University of Illinois Urbana-Champaign

On learning how to program via an interactive eBook with adaptive Parsons problems: Poster

Haynes-Magyar, Carl Christopher

Content Files
ALISEACP2022_Haynes-Magyar.pdf

Permalink

https://hdl.handle.net/2142/118045

Description

Title
On learning how to program via an interactive eBook with adaptive Parsons problems: Poster
Author(s)
Haynes-Magyar, Carl Christopher
Issue Date
2022-10
Keyword(s)
  • Introductory computer programming
  • Parsons problems
  • Cognitive load
  • Help-seeking
  • Adaptive scaffolding
  • Education
  • Online learning
  • User interfaces
Abstract
Traditional computer programming practice, such as writing pseudocode, code tracing, and code writing, can be arduous, time-intensive, and frustrating. But adaptive Parsons problems, which require learners to place mixed-up code blocks in the correct order, are designed to support learners' individual differences in knowledge acquisition, reduce extraneous cognitive load, and improve affect while learning how to program. These problems modify the difficulty of the current or next problem based on a learner's prior performance and help-seeking behavior. Adaptive Parsons problems are a more interactive way to learn stereotypical solutions to programming problems. Hence, they can help novice programmers build up the kind of mental library of solutions experts have at their disposal when writing code from scratch to solve any number of critical problems related to computing.
Series/Report Name or Number
Proceedings of the ALISE Annual Conference, 2022
Type of Resource
text
Genre of Resource
Conference Poster
Language
eng
Handle URL
https://hdl.handle.net/2142/118045
DOI
https://doi.org/10.21900/j.alise.2022.990
Copyright and License Information
  • Copyright 2022 Carl Christopher Haynes-Magyar
  • This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-sa/4.0/).

Owning Collections

Proceedings of the ALISE Annual Conference: ALISE 2022 PRIMARY
Go Back and Get It: From One Narrative to Many