Vasquez, A. N., & Henderson, T. S., & Zabner, D. (2024, June), Algorithmic Thinking: Why Learning Cannot Be Measured By Code-Correctness in a CS Classroom   Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/46537

\bibitem{asee_peer_46537}
  Vasquez, A. N., \& Henderson, T. S., \& Zabner, D. (2024, June), \emph{Algorithmic Thinking: Why Learning Cannot Be Measured By Code-Correctness in a CS Classroom }
  Paper presented at 2024 ASEE Annual Conference \& Exposition, Portland, Oregon.
  https://peer.asee.org/46537

Alejandra Noemi Vasquez, Trevion S Henderson, and David Zabner.  "Algorithmic Thinking: Why Learning Cannot Be Measured By Code-Correctness in a CS Classroom ".  2024 ASEE Annual Conference & Exposition, Portland, Oregon, 2024, June.  ASEE Conferences, 2024.  https://peer.asee.org/46537 Internet.  30 Jul, 2024

\bibitem{asee_peer_46537}
  Alejandra Noemi Vasquez, Trevion S Henderson, and David Zabner.
  "Algorithmic Thinking: Why Learning Cannot Be Measured By Code-Correctness in a CS Classroom ".
  \emph{2024 ASEE Annual Conference \& Exposition, Portland, Oregon, 2024, June}.
  ASEE Conferences, 2024.
  https://peer.asee.org/46537 Internet.  30 Jul, 2024

@INPROCEEDINGS{asee_peer_46537
author = "Alejandra Noemi Vasquez, Trevion S Henderson, and David Zabner"
title = "Algorithmic Thinking: Why Learning Cannot Be Measured By Code-Correctness in a CS Classroom "
booktitle = "2024 ASEE Annual Conference \& Exposition"
year = "2024"
month = "June"
address = "Portland, Oregon"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/46537}
}

TY  - CPAPER
AB  - Algorithmic thinking (AT) is a core learning outcome of introductory computing education. While many definitions of algorithmic thinking exist in the literature, we focus on the definition from the National Research Council, cited by Cooper and colleagues, which focuses on students’ capacity for functional decomposition of programming problems, generalization and parameterization, top-down design, refinement, and the appropriate use of iteration and data structures. Algorithmic thinking is a core cognitive learning outcome because it requires problem analyses, detailed breakdown of said problems, and versatile approaches that can be applied to many cases in which students use programming to solve problems. 

While studies of computational thinking and algorithmic thinking in mathematics education have found that AT is a strong predictor of academic performance, studies of AT in computing courses have found no relationship between AT and academic performance. We contend that the ubiquity of auto-graders in computer science courses may be one reason that the link between AT and academic performance is tentative in the empirical literature. It is possible, for example, for students to pass auto-graders while failing to adopt well-practiced programming techniques or engage in appropriate algorithmic thinking. As such, the scores from auto-graders may not adequately reflect student learning or algorithmic thinking. Thus, it is imperative that researchers and educators develop more appropriate approaches to assessing students’ algorithmic thinking in the context of computing coursework. 

Our work was guided by the following research questions: (1) What is the relationship between students’ computing self-efficacy beliefs and their self-assessed algorithmic thinking? (2) What is the relationship between students’ self-assessed algorithmic thinking and their approaches to solving problems using programming? (3) How are students’ algorithmic thinking skills manifested in their approaches to solving problems using programming? 

The setting for this mixed-method research study was an introductory computing course for first-year engineering students. We draw on six sources of data, which were collected as students completed their team-based final project: (a) students’ code from individual coding assignments, (b) students’ code from team-based coding assignments, (c) video and audio recordings of student teams as they complete team-based coding assignments, (d) fieldnotes from ongoing observations of student teams, (e) pre- and post-survey data, and (f) video and audio recordings of students’ participation in revise and resubmit sessions, during which they described revisions to coding assignments. 

In analyzing these data we found examples of students successfully completing assignments while failing to adopt well-practiced coding strategies, indicating that it is possible to perform well in introductory computing courses without demonstrating AT in situ. We discuss implications for teaching and learning in introductory computing courses, as well as implications for research methods in computer science education. 
AU  - Alejandra Noemi Vasquez
AU  - Trevion S Henderson
AU  - David Zabner
CY  - Portland, Oregon
DA  - 2024/06/23
PB  - ASEE Conferences
TI  - Algorithmic Thinking: Why Learning Cannot Be Measured By Code-Correctness in a CS Classroom 
UR  - https://peer.asee.org/46537
ER  -