Special Issue "Computational Thinking"

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Techno-Social Smart Systems".

Deadline for manuscript submissions: closed (31 July 2020).

Special Issue Editor

Adj.-Prof. Dr. Martin Ebner
Website
Guest Editor
Graz University of Technology, Educational Technology, Graz, Austria
Interests: technology enhanced learning; seamless learning; learning analytics; open educational resources; maker education; computer science for children
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue highlights the importance and possibilities of computational thinking (CT) in an educational, as well as a broader societal context. The problem-solving approach of computational thinking allows for learners and practitioners to better deal with the complexity and open ended non-trivial problems posed by a world ever more uncertain and unpredictable. This will lead to a generation more adept to tackle the imminent challenges posed by the climate crisis, automation, and artificial intelligence (AI). To collaborate on these big issues and the corresponding opportunities, students need to be equipped with a solid baseline understanding and the interdisciplinary vision for a better future.

Over the last decade, CT was mainly taught in informal settings, educating privileged youth in makerspaces and mostly urban after-school programs. These valuable lessons are slowly transformed in compulsory education and national curricula, establishing a more inclusive environment to have all young minds interact with CT.

We invite you to participate in our open call for papers to share ideas, experiments, and ultimately knowledge in this emerging area of public interest.

Finally, I would like to thank Michael Pollak and his valuable work for assisting me with this Special Issue.

Kind Regards,

Prof. Dr. Martin Ebner
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Future Internet is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Compulsory Education
  • Computational Thinking
  • Education
  • Informal Learning
  • K-12
  • Learning
  • Makerspaces
  • Maker Education
  • Problem Solving
  • Teaching

Published Papers (3 papers)

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Research

Open AccessArticle
Generic Tasks for Algorithms
Future Internet 2020, 12(9), 152; https://doi.org/10.3390/fi12090152 - 03 Sep 2020
Cited by 1
Abstract
Due to its links to computer science (CS), teaching computational thinking (CT) often involves the handling of algorithms in activities, such as their implementation or analysis. Although there already exists a wide variety of different tasks for various learning environments in the area [...] Read more.
Due to its links to computer science (CS), teaching computational thinking (CT) often involves the handling of algorithms in activities, such as their implementation or analysis. Although there already exists a wide variety of different tasks for various learning environments in the area of computer science, there is less material available for CT. In this article, we propose so-called Generic Tasks for algorithms inspired by common programming tasks from CS education. Generic Tasks can be seen as a family of tasks with a common underlying structure, format, and aim, and can serve as best-practice examples. They thus bring many advantages, such as facilitating the process of creating new content and supporting asynchronous teaching formats. The Generic Tasks that we propose were evaluated by 14 experts in the field of Science, Technology, Engineering, and Mathematics (STEM) education. Apart from a general estimation in regard to the meaningfulness of the proposed tasks, the experts also rated which and how strongly six core CT skills are addressed by the tasks. We conclude that, even though the experts consider the tasks to be meaningful, not all CT-related skills can be specifically addressed. It is thus important to define additional tasks for CT that are detached from algorithms and programming. Full article
(This article belongs to the Special Issue Computational Thinking)
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Open AccessArticle
Progressive Teaching Improvement For Small Scale Learning: A Case Study in China
Future Internet 2020, 12(8), 137; https://doi.org/10.3390/fi12080137 - 17 Aug 2020
Cited by 1
Abstract
Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, [...] Read more.
Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term. Full article
(This article belongs to the Special Issue Computational Thinking)
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Open AccessArticle
COVID-19 Epidemic as E-Learning Boost? Chronological Development and Effects at an Austrian University against the Background of the Concept of “E-Learning Readiness”
Future Internet 2020, 12(6), 94; https://doi.org/10.3390/fi12060094 - 26 May 2020
Cited by 9
Abstract
The COVID-19 crisis influenced universities worldwide in early 2020. In Austria, all universities were closed in March 2020 as a preventive measure, and meetings with over 100 people were banned and a curfew was imposed. This development also had a massive impact on [...] Read more.
The COVID-19 crisis influenced universities worldwide in early 2020. In Austria, all universities were closed in March 2020 as a preventive measure, and meetings with over 100 people were banned and a curfew was imposed. This development also had a massive impact on teaching, which in Austria takes place largely face-to-face. In this paper we would like to describe the situation of an Austrian university regarding e-learning before and during the first three weeks of the changeover of the teaching system, using the example of Graz University of Technology (TU Graz). The authors provide insights into the internal procedures, processes and decisions of their university and present figures on the changed usage behaviour of their students and teachers. As a theoretical reference, the article uses the e-learning readiness assessment according to Alshaher (2013), which provides a framework for describing the status of the situation regarding e-learning before the crisis. The paper concludes with a description of enablers, barriers and bottlenecks from the perspective of the members of the Educational Technology department. Full article
(This article belongs to the Special Issue Computational Thinking)
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