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Course info
KVD / 9ALGV
:
Course description
Department/Unit / Abbreviation
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KVD
/
9ALGV
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Algorithmics for Education
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Form of course completion
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Pre-Exam Credit
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Form of course completion
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Pre-Exam Credit
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Accredited / Credits
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No,
1
Cred.
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Type of completion
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-
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Type of completion
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-
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Time requirements
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Lecture
10
[Hours/Semester]
Seminar
18
[Hours/Semester]
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Course credit prior to examination
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No
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Course credit prior to examination
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No
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Included in study average
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NO
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Language of instruction
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-
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Occ/max
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|
|
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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NO
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Winter semester
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0 / -
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0 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Summer semester
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Semester taught
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Summer semester
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Minimum (B + C) students
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not determined
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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-
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
S|N |
Periodicity |
každý rok
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Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
No
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Fundamental theoretical course |
No
|
Evaluation scale |
S|N |
Substituted course
|
KVD/ALGV
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Preclusive courses
|
N/A
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Prerequisite courses
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N/A
|
Informally recommended courses
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N/A
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
,
XLS
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Course objectives:
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The aim is to present the possibilities of development of algorithmic thinking through various methods, activities and programming environments.
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Requirements on student
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Active participation of tackling practical examples available for teaching various educational areas.
Elaboration of assigned seminar task thematically oriented situations from the school environment.
Fulfillment of practical tests.
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Content
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The term algorithm and associated properties.
Algorithms ways.
Basic algorithmic structures.
Enrollment forms algorithms.
The programming environment for the development of algorithmic thinking.
Algorithmic complexity.
Graph theory.
Activities developing algorithmic thinking.
Examples of usable solutions to the development of algorithmic thinking in their chosen programming environment.
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Activities
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Fields of study
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Guarantors and lecturers
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-
Guarantors:
PhDr. Tomáš Jakeš, Ph.D. (100%),
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Lecturer:
PhDr. Zbyněk Filipi, Ph.D. (100%),
Mgr. Filip Frank, Ph.D. (100%),
PhDr. Tomáš Jakeš, Ph.D. (100%),
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Seminar lecturer:
PhDr. Zbyněk Filipi, Ph.D. (100%),
Mgr. Filip Frank, Ph.D. (100%),
PhDr. Tomáš Jakeš, Ph.D. (100%),
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Literature
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Contact hours
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26
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Preparation for formative assessments (2-20)
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10
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Undergraduate study programme term essay (20-40)
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40
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Practical training (number of hours)
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26
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Total
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102
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
Logical thinking developed at the output level of high school RVP. |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
Student explained on the basis of the historical development of the concept of the algorithm.
Student recognizes algorithm based on the properties.
Students use various methods of algorithm.
Student uses basic algorithmic structures.
Student distinguishes the pros and cons of different forms of writing algorithms.
Students are able to choose the environment for the development of algorithmic thinking by use.
The student perceives the fundamental problems associated with algorithmic complexity.
The student can use knowledge with algorithms from graph theory.
Student solves examples useful for the development of algorithmic thinking in their chosen environment.
Student analyzes problems from different areas and draws up solutions to an appropriate algorithm in various forms based on rules. |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Test |
Skills demonstration during practicum |
Seminar work |
Continuous assessment |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture supplemented with a discussion |
E-learning |
Task-based study method |
Skills demonstration |
Individual study |
Seminar classes |
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