Computational Thinking Education in K–12

A guide to computational thinking education, with a focus on artificial intelligence literacy and the integration of computing and physical objects. Computing has become an essential part of today's primary and secondary school curricula. In recent years, K–12 computer education has shifted from computer science itself to the broader perspective of computational thinking (CT), which is less about technology than a way of thinking and solving problems—“a fundamental skill for everyone, not just computer scientists,” in the words of Jeanette Wing, author of a foundational article on CT. This volume introduces a variety of approaches to CT in K–12 education, offering a wide range of international perspectives that focus on artificial intelligence (AI) literacy and the integration of computing and physical objects. The book first offers an overview of CT and its importance in K–12 education, covering such topics as the rationale for teaching CT; programming as a general problem-solving skill; and the “phenomenon-based learning” approach. It then addresses the educational implications of the explosion in AI research, discussing, among other things, the importance of teaching children to be conscientious designers and consumers of AI. Finally, the book examines the increasing influence of physical devices in CT education, considering the learning opportunities offered by robotics. Contributors Harold Abelson, Cynthia Breazeal, Karen Brennan, Michael E. Caspersen, Christian Dindler, Daniella DiPaola, Nardie Fanchamps, Christina Gardner-McCune, Mark Guzdial, Kai Hakkarainen, Fredrik Heintz, Paul Hennissen, H. Ulrich Hoppe, Ole Sejer Iversen, Siu-Cheung Kong, Wai-Ying Kwok, Sven Manske, Jesús Moreno-León, Blakeley H. Payne, Sini Riikonen, Gregorio Robles, Marcos Román-González, Pirita Seitamaa-Hakkarainen, Ju-Ling Shih, Pasi Silander, Lou Slangen, Rachel Charlotte Smith, Marcus Specht, Florence R. Sullivan, David S. Touretzk

Semi-automatic assessment of unrestrained Java code: a Library, a DSL, and a workbench to assess exams and exercises

© ACM 2015. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in http://dx.doi.org/10.1145/2729094.2742615Automated marking of multiple-choice exams is of great interest in university courses with a large number of students. For this reason, it has been systematically implanted in almost all universities. Automatic assessment of source code is however less extended. There are several reasons for that. One reason is that almost all existing systems are based on output comparison with a gold standard. If the output is the expected, the code is correct. Otherwise, it is reported as wrong, even if there is only one typo in the code. Moreover, why it is wrong remains a mystery. In general, assessment tools treat the code as a black box, and they only assess the externally observable behavior. In this work we introduce a new code assessment method that also verifies properties of the code, thus allowing to mark the code even if it is only partially correct. We also report about the use of this system in a real university context, showing that the system automatically assesses around 50% of the work.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Economíay Competitividad (Secretaría de Estado de Investigación, Desarrollo e Innovación) under grant TIN2013-44742-C4-1-R and by the Generalitat Valenciana under grant PROMETEOII2015/013. David Insa was partially supported by the Spanish Ministerio de Educación under FPU grant AP2010-4415.Insa Cabrera, D.; Silva, J. (2015). Semi-automatic assessment of unrestrained Java code: a Library, a DSL, and a workbench to assess exams and exercises. ACM. https://doi.org/10.1145/2729094.2742615SK. A Rahman and M. Jan Nordin. A review on the static analysis approach in the automated programming assessment systems. In National Conference on Programming 07, 2007.K. Ala-Mutka. A survey of automated assessment approaches for programming assignments. In Computer Science Education, volume 15, pages 83--102, 2005.C. Beierle, M. Kula, and M. Widera. Automatic analysis of programming assignments. In Proc. der 1. E-Learning Fachtagung Informatik (DeLFI '03), volume P-37, pages 144--153, 2003.J. Biggs and C. Tang. Teaching for Quality Learning at University : What the Student Does (3rd Edition). In Open University Press, 2007.P. Denny, A. Luxton-Reilly, E. Tempero, and J. Hendrickx. CodeWrite: Supporting student-driven practice of java. In Proceedings of the 42nd ACM technical symposium on Computer science education, pages 09--12, 2011.R. Hendriks. Automatic exam correction. 2012.P. Ihantola, T. Ahoniemi, V. Karavirta, and O. Seppala. Review of recent systems for automatic assessment of programming assignments. In Proceedings of the 10th Koli Calling International Conference on Computing Education Research, pages 86--93, 2010.H. Kitaya and U. Inoue. An online automated scoring system for Java programming assignments. In International Journal of Information and Education Technology, volume 6, pages 275--279, 2014.M.-J. Laakso, T. Salakoski, A. Korhonen, and L. Malmi. Automatic assessment of exercises for algorithms and data structures - a case study with TRAKLA2. In Proceedings of Kolin Kolistelut/Koli Calling - Fourth Finnish/Baltic Sea Conference on Computer Science Education, pages 28--36, 2004.Y. Liang, Q. Liu, J. Xu, and D. Wang. The recent development of automated programming assessment. In Computational Intelligence and Software Engineering, pages 1--5, 2009.K. A. Naudé, J. H. Greyling, and D. Vogts. Marking student programs using graph similarity. In Computers & Education, volume 54, pages 545--561, 2010.A. Pears, S. Seidman, C. Eney, P. Kinnunen, and L. Malmi. Constructing a core literature for computing education research. In SIGCSE Bulletin, volume 37, pages 152--161, 2005.F. Prados, I. Boada, J. Soler, and J. Poch. Automatic generation and correction of technical exercices. In International Conference on Engineering and Computer Education (ICECE 2005), 2005.M. Supic, K. Brkic, T. Hrkac, Z. Mihajlovic, and Z. Kalafatic. Automatic recognition of handwritten corrections for multiple-choice exam answer sheets. In Information and Communication Technology, Electronics and Microelectronics (MIPRO), pages 1136--1141, 2014.S. Tung, T. Lin, and Y. Lin. An exercise management system for teaching programming. In Journal of Software, 2013.T. Wang, X. Su, Y. Wang, and P. Ma. Semantic similarity-based grading of student programs. In Information and Software Technology, volume 49, pages 99--107, 2007

Introduction

"The 13th volume of the Series on E-learning monograph is “E-learning in the Time of COVID-19” and includes articles of authors from twelve countries and from more than twenty universities – participants of the 13th annual international scientific conference ”Theoretical and Practical Aspects of Distance Learning”, subtitled: “E-learning in the Time of COVID-19”, held online on 11–12 October 2021, organized by the University of Silesia in Katowice, Poland – Faculty of Arts and Sciences of Education, Faculty of Social Sciences, Institute of Pedagogy, Faculty of Science and Technology, Institute of Computer Science." [...] (fragm.

Partial Correctness of a Power Algorithm

This work continues a formal verification of algorithms written in terms of simple-named complex-valued nominative data [6],[8],[15],[11],[12],[13]. In this paper we present a formalization in the Mizar system [3],[1] of the partial correctness of the algorithm: i := val.1 j := val.2 b := val.3 n := val.4 s := val.5 while (i n) i := i + j s := s * b return s computing the natural n power of given complex number b, where variables i, b, n, s are located as values of a V-valued Function, loc, as: loc/.1 = i, loc/.3 = b, loc/.4 = n and loc/.5 = s, and the constant 1 is located in the location loc/.2 = j (set V represents simple names of considered nominative data [17]).The validity of the algorithm is presented in terms of semantic Floyd-Hoare triples over such data [9]. Proofs of the correctness are based on an inference system for an extended Floyd-Hoare logic [2],[4] with partial pre- and post-conditions [14],[16],[7],[5].Institute of Informatics, University of Białystok, PolandGrzegorz Bancerek, Czesław Byliński, Adam Grabowski, Artur Korniłowicz, Roman Matuszewski, Adam Naumowicz, and Karol Pąk. The role of the Mizar Mathematical Library for interactive proof development in Mizar. Journal of Automated Reasoning, 61(1):9–32, 2018. doi:10.1007/s10817-017-9440-6.R.W. Floyd. Assigning meanings to programs. Mathematical aspects of computer science, 19(19–32), 1967.Adam Grabowski, Artur Korniłowicz, and Adam Naumowicz. Four decades of Mizar. Journal of Automated Reasoning, 55(3):191–198, 2015. doi:10.1007/s10817-015-9345-1.C.A.R. Hoare. An axiomatic basis for computer programming. Commun. ACM, 12(10): 576–580, 1969.Ievgen Ivanov and Mykola Nikitchenko. On the sequence rule for the Floyd-Hoare logic with partial pre- and post-conditions. In Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, Kyiv, Ukraine, May 14–17, 2018, volume 2104 of CEUR Workshop Proceedings, pages 716–724, 2018.Ievgen Ivanov, Mykola Nikitchenko, Andrii Kryvolap, and Artur Korniłowicz. Simple-named complex-valued nominative data – definition and basic operations. Formalized Mathematics, 25(3):205–216, 2017. doi:10.1515/forma-2017-0020.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. Implementation of the composition-nominative approach to program formalization in Mizar. The Computer Science Journal of Moldova, 26(1):59–76, 2018.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. On an algorithmic algebra over simple-named complex-valued nominative data. Formalized Mathematics, 26(2):149–158, 2018. doi:10.2478/forma-2018-0012.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. An inference system of an extension of Floyd-Hoare logic for partial predicates. Formalized Mathematics, 26(2): 159–164, 2018. doi:10.2478/forma-2018-0013.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. Partial correctness of GCD algorithm. Formalized Mathematics, 26(2):165–173, 2018. doi:10.2478/forma-2018-0014.Ievgen Ivanov, Artur Korniłowicz, and Mykola Nikitchenko. On algebras of algorithms and specifications over uninterpreted data. Formalized Mathematics, 26(2):141–147, 2018. doi:10.2478/forma-2018-0011.Artur Kornilowicz, Andrii Kryvolap, Mykola Nikitchenko, and Ievgen Ivanov. Formalization of the algebra of nominative data in Mizar. In Maria Ganzha, Leszek A. Maciaszek, and Marcin Paprzycki, editors, Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, Prague, Czech Republic, September 3–6, 2017., pages 237–244, 2017. ISBN 978-83-946253-7-5. doi:10.15439/2017F301.Artur Kornilowicz, Andrii Kryvolap, Mykola Nikitchenko, and Ievgen Ivanov. Formalization of the nominative algorithmic algebra in Mizar. In Leszek Borzemski, Jerzy Świątek, and Zofia Wilimowska, editors, Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017 – Part II, Szklarska Poręba, Poland, September 17–19, 2017, volume 656 of Advances in Intelligent Systems and Computing, pages 176–186. Springer, 2017. ISBN 978-3-319-67228-1. doi:10.1007/978-3-319-67229-8_16.Artur Korniłowicz, Andrii Kryvolap, Mykola Nikitchenko, and Ievgen Ivanov. An approach to formalization of an extension of Floyd-Hoare logic. In Vadim Ermolayev, Nick Bassiliades, Hans-Georg Fill, Vitaliy Yakovyna, Heinrich C. Mayr, Vyacheslav Kharchenko, Vladimir Peschanenko, Mariya Shyshkina, Mykola Nikitchenko, and Aleksander Spivakovsky, editors, Proceedings of the 13th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Kyiv, Ukraine, May 15–18, 2017, volume 1844 of CEUR Workshop Proceedings, pages 504–523. CEUR-WS.org, 2017.Artur Korniłowicz, Ievgen Ivanov, and Mykola Nikitchenko. Kleene algebra of partial predicates. Formalized Mathematics, 26(1):11–20, 2018. doi:10.2478/forma-2018-0002.Andrii Kryvolap, Mykola Nikitchenko, and Wolfgang Schreiner. Extending Floyd-Hoare logic for partial pre- and postconditions. In Vadim Ermolayev, Heinrich C. Mayr, Mykola Nikitchenko, Aleksander Spivakovsky, and Grygoriy Zholtkevych, editors, Information and Communication Technologies in Education, Research, and Industrial Applications: 9th International Conference, ICTERI 2013, Kherson, Ukraine, June 19–22, 2013, Revised Selected Papers, pages 355–378. Springer International Publishing, 2013. ISBN 978-3-319-03998-5. doi:10.1007/978-3-319-03998-5_18.Volodymyr G. Skobelev, Mykola Nikitchenko, and Ievgen Ivanov. On algebraic properties of nominative data and functions. In Vadim Ermolayev, Heinrich C. Mayr, Mykola Nikitchenko, Aleksander Spivakovsky, and Grygoriy Zholtkevych, editors, Information and Communication Technologies in Education, Research, and Industrial Applications – 10th International Conference, ICTERI 2014, Kherson, Ukraine, June 9–12, 2014, Revised Selected Papers, volume 469 of Communications in Computer and Information Science, pages 117–138. Springer, 2014. ISBN 978-3-319-13205-1. doi:10.1007/978-3-319-13206-8_6.27218919

Score Reporting Research and Applications

Score reporting research is no longer limited to the psychometric properties of scores and subscores. Today, it encompasses design and evaluation for particular audiences, appropriate use of assessment outcomes, the utility and cognitive affordances of graphical representations, interactive report systems, and more. By studying how audiences understand the intended messages conveyed by score reports, researchers and industry professionals can develop more effective mechanisms for interpreting and using assessment data. Score Reporting Research and Applications brings together experts who design and evaluate score reports in both K-12 and higher education contexts and who conduct foundational research in related areas. The first section covers foundational validity issues in the use and interpretation of test scores; design principles drawn from related areas including cognitive science, human-computer interaction, and data visualization; and research on presenting specific types of assessment information to various audiences. The second section presents real-world applications of score report design and evaluation and of the presentation of assessment information. Across ten chapters, this volume offers a comprehensive overview of new techniques and possibilities in score reporting

Learning Styles in Higher Education: The use of Moodle platform

A documentary review was carried out on the production and publication of research papers concerning the study of the variables Learning Styles in Higher Education in the use of Moodle platform. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the period 2016-2021, achieving the identification of a total of 123 publications. The information provided by the said platform was organized through tables and figures categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics were described, a qualitative analysis was used to refer to the position of different authors on the proposed topic. Among the main findings of this research, it is found that Indonesia, with 12 publications, was the country with the highest scientific production registered in the name of authors affiliated with institutions of that country. The Knowledge Area that made the greatest contribution to the construction of bibliographic material referring to the study of Learning Styles in Higher Education in the use of the Moodle platform was Computer Science with 77 published documents, and the type of publication that was most used during the aforementioned period was the conference proceedings, representing 49% of the total scientific production

Chemistry education students’ perception toward their learning outcomes during online learning

This study aims to determine the self-perception of chemistry education students related to online learning in the UIN Ar-Raniry. This descriptive research involved chemistry education students of the 2019 batch. Their perception was obtained using an online questionnaire with 15 questions. It can be concluded that implementing the online learning system does not entirely affect students' learning outcomes. REFERENCESAl-Idrus, S. W., Muti’ah, M., & Rahmawati, R. (2021). Analisis Proses Pembelajaran Daring Selama Pandemi Covid-19 pada Mahasiswa Program Studi Pendidikan Kimia FKIP UNRAM. PENSA, 3(1 SE-Articles). https://ejournal.stitpn.ac.id/index.php/pensa/article/view/1246Arisandi, Y., Dasna, I. W., Sumari, S., Habiddin, H., Ibnu, S., & Subandi. (2021). Promoting vocational students' perception and achievement towards Chemistry: SPARE Learning Strategy for students majoring in Automotive Engineering. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(13), 6357–6365.Belawati, T. (2019). Pembelajaran Online. Universitas Terbuka.Habiddin, H., Yahmin, Y., Retnosari, R., Muarifin, M., Aziz, A. N., Husniah, I., & Anwar, L. (2020). Chemistry students' attitude towards chemistry. AIP Conference Proceedings, 2215(1), 20005. https://doi.org/10.1063/5.0000496Mandailina, V., Pramita, D., & Haifaturrahmah, H. (2021). Pembelajaran Daring Dalam Meningkatkan Motivasi dan Hasil Belajar Peserta Didik Selama Pandemi Covid-19: Sebuah Meta-Analisis. Indonesian Journal of Educational Science (IJES), 3(2), 120–129.Pratiwi, N. N., & Puspasari, D. (2021). PENGARUH PENGGUNAAN PEMBELAJARAN DARING TERHADAP HASIL BELAJAR SISWA. JAMP : Jurnal Administrasi Dan Manajemen Pendidikan; Vol 4, No 4 (2021): Volume 4 No 4 Desember 2021. https://doi.org/10.17977/um027v4i42021p320Purwanto, A., Pramono, R., Asbari, M., Hyun, C. C., Wijayanti, L. M., Putri, R. S., & Santoso,  priyono B. (2020). Studi Eksploratif Dampak Pandemi COVID-19 Terhadap Proses Pembelajaran Online di Sekolah Dasar. EduPsyCouns: Journal of Education, Psychology and Counseling, 2(1), 1–12.Putri, M., & Kurniawati, Y. (2021). Students' learning interest using computer and android in acid base teaching. J-PEK (Jurnal Pembelajaran Kimia), 6(2), 63–71. http://dx.doi.org/10.17977/um026v6i22021p063Putria, H., Maula, L. H., & Uswatun, D. A. (2020). Analisis Proses Pembelajaran dalam Jaringan (DARING) Masa Pandemi Covid- 19 Pada Guru Sekolah Dasar. Jurnal Basicedu, 4(4), 861–872.Riyana, C. (2019). Produksi Bahan Pembelajaran Berbasis Online. Universitas Terbuka.Wardani, S., Haryani, S., Harmiasri, R., & Sari, N. N. (2022). Implementation of Online Learning to Prepare the Youth Generation in the Disruptive Era. J-PEK (Jurnal Pembelajaran Kimia), 7(1), 9–21. https://doi.org/http://dx.doi.org/10.17977/um026v7i12022p00

Score Reporting Research and Applications

Score reporting research is no longer limited to the psychometric properties of scores and subscores. Today, it encompasses design and evaluation for particular audiences, appropriate use of assessment outcomes, the utility and cognitive affordances of graphical representations, interactive report systems, and more. By studying how audiences understand the intended messages conveyed by score reports, researchers and industry professionals can develop more effective mechanisms for interpreting and using assessment data. Score Reporting Research and Applications brings together experts who design and evaluate score reports in both K-12 and higher education contexts and who conduct foundational research in related areas. The first section covers foundational validity issues in the use and interpretation of test scores; design principles drawn from related areas including cognitive science, human-computer interaction, and data visualization; and research on presenting specific types of assessment information to various audiences. The second section presents real-world applications of score report design and evaluation and of the presentation of assessment information. Across ten chapters, this volume offers a comprehensive overview of new techniques and possibilities in score reporting

Diagrams in Essays: Exploring the Kinds of Diagrams Students Generate and How Well They Work

Part of the Lecture Notes in Computer Science book series (LNAI, volume 12909)12th International Conference, Diagrams 2021, Virtual, September 28–30, 2021, ProceedingsUsing appropriate diagrams is generally considered efficacious in communication. However, although diagrams are extensively used in printed and digital media, people in general rarely construct diagrams to use in common everyday communication. Furthermore, instruction on diagram use for communicative purposes is uncommon in formal education and, when students are required to communicate what they have learned, the usual expectation is they will use words --not diagrams. Requiring diagram inclusion in essays, for example, would be almost unheard of. Consequently, current understanding about student capabilities in this area is very limited. The aim of this study therefore was to contribute to addressing this gap: it comprised a qualitative exploration of 12 undergraduate students’ diagram use in two essays (in which they were asked to include at least one diagram). Analysis focused on identifying the kinds of diagrams produced, and the effectiveness with which those diagrams were used. Useful functions that the diagrams served included clarification, summarization, integration of points, and provision of additional information and/or perspectives in visual form. However, there were also redundancies, as well as unclear, schematically erroneous, and overly complicated representations in some of the diagrams that the students constructed. These findings are discussed in terms of needs, opportunities, and challenges in instructional provision