Category Archives: constructivism

Join us to discuss why we teach Computing at School (and University) on Monday 7th April at 2pm BST

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Why do we even bother? What (exactly) is the point? In this age of AI why would anyone need to learn about Computing? What value does it add, what skills do students learn and what knowledge do students actually need to develop? Join us on Monday 7th April at 2pm BST (UTC+1) to discuss a paper co-authored by Sue Sentance and published at iticse.acm.org [1]. From the abstract:

K-12 computing education research is a rapidly growing field of research, both driven by and driving the implementation of computing as a school and extra-curricular subject globally. In the context of discipline-based education research, it is a new and emerging field, drawing on areas such as mathematics and science education research for inspiration and theoretical bases. The urgency around investigating effective teaching and learning in computing in school alongside broadening participation has led to much of the field being focused on empirical research. Less attention has been paid to the underlying philosophical assumptions informing the discipline, which might include a critical examination of the rationale for K-12 computing education, its goals and perspectives, and associated inherent values and beliefs. In this working group, we conducted an analysis of the implicit and hidden values, perspectives and goals underpinning computing education at school in order to shed light on the question of what we are talking about when we talk about K-12 computing education. To do this we used a multi-faceted approach to identify implicit rationales for K-12 computing education and examine what these might mean for the implemented curriculum. Methods used include both traditional and natural language processing techniques for examining relevant literature, alongside an examination of the theoretical literature relating to education theory. As a result we identified four traditions for K-12 computing education: algorithmic, design-making, scientific and societal. From this we have developed a framework for the exemplification of these traditions, alongside several potential use cases. We suggest that while this work may provoke some discussion and debate, it will help researchers and others to identify and express the rationales they draw on with respect to computing education.

We’ll be joined by one of the papers co-authors, Sue Sentance from the University of Cambridge.  Sue is Director of the Raspberry Pi Computing Education Research Centre, recipient of the BCS Lovelace medal and an editor of the book Computer Science Education: Perspectives on Teaching and Learning in School published by Bloomsbury Academic. Sue will give us a lightning talk on the paper which is also summarised on the computing education research blog and in the slides from her talk.

All welcome, meeting URL is public at zoom.us/j/96465296256 (meeting ID 9646-5296-256) but the password is private and pinned in the slack channel which you can join by following the instructions at sigcse.cs.manchester.ac.uk/join-us

References

  1. Carsten Schulte, Sue Sentance, Sören Sparmann, Rukiye Altin, Mor Friebroon-Yesharim, Martina Landman, Michael T. Rücker, Spruha Satavlekar, Angela Siegel, Matti Tedre, Laura Tubino, Henriikka Vartiainen, J. Ángel Velázquez-Iturbide, Jane Waite and Zihan Wu (2024) What We Talk About When We Talk About K-12 Computing Education Working Group Reports on Innovation and Technology in Computer Science Education (ITiCSE 2024), Pages 226 – 257 DOI:10.1145/3689187.37096

Join us to discuss developing students professional competencies in software engineering on Monday 8th April at 2pm BST (UTC +1)

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Some competencies in software engineering are either difficult to teach and/or hard to measure, especially in a purely academic environment. Professional competencies in software engineering are often easier to learn in the workplace, rather than taught in a University lab, workshop or lecture theatre. What evidence can students provide of the professional competencies they develop while employed in a, workplace? Join us on Monday 8th April at 2pm BST (UTC+1) to discuss a paper on this published in this years SIGCSE technical symposium (sigcse2024.sigcse.org) by Matthew Barr, Oana Andrei, Alistair Morrison and Syed Waqar Nabi at the University of Glasgow [1]. From the abstract:

Competencies may be defined as the knowledge, skills, and professional dispositions that an individual is required to demonstrate in order to be considered professionally competent. Competency-based education has long been a feature of professional degree programs, but the discipline of Computing Science has only recently begun to embrace competencies as a means of structuring or evaluating students’ learning. Meanwhile, the practice of work-based learning – also well-established in other professional disciplines– has become more prevalent in Computing Science education, with increasing emphasis placed on work-based modes of learning, such as internships and apprenticeships. In this paper, we examine how students enrolled on a degree-level apprenticeship in Software Engineering have developed their professional competencies in the workplace. The paper is based on an analysis of 38 student assignments, wherein apprentices were asked to identify the competencies they have demonstrated, with reference to a portfolio of work. The UK Standard for Professional Engineering Competence and Commitment, which outlines the competencies required for certification as an Incorporated Engineer, provided the necessary framework. Competencies relating to communication and inter-personal skills were among those most often cited by apprentices, with competencies relating to knowledge and understanding and design and development systems also featuring prominently. Competencies relating to responsibility, management, or leadership were less prevalent, with professional commitment proving to be the least commonly cited category of competencies. We provide examples of how apprentices claim to have demonstrated each competency, and discuss the implications of these findings for competency-based learning in Computing Science education

We’ll be joined by the co-authors who will give us a five-minute lightning talk summary of their paper to kick-off our discussion. All welcome, joining details at sigcse.cs.manchester.ac.uk/join-us

References

  1. Matthew Barr, Oana Andrei, Alistair Morrison, Syed Waqar Nabi (2024) The Development of Students’ Professional Competencies on a Work-Based Software Engineering Program, SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education, Pages 81–87, DOI:10.1145/3626252.3630944

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Join us to discuss learning sciences for computing education on Monday 12th April at 2pm BST

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Learning sciences aims to improve our theoretical understanding of how people learn while computing education investigates with how people learn to compute. Historically, these fields existed independently, although attempts have been made to merge them. Where do these disciplines overlap and how can they be integrated further? Join us to discuss learning sciences for computing education via a paper by Lauren Margulieux, Brian Dorn and Kristin Searle, from the abstract:

This chapter discusses potential and current overlaps between the learning sciences and computing education research in their origins, theory, and methodology. After an introduction to learning sciences, the chapter describes how both learning sciences and computing education research developed as distinct fields from cognitive science. Despite common roots and common goals, the authors argue that the two fields are less integrated than they should be and recommend theories and methodologies from the learning sciences that could be used more widely in computing education research. The chapter selects for discussion one general learning theory from each of cognition (constructivism), instructional design (cognitive apprenticeship), social and environmental features of learning environments (sociocultural theory), and motivation (expectancy-value theory). Then the chapter describes methodology for design-based research to apply and test learning theories in authentic learning environments. The chapter emphasizes the alignment between design-based research and current research practices in computing education. Finally, the chapter discusses the four stages of learning sciences projects. Examples from computing education research are given for each stage to illustrate the shared goals and methods of the two fields and to argue for more integration between them.

There’s a 5 minute summary of the chapter ten minutes into the video below:



All welcome. As usual, we’ll be meeting on zoom, see sigcse.cs.manchester.ac.uk/join-us for details. Thanks to this months paper suggestions from Sue Sentance and Nicola Looker.

References

  1. Margulieux, Lauren E.; Dorn, Brian; Searle, Kristin A. (2019). “Learning Sciences for Computing Education“: 208–230. doi:10.1017/9781108654555.009. in In S. A. Fincher & A. V. Robins (Eds.) The Cambridge Handbook of Computing Education Research. Cambridge, UK: Cambridge University Press

Join us to discuss why minimal guidance doesn’t work on Monday 2nd November at 2pm GMT

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Minimal guidance is a popular approach to teaching and learning. This technique advocates teachers taking a back seat to facilitate learning by letting their students get on with it. Minimal guidance comes in many guises including constructivism, discovery learning, problem-based learning, experiential learning, active learning, inquiry-based learning and even lazy teaching. According to its critics, unguided and minimally guided approaches don’t work. Join us to discuss why via a paper [1] published by Paul Kirschner, John Sweller and Richard Clark, here is the abstract:

Evidence for the superiority of guided instruction is explained in the context of our knowledge of human cognitive architecture, expert–novice differences, and cognitive load. Although unguided or minimally guided instructional approaches are very popular and intuitively appealing, the point is made that these approaches ignore both the structures that constitute human cognitive architecture and evidence from empirical studies over the past half-century that consistently indicate that minimally guided instruction is less effective and less efficient than instructional approaches that place a strong emphasis on guidance of the student learning process. The advantage of guidance begins to recede only when learners have sufficiently high prior knowledge to provide “internal” guidance. Recent developments in instructional research and instructional design models that support guidance during instruction are briefly described.

This is a controversial, heavily cited and politically motivated paper which has provoked numerous rebuttals, making it an ideal candidate for a juicy journal club discussion! Thanks to Quintin Cutts for this months #paper-suggestions via our slack channel at uk-acm-sigsce.slack.com.

As usual, we’ll be meeting on zoom, see sigcse.cs.manchester.ac.uk/join-us for details and meeting URLs.

References

  1. Kirschner, Paul A.; Sweller, John; Clark, Richard E. (2006). “Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching”. Educational Psychologist. 41 (2): 75–86. DOI: 10.1207/s15326985ep4102_1 (see also altmetric.com/details/564640 for online attention scores)