Related Experiment Video
Updated: Nov 5, 2025
10:58
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
Published on: July 25, 2013
17.2K
Making Sense of Algorithms in Discrete Mathematics
1School of Teacher Education and Leadership, Faculty of Creative Industries, Education and Social Justice, Queensland University of Technology, E Block, Level 3, Victoria P ark Road, Kelvin Grove, Queensland 4059 Australia.
International Journal of Science and Mathematics Education
|May 19, 2021
Summary
Students successfully understood the Hungarian algorithm for network analysis and assignment problems. This study explored how secondary students learn complex network algorithms through a structured teaching approach.
Area of Science:
- Mathematics Education
- Computer Science Education
- Algorithm Comprehension
Background:
- Network analysis is crucial for modeling technology and engineering problems in secondary mathematics.
- Student understanding of network analysis algorithms is not well-researched.
- The Hungarian algorithm is a key method for solving assignment problems.
Purpose of the Study:
- To investigate how Year 12 students make sense of the Hungarian algorithm.
- To explore the effectiveness of a teaching experiment in explaining network algorithms.
- To identify student comprehension of algorithm steps and problem-solving applications.
Main Methods:
- A design-based research project involving eight Year 12 students.
- A teaching experiment with four 60-minute lessons.
- Incremental introduction of the Hungarian algorithm steps within a hypothetical learning trajectory.
Main Results:
- Students demonstrated understanding of the Hungarian algorithm's intermediate steps.
- Participants grasped the results generated by each step of the algorithm.
- Students could relate the algorithm's process to solving assignment problems.
Conclusions:
- The developed teaching approach facilitated student sense-making of the Hungarian algorithm.
- Students successfully applied the algorithm to solve assignment problems.
- Insights into student difficulties with network algorithms were identified for future research.
Related Concept Videos
01:12Trial and Error and Algorithm
238
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
238
01:28Synthetic Disvision of Polynomials
27
Synthetic division is an efficient algorithmic approach for dividing a polynomial by a linear binomial of the form x - c, where c is a real number. This method is helpful due to its streamlined process, which avoids the more cumbersome steps involved in the traditional long division of polynomials. It simplifies computation and serves as a practical tool for evaluating polynomials and identifying their factors.To perform synthetic division, one begins by listing the coefficients of the...
27
01:16Basic Discrete Time Signals
413
The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
413
01:26Algebraic Expressions
55
Algebraic expressions are essential in mathematics. They represent relationships through variables, constants, and operations. These expressions help describe patterns and solve problems in various mathematical fields. Understanding their components, classifications, and operations allows for efficient simplification and manipulation.Each algebraic expression consists of individual parts, including numbers and symbols, that work together to form meaningful mathematical statements. The numerical...
55
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
153
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
153
01:30Fundamental Theorem of Algebra
42
The Fundamental Theorem of Algebra is central to the study of polynomial equations, asserting that every non-constant polynomial with complex coefficients has at least one complex zero. This means that a polynomial of degree n ≥ 1, written as: with an ≠ 0, has at least one solution in the complex number system. Since the set of real numbers is a subset of complex numbers, this theorem applies equally to polynomials with real coefficients.Building on this result, the Complete...
42