The Cambridge Handbook of Computing Education Research

doi:10.1017/9781108654555

Cited by 62 publications

(28 citation statements)

References 1,550 publications

(70 reference statements)

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“…As we will argue throughout the paper, the major way to foster CT is to teach Computer Science fundamental ideas, concepts, and practices: given the historical and philosophical nature of this paper, we refer to the book edited by Grover (2020) for a comprehensive collection of examples of activities to teach CS in K-12 education, rooted in research findings. For scholars, we refer to the handbook edited by Fincher and Robins (2019) for a comprehensive picture of state of the art in Computer Science Education Research.…”

Section: Introductionmentioning

confidence: 99%

Computational Thinking, Between Papert and Wing

2021

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The pervasiveness of Computer Science (CS) in today’s digital society and the extensive use of computational methods in other sciences call for its introduction in the school curriculum. Hence, Computer Science Education is becoming more and more relevant. In CS K-12 education, computational thinking (CT) is one of the abused buzzwords: different stakeholders (media, educators, politicians) give it different meanings, some more oriented to CS, others more linked to its interdisciplinary value. The expression was introduced by two leading researchers, Jeannette Wing (in 2006) and Seymour Papert (much early, in 1980), each of them stressing different aspects of a common theme. This paper will use a historical approach to review, discuss, and put in context these first two educational and epistemological approaches to CT. We will relate them to today’s context and evaluate what aspects are still relevant for CS K-12 education. Of the two, particular interest is devoted to “Papert’s CT,” which is the lesser-known and the lesser-studied. We will conclude that “Wing’s CT” and “Papert’s CT,” when correctly understood, are both relevant to today’s computer science education. From Wing, we should retain computer science’s centrality, CT being the (scientific and cultural) substratum of the technical competencies. Under this interpretation, CT is a lens and a set of categories for understanding the algorithmic fabric of today’s world. From Papert, we should retain the constructionist idea that only a social and affective involvement of students into the technical content will make programming an interdisciplinary tool for learning (also) other disciplines. We will also discuss the often quoted (and often unverified) claim that CT automatically “transfers” to other broad 21st century skills. Our analysis will be relevant for educators and scholars to recognize and avoid misconceptions and build on the two core roots of CT.

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Section: Introductionmentioning

confidence: 99%

Computational Thinking, Between Papert and Wing

2021

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“…Many works have tried to find ways to mitigate the problem of high failure rate (Ihantola et al, 2015;Hellas et al, 2018;Luxton-Reilly et al, 2018;Robins, 2019, Fonseca et al, 2020 in introductory programming courses (CS1). In this sense, researchers (Amra et al, 2017;Costa et al, 2017;Márquez-Vera et al, 2016;Aguiar & Pereira, 2018;Pereira et al, 2019a;Pereira et al, 2019b;Romero & Ventura, 2019) have argued that early performance prediction is vital.…”

Section: Introductionmentioning

confidence: 99%

“…With such understanding, we can offer valuable information to instructors and learners. However, Robins (2019) explains that currently there is no reliable predictor yet for programming students' performance, since even large-scale analysis of multiple features have resulted in only limited predictive power. Still, Ihantola et al (2015), Quille & Bergin (2018), Hellas et al (2018) and Robins (2019) state there is a need to advance further in this field.…”

Section: Introductionmentioning

confidence: 99%

“…One potential way to perform such early prediction is by extracting useful information from students' log-data and use this information as features in machine-learning algorithms (Carter et al, 2019). To illustrate, there are studies which perform prediction using log-data, to represent how students deal with errors (Jadud, 2006;Watson, 2013), deadlines (Robins, 2019;Edwards et al, 2009), attempts and correctness (Castro-Wunsch et al, 2017;Ahadi et al, 2016;Estey & Coady, 2016); static analysis of code (Otero et al, 2016), typing patterns (Leinonen et al, 2016), etc.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for early performance prediction of introductory programming students: a comparative and explanatory study

Pereira

Fonseca

Oliveira

et al. 2020

RBIE

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Introductory programming may be complex for many students. Moreover, there is a high failure and dropout rate in these courses. A potential way to tackle this problem is to predict student performance at an early stage, as it facilitates human-AI collaboration towards prescriptive analytics, where the instructors/monitors will be told how to intervene and support students - where early intervention is crucial. However, the literature states that there is no reliable predictor yet for programming students’ performance, since even large-scale analysis of multiple features have resulted in only limited predictive power. Notice that Deep Learning (DL) can provide high-quality results for huge amount of data and complex problems. In this sense, we employed DL for early prediction of students’ performance using data collected in the very first two weeks from introductory programming courses offered for a total of 2058 students during 6 semesters (longitudinal study). We compared our results with the state-of-the-art, an Evolutionary Algorithm (EA) that automatic creates and optimises machine learning pipelines. Our DL model achieved an average accuracy of 82.5%, which is statistically superior to the model constructed and optimised by the EA (p-value << 0.05 even with Bonferroni correction). In addition, we also adapted the DL model in a stacking ensemble for continuous prediction purposes. As a result, our regression model explained ~62% of the final grade variance. In closing, we also provide results on the interpretation of our regression model to understand the leading factors of success and failure in introductory programming.

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“…The tasks of responding to external events; scheduling them; calling the handlers; storing the updated world and passing it on to the next handler; and so on-tasks that are either administrative in nature or require knowledge of specialized and perhaps complex APIs-are built into the World framework, so the student programmer does not need to know how to do them. In particular, in contrast to many other introductory approaches, the student does not write an "outer event loop", so they write quite sophisticated programs without having to understand looping constructs (which induce many misconceptions in students [20]). the value given to init has type W the value given to on-tick has type W -> W the value given to on-key has type W, Key -> W the value given to stop-when has type W -> Boolean the value given to to-draw has type W -> Image The example above shows how a program can respond to stimuli.…”

Section: The World Programming Modelmentioning

confidence: 99%

Event Loops as First-Class Values: A Case Study in Pedagogic Language Design

et al. 2019

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The World model is an existing functional input-output mechanism for event-driven programming. It is used in numerous popular textbooks and curricular settings.The World model conflates two different tasks-the definition of an event processor and its execution-into one. This conflation imposes a significant (even unacceptable) burden on student users in several educational settings where we have tried to use it, e.g., for teaching physics.While it was tempting to pile on features to address these issues, we instead used the Scheme language design dictum of removing weaknesses that made them seem necessary. By separating the two tasks above, we arrived at a slightly different primitive, the reactor, as our basis. This only defines the event processor, and a variety of execution operators dictate how it runs.The new design enables programmatic control over event-driven programs. This simplifies reflecting on program behavior, and eliminates many unnecessary curricular dependencies imposed by the old design. This work has been implemented in the Pyret programming language. The separation of concerns has enabled new curricula, such as the Bootstrap:Physics curriculum, to take flight. Thousands of students use this new mechanism every year. We believe that reducing impedance mismatches improves their educational experience. ACM CCS 2012Applied computing → Education; Software and its engineering → Simulator / interpreter; Language features;

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The Cambridge Handbook of Computing Education Research

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 62 publications

References 1,550 publications

Computational Thinking, Between Papert and Wing

Computational Thinking, Between Papert and Wing

Deep learning for early performance prediction of introductory programming students: a comparative and explanatory study

Event Loops as First-Class Values: A Case Study in Pedagogic Language Design

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