we should rethink our prerequisites
Why Programming Fundamentals does not correlates?Why Computers and Society correlates with a lot of
courses?
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(3) Curriculum Coherence
How courses group together
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CORE - CS CURRICULUMBasic Physics
Integral Calculus
Multivariate Calculus
Electrical Networks
Digital Systems I
Hardware Architectures
Operative Systems
General Chemistry
ProgrammingFundamentals
Object-orientedProgramming
Data Structures
ProgrammingLanguages
Database Systems I
Software Engineering I
Software Engineering II
Oral and WrittenCommunication
Techniques
Computing and Society
Discrete Mathematics
Algorithms Analysis
Human-computerInteraction
Differential Calculus
Linear Algebra
Differential Equations
Ecology andEnvironmental Education
Statistics
Economic Engineering I
Artificial Intelligence
PROFESSIONAL TRAINING HUMANITIES BASIC SCIENCE
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Technique
Exploratory Factor Analysis
(EFA)
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31
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UNDERLYING STRUCTURE
Electrical Networks
Differential Equations
Software Engineering II
Software Engineering I
HCI
Oral and Written
Communication
Techniques
General Chemistry
Programming Languages
Object-Oriented Programming
Data Structures
Artificial Intelligence
Operative Systems
Software Engineering
Object-Oriented Programming
Economic Engineering
Hardware Architectures
Database Systems
Digital Systems I
HCI
Differential and Integral CalculusLinear Algebra
Multivariate CalculusDigital Systems I
Basic PhysicsProgramming Fundamentals
Discrete MathematicsGeneral Chemistry
StatisticsData Structures
Computing and SocietyAlgorithms Analysis
Differential EquationsEcology and Environmental Education
Object-Oriented Programming
FACTOR 1: The basic training factor
FACTOR 2: The advanced CS topics factor
FACTOR 3: The client interaction factor
FACTOR 4: The programming
factor
FACTOR 5: The ? factor
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Grouping is also off
Fundamental Programming is not in the Programming factor?What to do with Electrical Networks and Differential Equations?
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(4) Drop-out Paths
What courses lead the students to drop-out
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DROPOUT AND ENROLLING PATHSTime
(semesters)
0
1
2
3
4
Dropout
They are all happy, but as time goes by…
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Technique
Sequence Mining (Sequential PAttern Discovery using
Equivalence classes - SPADE)
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DROPOUT PATHS
Sequence Support<Physics A, Dropout> 0.6081967
21
<Differential Calculus , Dropout> 0.570491803
<Programming Fundamentals , Dropout> 0.532786885
<Integral Calculus , Dropout> 0.496721311
<Physics A, Differential Calculus , Dropout> 0.43442623
<Linear Algebra , Dropout> 0.432786885
<Differential Calculus, Integral Calculus , Dropout>
0.385245902
<Physics C , Dropout> 0.347540984
<Physics A, Integral Calculus , Dropout> 0.327868852
<General Chemistry , Dropout> 0.319672131
<Differential Equations , Dropout> 0.31147541
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Most drop-outs fail basic courses
Should students start with CS topics?Too much pressure in engineering courses?
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(5) Load/Performance Graph
What students think they can manage vs. what they can actually manage
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Technique
Simple Visualisation:Density Plot of
Difficulty taken vs. Difficulty approved
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LOAD/PERFORMANCE GRAPH
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LOAD/PERFORMANCE GRAPH
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LOAD/PERFORMANCE GRAPH
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Unrealistic Suggested Load
How to the present the Curriculum in a better way?How we can recommend students the right load?
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Our goals?
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Which are the hardest/more difficult courses?
What lead our students to success/failure?
How courses are related?
Are there courses that could be eliminated?
Is the work-load adequate for our students? ??
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??What makes a course difficult then?
Why Programming Fundamentals does not correlates?
Why Computers and Society correlates with a lot of courses?Fundamental Programming is not in the Programming
factor?
Should students start with CS topics?Too much pressure in engineering
courses?How to the present the Curriculum in a better way?How we can recommend students the right
load?
What to do with Electrical Networks and Differential Equations?
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Our ambitious goal?
Apply these techniques at your own data in your own institution
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Our more ambitious goal?
Make you think about LA techniques that can be easily transferred to
practitioners