2021
Guenaga, Mariluz; Eguiluz, Andoni; Garaizar, Pablo; Gibaja, Juanjo
How do students develop computational thinking? Assessing early programmers in a maze-based online game Journal Article
In: Computer Science Education, 2021, ISSN: https://www.tandfonline.com/loi/ncse20.
Abstract | Links | BibTeX | Tags: block-based programming, Computational Thinking, student assessment
@article{Guenaga2021,
title = {How do students develop computational thinking? Assessing early programmers in a maze-based online game},
author = {Mariluz Guenaga and Andoni Eguiluz and Pablo Garaizar and Juanjo Gibaja},
url = {https://www.tandfonline.com/doi/full/10.1080/08993408.2021.1903248},
doi = {https://doi.org/10.1080/08993408.2021.1903248},
issn = {https://www.tandfonline.com/loi/ncse20},
year = {2021},
date = {2021-04-13},
journal = {Computer Science Education},
abstract = {Background and Context: Despite many initiatives to develop Computational Thinking (CT), not much is known about how early programmers develop CT and how we can assess their learning.
Objective: Determine if the analysis of students’ interactions with an online platform allows understanding the development of CT, how we can convert data collected into valuable insights, and the aspects that should be considered in platforms design.
Method: We developed an online platform with a fine-grained log–recording system. We analysed the data collected from 1004 students (ages 8-14) to understand the difficulties they face. We explain our platform and the tools to process and filter the interaction logs. We calculate additional indicators that provide useful information about student’s behaviour.
Findings: Age and gender have shown to influence on CT learning. Generating additional indicators from basic interaction data provide valuable insights. We provide a list of recommendations for developing more effective programming learning platforms.},
keywords = {block-based programming, Computational Thinking, student assessment},
pubstate = {published},
tppubtype = {article}
}
Background and Context: Despite many initiatives to develop Computational Thinking (CT), not much is known about how early programmers develop CT and how we can assess their learning.
Objective: Determine if the analysis of students’ interactions with an online platform allows understanding the development of CT, how we can convert data collected into valuable insights, and the aspects that should be considered in platforms design.
Method: We developed an online platform with a fine-grained log–recording system. We analysed the data collected from 1004 students (ages 8-14) to understand the difficulties they face. We explain our platform and the tools to process and filter the interaction logs. We calculate additional indicators that provide useful information about student’s behaviour.
Findings: Age and gender have shown to influence on CT learning. Generating additional indicators from basic interaction data provide valuable insights. We provide a list of recommendations for developing more effective programming learning platforms.
Objective: Determine if the analysis of students’ interactions with an online platform allows understanding the development of CT, how we can convert data collected into valuable insights, and the aspects that should be considered in platforms design.
Method: We developed an online platform with a fine-grained log–recording system. We analysed the data collected from 1004 students (ages 8-14) to understand the difficulties they face. We explain our platform and the tools to process and filter the interaction logs. We calculate additional indicators that provide useful information about student’s behaviour.
Findings: Age and gender have shown to influence on CT learning. Generating additional indicators from basic interaction data provide valuable insights. We provide a list of recommendations for developing more effective programming learning platforms.