Module Database Search
| MODULE DESCRIPTOR | |||
|---|---|---|---|
| Module Title | |||
| Research Trends | |||
| Reference | CM4604 | Version | 2 |
| Created | February 2024 | SCQF Level | SCQF 10 |
| Approved | July 2020 | SCQF Points | 30 |
| Amended | April 2024 | ECTS Points | 15 |
| Aims of Module | |||
|---|---|---|---|
| To excite and enthuse students in the field of computer science with the stat-of-the-art and current research trends conveyed through a mix of seminars, research outputs and discussions. To use a combination of peer review methods, surveys and critical evaluation of current research and advanced scholarship in Artificial Intelligence (AI) and Data Science (DS). | |||
| Learning Outcomes for Module | |
|---|---|
| On completion of this module, students are expected to be able to: | |
| 1 | Develop research skills including critical analysis, literature searching, academic writing and referencing for the highly dynamic area of Artificial Intelligence and Data Science. |
| 2 | Examine current and emerging trends in Artificial Intelligence and Data Science and how they affect the development of intelligent systems collaboratively within a team. |
| 3 | Develop a research proposal to address a specific problem or apply new knowledge using technologies in Artificial Intelligence and Data Science. |
| 4 | Develop project presentations to analyse the effect of current technologies on the users, consumers and companies including legal, ethical, professional and social issues. |
| Indicative Module Content |
|---|
| Artificial Intelligence and Data Science trends: The series of departmental seminars will be used to deliver an overview of the state-of-the-art in research topics focused on Intelligent data driven research and applications. These will cover "hot areas" from Artificial Intelligence and Data Science (such as trends in robotics; neurotechnology, deep learning trends; dialogue systems, AI and ethics, wearables and sensing technologies. User Studies: Observational, questionnaires, surveys and feasibility assessment of a chosen state-of-the-art topic. Use of secondary sources: Literature searches; information sources (online and offline) and gathering. Reading and understanding research papers. Use selected CS databases; use systems for bibliography construction. Critique: Conduct peer reviewing in a professional manner and communicate results; conduct a comparative critical analysis of scholarship in the field. Academic writing: Distinguish between different types of academic writing strategies; write an effective and feasible research proposal; use electronic systems of bibliographic citation. Dissemination: Technical writing, referencing, bibliographies. Practical skills in formatting, building contents and indices. Presentation skills, written and oral. Avoid plagiarism of secondary sources by using a variety of writing strategies. |
| Module Delivery |
|---|
| The course content is delivered by a combination of seminars; lectures and is based on extensive use of invited talks and review of literature in premier AI conferences (such as the international joint conference on AI) and by interactive group sessions. |
| Indicative Student Workload | Full Time | Part Time |
|---|---|---|
| Contact Hours | 96 | N/A |
| Non-Contact Hours | 204 | N/A |
| Placement/Work-Based Learning Experience [Notional] Hours | N/A | N/A |
| TOTAL | 300 | N/A |
| Actual Placement hours for professional, statutory or regulatory body |
| ASSESSMENT PLAN | |||||
|---|---|---|---|---|---|
| If a major/minor model is used and box is ticked, % weightings below are indicative only. | |||||
| Component 1 | |||||
| Type: | Coursework | Weighting: | 40% | Outcomes Assessed: | 3, 4 |
| Description: | A project proposal for a research work on a trending research topic in AI and Data Science (Group Coursework). | ||||
| Component 2 | |||||
| Type: | Coursework | Weighting: | 60% | Outcomes Assessed: | 1, 2 |
| Description: | A Review Paper written, targeting a well reputed research journal/conference (Individual Coursework). | ||||
| MODULE PERFORMANCE DESCRIPTOR | ||||||||
|---|---|---|---|---|---|---|---|---|
| Explanatory Text | ||||||||
| The calculation of the overall grade for this module is based on 40% weighting of Component 1 (Project Proposal) and 60% weighting of Component 2 (Group Coursework). An overall minimum grade D is required to pass the module. | ||||||||
| Coursework: | ||||||||
| Coursework: | A | B | C | D | E | F | NS | |
| A | A | A | B | B | C | E | ||
| B | B | B | B | C | C | E | ||
| C | B | C | C | C | D | E | ||
| D | C | C | D | D | D | E | ||
| E | C | D | D | E | E | E | ||
| F | E | E | E | E | F | F | ||
| NS | Non-submission of work by published deadline or non-attendance for examination | |||||||
| Module Requirements | |
|---|---|
| Prerequisites for Module | None. |
| Corequisites for module | None. |
| Precluded Modules | None. |
| INDICATIVE BIBLIOGRAPHY | |
|---|---|
| 1 | Bott, F. 2014. Professional Issues in Information Technology. 2nd ed. BCS. |
| 2 | Wouter de Nooy, Andrej Mrvar, Vladimir Batagelj (2019)Exploratory Social Network Analysis with Pajek: Revised and Expanded Edition for Updated Software. |
| 3 | Dragt (2017) How to Research Trends: Move Beyond Trendwatching to Kickstart Innovation. |
| 4 | Victor X. Wang (2019) Transdisciplinary Knowledge Generation. |
| 5 | Victor C.X. Wang (2018). Handbook of Research on Innovative Techniques, Trends, and Analysis for Optimized Research Methods. |