Self-organizing maps to find computational thinking features in a game building workshop

Souza, Alexandra; Barcelos, Thiago; Castro-Muñoz, Roberto; Silveira, Ismar Frango; Omar, Nizam; Silva, L. A.

doi:10.1109/clei.2017.8226407

Cited by 2 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, SOMs have demonstrated the potential to identify important elements of object-oriented programming, such as classes and objects, by detecting common features in software code [12]. SOMs can also aid in the analysis of completed programming code, such as computer games, to identify the development of computational thinking skills [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps

2023

View full text Add to dashboard Cite

Due to the everchanging and evergrowing nature of programming technologies, the gap between the programming industry’s needs and the educational capabilities of both formal and informal educational environments has never been wider. However, the need to learn computer programming has never been greater, regardless of the motivation behind it. The number of programming concepts to be taught is increasing over time, while the amount of time available for education and training usually remains the same. The objective of this research was to analyze the source codes used in many educational systems to teach fundamental programming concepts to learners, regardless of their prior experience in programming. A total of 25 repositories containing 3882 Python modules were collected for the analysis. Through self-organization of the collected content, we obtained very compelling results about code structure, distribution, and differences. Based on those results, we concluded that Self-Organizing Maps are a powerful tool for both content and knowledge management, because they can highlight problems in the curriculum’s density as well as transparently indicate which programming concepts it has successfully observed and learned to recognize. Based on the level of transparency exhibited by Self-Organizing Maps, it is safe to say that they could be used in future research to enhance both human and machine learning of computer programming. By achieving this level of transparency, such an Artificial Intelligence system would be able to assist in overall computer programming education by communicating what should be taught, what needs to be learned, and what is known.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps

2023

View full text Add to dashboard Cite

show abstract

“…The analyses are based on statistical techniques such as the mean, median, standard deviation, histograms, and hierarchical clustering. More specifically, the studies of [16], [25], [26] that analyze data generated by the Dr. Scratch tool from artifacts available in the Scratch online community have limited results due to the use of the duplicate code, employment of bad programming practices and the various nature of the files (stories, animations and games) as shown by [10], [27], [28]. In addition, the analysis of artifacts indicates their complexity, i.e., it has limitations in the process of measuring the acquisition of Computational Thinking.…”

Section: Data Mining Framework To Analyze the Evolution Of Computational Thinking Skillsmentioning

confidence: 99%

“…In addition, the analysis of artifacts indicates their complexity, i.e., it has limitations in the process of measuring the acquisition of Computational Thinking. The authors indicated as possible causes the following problems in the proposal of the workshop: activities of increasing complexity, an educational rubric structure and the long term [10], [27], [28].…”

Section: Data Mining Framework To Analyze the Evolution Of Computational Thinking Skillsmentioning

confidence: 99%

“…Another aspect to be noted in these studies is the analysis of data produced through artifacts available in the online community from Scratch. Studies such as [1], [10], [27]- [30] have shown that the absence of knowledge about how the artifacts were generated can provide information that does not reflect the real progress in Computational Thinking skills in the student.…”

Section: Data Mining Framework To Analyze the Evolution Of Computational Thinking Skillsmentioning

confidence: 99%

See 1 more Smart Citation

Data Mining Framework to Analyze the Evolution of Computational Thinking Skills in Game Building Workshops

et al. 2019

View full text Add to dashboard Cite

Computational thinking has become a required capability in the student learning process, and digital games as a teaching approach have presented promising educational results in the development of this competence. However, properly evaluating the effectiveness and, consequently, student progress in a course using games is still a challenge. One of the most widely implemented ways of evaluation is with an automated analysis of the code developed in the classes during the construction of digital games. Nevertheless, this topic has not yet been explored in aspects such as incremental learning, the model and teaching environment and the influences of acquiring skills and competencies of computational thinking. Motivated by this knowledge gap, this paper introduces a framework proposal to analyze the evolution of computational thinking skills in digital games classes. The framework is based on a data mining technique that aims to facilitate the discovery process of the patterns and behaviors that lead to the acquisition of computational thinking skills, by analyzing clusters with an unsupervised neural network of self-organizing maps (SOM) for this purpose. The framework is composed of a collection of processes and practices structured in data collection, data preprocessing, data analysis, and data visualization. A case study, using Scratch, was executed to validate this approach. The results point to the viability of the framework, highlighting the use of the visual exploratory data analysis, through the SOM maps, as an efficient tool to observe the acquisition of computational thinking skills by the student in an incremental course.

show abstract

Self-organizing maps to find computational thinking features in a game building workshop

Cited by 2 publications

References 0 publications

Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps

Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps

Data Mining Framework to Analyze the Evolution of Computational Thinking Skills in Game Building Workshops

Contact Info

Product

Resources

About