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 Ochoaxavier@cti.espol.edu.echttp://ariadne.cti.espol.edu.ec/xavierTwitter: @xaoch