Conditions of Learning in Novice Programmers

Perkins, David N.; Hancock, Chris; Hobbs, Renée; Martin, F. Elizabeth; Simmons, Rebecca

doi:10.2190/gujt-jcbj-q6qu-q9pl

Cited by 164 publications

(128 citation statements)

References 13 publications

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“…It resonates with the 'movers and stoppers' findings of Perkins et al (1986). A stopper is categorised as person who is halted abruptly by an error or difficulty and does not have the inclination to tackle the problem independently.…”

Section: Emotional Responsementioning

confidence: 63%

“…A novice who becomes very frustrated by unforeseen problems is likely to become a 'stopper'. In contrast, a 'mover' is a learner with enthusiasm who views an error as a challenge rather than an obstacle (Perkins et al, 1986). The ability to modify and adapt programs effectively in response to errors is likely to reinforce a mover's ability to self-support his or her problem solving and progress.…”

Section: Emotional Responsementioning

confidence: 99%

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Learning Experiences in Programming: The Motivating Effect of a Physical Interface

Martin

Hughes

Richards

2017

Proceedings of the 9th International Conference on Computer Supported Education

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Abstract:A study of undergraduate students learning to program compared the use of a physical interface with use of a screen-based equivalent interface to obtain insights into what made for an engaging learning experience. Emotions characterized by the HUMAINE scheme were analysed, identifying the links between the emotions experienced during programming and their origin. By capturing the emotional experiences of learners immediately after a programming experience, evidence was collected of the very positive emotions experienced by learners developing a program using a physical interface (Arduino) in comparison with a similar program developed using a screen-based equivalent interface.

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Section: Emotional Responsementioning

confidence: 63%

Section: Emotional Responsementioning

confidence: 99%

Learning Experiences in Programming: The Motivating Effect of a Physical Interface

Martin

Hughes

Richards

2017

Proceedings of the 9th International Conference on Computer Supported Education

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“…Novice programmers can be divided into three classes [49,68]: stoppers, who give up; tinkerers, who appear to modify their code at random; and movers, who are already able to engage with feedback to make progress. Even though the movers already demonstrate debugging skills beyond the average of their cohort, all three classes still need high-quality, novice-friendly debugging feedback to allow a self-guided refinement of their solutions towards a correct answer.…”

Section: Student Programmingmentioning

confidence: 99%

Fast test suite-driven model-based fault localisation with application to pinpointing defects in student programs

2017

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Fault localisation, i.e. the identification of program locations that cause errors, takes significant effort and cost. We describe a fast model-based fault localisation algorithm that, given a test suite, uses symbolic execution methods to fully automatically identify a small subset of program locations where genuine program repairs exist. Our algorithm iterates over failing test cases and collects locations where an assignment change can repair exhibited faulty behaviour. Our main contribution is an improved search through the test suite, reducing the effort for the symbolic execution of the models and leading to speed-ups of more than two orders of magnitude over the previously published implementation by Griesmayer et al. We implemented our algorithm for C programs, using the KLEE symbolic execution engine, and demonstrate its effectiveness on the Siemens TCAS variants. Its performance is in line with recent alternative model-based fault localisation techniques, but narrows the location set further without rejecting any genuine repair locations where faults can be fixed by changing a single assignment. We also show how our tool can be used in an educational context to improve self-guided learning and accelerate assessment. We apply our algorithm to a large selection of actual student coursework submissions, providCommunicated by Prof. Alfonso Pierantonio, Jasmin Blanchette, Francis Bordeleau, Nikolai Kosmatov, Gabi Taentzer, and Manuel Wimmer. We show this using small test suites, already provided in the coursework management system, and on expanded test suites, demonstrating the scalability of our approach. We also show compliance with test suites does not reliably grade a class of "almost-correct" submissions, which our tool highlights, as being close to the correct answer. Finally, we show an extension to our tool that extends our fast localisation results to a selection of student submissions that contain two faults.

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“…In so doing, we assume our students can read and understand those examples…. Perkins [8] claim that the ability to perform a walkthrough is an important skill for diagnosing bugs and therefore the ability to review code is an important skill in writing code…”

Section: Classmentioning

confidence: 99%

Memory Tracing

Cogan¹,

Gurwitz²

2009

J. of Computer Science

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Problem statement: Students completing introductory computing courses did not know how to program at the expected level. Seeking the underlying problem, we came to believe that students were focusing only on results and not connecting with the inner workings of their code. This left them poorly prepared to master increasingly complex problems. Approach: We hoped that by promoting memory tracing as a core competence as early as possible in introductory programming courses we would hone the understanding and skills of our students and improve their chances for succeeding in computer science. We emphasized a basic and manual approach to memory tracing--in the classroom, in conjunction with homework assignments and on exams--to help our students gain the ability to write good programs, test them and, should it become necessary, debug them. Results: Having received gratifying results from our approach in our own classes, we had moved to get the word out as quickly as possible to motivate other educators to implement it. We described how we derived benefit from memory tracing in the various contexts and we presented the details of our method for teaching students how to best use this technique. Conclusion/Recommendations: Training students early on to actively carry out a manual memory trace of programs (as opposed to relying on debuggers or print statements) will help them develop their coding skill and comfort, quite apart from any facility for finding and fixing errors. Although experienced programmers trace intuitively, beginning students do not; they need to be trained. Therefore we felt that tracing should be an explicit, emphasized component of the introductory courses.

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Conditions of Learning in Novice Programmers

Cited by 164 publications

References 13 publications

Learning Experiences in Programming: The Motivating Effect of a Physical Interface

Learning Experiences in Programming: The Motivating Effect of a Physical Interface

Fast test suite-driven model-based fault localisation with application to pinpointing defects in student programs

Memory Tracing

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