TASNOP: A tool for teaching algorithms to solve network optimization problems

Lourenço, Wilson da Silva; Lima, Stanley Jefferson de Araujo; Araújo, Sidnei Alves de

doi:10.1002/cae.21864

Cited by 12 publications

(8 citation statements)

References 11 publications

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“…TASNOP is a resource written in Java designed to make it easier for students to comprehend network optimization concepts and the operation of some optimization algorithms (A*, Greedy Search, Dijkstra, Ford‐Fulkerson, and Nearest Neighbor).…”

Section: Related Workmentioning

confidence: 99%

Seshat — a web‐based educational resource for teaching the most common algorithms of lexical analysis

Arnaiz‐González

Díez-Pastor

Ramos‐Pérez

et al. 2018

Comp Applic In Engineering

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The theoretical background to automata and formal languages represents a complex learning area for students. Computer tools for interacting with the algorithm and interfaces to visualize its different steps can assist the learning process and make it more attractive. In this paper, we present a web application for learning some of the most common algorithms in an appealing way. They are specifically linked to the recognition of regular languages that are, taught in classes on both automata theory and compiler design. Although several simulators are available to students, they usually only serve to validate grammars, automata, and languages, rather than helping students to learn the internal processes that an algorithm can perform. The resource presented here can execute and display each algorithm process, step by step, providing explanations on each step that assist student comprehension. Additionally, as a web‐based resource, it can be used on any device with no need for specific software installation.

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Section: Related Workmentioning

confidence: 99%

Seshat — a web‐based educational resource for teaching the most common algorithms of lexical analysis

Arnaiz‐González

Díez-Pastor

Ramos‐Pérez

et al. 2018

Comp Applic In Engineering

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“…Representation of knowledge and algorithms covered by the Intelligent systems course are thematically very diverse and comprise: search algorithms—uninformed search (breadth‐first, depth first, depth‐first with iterative deepening) and heuristics and informed search (hill‐climbing, best‐first, branch and bound, A*), Minimax search and Minimax with alpha‐beta pruning, resolution theorem proving, fuzzy logic, semantic networks and frames, production systems, representations for reasoning under uncertainty, planning systems, inductive systems, and Bayesian networks. The listed algorithms done in lectures and auditory exercises can in some cases seem quite abstract and unclear to students, so interactive simulations of those algorithms facilitate their understanding and learning .…”

Section: Problem Description and Related Workmentioning

confidence: 99%

SAIL—Software system for learning AI algorithms

Draskovic

Cvetanović

Nikolić

2018

Comp Applic In Engineering

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Artificial intelligence (AI) comprises a large spectrum of groups of algorithms: heuristic algorithms for search and planning, formal methods for representation of knowledge and reasoning, algorithms for machine learning and many more. Since these algorithms are complex, there is a need for a system which would enable their application both in everyday work and education processes. This paper describes a software system for learning AI algorithms called SAIL (Software System for AI Learning), which can be used both on computers and mobile devices. The paper gives examples of lab exercises and self‐study tasks that through graphic representation and detailed procedures help students master this area. Students can enter their examples into the system and obtain correct solutions for those examples. At any point when an example is simulated, a student can proceed to the next step or go back to the previous one, save the current simulation as a file, or print the detailed procedure as a task solution. SAIL helps lecturers go through the syllabus more efficiently and improve class material, while at the same time it helps students get a better grasp of implemented algorithms. SAIL can also benefit software engineers, who can select and simulate an adequate algorithm to solve a specific problem. The results of the SAIL system are verified within the AI introductory course at the School of Electrical Engineering University of Belgrade and they are presented in this paper.

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Section: Introductionmentioning

confidence: 99%

“…Network Optimization Problems (NOP) can be better analyzed by graph [1]. Shortest Path Problem (SPP) is one of NOP where its solution mostly used Dijkstra's algorithm [1] to find minimum distance between two vertices [2]. The algorithm commonly used in manufacturing, computer networks, transport, and telecommunications, making it a crucial topic to be understood by students.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Question-led approach in designing Dijkstra algorithm game-based learning: A pilot study

Ramle

Rosli

Nathan

et al. 2020

IJERE

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Dijkstra algorithm is important to be understood because of its many uses. However, understanding it is challenging. Various methods to teach and learn had been researched, with mixed results. The study proposes questionled approach of the algorithm in a game-based learning context. The game designed based on an existing game model, developed and tested by students. Pre- and post-game tests compared and game feedback survey analysed. Results showed that students’ performance in graph data structure Dijkstra algorithm improved after playing the game where post-test mark was higher than pre-test. Game feedback were mostly positive, with areas of improvement. Students may use the game as a learning tool for self-regulated learning. Educators may get some ideas on how to design teaching tool using question-led approach.

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TASNOP: A tool for teaching algorithms to solve network optimization problems

Abstract: This work presents the TASNOP-Teaching Algorithms for Solving Network

Cited by 12 publications

References 11 publications

Seshat — a web‐based educational resource for teaching the most common algorithms of lexical analysis

Seshat — a web‐based educational resource for teaching the most common algorithms of lexical analysis

SAIL—Software system for learning AI algorithms

Question-led approach in designing Dijkstra algorithm game-based learning: A pilot study

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