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Methodology of Probability Theory Instruction

General data

Course ID: 1000-135MRP
Erasmus code / ISCED: 11.013 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0540) Mathematics and statistics, not further defined The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title: Methodology of Probability Theory Instruction
Name in Polish: Metodyka nauczania rachunku prawdopodobieństwa
Organizational unit: Faculty of Mathematics, Informatics, and Mechanics
Course groups: (in Polish) Przedmioty obieralne na studiach drugiego stopnia na kierunku bioinformatyka
Courses resulting in teaching certificates
Elective courses for 1st degree studies in mathematics
Elective courses for 2nd stage studies in Mathematics
Pedagogical courses
ECTS credit allocation (and other scores): 6.00 Basic information on ECTS credits allocation principles:
  • the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS;
  • the student’s weekly hourly workload is 45 h;
  • 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes;
  • weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS;
  • work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load.

view allocation of credits
Language: Polish
Type of course:

elective courses
pedagogical qualifications

Short description:

We will discuss teaching methodology for combinatorics and theory of probability and work on developing probabilistic intuition.

Full description:

Topics:

How to present axioms of probability theory and the properties of probability? Classical probability. Elements of "college" probability. The sum rule and the product rule. The methodology of introducing notions of "college" probability. Problems with interesting numerical results. Paradoxes in probability theory. Stochastic trees - a a method for illustrating the notion of conditional probability. Explaining basic probability theorems on stochastic trees. How to introduce the notion of independent events? Bernoulli's scheme. Stochastic games as a method for introducing notions related to random variables. Applications of the theorem on the expected value of a sum of random variables. Using stochastic graphs in the analysis of some random experiments.

Bibliography:

Literature will be given durign the course.

Learning outcomes:

(Each effect is followed by the code of the corresponding requirement of the Teachers' Education Standard)

In the scope of knowledge a graduate knows:

the national curriculum of mathematics in the scope of the probability theory, the teaching objectives and the content knowledge at different education levels (D.1/E.1.W2.);

methods of teaching of probability theory - substantive and methodical solutions, good practices, how to adapt the teaching to needs and abilities of students of divirsified learning potentials, typical students' errors, their role and how to makee use of them while teaching (D.1/E.1.W6.);

the need to build a positive attitute of students towards studying, developing their curiosity, activity and coginitive independence, logical and critical thinking, to build the motivation to learn mathematics in a systematic way, to use different knowlegde sources, incuding the Internet and to prepare students for life-long learning through self-reliant learning (D.1/E.1.W15.);

In the scope of skills a graduate can:

identify typical school exercises with the learning objectives, in particular with the general requirements of the national curriculum and with the key competemces (D.1/E.1.U1.);

identify the probability theory topics with other learning content topics (D.1/E.1.U3.);

addopt the communication style to the level of development of his/her students (D.1/E.1.U4.);

create didactical situations invoking students' activity and aimed at broadening of their interests and at the knowledge popularization (D.1/E.1.U5.);

recognize typical students' errors and use them in the teaching practice (D.1/E.1.U10.).

In the scope of social competences, a graduate is ready:

to popularize knowledge among students, within and outide the school (D.1/E.1.K2.);

to encourage students to research attempts (D.1/E.1.K3.);

to promote a responsible and critical use of digital media and to obey the copyright laws (D.1/E.1.K4.);

to develop students' curiosity, activity and cognitive independence as well as the logical and critical thinking (D.1/E.1.K7.);

to stimulate students to life-long learning through self-reliant learning (D.1/E.1.K9.).

Assessment methods and assessment criteria:

The final grade is based on the number of points gained during classes, the midterm exam and the final exam.

Classes in period "Summer semester 2023/24" (in progress)

Time span: 2024-02-19 - 2024-06-16
Selected timetable range:
Navigate to timetable
Type of class:
Classes, 30 hours more information
Lecture, 30 hours more information
Coordinators: Adam Osękowski
Group instructors: Adam Osękowski
Students list: (inaccessible to you)
Examination: Examination

Classes in period "Summer semester 2024/25" (future)

Time span: 2025-02-17 - 2025-06-08
Selected timetable range:
Navigate to timetable
Type of class:
Classes, 30 hours more information
Lecture, 30 hours more information
Coordinators: Adam Osękowski
Group instructors: Adam Osękowski
Students list: (inaccessible to you)
Examination: Examination
Course descriptions are protected by copyright.
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Krakowskie Przedmieście 26/28
00-927 Warszawa
tel: +48 22 55 20 000 https://uw.edu.pl/
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