# CogTax: A Four-Level Cognitive Taxonomy for Command-Line Computing Education

arXiv: https://arxiv.org/abs/2607.00140 · HTML: https://mlsanity.com/paper/2607.00140/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Builds on (most cited first, 12 of 12)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [MTEB: Massive Text Embedding Benchmark](https://mlsanity.com/paper/2210.07316/) (2022)
- [Multilingual E5 Text Embeddings: A Technical Report](https://mlsanity.com/paper/2402.05672/) (2024)
- [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://mlsanity.com/paper/2212.09741/) (2022)
- [Problem-Solving Guide: Predicting the Algorithm Tags and Difficulty for Competitive Programming Problems](https://mlsanity.com/paper/2310.05791/) (2023)
- [Automated Analysis of Learning Outcomes and Exam Questions Based on Bloom's Taxonomy](https://mlsanity.com/paper/2511.10903/) (2025)
- [Estimating Difficulty Levels of Programming Problems with Pre-trained Model](https://mlsanity.com/paper/2406.08828/) (2024)
- [Smart Learning in the 21st Century: Advancing Constructionism Across Three Digital Epochs](https://mlsanity.com/paper/2501.07486/) (2025)
- [Clustering MOOC Programming Solutions to Diversify Their Presentation to Students](https://mlsanity.com/paper/2403.19398/) (2024)
- [Beyond SELECT: A Comprehensive Taxonomy-Guided Benchmark for Real-World Text-to-SQL Translation](https://mlsanity.com/paper/2511.13590/) (2025)

## Built on by (most cited first, 1 of 1)

- [Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach](https://mlsanity.com/paper/2607.02432/) (2026)

## Abstract (arXiv)

As computing education expands beyond traditional programming into operational domains such as systems administration and command-line environments, existing pedagogical frameworks struggle to capture a dimension that is critical in these contexts: the real-world consequences of learner actions. Existing cognitive taxonomies classify learning objectives by mental operations but do not account for system impact, leaving a critical gap in command-line education where conceptually simple commands can have severe consequences. This work presents CogTax, a four-level cognitive taxonomy that integrates two dimensions: cognitive complexity, derived from Bloom's Revised Taxonomy, and operational impact, which distinguishes observational, reversible, structural, and administrative operations. The four progressive levels range from safe read-only inspection to advanced system management requiring integration of multiple abstract models. Then, the taxonomy level is defined as the maximum of these dimensions, ensuring that both conceptual understanding and operational awareness are addressed. CogTax gives instructors a principled framework for sequencing course material and calibrating assessment difficulty, and gives students an explicit reference for self-assessment and gap identification. To demonstrate that taxonomy levels are automatically assignable, making the framework scalable without manual expert annotation, a classifier that combines syntactic representations derived from abstract syntax trees with semantic embeddings is trained. Evaluated on 585 expert-annotated Linux/bash commands, this combined approach achieves 89% accuracy, outperforming either representation alone, and demonstrates cross-language extensibility through structural equivalences across command languages.