Learning and Teaching Algorithm Design and Optimisation Using Contests Tasks

Combéfis, Sébastien; BARRY, Saïkou Ahmadou; CRAPPE, Martin; David, Mathieu; Moffarts, Guillaume de; HACHEZ, Hadrien; KESSELS, Julien

doi:10.15388/ioi.2017.02

Cited by 7 publications

(8 citation statements)

References 11 publications

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“…The solution from the perspective of learning theory does not solve the problem. Whereas Combéfis et al (2017) argue that computer scientists must have the skills to design efficient algorithms. This means that according to them, computer scientists are not only able to make algorithms, but also must produce efficient algorithms.…”

Section: Describe Relevant Scholarshipmentioning

confidence: 99%

Identifying the Factor of Mathematical Reasoning That Affects the Ability to Programming Algorithm

Hartati¹,

Vitianingsih²,

Kurniati³

et al. 2020

IES

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This paper examines the limited proficiency to engage in programming algorithms among university students in information technology and information system in several universities across Surabaya, Indonesia. The purpose of this research is to find the most influential factor in learning programming algorithm using a quantitative approach. The research subjects were second-semester information technology students in several private universities in Surabaya, Indonesia. The research instruments were mathematical reasoning and basic algorithm programming test. Mathematical reasoning tests incorporated linear algebraic, basic calculus, and mathematical logic. The data analysis used was variant-based Structural Equation Modelling, also known as Partial Least Squares - Structural Equation Modelling based on Smart-PLS 3. With α = 5%, the research results conclude that mathematical reasoning positively influences algorithm programming ability with an R score of 0.999, and that the most influential variable among mathematical reasoning abilities was algebra with an R score of 0.732.

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Section: Describe Relevant Scholarshipmentioning

confidence: 99%

Identifying the Factor of Mathematical Reasoning That Affects the Ability to Programming Algorithm

Hartati¹,

Vitianingsih²,

Kurniati³

et al. 2020

IES

View full text Add to dashboard Cite

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“…Algorithm learning as a fundamental course in information engineering school is emphasized to develop the ability to analyze computing problem and to design a possible computing solution. Thus, metacognitive training to be implemented must support the development of problem-solving skill (Combefis, Barry, Crappe, & David, 2017). Furthermore, the presence of recommendation from the system will guide and ensure the effectiveness of the training system.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Learning Strategy Recommendation System to Train Metacognition and Self-Regulated Learning in Algorithm and Data Structure Course

Indriana

Setio

Rahmawati³

et al. 2019

ICEAP Proceeding 2019

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Metacognitive training system (MTS) which is developed for Algorithm and Data Structure course facilitates students to choose, use, and evaluate different learning strategies. Therefore, students are encouraged to be self-regulated. However, the system is also expected to know the best learning strategies of students to generate recommendation that will help students to better understand their self. The recommendation is generated by using various parameters such as post-test scores, learning strategy evaluation values, learning strategy access time, number of clicks and number of summary words to determine the best level of metacognition and learning strategies for students. The learning strategy recommendation system developed using Simple Additive Fuzzy Weighting Algorithm. This method adds the weight of each criterion in each learning strategy. The system's functionality is tested using Black Box method as well as validated by experts. A user acceptance test (UAT) is also carried out to prove that the system is accepted by users and requirements have been met. The UAT resulted in an average of 82.5% which is considered as very good.

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“…Algorithm-learning is very important in computing education. Ability to design and optimise an algorithm, as well as to implement it for solving real-world problems is required in the related professions [1]. In addition, designing an algorithm is basically performing a general problem-solving process; thus, it is also applicable to non-computing problems.…”

Section: Introductionmentioning

confidence: 99%

“…Teaching how to design algorithms requires a different approach than teaching other topics. The skill for designing and implementing algorithm can be achieved by experimentation and trial errors in real computing problems [1]. Thus, instead of only relying on classroom lectures, students need to be exposed to a kind of computer‐based system which facilitates students to participate actively in the learning process, i.e.…”

Section: Introductionmentioning

confidence: 99%

Development and evaluation of adaptive metacognitive scaffolding for algorithm‐learning system

2019

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Adaptive metacognitive scaffolding is developed to provide learning assistance on an as-needed basis; thus, advances the effectiveness of computer-based learning systems. Metacognitive scaffoldings have been developed for some science subjects; however, not for algorithm-learning. The learning algorithm is different from learning science as it is more oriented to problem-solving; therefore, this study is aimed to describe the modelling, development, and evaluation of the adaptive metacognitive scaffolding which is dedicated for encouraging algorithm-learning. In addition, the authors present a new approach for learner modelling to find students' metacognitive state. Adaptivity of the scaffolding is based on the learner modelling. To evaluate the effectiveness of the developed system, it is deployed in a real algorithm-learning classroom of 38 students. The class is randomly divided into two groups: experiment and control. Two parameters are measured from both groups, i.e. academic success and academic satisfaction. Non-parametric statistical test, i.e. Mann-Whitney U-test (significance level 0.01) rejects the null hypothesis (U-value = 86.5 and U-critical = 101). This result verifies that the academic success of the experiment group is significantly higher than that of the control group. In addition, an academic satisfaction survey shows that adaptive scaffolding is valid in assisting students while learning with the system.

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Learning and Teaching Algorithm Design and Optimisation Using Contests Tasks

Cited by 7 publications

References 11 publications

Identifying the Factor of Mathematical Reasoning That Affects the Ability to Programming Algorithm

Identifying the Factor of Mathematical Reasoning That Affects the Ability to Programming Algorithm

Development of Learning Strategy Recommendation System to Train Metacognition and Self-Regulated Learning in Algorithm and Data Structure Course

Development and evaluation of adaptive metacognitive scaffolding for algorithm‐learning system

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