Even though the International Olympiad in Informatics directly involves a restricted number of pupils from each country, one of its primary goals is stimulating interest in computer science and information technology over the whole younger segment of the world’ s population. In several countries, this aim has to be accomplished without an active intervention of the Ministry of Education on school programs, relying on the efforts of small devoted organizations. In this context, promoting the involvement of a large number of school teachers may be as crucial as difficult to achieve. Following a 9-year experience of teams competitions in Italy, recently shared with other European countries, we argue that teams Olympiad may be an effective tool for widening the participation of high-school students and teachers, synergistically cooperating with existing individual competitions. On the one hand, teams contests foster peer education, encouraging talented students to help training fellows. On the other hand, these competitions can be more appealing both for average students, valuing group membership more than personal accomplishments, and most importantly, teachers: team achievements are more recognizably linked with the overall school or teacher performance than solitary excellences, resulting in increased returns rewarding the involved subjects.

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Fostering Informatics Education through Teams Olympiad

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.

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

Metacognitive scaffolding is an important pedagogical support to encourage self-regulated learning, especially in computer-based learning environments. Currently, an e-learning system which is complemented by metacognitive scaffolding has been developed. The scaffolding includes a recommendation system given by a virtual pedagogical agent. However, an academic satisfaction evaluation on the system reveals that the recommendation is less helpful. Therefore, this paper proposes a framework for providing a better recommendation. Two types of recommendations are aimed, including recommendation for the definition of learning goal/sub-goal and learning strategy. Goal/sub-goal recommendation is generated by identifying students' level of prior knowledge by using text classification algorithm. On the other hand, the strategy-use recommendation is generated by modeling the fitness of previously used learning strategy using fuzzy inference system and analyzing students' interaction log with the learning strategy. Implementation of the proposed scheme produces a prototype of the recommendation system. An A/B testing is conducted to compare previous and newer recommendation systems. The test shows that most of the users prefer to use the improved recommendation system.

A Framework for Improving Recommendation in Adaptive Metacognitive Scaffolding

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.

/pdf/identifying-the-factor-of-mathematical-reasoning-that-3sh828c3fd.pdf

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

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.

https://iceap.kemdikbud.go.id/index.php/iceap/article/download/222/79

Development of learning strategy recommendation system to train metacognition and self-regulated learning in algorithm and data structure course

It is important for a future computer science engineer or scientist to master algorithm design and to know how to optimise algorithms to solve real-world problems. Most programming and IT contests require their contestants to design algorithms to solve problems and to optimise their code to get the best temporal and spatial performances. This paper presents training materials built from contest tasks to teach and learn how to design algorithms that solve concrete and contextualised problems. The first learning modules will be built thanks to a pedagogical device that will be deployed during the 2017–2018 academic year at ECAM in the frame of the LADO project. All the produced materials will be open sourced and available in English.

Saïkou Ahmadou Barry

Papers

Learning and Teaching Algorithm Design and Optimisation Using Contests Tasks