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2022_06_30 «Enseñanza de las Ciencias de la Computación: algunas líneas de investigación sobre el aprendizaje introductorio a la programación»

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2022_06_30 «Enseñanza de las Ciencias de la Computación: algunas líneas de investigación sobre el aprendizaje introductorio a la programación»

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2022_06_30 «Enseñanza de las Ciencias de la Computación: algunas líneas de investigación sobre el aprendizaje introductorio a la programación». António Mendes

2022_06_30 «Enseñanza de las Ciencias de la Computación: algunas líneas de investigación sobre el aprendizaje introductorio a la programación». António Mendes

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2022_06_30 «Enseñanza de las Ciencias de la Computación: algunas líneas de investigación sobre el aprendizaje introductorio a la programación»

  1. 1. Computer Science Education Research Some research lines on introductory programming learning António José Mendes
  2. 2. Agenda Motivation and Context 01 Understand the student 02 Tools to support learning 03 Future research lines 04 2 Computer Science Education Research
  3. 3. Agenda Motivation and Context 01 Understand the student 02 Tools to support learning 03 Future research lines 04 3 Computer Science Education Research
  4. 4. Motivation and Context 4 Difficulties that students often feel when learning to program High failure rates Superficial learning shown by many students Big performance differences Impact in other courses Computer Science Education Research
  5. 5. Motivation and Context 5 CSER has been established as a research area The involvement of international organizations, like ACM and IEEE Research developed in the Educational Technology Lab, at CISUC, for more than 25 years Focus on introductory programming learning in Higher Education Computer Science Education Research
  6. 6. Agenda Motivation and Context 01 Understand the student 02 Tools to support learning 03 Future research lines 04 6 Computer Science Education Research
  7. 7. Understand the student 7 Learning to program Misconceptions Student motivation Experienced vs novices Anxiety while learning programming Problem contextualization ... Computer Science Education Research
  8. 8. Understanding the student Learning to program 8 What is involved? Knowledge about a programming language (and an IDE) Develop problem solving strategies within the language limits Create mental models about how programs are executed and what is the result of executing each instruction Computer Science Education Research
  9. 9. Understanding the student Learning to program 9 Causes of difficulties Complexity and cognitive load associated with learning to program Deficient learning conditions and pedagogical strategies used Student’s previous skills, attitudes, work habits and study methods Computer Science Education Research
  10. 10. Understanding the student Misconceptions 10 Syntactic knowledge – Knowing the caracteristics of the programming language used Programming languages are rigid, including details that are not easy to understand by novices Basic errors are common: () {} ; int x; Important disturbance factor for inexperienced and / or insecure students, who are blocked by error messages Computer Science Education Research
  11. 11. Understanding the student Misconceptions 11 Conceptual knowledge - Understand the flow of program execution and how the different control structures affect it Misconceptions about simple concepts, like variables, selection or repetition are common The dynamics involved in calling and returning a function creates difficulties for many students Very basic operations can be complex for some students Computer Science Education Research
  12. 12. Understanding the student Misconceptions 12 Strategic knowledge - Ability to plan, write and debug programs using syntactic and conceptual knowledge Difficulties to understand the problem to be solved Difficulty dividing the problem into parts and planning the different steps for solving each one Inability to understand, locate and correct existing logic errors Computer Science Education Research
  13. 13. Understanding the student Misconceptions Some pointers 13 A. Ettles, A. Luxton-Reilly, and P. Denny, “Common logic errors made by novice programmers,” in Proceedings of the 20th Australasian Computing Education Conference, 2018, pp. 83–89. Y. Qian and J. Lehman, “Students’ Misconceptions and Other Difficulties in Introductory Programming,” ACM Trans. Comput. Educ., vol. 18, no. 1, 2017. D. McCall and M. Kölling, “Meaningful categorisation of novice programmer errors,” in FIE 2015 - Proceedings of the Frontiers in Education Conference, 2015. A. Altadmri and N. Brown, “37 million compilations: Investigating novice programming mistakes in large-scale student data,” in Proceedings of the 46th ACM Technical Symposium on Computer Science Education, 2015, pp. 522–527. A. Gomes and A. J. Mendes, “A teacher’s view about introductory programming teaching and learning: Difficulties, strategies and motivations,” in Proceedings of the Frontiers in Education Conference, 2015. Computer Science Education Research
  14. 14. Understanding the student Motivation 14 Psychological mechanisms that cause people to get involved and persist in certain behaviors Learning to program requires effort, persistence and the ability to resist when the student feels unable to progress Student motivation is very important, which justifies the relevance of this area in CSER It integrates several relevant domains, some of which are more studied at CSER Computer Science Education Research
  15. 15. Understanding the student Motivation 15 Self Regulation of Learning Constructs linked to beliefs, goals and behaviors related to the way students manage their learning processes How students respond to feedback received, how they maintain the belief that they will be successful, and how they make plans to achieve it Can be considered in an individual (SRL - Self Regulated Learning) and social (SSRL - Socially Shared Regulation of Learning) point of view Computer Science Education Research
  16. 16. Understanding the student Motivation Self-Regulation of Learning 16 Self-efficacy One of the most studied components of SRL in the context of CSER and one of the most important Belief that a person has in being able to achieve a given objective through appropriate behaviors Previous positive experiences tend to increase the level of self-efficacy There is a direct link between academic performance and self-efficacy, with one directly influencing the other Computer Science Education Research
  17. 17. Understanding the student Motivation Self-Regulation of Learning 17 Metacognitive self-regulation Self-assessment behaviors and strategies used to overcome learning difficulties Successful students generally show a good ability to assess the difficulty of the task to be solved, make a proper decomposition of the problem and have careful planning and time management Computer Science Education Research
  18. 18. Understanding the student Motivation Some pointers 18 L. Silva, A. J. Mendes, A. Gomes and G. Macedo, “Regulation of Learning Interventions in Programming Education: A Systematic Literature Review and Guideline Proposition,” in SIGCSE 2021 - Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, 2021. A. Lishinski and A. Yadav, “Motivation, Attitudes, and Dispositions,” in The Cambridge Handbook of Computing Education Research, Cambridge University Press, 2019, pp. 801–826. A. Gomes, W. Ke, C.-T. Lam, M. J. Marcelino, and A. J. Mendes, “Student motivation towards learning to program,” in FIE 2018 - Proceedings of Frontiers in Education Conference, 2018. D. Zingaro and L. Porter, “Impact of student achievement goals on CS1 outcomes,” in SIGCSE 2016 - Proceedings of the 47th ACM Technical Symposium on Computing Science Education, 2016, pp. 279–284. Computer Science Education Research
  19. 19. Understanding the student Key ideas 19 Students' motivation is fundamental, particularly with regard to their self-efficacy It is important to instill in students a positive attitude about learning programming There should be as much individualized support as possible Easy communication between student and teacher is essential Committed teachers can make a big difference Computer Science Education Research
  20. 20. Agenda Motivation and Context 01 Understand the student 02 Tools to support learning 03 Future research lines 04 20 Computer Science Education Research
  21. 21. Tools to support learning 21 Animation and Simulation Tools Monitoring and Feedback tools Support to Self-Regulation of Learning Adaptive development environments Games Open Educational Resources ... Computer Science Education Research
  22. 22. Tools to support learning Animation and Simulation 22 Animation (or visualization) tools allow you to animate a certain predefined algorithm or program Simulation tools receive a program and proceed to its simulation, allowing you to see the effects of the instructions as they are executed Animated graphic formats are expected to contribute to a better understanding of algorithms and programs Simulation can help students understand and correct the logical errors they make Computer Science Education Research
  23. 23. Tools to support learning Algorthims and Data Structures simulation SICAS 23 Computer Science Education Research
  24. 24. Tools to support learning Animation and simulation 24 There are not a large number of independent studies on the educational effectiveness of this type of tools Most studies are linked to the process of developing a given tool Difficulty moving from academic prototypes to general purpose products Some consensus on its usefulness for students already with some programming skills, but of reduced utility for the rest Computer Science Education Research
  25. 25. Tools to support learning Animation and simulation Some pointers 25 L. Malmi, I. Utting, and A. J. Ko, “Tools and Environments,” in The Cambridge Handbook of Computing Education Research, Cambridge University Press, 2019, pp. 639–662. J. Sorva, V. Karavirta, and L. Malmi, “A review of generic program visualization systems for introductory programming education,” ACM Trans. Comput. Educ., vol. 13, no. 4, Nov. 2013. M. Ben-Ari et al., “A decade of research and development on program animation: The Jeliot experience,” J. Vis. Lang. Comput., vol. 22, no. 5, pp. 375–384, Oct. 2011. A. J. Mendes, M. J. Marcelino, A. Gomes, C. Bravo, M. Esteves, and M. Redondo, “Using simulation and collaboration in CS1 and CS2,” in ITiCSE 2005 – Proc. of the 10th Annual Conf. on Innovation and Techn. in Computer Science Education, 2005. Computer Science Education Research
  26. 26. Tools to support learning Monitoring and Feedback 26 Evaluation and feedback tools have also attracted a lot of attention over time There are systems that evaluate several aspects, such as the correctness of the program, the style and complexity It is important to provide quality pedagogical feedback There are many tools that do not go beyond the indication of test cases in which the program passes successfully and those in which it fails Computer Science Education Research
  27. 27. Tools to support learning Monitoring and Feedback CodeInsights 27 Computer Science Education Research
  28. 28. Tools to support learning Monitoring and feedback Some pointers 28 A. Carter, C. Hundhausen and Olivares, D., “Leveraging the Integrated Development Environment for Learning Analytics,” in The Cambridge Handbook of Computing Education Research, Cambridge University Press, 2019, pp. 679–706. P. Ihantola, T. Ahoniemi, V. Karavirta, and O. Seppälä, “Review of recent systems for automatic assessment of programming assignments,” in Proceedings of the 10th Koli Calling Int. Conf. on Computing Education Research, 2010, pp. 86–93. H. Keuning, J. Jeuring, and B. Heeren, “A Systematic Literature Review of Automated Feedback Generation for Programming Exercises,” ACM Trans. Comput. Educ., vol. 19, no. 1, 2018. N. G. Fonseca, L. Macedo, and A. J. Mendes, “Supporting differentiated instruction in programming courses through permanent progress monitoring,” in SIGCSE 2018 - Proceedings of the 49th ACM Technical Symposium on Computer Science Education, 2018. Computer Science Education Research
  29. 29. Tools to support learning SRL and SSRL support 29 A more recente trend is to include SRL and SSRL support in programming learning environments Use of scaffoldings to stimulate the development of some SRL constructs, like planning, progress awareness or self-efficacy Some tools consider the social influence on SRL Computer Science Education Research
  30. 30. Tools to support learning SRL and SSRL support RESPE 30 Computer Science Education Research
  31. 31. Tools to support learning SRL and SSRL support RESPE 31 Computer Science Education Research
  32. 32. Tools to support learning SRL and SSRL support RESPE 32 Computer Science Education Research
  33. 33. Tools to support learning SRL and SSRL support Some pointers 33 L. Silva, A. J. Mendes, A. J. Gomes, G. Fortes, C. T. Lam and C. Chan, Exploring the Association Between Self-Regulation of Learning and Programming Learning: A Systematic Literature Review and Multinational Investigation, in FIE 2021 - Proceedings of Frontiers in Education, 2021 L. Silva, A. J. Mendes, A. Gomes and G. Macedo, “Regulation of Learning Interventions in Programming Education: A Systematic Literature Review and Guideline Proposition,” in SIGCSE 2021 - Proceedings of the 52nd ACM Technical Symposium on Computer Science Education, 2021. Computer Science Education Research
  34. 34. Agenda Motivation and Context 01 Understand the student 02 Tools to support learning 03 Future research lines 04 34 Computer Science Education Research
  35. 35. Future development lines 35 Support environments for programming learning in higher education Tools to support the development of computational thinking in lower age groups Visualization languages for specific audiences, namely in the area of design and multimedia Understanding the mental mechanisms linked to learning programming (using BCI) Computer Science Education Research
  36. 36. Future development lines Environment to support programming learning 36 Adaptability to student characteristics and past performance Variable learning paths, either in the degree of difficulty or in the type of exercises proposed Variable degree of support and type of feedback, possibly using programming schemes Support for student motivation, with particular attention to self-regulation (individual and social) and self-efficacy Monitoring of learning facilitating teacher intervention whenever necessary Computer Science Education Research
  37. 37. Thank you toze@dei.uc.pt

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