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Design Methodology of Introductory Information Technology for Major in Languages and Translation

Published:13 October 2023Publication History

ABSTRACT

This paper presents a design methodology of Introductory Information Technology offered to non-majors in computer science, such as the major in languages and translation, at the Macao Polytechnic University (MPU). The main objectives of this course are to introduce the fields of computer science for major in languages and translation, keep them engaged in the course and effectively gain their interest in learning information technology. Furthermore, they can apply what they have learned from the course of introductory information technology to their professional learning and future careers. Also, the learning situation of the students in this major, and the results of students’ perceptions and reactions are presented correspondingly.

References

  1. MPU Student Engagement Survey (for BSc in Computing): from 2009-2010 till 2021-2022Google ScholarGoogle Scholar
  2. MPU Student Engagement Survey (for MSc in Big Data and IoT): from 2019-20 till 2021-22Google ScholarGoogle Scholar
  3. MPU Graduate Employment Survey (for BSc in Computing): from 2004-2005 till 2020-2021Google ScholarGoogle Scholar
  4. Ngai Cheong, (2022), “Personalized Learning in Science Recommendation System Based on Learners’ Preferences”, The proceedings of 2022 3rd International Conference on Education Development and Studies, pp.22-27.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Ngai Cheong, (2021), “Knowledge-based Recommender System of Conceptual Learning in Science”, The proceedings of 5th International Conference on Education and E-Learning, pp.9-15.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Ngai Cheong, (2022), “Revitalizing Introductory Computer Courses for Non-CS Majors : A Comparative Study”, The 2022 4th International Conference on Education and Training Technologies, pp.115-119.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Jeannette M.Wing (2006), Computational thinking. Commun. ACM;49(3):pp. 33-35.Google ScholarGoogle Scholar
  8. Daniel D.Garcia, Collcen M. Lewis, John P.Dougherty, and Matthew C.Jadud, (2010), “If you might be a computational thinker!”, In Proceedings of the 41st ACM SIGCSE. ACM, New York, USA, pp.263-264.Google ScholarGoogle Scholar
  9. Dennis Kafura and Deborah Tatar, (2010), “ Initial experience with a computational thinking course for computer science students” In Proceedings of the 42nd ACM SIGCSE. ACM,New York,USA, pp.251-256.Google ScholarGoogle Scholar
  10. Paul Curzon, Computing Without Computers: A Gentle Introduction to Computer Programming, Data Structures and Algorithms. http://www.eecs.qmul.ac.uk/pc/research/education/puzzles/reading.Google ScholarGoogle Scholar
  11. A. C. Bart, R. Whitcomb, D. Kafura, C. A. Shaffer and E. Tilevich, (2017), "Computing with corgis: Diverse real-world datasets for introductory computing", Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer science Education, pp. 57-62.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. M. Han, Z. Li, J. S. He and X. S. Tian, (2019), "Understand the emerging demands of computing education for non-cs major students", Proceedings of the 50th ACM Technical Symposium on Computer science Education, pp. 1266-1266.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. G. McKinsey , (2011), Big data: the next frontier for innovation competition and productivity.Google ScholarGoogle Scholar
  14. S. Y. Lye and J. H. L. Koh, (2014), "Review on teaching and learning of computational thinking through programming: What is next for k-12", Computers in Human Behavior, vol. 41, pp. 51-61.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. K. Hoenig, R. Pollak, B. Schulz and V. Stocke, (2016), "Social capital participation in adult education and labor market success: Constructing a new instrument", Methodological issues of longitudinal surveys, pp. 291-312.Google ScholarGoogle ScholarCross RefCross Ref
  16. K. Antonis, T. Daradoumis, S. Papadakis and C. Simos, (2010), "Evaluation of the effectiveness of a web-based learning design for adult computer science courses", IEEE Transactions on Education, vol. 54, no. 3, pp. 374-380.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. D. Weintrop, E. Beheshti, M. Horn, K. Orton, K. Jona, L. Trouille, ,(2016), "Defining computational thinking for mathematics and science classrooms", Journal of Science Education and Technology, vol. 25, no. 1, pp. 127-147.Google ScholarGoogle ScholarCross RefCross Ref
  18. K. Rich, C. Strickland and D. Franklin, (2017), "A literature review through the lens of computer science learning goals theorized and explored in research", Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer science Education, pp. 495-500.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. T. Van. DeGrift, (2017), "Pogil activities in data structures: What do students value", Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer science Education, pp. 597-602.Google ScholarGoogle Scholar
  20. Ngai Cheong, (2014), “Analysis of Multi-rate and Multi-stage Switched-Capacitor Filters Using ISCMRATE”, Journal of Circuits, Systems, and Computers, No. 23(7), pp.1-14.Google ScholarGoogle Scholar
  21. Ngai Cheong, (2014), “An Optimization Applied to Design of SC Biquad-Based Circuits”, Recent Advances in Electrical & Electronic Engineering, No. 7(1), pp.47-56.Google ScholarGoogle Scholar
  22. Ngai Cheong, (2012), “Optimizations in Switched Circuits Design Using Constraint Programming”, International Conference on System Science and Engineering, pp.143-146.Google ScholarGoogle Scholar
  23. S. Beyer ,(2003), “Gender Differences in Computer science Students,” in Proc. SIGSCE 2003, pp. 49-53.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. A. Forte, M. Guzdial, (2005), “Motivation and Nonmajors in Computer science: Identifying Discrete Audiences for Introductory Courses,” IEEE Transactions On Education, Vol. 48, No. 2, pp. 248-253.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. N. Herrmann, (2003), “Redesigning Introductory Computer Programming Using Multilevel Online Modules for a Mixed Audience,” in Proc. 34th ACM Special Interest Group Computer science Education (SIGCSE) Tech. Symp. Computer Science Education, pp. 196–200.Google ScholarGoogle Scholar
  26. D. Joyce, (1998), “The computer as a problem solving tool: a unifying view for a nonmajors course,” in Proc. 29th SIGCSE Tech. Symp. Computer Science Education, pp. 63-67.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. N. Kock, R. Aiken, C. Sandas, (2002), “Using complex IT in specific domains: Developing and assessing a course for nonmajors,” IEEE Trans. Educ., vol. 45, no. 1, pp. 50–56.Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. B. Wilson, S. Shrock,(2001), “Contributing to success in an introductory computer science course: A study of twelve factors,” in Proc. SIGSCE 2001, pp. 184–188.Google ScholarGoogle ScholarDigital LibraryDigital Library

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          • Published in

            cover image ACM Other conferences
            ICETT '23: Proceedings of the 9th International Conference on Education and Training Technologies
            April 2023
            216 pages
            ISBN:9781450399593
            DOI:10.1145/3599640

            Copyright © 2023 ACM

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            Publication History

            • Published: 13 October 2023

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