International Internship - Aviation (Extended)

AVA20018 25 Credit Points

Duration

  • One Semester or equivalent
     

Contact hours

  • 30 - 60 full days in internship plus 18 hours online

On-campus unit delivery combines face-to-face and digital learning.

2025 teaching periods

Hawthorn

Summer		 Hawthorn

Winter

Dates:
6 Jan 25 - 16 Feb 25

Results:
4 Mar 25

Last self enrolment:
6 Jan 25

Census:
17 Jan 25

Last withdraw without fail:
31 Jan 25

Dates:
23 Jun 25 - 3 Aug 25

Results:
19 Aug 25

Last self enrolment:
23 Jun 25

Census:
4 Jul 25

Last withdraw without fail:
18 Jul 25

Prerequisites

150 credit points

Aims and objectives

The International Internship Extended is an opportunity for students to undertake an extended 'real world learning' experience and to gain valuable work experience in an international setting. It provides students with an opportunity to apply concepts of their discipline in a workplace setting, and to experience employment in the industry internationally. The unit allows students to work with an organisation for an extended period (30 to 60 days) that permits access to complex longer projects. On completion of the unit, students will be able to:
  • Engage meaningfully in work environments
  • Critically evaluate the relationship between theory and practice within a disciplinary area
  • Describe the interactions between disciplinary knowledge, practice and career opportunities
  • Develop cultural competency
Unit Learning Outcomes (ULO)
 
Students who successfully complete this unit will be able to:
 
1 Identify and apply appropriate disciplinary skills and knowledge in a real-world environment
2 Communicate effectively for a range of audiences within the workplace, industry and cultural contexts
3 Develop cross-cultural competence through experiential learning
4 Reflect on and evaluate own experiences, both within the workplace, and regional and global industry trends, that might lead to or affect future employment
5 Demonstrate an understanding of intercultural adjustment models through reflexive analysis