UCD_Module_analysis_Geary

This script loads the module summary spreadsheet provided by M. Norris and essentially clusters and filters out irrelevant courses across the Schools and Units. Remaining information is there after classified into thematic clusters.

The script is written by EA on 3 OCT 2025. Errors in the raw secondary data should be cross checked with the UCD admin; Errors and inconsistencies in the script are authors own.

STEP 1: load the data & packages

Specifically, I am using the spreadsheet: 202526 Descriptors with Purpose & Overarching Content 280825.xlsx”

STEP 2: Summarise the content and Clean up content

## [1] "School / Unit"                 "Module ID"                    
## [3] "Title"                         "Credits"                      
## [5] "Level"                         "Trimester"                    
## [7] "Purpose & Overarching Content"

## 
##    0    1    2    3    4    5 <NA> 
##   72  648  816 1495 2956  150    4

Seems that at least 4 modules lack proper description. Next, check the offer of modules covering Level 3 and above across the schools to understand how rich the curriculum is in the first place.

## 
##  Agriculture and Food Science      Arch Plan and Env Policy 
##                           295                           137 
##                   Archaeology Art History & Cultural Policy 
##                            41                            38 
##   Beijing Dublin Intl College Biology and Environmental Sci 
##                            19                           138

Then we filter out the schools that are irrelevant ( base ont he prevous discussion). See. the code for th school selection

STEP 3. plot the schools/ units that have the offers on the level of 3-5

–> later on separate the levels as well –> UG== c(3-4), and PG ==5

I table the info to see the distribution by school and proportion-like

##                          School  3   4 5
##    Agriculture and Food Science 93 200 2
##        Arch Plan and Env Policy 35 100 2
##   Biology and Environmental Sci 34 102 2
##    C.O. - Social Sciences & Law  2   0 0
##                       Economics 40  35 8
##                       Geography 24  39 0
##      Politics and Int Relations 29  36 5
##   Public Hlth Physio & Sport Sc 42  91 1
##  Soc Pol Soc Work & Soc Justice 16  51 3
##                       Sociology 21  27 1

STEP 4. identify Relevant modules

To do so, I opt to check the titles of the modules for the following terms:

“policy-making” // “public” // “politics”// “policy” // “public administration” /“administration”/ “governance” and variation of the terms above. Please see the variations in the code for the stemming approach.

#create a category where module explicitly reference policy making
library(tidyverse)

policy_making=dt %>%
  filter(str_detect(Title, 'policy making|Policy-making|policymaking|Policy Making|policy making'))



policy=  dt %>%
  filter(str_detect(Title, 'policy|Policy'))

politics= dt %>%
  filter(str_detect(Title, 'politics|Polit|polit|Politics'))

implementation=  dt %>%
  filter(str_detect(Title, 'implementation|implement|Implementation|Implement'))


public_admin=     dt %>%
  filter(str_detect(Title, 'public admin|Public Admin|Public admin'))


public=
    dt %>%
  filter(str_detect(Title, 'public|Public'))

administr= 
    dt %>%
  filter(str_detect(Title, 'Admin|admin'))

governance=
     dt %>%
  filter(str_detect(Title, 'Govern|govern'))

regulation=
    dt %>%
  filter(str_detect(Title, 'Regula|regula'))

This results in 102 modules across school and levels.

STEP 5.Find and drop the overlap of the modules between the the categories

Going through the Module titles, description and nesting within the school I remove the duplicates and modules that relate to Thesis and/or Internship(s).I also drop the modules labelled as Connected _politics To cross-check the results, I simply list the modules– those with the count>1 need to be double checked.

##                                     clean_dt$Title n     percent
##                     Advanced Seminar in Politics 1 1 0.009803922
##                     Advanced Seminar in Politics 2 1 0.009803922
##            Agri-Environmental Economics and Policy 1 0.009803922
##               Agri-Environmental Issues and Policy 1 0.009803922
##  Applications of Environmental Policy & Regulation 1 0.009803922
##                              Applied Public Policy 1 0.009803922

Having completed the automatic checks and clean ups , I went through the remaining 97 observations. Combing through the descriptions allowed to identify modules which appear to be identical based on their description BUT could vary in terms of credits they assign or other details.

Summary: 1. ECON42580 & ECON42680. Differ in the Number of Credits assigning ( 5 vs 10) 2. SPOL30360 & SPOL41130. Differ in the level they target ( 3 vs 4) 3. PLAN30170 & PLAN40640 . Differ in the level they target ( 3 vs 4) 4. BIOL40140 & ENVB40380. (ENV module offered online in Spring; & BIO labelled module is offered in Autumn) 5. ECON42360 & ECON42270. Differ in the number of credits (the latter offers 7.5 credits while the former 5) 6. PHPS40740 & PHPS40690. Not clear substantive differences 7. FDSC40740 & FDSC40700. The former module is for the Professional diploma students otherwise, they are the same

Next, I have manually filtered out modules which hardly touch upon the key focus of micro credentials. Those include 1. Northern Ireland Politics–> POL36160 2. Social Justice: Critical Theories and Radical Politics –> SSJ40070 3. Social and Political Thought: Basic texts and discussions –> SOC40730 4. Gender, Sexuality, and the Body; The Politics of Belonging –> GS40110 5. Public Health Nutrition –> BOTH duplicated modules PHPS40690 & PHPS40740
6. Critical Political Economy –> EQUL40390 7. Public Health Nutrition –> PHPS40740
8. Public Health Nutrition O/L –> PHPS40720 9. Deliberative Mini-Publics –> POL30640 10. Politics of International Trade and Investment –> POL42530 11. Politics of Authoritarianism –> POL42500 12. Gender & the Political System –> POL42040 13. Politics and Change in the Middle East and North Africa –> POL41510 14. Politics of Human Rights –> POL41020 15. International Political Economy –> POL40370 16. EU Foreign Policy: understanding how the European Union –> INRL30400

17. Gender and Politics –> POL30680

18. Introduction to Asian Politics –> POL30690

19. Political Activism in the Middle East –> POL30730

20. Political Risk and Foreign Direct Investment -> POL30820

21. Advanced Seminar in Politics 1 –> POL30860

22. Advanced Seminar in Politics 2 –> POL30870

23. The Politics of Organised Crime –> POL30890

24. Strategies of Politics –> POL36100

25. Comparative Political Theory –> POL36110

26. Politics of Care –> POL36180

27. Media Politics under Authoritarianism –> POL36250

28. Politics of Development –> POL40100

29. Food Poverty and Policy –> RDEV30160

30. World Politics and HA –> HACT40120

31. Food Regulation –> FDSC40740 # repeated module–> to be dropped

32. Public Health Medicine, Epidemiology & International Health –> PHPS30020

33. Critical Geopolitics and Diplomacy–> GEOG40450

34. Far-right, Hate and Political Polarisation –> GEOG31080

35. Political Geography of European Integration –> GEOG30240

36. Conservation History, Theory and Policy –> ARCT40170

37. Managing the Interface between Science and Policy (On-line) –> ENVB40380

38. Gov the City: Admin & Transp –> PLAN4000W

39. European Environmental Policy –> ENVP40020 # relies on field trip

40. Environ Change & Trans Policy –> ENVP3000W

41. World Politics and HA –> HACT40120

42. Food Poverty and Policy –> RDEV30160

Having identified the duplicated cases, I drop them out from the main sample. There after I also remove the modules with the duplicated content.

Thereafter, I a categorical indicator capturing level of relevance modules across schools have for micro-credentials.

## 
##     Highly Relevant Marginally Relevant   Somewhat Relevant             UNCLEAR 
##                  20                  10                  18                   3

STEP 6. Plot the number of the relevant course across schools by levels using remaining observations

Having gone through the description of the modules provided in the data set, I have identified modules which were fully irrelevant (listed above). The underlying logic for the selection is to filter out modules which are subject specific YET have no bearing or direct relevance to public policy OR policy making.

The observations remaining within the sample after manual checks were classified into 4 categories capturing the level of relevance of the module, based on the provided description to the topic of credentials.

However, as a first step, I plot he distribution of courses which appear to bear relevance to the micro credentials overall. I separate the offered modules by level.

STEP 7: Assessing Relevance of the sample

When plotting the results using the introduced degree of relevance to the Public policy micro credentials, it is clear that high input of highly relevant classes comes from Social Policy, and Politics, followed by Economics, AGRI and Food science. Others fall quite a bit behind.

Please note that the category of “unclear” here refers to the modules which either lack description OR seem to contain description of a program broadly defined ( # in Public health this refers to the “European Public Health” described as The European Masters in Public Health Program)

Let’s take a look at the pattern by the level at which these modules are offered separating relevance levels as well. As the last step, I group the modules by school and preserve the opportunity to check the N of credits they grant. For details description, please consult the spreadsheet.