This course is part of the programme:
Master in Engineering and Management (Second Level)
Objectives and competences
The aim of this course is to develop students’ capacity to work in interdisciplinary project teams, and to upgrade in practice their knowledge and skills needed for problem solving in engineering and management.
Students will acquire the following competences:
- Ability to find and understand relevant examples of good practice for a selected topic, and to critically assess their potential usability in the context of the project task,
-Ability to acquire missing information and specific knowledge in the field of engineering and management, connected with the specific context of the project task;
-Ability of solving real interdisciplinary problems in a team,
-Ability to present their project in a written form and orally, and to discuss about the project and its topics with professional and other interested audience.
Students should have knowledge acquired during the courses Introduction to modern technological systems and Economics for Engineers. Students should be prepared to work in teams.
Content (Syllabus outline)
During the course students will be solving a spe-cific problem in the field of engineering and man-agement. Through the practical work, students will learn about the principles of project manage-ment and project group work. The problem will be complex enough to require an interdisciplinary approach and teamwork. Every year, a new pro-ject task will be carefully selected on the basis of real problems in specific environments (compa-nies, organizations, local communities) and will require concrete solutions with practical value.
Study process will consist of the following phas-es:
- Presentation of course objectives
- Instructions for project work
2. Project set-up
- Project task presentation
- Project team organization
3. Detailed project work plan
- Project coordination and means of work
- The division of work
- Determination of the phases of the project
- Time Plan
- Discussion and Consultations
- Problem-solving for the project task and coordination
- Regular (online) meetings with presentation of intermediate results
- Discussion and Consultations
5. Final report
- Preparation of a written report
- Preparing for a public presentation
- Public presentation
6. Final analysis of the project
- Critical evaluation of project results
- Evaluation of the workflow
- Summary of experience – Lessons learned
Intended learning outcomes
After the end of the course, students will have
- Knowledge about basic principles of project management, and know how to act as a re-sponsible member of a team;
- Skills how to acquire additional knowledge and information needed for solvong a concrete complex problem from engineering and man-agement, connected to the project task;
- Knowledge about group dynamics within the project team;
- Ability to communicate about open issues in an efficient and assertive way;
- Enhanced ability to critically evaluate their personal contribution to the results of the team.
To prepare for the project work:
R. Newton: The Theory and Practice of Project Management: Creating Value Thorough Change. Palgrave Macmillan, 2009. ISBN 978‐0‐230‐53667‐8
Due to the nature of the subject other literature is not prescribed in advance. It is determined each year and for each group according to the contents of selected projects by mentors. Additional sources are found by students with a help of their mentors as a part of the study process.
Descriptive grades »pass« and »fail« are used. The elements contributing to the assessment include monitoring of project developments and deliverables, including capabilities for project communication and management of group dynamics, with elements of peer- and self-evaluation, interim presentations, written report and final presentation of project results with discussion.
Assist. Prof. Aneta Trajanov, Assist. Prof. in the field of computer sience and informatics, is a re-searcher at the Department of Knowledge Technologies, JSI, and an assistant professor at the University of Nova Gorica. She completed her PhD on machine learning in 2010 at the Jozef Stefan International Postgraduate School, and a post-doc at Ruđer Boškovič Institute, Zagreb, Croatia in 2015/2016. Her main research interests are machine learning and knowledge discovery from environmental data, deci-sion support, inductive logic programming and equation discovery. She works on several European, as well as national, projects in the area of agroecology, where she applies different machine learning methods for analysing (agro)ecological data.
1. WALL, David P., DELGADO, Antonio, O’SULLIVAN, Lilian, CREAMER, Rachel, TRAJANOV, Aneta, KUZMANOVSKI, Vladimir, HENRICKSEN, Christian B., DEBELJAK, Marko. A decision support model for assessing the water regulation and purification potential of agricultural soils across Europe. Frontiers in sustainable food systems. 2020, 15 str. ISSN 2571-581X. DOI: 10.3389/fsufs.2020.00115.
2. SANDÉN, Taru, TRAJANOV, Aneta, SPIEGEL, Heide, KUZMANOVSKI, Vladimir, SABY, Nico-las, PICAUD, Calypso, HENRIKSEN, Christian B. H., DEBELJAK, Marko. Development of an agri-cultural primary productivity decision support model: a case study in France. Frontiers in environ-mental science, ISSN 2296-665X, 2019, vol. 7, str. 58-1-58-13, doi: 10.3389/fenvs.2019.00058. [COBISS.SI-ID 32342311]
3. BAMPA, Francesca, TRAJANOV, Aneta, DEBELJAK, Marko, et al. Harvesting European knowledge on soil functions and land management using multi%criteria decision analysis. Soil use and management, ISSN 0266-0032, 2019, vol. 35, no. 6, spec. iss., str. 6-20, doi: 10.1111/sum.12506. [COBISS.SI-ID 32292903]
4. TRAJANOV, Aneta, KUZMANOVSKI, Vladimir, RÉAL, Benoît, MARKS PERREAU, Jonathan, DŽEROSKI, Sašo, DEBELJAK, Marko. Modeling the risk of water pollution by pesticides from im-balanced data. Environmental science and pollution research international, ISSN 0944-1344. [Print ed.], 2018, vol. 25, no. 19, str. 18781-18792,doi: 10.1007/s11356-018-2099-7.[COBISS.SI-ID 31356967]
5. TRAJANOV, Aneta, SPIEGEL, Heide, DEBELJAK, Marko, SANDÉN, Taru. Using data mining techniques to model primary productivity from international long-term ecological research (ILTER) agricultural experiments in Austria. Regional environmental change, ISSN 1436-3798, [in press] 2018, 15 str., doi: 10.1007/s10113-018-1361-3. [COBISS.SI-ID 31437607]
6. TRAJANOV, Aneta, KUZMANOVSKI, Vladimir, LEPRINCE, Florence, RÉAL, Benoît, DUTER-TRE, Alain, MAILLET-MEZERAY, Julie, DŽEROSKI, Sašo, DEBELJAK, Marko. Estimating drainage periods for agricultural fields from measured data : data-mining methodology and a case study (La JailliÈRe, France. Irrigation and drainage : International commission on irrigation and drainage, ISSN 1531-0353. [Print ed.], 2015, vol. 64, no. 5, str. 703-516, doi: 10.1002/ird.1933. [COBISS.SI-ID 28858919]
7. KUZMANOVSKI, Vladimir, TRAJANOV, Aneta, LEPRINCE, Florence, DŽEROSKI, Sašo, DEBELJAK, Marko. Modeling water outflow from tile-drained agricultural fields. Science of the total environment, ISSN 0048-9697, feb. 2015, vol. 505, str. 390-401, doi: 10.1016/j.scitotenv.2014.10.009. [COBISS.SI-ID 28041255]
8. DEBELJAK, Marko, TRAJANOV, Aneta, STOJANOVA, Daniela, LEPRINCE, Florence, DŽERO-SKI, Sašo. Using relational decision trees to model out-crossing rates in a multi-field setting. V: JORDÁN, Ferenc (ur.), SCOTTI, Marco (ur.). Proceedigs of the 7th ECEM, European Conference on Ecological Modelling, 30 May – 2 June 2011, Riva el Garda, Italy, (Ecological modelling, ISSN 0304-3800, vol. 245, 2012). Amsterdam: Elsevier. 2012, vol. 245, str. 75-83, doi: 10.1016/j.ecolmodel.2012.04.015. [COBISS.SI-ID 25848103]
9. TRAJANOV, Aneta. Analysis of results of ecological simulation models with machine learn-ing. Informatica : an international journal of computing and informatics, ISSN 0350-5596, jun. 2011, vol. 35, no. 2, str. 285-286. [COBISS.SI-ID 24882471]
10. TRAJANOV, Aneta, TODOROVSKI, Ljupčo, DEBELJAK, Marko, DŽEROSKI, Sašo. Modelling the outcrossing between genetically modified and conventional maize with equation discov-ery. Ecological modelling, ISSN 0304-3800. [Print ed.], 2009, vol. 220, no. 8, str. 1063-1072. [CO-BISS.SI-ID 22574375]
11. TRAJANOV, Aneta, VENS, Celine, COLBACH, Nathalie, DEBELJAK, Marko, DŽEROSKI, Sašo. The feasibility of co-existence between conventional and genetically modified crops : using machine learning to analyse the output of simulation models. V: Proceedings of ICEM 2006, International Con-ference on Ecological Modelling, Mamaguchi, Japan : 28 August – 1 September 2006, Yamaguchi Uni-versity, Japan, (Ecological modeling, ISSN 0304-3800, Vol. 215, no. 1/3, 2008). Amsterdam: Elsevier Scientific Publ. Co. 2008, issues 1-3, vol. 215, str. 262-271, doi: 10.1016/j.ecolmodel.2008.02.031. [COBISS.SI-ID 21667367]
University course code: 2GI004
Year of study: 1
- Silvester Vončina, univ.dipl.ekon.
- prof. dr. Marko Bohanec
- prof. dr. Nada Lavrač
- doc. dr. Aneta Trajanov
- Lectures: 24 hours
- Individual work: 201 hours
Course type: mandatory
Languages: slovenian or english
Learning and teaching methods:
• learning-by-doing: theoretical introduction will be limited to the basic principles of the ol design and project work. then students will be acquainted with the project objectives that will have to be fulfilled by them working together as a team. the project task and the method of work will provide as much as possible an authentic experience of project work in an interdisciplinary team; • studying examples of best practices; • group research and project work; • group consultations (discussion, additional explanation, questions and answers, handling specific questions); • students’ progress will be regularly followed by the mentors, external consultants will be engaged if needed;