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New Technologies (5 cr)

Code: TLTT4500-3025

General information


Enrollment
01.08.2024 - 22.08.2024
Registration for the implementation has ended.
Timing
26.08.2024 - 18.12.2024
Implementation has ended.
Number of ECTS credits allocated
5 cr
Local portion
0 cr
Virtual portion
5 cr
Mode of delivery
Online learning
Unit
School of Technology
Campus
Main Campus
Teaching languages
Finnish
Seats
10 - 15
Degree programmes
Bachelor's Degree Programme in Logistics
Teachers
Pasi Poutiainen
Groups
UTIVERKKO
Institute of New Industry, online learning (mechanical, logistics and civil engineering)
Course
TLTT4500
No reservations found for realization TLTT4500-3025!

Evaluation scale

0-5

Objective

Technological development in today's work environments is accelerating. Logistics is already frontrunner of some technologies today. As a future engineer, you need to understand the concepts and capabilities of the latest technologies. The engineer must be able to utilize the opportunities provided by technology in accordance with the principles of continuous improvement. As an engineer of the future, you should have an innovative perspective on new technologies, so that companies have the courage to start using technologies to improve their performance.

In this course you will learn
- search information about new technologies
- know different technologies and their principles
- understand the potential of new technologies from logistic point of view
- apply the use of new technologies

After completing this course, you will have wide understanding about the terminology of the technologies covered in the course. You will understand what might are potential of new technologies in future. You gain understanding how technologies can be utilized for real applications. You will have the knowledge and skills to search information about the latest technologies.

Competencies: technological knowledge, engineering practise.

Learning outcome:
You will understand the terminology of new technologies and their relevance to your future work, be familiar with key concepts and theories.

Content

The course covers following main themes:
- modern robotization,
- wearable technologies,
- AI, Big Data & IoT,
- additive manufacturing,
- other technologies and trends.

Materials

Lecture materials related to the course can be found in the course workspace. Relevant open-source online material can also be used.

Teaching methods

The course is implemented virtually, which includes self-study (independent learning tasks or group learning tasks). The course includes possible online laboratory work. Pay attention the start and end times of the course, and returning dates of assignments.

The course also includes larger project work

Course assignments are described in the course workspace.

Employer connections

Seminars, webinars, etc. that take place during the course can be included in the course. This will be discussed in more detail during the course.

Exam schedules

Assessment is based on learning assignments.

Completion alternatives

The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.

Student workload

One credit corresponds to an average of 27 hours of work, which means that the load of five credits is approximately 135 hours. The load is distributed in different ways depending on the course implementation.

Assessment criteria, satisfactory (1)

1: Student has gained knowledge at all the subjects described in the learning outcomes.

2: Student has gained knowledge at all the subjects described in the learning outcomes and is able to utilize this knowledge.

Assessment criteria, good (3)

3: Student understands all the subjects described in the learning outcomes and is able to apply the skills one has learned.

4: Student has attained an excellent level in almost all the subjects described in the learning outcomes and is able to apply the skills one has learned.

Assessment criteria, excellent (5)

5: The student has attained an excellent level in all subjects described in the learning outcomes and is able to apply the skills one has learned.

Further information

Toteutuksen aktiviteettien arviointipisteet lasketaan yhteen. Pisteiden kokonaismäärä määrittelee lopullisen arvioinnin. Maksipistemäärä on 100p ja läpäisyvaatimus on 50p. Arvosanataulukko pisterajoineen esitetään tarkemmin toteutuksen oppimistilassa.

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