Information Encoding Modeling in Computer Science Education

Modeling is a major component of Computer Science (CS) education implemented in K-12. We propose Information Encoding Modeling (IEM) to facilitate the development of modeling competencies, emphasizing semiotic competence and the creative representation of complex information. This research situates IEM within the context of existing modeling techniques, evaluates its application through various examples, and proposes an Encoding Diagram as a key representational tool. We present the potential integration of IEM into CS education to enhance students’ understanding of information encoding and Computational Thinking principles.

Authors

Institutions

Publication Details

Published
2024-09-05
DOI
https://doi.org/10.1145/3677619.3678125
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Information Encoding Modeling in Computer Science Education

Johannes Krugel, Heike Buttke
Online Learning and Analytics
article

Information Encoding Modeling in Computer Science Education

Johannes Krugel, Heike Buttke
article en

Abstract

Modeling is a major component of Computer Science (CS) education implemented in K-12. We propose Information Encoding Modeling (IEM) to facilitate the development of modeling competencies, emphasizing semiotic competence and the creative representation of complex information. This research situates IEM within the context of existing modeling techniques, evaluates its application through various examples, and proposes an Encoding Diagram as a key representational tool. We present the potential integration of IEM into CS education to enhance students’ understanding of information encoding and Computational Thinking principles.

Leibniz University Hannover (DE)
Quality Education
Openalex Percentile: Top 84%
Online Learning and Analytics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.