A theory of instruction for introductory programming skills

Xie, Benjamin; Loksa, Dastyni; Nelson, Greg L.; Davidson, Matthew J.; Dong, Dongsheng; Kwik, Harrison; Tan, Alex Hui; Hwa, Leanne; Li, Min; Ko, Andrew J.

doi:10.1080/08993408.2019.1565235

Cited by 113 publications

(58 citation statements)

References 51 publications

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“…There has been some interesting work by others on related, but different, teaching approaches [11,28,39]. As I have not yet given those papers the thought deserved, I will refrain from commenting here, but instead merely recommend those papers to the reader.…”

Section: Reflections On Teachingmentioning

confidence: 98%

Proceedings of the 9th Computer Science Education Research Conference

2020

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This paper is a companion to my keynote address at the 9th Computer Science Education Research Conference (CSERC '20). I review the research that led to my three stage neo-Piagetian model of how novices understand code. Code tracing is the key. In the first stage, the novice cannot trace code. In the second stage, the novice has mastered tracing, but, crucially, that is the only skill they have mastered. It is only when novices reach the third stage that they begin to reason about code in a more general, abstract way. The principal failure of traditional approaches to teaching programming has been the assumption that the novices begin at the third stage.

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Section: Reflections On Teachingmentioning

confidence: 98%

Proceedings of the 9th Computer Science Education Research Conference

2020

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“…For instance, when debugging their own programs, learners may be influenced by what they want the program to do instead of identifying the exact actions prescribed by the code [63]. Xie et al show that explicitly teaching tracing strategies can improve novices performances [93] and present a pedagogical approach where tracing acts as the first of four skills that the novice should develop [92].…”

Section: Tracing Skillsmentioning

confidence: 99%

Differentiated Assessments for Advanced Courses that Reveal Issues with Prerequisite Skills

Nelson

Strömbäck

Korhonen

et al. 2020

Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education

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Computing learners may not master basic concepts, or forget them between courses or from infrequent use. Learners also often struggle with advanced computing courses, perhaps from weakness with prerequisite concepts. One underlying challenge for researchers and instructors is determining the reason why a learner gets an advanced question wrong. Was the wrong answer because the learner lacked prerequisite skills, has not mastered the advanced skill, or some combination of the two? We contribute a design investigation into how to create differentiated questions which diagnose prerequisite and advanced skills at the same time. We focused on tracing and related skills as prerequisites, and on advanced object-oriented programming, concurrency, algorithm and data structures as the advanced skills. We conducted an inductive qualitative analysis of existing assessment questions from instructors and from a concept inventory with a validity argument (the Basic Data Structures Inventory). We found dependencies on a variety of prerequisite knowledge and mixed potential for diagnosing difficulties with prerequisites. Inspired by this analysis, we developed examples

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“…The results from plans and plan-composition strategies have been influential in CER. Although several important questions regarding how students decompose code and how different plan-composition strategies have an impact on students' comprehension of code remain, their results have spawned several pedagogical approaches that emphasize the teaching of higher-level abstractions over low-level syntax and semantics with promising results [303,306,176,75,395]. When designing learning materials, or assessment instruments, instructors must consider the path learners take while learning.…”

Section: Plan-composition Strategiesmentioning

confidence: 99%

“…by using a program comprehension framework, the Block Model, and a model of program comprehension cognitive complexity, the CCCP, to create our learning trajectories. We expect that the design methods presented in Publication V could better support practitioners and researchers developing pedagogy that emphasize program comprehension as a precursory skill (e.g., [319,395]) to create and adapt a diverse set of tasks aimed to foster program comprehension.…”

Section: What Kind Of Activities Can Foster Program Comprehension Andmentioning

confidence: 99%