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Simple Metrics for Curricular Analysis
Xavier OchoaEscuela Superior Politécnica del Litoral
We already use metrics to talk about our
curriculaPassing rates, Final Effiency
Which other metrics can we all of us use?
Depends on the data
What data all of we have?
The humble academic records
There is much information stored in
our vaults alreadyIt is the low-hanging fruit of
Curriculum Analytics
It make sense to develop shared metrics
It will enable compartive-studies
Temporal MetricsWhen we plan the courses vs. when our
students take them
Course Temporal Position
Average semester/year when the students take the course
Semester 3
CTP = 5.77
Temporal Distance Between Courses
We think that they should be taken sequentially vs. How much apart they take them
TDI=2.07
Course DurationHow many semesters/years a student need to
pass the course
COURSE CDU
BASIC CALCULUS 2.21
PROGRAMMING FUNDAMENTALS 1.87
STATISTICS 1.80
BASIC PHYSICS 1.74
DIFERENCIAL EQUATIONS 1.73
Difficulty MetricsTrying to understand what makes a
course difficult
Simple Difficulty Metrics
How the course affect the student GPA
Course Alfa BetaPhysics A 1.2302 2.4057Programming Fundamentals 1.3458 2.0529
Linear Algebra 1.3042 1.8891Differential Equations 1.3066 1.8509
Statistics 1.2519 1.7823
Algorithm AnalysisOral Communication
Profile Based MetricsHow the course affect the student with different
GPA
>8.5 8.5 to 7.5 7.5 to 6.5 <6.50
0.2
0.4
0.6
0.8
1
1.2
Oral Communications - Profiled Approval Rate
>8.5 8.5 to 7.5 7.5 to 6.5 <6.50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Programming Fundamentals -Profiled Approval Rate
>8.5 8.5 to 7.5 7.5 to 6.5 <6.50
0.10.20.30.40.50.60.70.80.9
1
Computers & Society -Profiled Approval Rate
>8.5 8.5 to 7.5 7.5 to 6.5 <6.50
0.2
0.4
0.6
0.8
1
1.2
Differential Equations -Profiled Approval Rate
>8.5 8.5 to 7.5 7.5 to 6.5 <6.50
1
2
3
4
5
6
7
8
9
Programming Fundamentals - Profiled Performance
>8.5 8.5 to 7.5 7.5 to 6.5 <6.50
1
2
3
4
5
6
7
8
9
10
Discrete Mathematics -Profiled Performance
>8.5 8.5 to 7.5 7.5 to 6.5 <6.50
0.5
1
1.5
2
2.5
3
Programming Fundamentals -Profiled Difficutly Beta
>8.5 8.5 to 7.5 7.5 to 6.5 <6.50
0.10.20.30.40.50.60.70.80.9
1
Computer & Society – Profiled Difficulty Beta
>8.5 8.5 to 7.5 7.5 to 6.5 <6.5
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
Economic Engineering -Profiled Difficulty Beta
What do to whit these metrics
Ideas•Course concurrency•Find neglected courses•Bottlenecks identification•Section Planning•Course clustering
Conclusions
Very simple metrics could provide valuable
informationAdding and multiplying
Which other metrics as a community we can create and share
Let’s start the discussion
Gracias / Thank youQuestions?
Xavier [email protected]://ariadne.cti.espol.edu.ec/xavierTwitter: @xaoch