Taking Advantage of Scale by Analyzing Frequent Constructed-Response, Code Tracing Wrong Answers

Stephens-Martinez, Kristin; Ju, An; Parashar, Krishna; Ongowarsito, Regina; Jain, Nikunj; Venkat, Sreesha; Fox, Armando

doi:10.1145/3105726.3106188

Cited by 45 publications

(3 citation statements)

References 9 publications

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“…For example, Qian and Lehman (2017) summarize misconceptions research that described misconceptions observed in a single student's response. In contrast, Stephens-Martinez et al (2017) found that a small set of wrong answers (approximately 5 percent of all distinct wrong answers) accounted for approximately 60 percent of the wrong answers that students submitted. however, these wrong answers could include not distinguishing between whether the string X is printed as "x", 'x', or x.…”

Section: What Evidence Should Be Required To Document a Misconception?mentioning

confidence: 76%

Student Knowledge and Misconceptions

Lewis¹,

Clancy²,

Vahrenhold³

2019

The Cambridge Handbook of Computing Education Research

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Section: What Evidence Should Be Required To Document a Misconception?mentioning

confidence: 76%

Student Knowledge and Misconceptions

Lewis¹,

Clancy²,

Vahrenhold³

2019

The Cambridge Handbook of Computing Education Research

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“…In contrast to several proposals to help students in the learning of programming involving the use of some programming language or computer tool [14][15][16][17][18][19][20][21], the approach behind the motivation of the authors' proposal is based on observations of situations in day-to-day life in which people successfully use methods to solve problems or perform tasks such as games, ordering of objects, different kinds of searches, mathematics problems, etc. In such situations an action or a sequence of actions is repeated until a special state is reached, which can be solved easily by a straight-forward action.…”

Section: Defining a Theoretical Framework For Didactics Of Programmingmentioning

confidence: 99%

A research model in didactics of programming

Rosa¹,

Gómez²

2020

CLEIej

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This paper presents a research model in didactics of programming elaborated within the theoretical framework of the epistemological theory of Jean Piaget. That theory explains the construction of scientific knowledge based on empirical studies made by Piaget over many years. The model arises from the analysis of the results of the application of principles of the theory, especially the triad of intra-inter-trans stages, to the empirical study of the construction of the concepts of algorithm, data structure and program. The elaboration of the model is a contribution to the development of the didactics of programming and, in general, of the didactics of computer science, since the model can be used in other computer science topics. Didactics is a specific area within computer science, with its own foundations and methods, which studies in depth topics related to education in the discipline. Two empirical studies about the construction of knowledge of algorithms and data structures, and of the corresponding programs as executable objects, are briefly described to illustrate the model.Both examples use a search algorithm (binary and linear) and the implementations are in the programming language C.

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“…In particular, we focus on code tracing, a foundational skill in programming that involves stepping through a program to predict values of variables and outputs. Novices struggle with code tracing across variations of how programming constructs are used together (Lister et al, 2004;Sorva, 2012;Stephens-Martinez et al, 2017). For example, executing an addition assignment statement inside a for loop for computing a cumulative sum (i.e., integrating an addition assignment statement and a for statement) already posed a difficulty for novices and revealed misconceptions that students harboured after standard lectures and exercises according to our prior work (Huang, 2018).…”

Section: Introductionmentioning

confidence: 99%

Supporting skill integration in an intelligent tutoring system for code tracing

Huang

Brusilovsky

Guerra

et al. 2022

Computer Assisted Learning

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Background Skill integration is vital in students' mastery development and is especially prominent in developing code tracing skills which are foundational to programming, an increasingly important area in the current STEM education. However, instructional design to support skill integration in learning technologies has been limited. Objectives The current work presents the development and empirical evaluation of instructional design targeting students' difficulties in code tracing particularly in integrating component skills in the Trace Table Tutor (T3), an intelligent tutoring system. Methods Beyond the instructional features of active learning, step‐level support, and individualized problem selection of intelligent tutoring systems (ITS), the instructional design of T3 (e.g., hints, problem types, problem selection) was optimized to target skill integration based on a domain model where integrative skills were represented as combinations of component skills. We conducted an experimental study in a university‐level introductory Python programming course and obtained three findings. Results and Conclusions First, the instructional features of the ITS technology support effective learning of code tracing, as evidenced by significant learning gains (medium‐to‐large effect sizes). Second, performance data supports the existence of integrative skills beyond component skills. Third, an instructional design focused on integrative skills yields learning benefits beyond a design without such focus, such as improving performance efficiency (medium‐to‐large effect sizes). Major Takeaways Our work demonstrates the value of designing for skill integration in learning technologies and the effectiveness of the ITS technology for computing education, as well as provides general implications for designing learning technologies to foster robust learning.

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Taking Advantage of Scale by Analyzing Frequent Constructed-Response, Code Tracing Wrong Answers

Cited by 45 publications

References 9 publications

Student Knowledge and Misconceptions

Student Knowledge and Misconceptions

A research model in didactics of programming

Supporting skill integration in an intelligent tutoring system for code tracing

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