Engage Against the Machine: Rise of the Notional Machines as Effective Pedagogical Devices

Dickson, Paul E.; Brown, Neil C. C.; Becker, Brett A.

doi:10.1145/3341525.3387404

Cited by 17 publications

(5 citation statements)

References 28 publications

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“…There exist numerous strategies to help learners acquire this abstract understanding. One strategy is to explicitly teach a notional machine , ,, i.e., an abstract model of the program execution environment. Another strategy is to teach using many relevant examples, each showing different representations of the same concepts. , Exposure to many representations or orientations of the same concept can help students perceive the abstract principles that underlie the surface-level characteristics of any single example.…”

Section: Discussionmentioning

confidence: 99%

“…Every programming course involves a notional machine, either implicitly in the programming language and features used or explicitly taught by the instructor. 28 It has been argued that notional machines ought to be explicitly taught in introductory programming courses (e.g., Sorva, 28 Dickson et al 30 ).…”

Section: Mental Models and Notional Machinesmentioning

confidence: 99%

“…There exist numerous strategies to help learners acquire this abstract understanding. One strategy is to explicitly teach a notional machine, 27,29,30 i.e., an abstract model of the program execution environment. Another strategy is to teach using many relevant examples, each showing different representations of the same concepts.…”

Section: Abstractionmentioning

confidence: 99%

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Recommendations for Improving End-User Programming Education: A Case Study with Undergraduate Chemistry Students

Fuchs,

McDonald,

Gautam

et al. 2024

J. Chem. Educ.

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Programming is widespread in multiple domains and is being integrated into various discipline-specific university courses where, like students in a typical introductory computing course, students from other disciplines face challenges with learning to program. We offer a case study in which we study undergraduate students majoring in either chemistry or biochemistry as they learn programming in a physical chemistry course sequence. Using surveys and think-aloud sessions with students, we conducted a thematic content analysis to explain the challenges they face in this endeavor. We found that students struggled to transfer their programming knowledge to new representations and problems, and they did not have strategies in place for solving problems with programming. These facts combine to lower students' confidence in their programming abilities, making it less likely that they will reach for computing to help solve domain-specific problems. We recommend that students in end-user programming contexts be explicitly taught the skills of abstraction, decomposition, and metacognitive awareness as they pertain to programming.

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Section: Discussionmentioning

confidence: 99%

Section: Mental Models and Notional Machinesmentioning

confidence: 99%

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Recommendations for Improving End-User Programming Education: A Case Study with Undergraduate Chemistry Students

Fuchs,

McDonald,

Gautam

et al. 2024

J. Chem. Educ.

View full text Add to dashboard Cite

show abstract

“…Bayman and Mayer [1988] first showed that an appropriate conceptual model for BASIC could help students "develop fewer misconceptions [...] and perform better on transfer tests. " du Boulay [1986] coined the term "notional machine" for conceptual models specifically of a language's dynamic semantics, which has received renewed focus in recent years [Dickson et al 2020]. Our work differs from most research on notional machines by focusing equally on a conceptual model of static semantics.…”

Section: Related Workmentioning

confidence: 99%

A Grounded Conceptual Model for Ownership Types in Rust

Crichton,

Gray,

Krishnamurthi

2023

Proc. ACM Program. Lang.

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Programmers learning Rust struggle to understand ownership types, Rust’s core mechanism for ensuring memory safety without garbage collection. This paper describes our attempt to systematically design a pedagogy for ownership types. First, we studied Rust developers’ misconceptions of ownership to create the Ownership Inventory, a new instrument for measuring a person’s knowledge of ownership. We found that Rust learners could not connect Rust’s static and dynamic semantics, such as determining why an ill-typed program would (or would not) exhibit undefined behavior. Second, we created a conceptual model of Rust’s semantics that explains borrow checking in terms of flow-sensitive permissions on paths into memory. Third, we implemented a Rust compiler plugin that visualizes programs under the model. Fourth, we integrated the permissions model and visualizations into a broader pedagogy of ownership by writing a new ownership chapter for The Rust Programming Language , a popular Rust textbook. Fifth, we evaluated an initial deployment of our pedagogy against the original version, using reader responses to the Ownership Inventory as a point of comparison. Thus far, the new pedagogy has improved learner scores on the Ownership Inventory by an average of 9

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“…These tools range from automated assessment systems (for reviews, see e.g. [15]- [18]) and systems used to illustrate how programs work [19], [20]. While automated assessment systems originally focused on functional correctness of programs (cf.…”

Section: Background a Automated Assessment And Feedback In Programmingmentioning

confidence: 99%

How to Help to Ask for Help? Help Request Prompt Structure Influence on Help Request Quantity and Course Retention

Sarsa

Pettersson

Hellas

2022

2022 IEEE Frontiers in Education Conference (FIE)

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Full research paper-Feedback and support are at the core of efficient learning. While the effect of feedback has been explored in a multitude of studies, the effect of asking for help and the effect of how that help is asked for is an under-explored area. In this work, we present the results of a randomized controlled trial organized in an introductory programming course for lifelong learners. In the study, we explored the usefulness of different types of help prompts used to guide learners asking for help. Gauging the effect of different combinations of three prompts used to scaffold writing out the help request, (1) "Describe the issue with your program", (2) "Explain how your program works", and (3) "What have you tried to do to resolve the issue", we study how prompts influence learners' behavior. Using log data collected from the online platform where the prompts were explored, we study how the prompts affect whether learners end up sending a help request instead of simply considering to send one, how the scaffolding prompts affect whether learners send further help requests, and whether the questions have an effect on course retention. Our results show that the help request prompts have an impact on whether learners end up actually writing a help request, which in turn also influences whether the learner will ask for help on another occasion. Further, we observe that help request prompts may have an effect on whether learners figure out a solution to a problem on their own. Alarmingly, we also observe that the prompts can affect how far learners go in a course. Based on our results, we advise teachers and researchers to pay attention to how learners are guided into asking for help.

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Engage Against the Machine: Rise of the Notional Machines as Effective Pedagogical Devices

Cited by 17 publications

References 28 publications

Recommendations for Improving End-User Programming Education: A Case Study with Undergraduate Chemistry Students

Recommendations for Improving End-User Programming Education: A Case Study with Undergraduate Chemistry Students

A Grounded Conceptual Model for Ownership Types in Rust

How to Help to Ask for Help? Help Request Prompt Structure Influence on Help Request Quantity and Course Retention

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