Module POLM150 for 2021/2
- Overview
- Aims and Learning Outcomes
- Module Content
- Indicative Reading List
- Assessment
Postgraduate Module Descriptor
POLM150: Text as Data
This module descriptor refers to the 2021/2 academic year.
Module Aims
There are three primary aims of the module. First, the module will provide an applied introduction to the use of text analysis in social scientific research. You are introduced to the entire research “pipeline” for a typical text-based project, including: a) collecting textual information online (e.g., web scraping), b) key approaches to text preprocessing and “feature extraction,” and c) supervised and unsupervised approaches to text classification. These methods are essential for data scientists interested social science questions. Second, the module introduces you to the Python programming language. Python is a popular language for scientific computing and knowledge of Python will place you at a competitive advantage in industry, government, or when pursing further education. Third, the module assessments aim to further reinforce the importance research design and thus provide students with yet another opportunity to hone critical research skills.
On successfully completing the programme you will be able to: | |
---|---|
Module-Specific Skills | 1. apply appropriate tools for collecting and preprocessing textual information; 2. understand and apply a variety of text analysis methods to answer questions in social science and public policy; 3. critically evaluate the strengths and weaknesses of particular text analytic tools for answering research questions in the social and policy sciences; |
Discipline-Specific Skills | 4. employ text analytic methods to empirically evaluate theories and hypotheses in the social and policy sciences; 5. evaluate the role of text analysis for supporting policy analysis and evaluation; 6. construct arguments based on textual data for both written and oral presentation; 7. demonstrate a strong command of research design through written and oral assessments; |
Personal and Key Skills | 8. gain a solid foundation in the Python programming language; 9. communicate effectively in speech and writing; 10. work independently and within a limited time frame to complete a specified task. |
Module Content
Syllabus Plan
Although the module’s precise content may vary from year to year, it is envisaged that the syllabus will cover the following topics:
- Programming in Python
- Collecting textual information online
- Preprocessing text for analysis and “feature selection”
- Dictionary-based methods for text classification
- Supervised and unsupervised learning for text classification
- Ideological scaling
- Using text-based measures in regression models
Learning and Teaching
This table provides an overview of how your hours of study for this module are allocated:
Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
---|---|---|
22 | 128 |
...and this table provides a more detailed breakdown of the hours allocated to various study activities:
Category | Hours of study time | Description |
---|---|---|
Scheduled Learning and Teaching Activity | 22 | 11 x 2 hour lectures |
Guided Independent Study | 40 | Activities to familiarize you with the Python programming language |
Guided Independent Study | 30 | Reading and preparing for lectures |
Guided independent study | 58 | Research and analysis for final essay and presentation |
Online Resources
This module has online resources available via ELE (the Exeter Learning Environment).
- Learn Python interactively online using Code School’s free Python course: https://www.codecademy.com/learn/python
Other Learning Resources
- For more information on downloading and installing Python: https://wiki.python.org/moin/BeginnersGuide/Download
How this Module is Assessed
In the tables below, you will see reference to 'ILO's. An ILO is an Intended Learning Outcome - see Aims and Learning Outcomes for details of the ILOs for this module.
Formative Assessment
A formative assessment is designed to give you feedback on your understanding of the module content but it will not count towards your mark for the module.
Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
---|---|---|---|
Practicals | 4 short assignments to reinforce programming skills. | 1-5,8-10 | Written |
Summative Assessment
A summative assessment counts towards your mark for the module. The table below tells you what percentage of your mark will come from which type of assessment.
Coursework | Written exams | Practical exams |
---|---|---|
85 | 0 | 15 |
...and this table provides further details on the summative assessments for this module.
Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
---|---|---|---|---|
Research proposal | 10 | 800 words | 2-7, 9-10 | Written |
Research essay | 75 | 4,000 words | 1-10 | Written |
Presentation (individual) | 15 | 10 minutes | 1-10 | Written |
Re-assessment
Re-assessment takes place when the summative assessment has not been completed by the original deadline, and the student has been allowed to refer or defer it to a later date (this only happens following certain criteria and is always subject to exam board approval). For obvious reasons, re-assessments cannot be the same as the original assessment and so these alternatives are set. In cases where the form of assessment is the same, the content will nevertheless be different.
Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
---|---|---|---|
Research proposal | 1-2 pages proposal | 2-7,9-10 | August/September reassessment period |
Research Essay | 4,000 words essay | 1-10 | August/September reassessment period |
Presentation (individual) | 10 minutes presentation | 1-10 | Spring term |