43801 Digital Engineering Fundamentals
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Subject handbook information prior to 2024 is available in the Archives.
Credit points: 6 cp
Result type: Grade and marks
Requisite(s): 120 credit points of completed study in Bachelor's Degree owned by FEIT OR 120 credit points of completed study in Bachelor's Honours Embedded owned by FEIT OR 120 credit points of completed study in Bachelor's Combined Degree owned byFEIT OR 120 credit points of completed study in Bachelor's Combined Honours degree owned by FEIT OR 120 credit points of completed study in Bachelor's Combined Degree co-owned by FEIT OR 120 credit points of completed study in Bachelor's Combined Honours degree co-owned by FEIT
These requisites may not apply to students in certain courses. See access conditions.
Description
This subject introduces students to digital engineering concepts and principles, and builds practical skills in support of digital delivery methods on building and civil infrastructure projects. Digital engineering assumes a strategic approach to asset information management. Students develop knowledge in the production and management of information and data, exploring the technologies and processes that support the use of open data formats, metadata schemas and asset classification systems, geometric and non-geometric data linking, and cross-enterprise collaboration via a common data environment. Students develop written and oral communication skills in the expression of these core enablers of digital engineering. By applying these central digital engineering concepts and principles, students develop their understanding of the digital engineer’s role in digital delivery. This subject is a starting point for students' ongoing development to be effective agents in digital engineering.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Apply digital engineering concepts and principles to explore core enablers of digital engineering relative to data exchange using open data formats, applying metadata, and establishing common data environments. (D.1) |
|---|---|
| 2. | Discuss how the exchange of information between stakeholders and the providers and receivers of information is key to project success. (D.1) |
| 3. | Evaluate digital engineering standards, including technical requirements and tools, and how they are implemented in infrastructure projects. (D.1) |
| 4. | Analyse examples of collaborative digital engineering methods using case study data. (C.1) |
| 5. | Reflect on the digital engineer’s role in the asset lifecycle and strategic leadership responsibilities. (F.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
- Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
- Reflective: FEIT graduates critically self-review their own and others' performance with a high level of responsibility to improve and practice competently for the benefit of professional practice and society. (F.1)
Teaching and learning strategies
The subject is delivered in seven modules and uses a mixture of workshops, computing labs, and online learning activities to support student learning. With the aim of developing foundational digital engineering knowledge and practical skills, students will utilise project-based learning and industry case studies to develop digital engineering capabilities. Engaging in regular pre-class activities and working through the assessments build essential knowledge in the implementation of consistent and consolidated data modelling workflows to support the collaborative production of information, and sharing of structured, high-quality data. Visual methods are used to communicate information workflows and engage student learning.
Each week students are expected to prepare for workshops and computing labs by completing a set of pre-class tasks provided on Canvas. The tasks involve such activities as reading from key resources, watching videos, and other online learning content. Responses to the pre-class tasks are used in online tutorial classes to add depth to discussion and are an essential component of the subject.
During computing labs, students will build on pre-class tasks to learn and apply concepts with the aim being to build digital engineering skills. These skills will be practiced in class and are designed to be relevant to the assessment tasks. Areas of learning in computing labs include model interrogation, open data exchange and data tagging activities, design of collaborative information production workflows, group discussion, and reflection. Activities support acquisition of digital engineering skills in a small group setting under the supervision of a tutor; these skills relate directly to the substantive summative assessment Tasks 2 & 3.
Content (topics)
The subject is divided into eight modules: (1) digital engineering concepts and principles, (2) digital engineering requirements, (3) data schemas and specifications, (4) use of open data formats, (5) application of metadata to support semantic interoperability, (6) establishing the contractor common data environment, (7) managing and disseminating project documentation, graphical models, and non-graphical data using a common data environment, and (8) student consultation and team presentations. Modules 2 to 7 are supported by industry examples of digital engineering and embedded in practical demonstrations of effective digital delivery.
Assessment
Assessment task 1: Pre-class and In-class Activities
| Intent: | To increase understanding of key digital engineering concepts and methods |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2 and 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 |
| Type: | Exercises |
| Groupwork: | Individual |
| Weight: | 20% |
| Length: | Type: Individual pre-class and in-class activities Groupwork: Individually assessed Weight: 20% Length: 3-5 pages of responses to activity sheets |
Assessment task 2: Individual Digital Engineering Standards Review
| Intent: | To recognise effective applications of digital engineering, it is essential that students are familiar with the requirements and related policy context of the digital delivery methods used in infrastructure projects. This individual assessment task will allow you to develop your research skills and information literacy (finding, evaluating and referencing information), and acquire knowledge of digital engineering frameworks and policies relevant to the case studies to be undertaken in Task 3. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 2 and 3 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 30% |
| Length: | Type: Individual Report Groupwork: Individually assessed Weight: 30% Length: 2500-3000 words |
Assessment task 3: Digital Engineering Case Studies
| Intent: | To provide students with an opportunity to consolidate their knowledge and digital skill sets by undertaking case studies of the main enablers of digital engineering. This task capitalises on a contextual approach to assessment by scaffolding previous tasks to support progressive learning across the subject. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
| Type: | Case study |
| Groupwork: | Group, group and individually assessed |
| Weight: | 35% |
| Length: | Type: Presentation Groupwork: Group, group and individually assessed Weight: 35% (25% Group, 10% Individual) Length: 20-minute presentation, at least 7-10-minute presentation per team member |
Assessment task 4: Digital Engineering Narratives
| Intent: | To reflect in different ways on your individual learning goals for this subject and beyond. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): D.1 and F.1 |
| Type: | Reflection |
| Groupwork: | Individual |
| Weight: | 15% |
| Length: | Type: Reflection Groupwork: Individual Weight: 15% Length: (i) 2-3-minute video recording, or (ii) 500-750-word written reflection |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Required texts
Borrmann, A., König, M., Koch, C., & Beetz, J. (2018). Building information modelling technology foundations and industry practice: Technology foundations and industry practice.
Recommended texts
Transport for NSW Digital Engineering Framework 2019 - https://www.transport.nsw.gov.au/news-and-events/reports-and-publications/digital-engineering-framework
Transport for NSW Digital Engineering Framework Document Library 2021 - https://www.transport.nsw.gov.au/digital-engineering/digital-engineering-framework-0