Mobile Technologies and Microlearning
Master in Leadership in Open Education (Second Level)
Objectives and competences
The main objective of the course Mobile Technologies through Microlearning is to present students with state-of-the-art mobile technologies and services, and to qualify them to independently design and develop complex applications for mobile devices. They will be able to use the acquired skills for solving problems that require advanced approaches.
Students will acquire the following competences:
Knowledge of up-to-date mobile technologies and services for contemporary micro and collaborative learning;
Ability to use modern computer tools in order to draw up micro-learning materials;
Ability to independently create and develop educational applications for mobile devices.
• Basic knowledge of computers and programming.
• Students should have the basic understanding of the concepts, stategies and didactical models of open education.
• They should be able to use computer supported tools for content generation and design and communication and collaboration tools, and should be prepared to work in interdisciplinary teams.
During the course students will be learning about the basics of mobile technologies and microlearning. The basics include concepts about mobile devices, mobile technologies and services, as well as micro and collaborative learning. Then they will combine classical lec-tures and microlearning nuggets, self-learning and hands-on, as well as engagement in real-life projects to get deep knowledge about es-sential topics on mobile technologies in the context of open education. Moreover, they will also learn how to develop study material using state-of-the art tools. The following topics will be explored in more detail:
• Aims and purpose of the course
• Syllabus presentation
• Presentation of teaching tools, resources and course execution
• Students' obligations
• Study instructions and suggestions
• Mobile/smart devices and their specific functions and specialities in comparison with personal computers (e.g. geolocation, smart systems)
• Brief history of mobile technologies
• Economic, environmental, human and social impacts of mobile technologies
• Etical issues in mobile computing
• Introduction to micro computing and computer tools to support micro learning
Mobile Technologies and Services
• Wireless and mobile networks
o Commerical mobile networks
o Data networks
o Personal networks
o Identification systems
• Mobile platforms
• Connecting mobile devices with sensor systems
• Cloud mobile computing
• Mobile application security
Programming mobile applications
• Development of native applications
• Development of cross-platform mobile applications
• Development of user interfaces for mobile devices
o Human-computer interaction principles
• Tools for coding, running and debugging mobile apps
Usage of computer tools for the develop-ment of study materials for microlearning
• Mobile apps for learning
o e.g., serious games
• Other kinds of study materials
o Short videos / interactive videos
o Whiteboard animations
o Kinetic text (or text-based animation)
o Interactive PDFs
Intended learning outcomes
- Students will learn the basic concepts of modelling and programming applications for mobile devices running on different mobile platforms.
- Students will be able to autonomously write the problem in the form of an algorithm and convert the algorithm into an application using modern software development tools.
- Students will acquire a basic understanding of mobile systems and architectures, and will be able to autonomously solve problems using mobile technologies and services.
- As microlearning based on the concept »Less is more« will be applied, students will acquire the ability of independent and adaptive (dynamic) searching and upgrading of knowledge in a continuous way.
Dharma Prakash Agrawal, Qing-An Zeng (2011) Introduction to wireless and mobile systems. Cengage Learning. ISBN 978-1-4390-6207-4.
Reza B'Far (2007) Mobile Computing Principles: Designing and Developing Mobile Applications with UML and XML. Cambridge University Press. ISBN 978-0521817332.
Frank H. P. Fitzek, Marcos D. Katz (2014) Mobile Clouds. Exploiting Distributed Resources in Wire-less, Mobile and Social Networks. Wiley. ISBN 978-0-470-97389-9.
Vasja Vehovar (ur.) (2007) MOBILNE REFLEKSIJE. Založba FDV. ISBN 978-961-235-275-2.
Seminar work with discussion in order to evaluate the ability of writing a mobile application for a selected practical problem. Written exam, which assesses knowledge of the fundamental concepts of programming applications for mobile devices and the ability of solving short programming problems.
Prof. dr. Bojan Cestnik, rang full professor, hab. Field computer scienc, is the general manager of software company Temida and a researcher in the department of Knowledge technologies at Jozef Stefan Institute in Ljubljana. He obtained his Ph.D. in Computer Science in 1991 at the Faculty of Electrical Engineering and Computer Science, University of Ljubljana, Slovenia. His professional and research interests include knowledge based information systems, business process modeling, decision support systems and machine learning. His research work was presented at several international con-ferences. He has been responsible for several large-scale software development and maintenance projects for supporting business operations.
CESTNIK, Bojan, FABBRETTI, Elsa, GUBIANI, Donatella, URBANČIČ, Tanja, LAVRAČ, Nada. Reducing the search space in literature-based discovery by exploring outlier documents : a case study in finding links between gut microbiome and Alzheimer's disease. Genomics and computational biology, ISSN 2365-7154, 2017, vol. 3, no. 3, str. e58-1-e58-10, doi: 10.18547/gcb.2017.vol3.iss3.e58. [COBISS.SI-ID 30497575]
PEROVŠEK, Matic, KRANJC, Janez, ERJAVEC, Tomaž, CESTNIK, Bojan, LAVRAČ, Nada. TextFlows : a visual programming platform for text mining and natural language processing. Science of computer programming, ISSN 0167-6423, 2016, vol. 121, str. 128-152, doi: 10.1016/j.scico.2016.01.001. [COBISS.SI-ID 29549095]
CESTNIK, Bojan, BOHANEC, Marko, URBANČIČ, Tanja. QTvity : advancing students' engagement during lectures by using mobile devices. V: RACHEV, Boris (ur.). CompSysTech'15 : proceedings of the 16th International Conference on Computer Systems and Technologies, June 25 - 26, 2015, Dublin, Ireland. New York: ACM. 2015, str. 334-341. http://dl.acm.org/citation.cfm?id=2812467&dl=ACM&coll=DL. [COBISS.SI-ID 29039143]
CESTNIK, Bojan, URBANČIČ, Tanja. Teaching supply chain management with the beer distribution game on mobile devices. V: CABALLERO-GIL, Pino (ur.). Proceedings. [S. l.: s. n.]. 2014, str. 111-117. [COBISS.SI-ID 3560187]
CESTNIK, Bojan, CHERNOGOROV, Fedor, KUKLIŃSKI, Slawomir, KRIŽMAN, Viljem. Framework for cognitive network implementation based on Cellar, Karaf, JADE and OSGi. V: BALANTIČ, Zvone (ur.), et al. Fokus 2020 : zbornik 33. mednarodne konference o razvoju organizacijskih znanosti = Focus 2020 : proceedings of the 33rd International Conference on Organizational Science Development. Kranj: Moderna organizacija. 2014, str. [1-8]. [COBISS.SI-ID 3009403]
PETRIČ, Ingrid, CESTNIK, Bojan, LAVRAČ, Nada, URBANČIČ, Tanja. Outlier detection in cross-context link discovery for creative literature mining. The Computer journal, ISSN 0010-4620, 2012, vol. 55, no. 1, str. 47-61, doi: 10.1093/comjnl/bxq074. [COBISS.SI-ID 1621243]
MACEDONI-LUKŠIČ, Marta, PETRIČ, Ingrid, CESTNIK, Bojan, URBANČIČ, Tanja. Developing a deeper understanding of autism : connecting knowledge through literature mining. autism res. treat., 2011, vol. 2011, 8 str. [COBISS.SI-ID 1916411]
PUR, Aleksander, BOHANEC, Marko, LAVRAČ, Nada, CESTNIK, Bojan. Primary health-care network monitoring : a hierarchical resource allocation modeling approach. Int. j. health plann. manage., 2010, vol. 25, no. 2, str. 119-135. [COBISS.SI-ID 23721255]
PETRIČ, Ingrid, URBANČIČ, Tanja, CESTNIK, Bojan, MACEDONI-LUKŠIČ, Marta. Literature mining method RaJoLink for uncovering relations between biomedical concepts. Journal of biomedical informatics, apr. 2009, vol. 42, no. 2, str. 219-227. [COBISS.SI-ID 929787]
CESTNIK, Bojan, KERN, Alenka, MODRIJAN, Helena. Semi-automatic ontology construction for improving comprehension of legal documents. Lect. notes comput. sci., 2008, lNCS 5184, str. 328-339. [COBISS.SI-ID 23096103]
PETRIČ, Ingrid, URBANČIČ, Tanja, CESTNIK, Bojan. Discovering hidden knowledge from biomedical literature. Informatica (Ljublj.), 2007, vol. 31, no. 1, str. 15-20, ilustr. [COBISS.SI-ID 634875]
LAVRAČ, Nada, CESTNIK, Bojan, GAMBERGER, Dragan, FLACH, Peter A. Decision support through subgroup discovery : three case studies and the lessons learned. Mach. learn.. [Print ed.], 2004, vol. 57, str. 115-143. [COBISS.SI-ID 18515239]
BOHANEC, Marko, CESTNIK, Bojan, RAJKOVIČ, Vladislav. Quasitative multi-attribute modeling and its application housting. Revue des systèmes de décision, 2001, vol. 10, str. 175-193. [COBISS.SI-ID 16555559]
CESTNIK, Bojan, SUŠNIK, Janko, BIZJAK, Breda. Computerised estimation of the compatibility of stresses and strains at work. Informatica medica slovenica, 1996, letn. 3, št. 1,2,3, str. 101-108. [COBISS.SI-ID 7897049]
University course code: 2LOE19
Year of study: 2. year
- prof. dr. Barbara Koroušić Seljak
- pridr. prof. dr. Bojan Cestnik
- Lectures: 18 hours
- Exercises: 12 hours
- Individual work: 120 hours
Course kind: elective
Languages: slovenian or english
Learning and teaching methods:
teaching will consist of three parts: the first part will consist of classical and online lectures, where the contents of the syllabus will be presented and explained. part of the contents will be acquired by the students in such a way that they will experience microlearning by themselves. the second part will include hands-on exercises, where the students will use the concepts from the lectures in practical problems and applications. group discussions will be encouraged. the third part will consist of individual work where the students will be solving homeworks throughout the course and at the end write a seminar work in the form of a longer mobile application. 50% of contact hours will be done face-to-face in class, and 50% will be online.