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Teaching algorithmic thinking

Computer Science and Mathematics, Second Cycle
1 in 2 year
Course director:

Janez Demšar

Hours per week – 1. semester:

There are no prerequisites.

Content (Syllabus outline)

The goal of the course is to train the future teachers for teaching algorithmic thinking. The approach is based on principles described on Concrete illustrations will roughly follow the list of topics proposed in the IEEE/ACM K12 curriculum for computer science:
binary presentation of data, representation of images and sound,
data compression, information theory, error detection,
searching algorithms, sorting algorithms,
routing and deadlock, finite state automata, and algorithms on graphs
and others.
Besides these concrete examples, students will learn about general didactical principles that need to be observed when teaching algorithmic thinking.
In addition to practice classes in partner schools under appropriate supervision, the students will gain practical experience by helping in the summer schools at the faculty, by teaching computer groups at schools, preparing school children for the international Bebras competition etc.


O. Hazzan, T. Lapidot, N. Ragonis: Guide to Teaching Computer Science: An Acticity-Based Approach, Springer, 2011.
T. Bell, I. H. Witten, M. Fellows: Computer Science Unplugged,, 2006.
R. Sedgewick, K. Wayne: Algorithms, 4th edition. Addison-Wesley, 2011.

Objectives and competences

Students will learn, both theoretically and through concrete examples, how to teach algorithmic thinking using methods that are appropriate for primary and high schools.

Intended learning outcomes

Students will understand the basics of computer science (from coding to algorithms and data structures to more specific topics) in a more intuitive way.
They will be able to apply this deeper understanding of CS to teach computer science in an approachable and attractive way.
They will learn how to prepare teaching activities, observe reactions of target audience, analyse and evaluate the activity and improve it.
Students will gain basic understanding of psychology, in particular developmental psychology, and apply it to their teaching practice.
With some training in speaking and presentation, students will be more capable to give public presentations of computer science to different target audiences.
As potential future teachers, students will know and understand the pitfalls of rigid taxonomies like the Bloom taxonomy - which is known to be a particularly bad fit for CS -- and its application for bureaucratization of school system, and hence avoid its use for planning, analysis and evaluation of their work.

Learning and teaching methods

Lectures and homeworks with special emphasis on intuitive understanding and gaining practical experience.


Continuing (homework, practical work)
Final (written exam)
grading: 5 (fail), 6-10 (pass) (according to the Statute of UL)

Lecturer's references

Pet najpomembnejših del:
1. DEMŠAR, Janez. Algorithms for subsetting attribute values with Relief. Mach. learn.. [Print ed.], Mar. 2010, vol. 78, no. 3, str. 421-428, graf. prikazi. [COBISS-SI-ID 7550548], [JCR, WoS, št. citatov do 9. 3. 2010: 0, brez avtocitatov: 0, normirano št. citatov: 0]
2. ŠTAJDOHAR, Miha, MRAMOR, Minca, ZUPAN, Blaž, DEMŠAR, Janez. FragViz : visualization of fragmented networks. BMC bioinformatics, 2010, vol. 11, str. 1-14, ilustr. [COBISS-SI-ID 7964756], [JCR, WoS, št. citatov do 6. 10. 2011: 1, brez avtocitatov: 1, normirano št. citatov: 1]
3. ZUPAN, Blaž, DEMŠAR, Janez. Open-source tools for data mining. Clin. lab. med., 2008, vol. 28, no. 1, str. 37-54, ilustr. [COBISS-SI-ID 6280532], [JCR, WoS, št. citatov do 6. 9. 2011: 2, brez avtocitatov: 2, normirano št. citatov: 1]
4. DEMŠAR, Janez, LEBAN, Gregor, ZUPAN, Blaž. FreeViz-An intelligent multivariate visualization approach to explorative analysis of biomedical data. Journal of biomedical informatics, 2007, vol. 40, no. 6, str. 661-671, ilustr. [COBISS-SI-ID 6188116], [JCR, WoS, št. citatov do 9. 3. 2010: 2, brez avtocitatov: 2, normirano št. citatov: 2]
5. DEMŠAR, Janez. Statistical comparisons of classifiers over multiple data sets. J. mach. learn. res.. [Print ed.], Jan. 2006, vol. 7, str. [1]-30, graf. prikazi. [COBISS-SI-ID 5134420], [JCR, WoS, št. citatov do 6. 11. 2011: 365, brez avtocitatov: 365, normirano št. citatov: 412]

Celotna bibliografija je dostopna na SICRISu:

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