Fostering Program Comprehension in Novice Programmers - Learning Activities and Learning Trajectories

Izu, Cruz; Schulte, Carsten; Aggarwal, Ashish; Cutts, Quintin; Duran, Rodrigo; Gutica, Mirela; Heinemann, Birte; Kraemer, Eileen; Lonati, Violetta; Mirolo, Claudio; Weeda, Renske

doi:10.1145/3344429.3372501

Cited by 61 publications

(41 citation statements)

References 292 publications

(379 reference statements)

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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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“…Although introductory programming education traditionally focuses on writing programs, there is a large body of research on reading, tracing and understanding programs, suggesting tracing knowledge is critical for learning writing skills [26,46]. The relationship between student skill of reading, writing, tracing, and explaining, as identified by assessments, is well studied in the BRACElet project, reported in [6], that originate after an ITiCSE working group in 2004 [44].…”

Section: Tracing Skillsmentioning

confidence: 99%

“…Tracing tasks can include, for example, predicting the output of a segment of code, or making diagrams representing the contents of memory after some code executes. The report of an 2019 ITICSE working group also include a catalog [26,Section 5] of program comprehension tasks, covering many different kinds of tracing tasks.…”

Section: Tracing Skillsmentioning

confidence: 99%

“…Tracing activities can also be part of program comprehension strategies, including building strategies at higher levels of abstraction. For instance, executing a piece of code on a series of specific inputs may guide the learner to understand what the given code computes [26]. This high-level skill also includes understanding which fragments of code to be traced, or knowing when an external representation (e.g., a tracing table or a diagram) is needed.…”

Section: Tracing Skillsmentioning

confidence: 99%

“…Several assessments use code tracing questions in order to assess programming knowledge; others assess tracing skills themselves, as the already mentioned [26,44,47]. Nelson et al developed a systematic, multi-part formative assessment for tracing skills of simple statements as well as larger combinations of code [55].…”

Section: Assessments In Computingmentioning

confidence: 99%

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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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Smart generation of code tracing questions for assessment in introductory programming

Stankov

Jovanov

Bogdanova

2022

Comp Applic In Engineering

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Teaching programming is a challenging activity nowadays, especially in introductory programming courses, which are typically massively attended. Writing functional programs is a cognitive skill, which many students, novices in programming, find it difficult to master. It is equally challenging to assess this ability of students. Research has shown that students need to learn how to read and understand, before they can learn to write programs. Code tracing questions are a suitable way to assess the knowledge of semantics of programming constructs (understanding what programs do). However, when designing these types of questions, teachers need to be aware of the complexity of the code and must always try to create questions with code snippets of same or similar complexity, especially when preparing different versions of the same test (for large student groups). In this paper we propose a new model for smart automatic generation of code tracing questions. The model uses a suitable source code metric to measure the code complexity, and to enable generation of questions containing code snippets of consistent complexity. We also introduce CodeCPP: a newly developed Moodle plugin, which is an implementation of the proposed model. The evaluation of CodeCPP, through a year‐long use of the system in teaching programming as well as feedback from selected teachers with many years of experience in teaching programming courses, shows positive and promising results. The system utilization confirms that the application of the proposed model enables a fair and objective knowledge assessment. Furthermore, the feedback provided by teachers and students is highly positive.

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Fostering Program Comprehension in Novice Programmers - Learning Activities and Learning Trajectories

Abstract: Morrison and Lauren Margulieux, for their insightful and detailed feedback of my thesis. I am honored to have Professor Matti Tedre as my opponent. Thanks for the CNPq and IFMS for their financial support.

Cited by 61 publications

References 292 publications

Proceedings of the 9th Computer Science Education Research Conference

Proceedings of the 9th Computer Science Education Research Conference

Differentiated Assessments for Advanced Courses that Reveal Issues with Prerequisite Skills

Smart generation of code tracing questions for assessment in introductory programming

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