| Unit name | Dissertation (Group Project) |
|---|---|
| Unit code | ECONM0017 |
| Credit points | 60 |
| Level of study | M/7 |
| Teaching block(s) |
Academic Year (weeks 1 - 52) |
| Unit director | Dr. Tran |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
Economic Analytics (TB1) |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Economics |
| Faculty | Faculty of Social Sciences and Law |
Why is this unit important?
The dissertation is an extended report of an independent study that identifies and investigates a particular question and explores it systematically over a sustained period of time. The dissertation provides students with the opportunity to read extensively and apply research skills to a chosen area of study selected from a range of projects. This unit requires each student to complete a substantial economics and data science project by collaborating in small groups. Students work on a topic of their choosing from a selection proposed by Industry partners. Students will first work on a data project in groups which prepares them for the more substantial work on their dissertation. Therefore, this unit provides students with the opportunity to apply their acquired economics and data science knowledge to real-world data to solve problems with potential real-world impact. To do so, students need to work in groups to independently plan, run, document, and present a substantial piece of original work. The aim of this unit is to give students a substantial opportunity to integrate material from all taught units in both economics and data science, to demonstrate the breadth and depth of their learning on the MSc, and to prepare them for the type of data problems they may encounter in their first placements after graduation.
How does this unit fit into your programme of study
This unit requires students to combine all material taught in the economics and data science units that they have studied as part of the programme to find solutions to real-world economics and data science problems. As such, the unit allows students to better appreciate the connections between the taught units they have studied and understand how economics and data science can be combined to enhance each other. As the final unit in this programme, this unit also aims to prepare students for work after graduation by allowing them to obtain first-hand experience in working on actual economics and data science related projects. The setting with industry partners and working in groups is meant to represent settings that students may encounter in their first placements after graduation – therefore this unit also prepares students by training group work and dissemination skills.
An overview of content
The dissertation group project is an extended report of an independent study that identifies and investigates a particular question and explores it systematically over a sustained period of time. Student groups are matched with industry partners and allow students to work on a real-world economics and data science problem. In doing so, students need to apply and combine their knowledge in economics and data science to plan, run, document, and present a substantial piece of original work.
For this unit, students are assigned to groups of approximately five students. The group assignment is done at the start of TB2 by the unit director, taking into account student preferences. Each group works on a project following the same structure with three main phases.
In phase 1 (about ten weeks in TB2), the groups work on a preliminary data project related to their main project. After an initial briefing session by industry partners, students conduct the project independently, under guidance of supervisors and teaching assistants. Supported by regular TA sessions, each group get the chance to understand their data and need to produce a short group report. This phase also features taught sessions on conducting group work, research ethics, and data handling skills. The short lab report determines the pass/fail of the dissertation.
In phase 2 (about ten weeks in the summer), the groups work on their main projects. These projects can come from a wide range of fields and may be in collaboration with an industry partner. In this phase, each group needs to produce an extended report.
In phase 3 (about two weeks), the groups need to disseminate their results by presenting their findings to supervisors and industry partners.
In phases 1 and 2, the TAs will keep records of individual engagement (attendance in the sessions and general participation in group work as a part of standard teaching practice). In addition, students will submit their individual assessment of each group member’s relative work contributions to the TAs. These pieces of information will be used in determining individual marks on the group’s final submission.
How will students, personally, be different as a result of the unit
The unit gives students hands-on experience in working with real-world data problems related to economics. It also exposes students to interaction with industry partners. As a results, students will have applied and combined their knowledge in economics and data science to a hands-on project. Students are required to work independently under guidance of supervisors. Therefore, they are leading the data exploration and determine the direction of analysis. From the beginning of phase 1, students will work with their peers with various backgrounds and industry partners. As a result, students will learn skills to start projects from scratch and complete them successfully, including the ability to manage time and how to engage effectively in larger teams with a diversity of competences. All these skills are meant to prepare them for their first placements after graduation.
Learning Outcomes
On successful completion of the dissertation, students will be able to
1. Work in a group to solve a real-world economics and data science related project with clear objectives.
2. Identify methodologically appropriate and ethical approaches towards addressing project aims and objectives.
3. Recognize a problem and apply relevant economics and data science methodology to create a suitable solution.
4. Critically evaluate and effectively communicate their findings in terms of their motivation, methodology, results, and in relation to existing work (in written, visual, and oral forms).
The supervision of every dissertation project is carried out by a qualified member of academic staff, sometimes with the assistance of a postdoctoral research associate, doctoral student, or external industrial advisor/supervisor.
Student groups are allocated at the start of the TB2. There is an initial briefing session by industry partners and academic staff which introduces the projects.
Students then work independently on projects related to the topic of their dissertation. During this phase, regular workshops and meetings with facilitators are scheduled to provide ongoing support for all students. The facilitators will also note the level of individual engagement in these meetings which feeds into the individual marks element.
Initial results are presented in a lab report in a tutor-assisted peer dialogue workshop which must be passed. In the end, the more substantial results are reported in an individual presentation and a written group dissertation.
Tasks which help you learn and prepare you for summative tasks (formative):
Lab report (pass/fail) in tutor assisted peer dialogue workshop
Tasks which count towards your unit mark (summative):
Dissertation (80%) - ILOs 1- 4
Presentation (20%) - ILOs 1-4
Each student will therefore receive an individual mark based on:
80% Group Dissertation mark (50% of which will be weighted by the facilitator’s assessment of team sessions– therefore no student can get less than 50% of the Group mark) + 20% Individual presentation. The facilitator’s assessment is informed by student input and evaluates each student’s input to the group work. Equal contribution and engagement by all group members results in each student’s weight being equal to 1. Student weights can also be above or below 1, but the group members’ weights will average to 1.
When assessment does not go to plan
If a student does not complete the project with their group for any reason, there is the possibility for individual submissions for the Phase 2 and Phase 3 assessments.
If a student has completed the phase 1 assessment these marks will be carried forward and used to calculate the individual report mark for phase 2.
If the student has not completed phase 1 then they will complete the project with the next cohort.
In the unlikely event that a student fails at the end of the unit they will be able to make an individual resubmission for phases 2 and 3.
Should the whole group fail, they will be able to resubmit as a group.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. ECONM0017).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
