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SPOL10030

Academic Year 2026/2027

Understanding Social Problems and Policies (SPOL10030)

Subject:
Social Policy
College:
Social Sciences & Law
School:
Soc Pol, Soc Wrk & Soc Justice
Level:
1 (Introductory)
Credits:
5
Module Coordinator:
Assoc Professor Stephan Koeppe
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module seeks to equip you with basic research and analytical skills that are needed to understand and respond to social policy problems. Its main focus is on statistical data available in online databases that are widely used to describe such problems and design policy solutions to them. You will learn about major relevant databases for Ireland and the EU and will receive guidance and hands-on experience on how to access those databases, search through them for data on specific social policy topics, select and extract particular relevant indicators into Excel spreadsheets, present the data in graphs and tables, and write brief descriptive commentaries on what the data reveal. For illustrative purposes, the module will focus on unemployment as a representative social problem and will concentrate on analysing that problem and policy responses to it in Ireland and in the EU. A special focus will be on the impact of Covid-19 on the labour market and its effects on employment and unemployment among younger cohorts in particular.

About this Module

Learning Outcomes:

What will I learn?
On completion of this module, you should be able to:
1. Identify the key social policy data sources that are available online for Ireland and the EU;
2. Understand what kinds of information those sources contain;
3. Access these sources and locate information within them on particular social policy topics;
4.Extract and download data on these topics;
5. Use Excel spreadsheets to organise the data and present them in graphs and tables;
6. Apply basic descriptive statistics in Excel;
7. Write short descriptive accounts of what the data reveal


How will I learn?
The course delivery is a mix of lectures and hands-on computer workshops in small groups. Additional videos and step-by-step guides will assist your learning.
Throughout the computer labs we encourage debate, mutual support and effective collaboration within small groups.

Indicative Module Content:

The key aim of the module is to introduce students to social science data analysis. Throughout the module the main examples are drawn from analysing the labour market which includes unemployment, education level, gender pay gap, occupational segregation, female managers and the effects of Covid-19 on remote working. With a focus on Ireland all data analysis compares European countries and contextualises Ireland within the European Union.

UNESCO highlights a set of key competencies that support individuals in their development and support society in achieving the UN Sustainable Development Goals (SDGs). UCD has adapted these competencies and is combining them with others to form a general framework of learning competencies. This module will help you develop the competencies below to the levels specified. 
Learning Competency Additional Information Level

Collaboration

 The ability to learn from others; to understand and respect the needs, perspectives and actions of others (empathy); to understand, relate to and be sensitive to others (empathic leadership); to deal with conflicts in a group; and to facilitate collaborative and participatory problem solving. Not addressed

Critical Thinking

 The ability to question norms, practices and opinions; to reflect on one’s own values, perceptions and actions. Novice

Systems Thinking

 The ability to recognize and understand relationships; to analyse complex systems; to think of how systems are embedded within different domains and different scales; and to deal with uncertainty. Novice

Strategic

 The ability to collectively develop and implement innovative actions that further sustainability at the local level and further afield. Novice

Integrated Problem Solving

 The overarching ability to apply different problem-solving frameworks to complex sustainability problems and develop viable, inclusive and equitable solution options that promote sustainable development, integrating the competencies in this list. Novice

Self-awareness

 The ability to reflect on one’s own role in the local community and (global) society; to continually evaluate and further motivate one’s actions; and to deal with one’s feelings and desires. Novice

Normative

 The ability to understand and reflect on the norms and values that underlie one’s actions; and to negotiate values, principles, goals, and targets, in a context of conflicts of interest and trade-offs, uncertain knowledge and contradictions. Novice

Anticipatory

 The ability to understand and evaluate multiple scenarios for the future – possible, probable and desirable; to create one’s own visions for the future; to apply the precautionary principle; to assess the consequences of actions; and to deal with risks and changes. Novice

Digital Literacy and Judgement

 The ability to access, evaluate, create and communicate information in digital environments; to engage critically, ethically and responsibly with digital technologies and digital information; to understand their opportunities, limitations, risks and impact on individual’s digital identities; and to exercise informed judgement in digital participation and decision-making. Advanced Beginner

AI Literacy & Agency

 The ability to understand, critically evaluate and responsibly engage with artificial intelligence; to recognise how AI systems are shaped by human values; to assess their ethical, social and environmental implications; and to exercise human judgement, agency and accountability in AI-related contexts. Not addressed

Wellbeing

 Wellbeing is having the resources and skills to meet life's challenges, including attributes such as personal development skills, resilience, stress management, strengths, lifestyle skills, nutrition, physical activity, sleep, alcohol/substance use, academic skills, time management, goal setting, interpersonal skills, group work, communication. Not addressed

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


Goal Name Description Coverage

End poverty in all its forms everywhere 1

Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all 3

Achieve gender equality and empower all women and girls 4

Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all 4

Reduce inequality within and among countries 4

Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development 1
 

Student Effort Hours:
Student Effort Type Hours
Lectures

10
Small Group

2
Computer Aided Lab

6
Specified Learning Activities

45
Autonomous Student Learning

40
Total

103

Approaches to Teaching and Learning:
The module consists of lectures and data analysis workshops.
1. The lectures will introduce the module and examine basic aspects of social policy data, with reference especially to data on unemployment. The lectures also discuss the practical data analysis and introduce you to critically assess tables, graphs and results.
2. In six weeks of data analysis workshops you will learn how to put into practice some of what you have learned in the lectures. The class will be divided into small groups for these workshops. Each student will have one workshop per week for this six-week period. The computer labs are practical and hands-on sessions where you can explore and familiarise yourself with social data.

Artificial Intelligence (AI) is not taught nor used in this module. In line with the UCD Academic Integrity Policy generative AI may only be used in assignments if transparently reported for which tasks AI was used. If detected without reporting, assignments are graded with an E or lower.

Requirements, Exclusions and Recommendations
Learning Recommendations:

To learn about the power of stats watch this video:
https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen


Purchase a full laptop for your studies. Do NOT buy a substandard tablet like Chromebook as you will not be able to use all Excel functions required for this course and your studies in general.


Module Requisites and Incompatibles
Equivalents:
Social Policy Research (SPOL1903)


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade Component repeat (in-module) Offered
Assignment(Including Essay): Individual end-of-term assignment. Students are asked to retrieve data, display it appropriately and analyse their findings in an essay. Week 11 Standard conversion grade scale 40% No
70
 No
Assignment(Including Essay): Individual mid-term assignment. Students are asked to retrieve data, display it appropriately and analyse their findings in an essay. Week 8 Standard conversion grade scale 40% No
30
 No

As part of UCD's student support, under the Additional Consideration policy, extensions may be available for the following assessments in the module: Assignment (including essay/poster), Portfolio, Reflective Assignment, Report(s), and Individual Project.


Carry forward of passed components
No
 

Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

The mid-term assignment will develop your analytical and writing skills with detailed feedback to improve data analysis. The final assignment asks for a more comprehensive and advanced data analysis and the previous feedback should be considered. In the data analysis workshops oral feedback is provided, but there will be no feedback on the submission of workshop outcomes.

Quirk, Thomas J. (2016) Excel 2016 for Social Science Statistics. A Guide to Solving Practical Problems. Cham: Springer. DOI:10.1007/978-3-319-39711-5
Guerrero, Hector (2014 or 2019) Excel Data Analysis. Modeling and Simulation. Heidelberg: Springer. DOI:10.1007/978-3-642-10835-8

Name Role
Dr Ausra Cizauskaite Lecturer / Co-Lecturer
Francesca Pignoloni Tutor
Gabriela Sepúlveda Tutor
Anastasiia Vasylchenko Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - 1, 2, 3, 5, 11, 12 Mon 16:00 - 16:50
Autumn Lecture Offering 1 Week(s) - 1, 2, 3, 5, 11, 12 Wed 14:00 - 14:50
Autumn Computer Aided Lab Offering 1 Week(s) - 4, 5, 6, 7, 9, 10 Thurs 11:00 - 11:50
Autumn Computer Aided Lab Offering 2 Week(s) - 4, 5, 6, 7, 9, 10 Tues 13:00 - 13:50
Autumn Computer Aided Lab Offering 3 Week(s) - 4, 5, 6, 7, 9, 10 Thurs 10:00 - 10:50
Autumn Computer Aided Lab Offering 4 Week(s) - 4, 5, 6, 7, 9, 10 Tues 14:00 - 14:50
Autumn Computer Aided Lab Offering 5 Week(s) - 4, 5, 6, 7, 9, 10 Thurs 12:00 - 12:50
Autumn Computer Aided Lab Offering 6 Week(s) - 4, 5, 6, 7, 9, 10 Thurs 13:00 - 13:50
Autumn Computer Aided Lab Offering 7 Week(s) - 4, 5, 6, 7, 9, 10 Thurs 14:00 - 14:50
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