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Paper

Automatic assessment of students' software models using a simple heuristic and machine learning

by Independent / Community 0038069fee6b7a4421dc5ddad8c80a8d5533902e

Free2AITools Nexus Index

67.8

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 81

P: Popularity 57

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Software models are increasingly popular. To educate the next generation of software engineers, it is important that they learn how to model software systems well, so that they can design them effectively in industry. It is also important that instructors have the tools that can help them assess students' models more effectively. In this paper, we investigate how a tool that combines a simple heuristic with machine learning techniques can be used to help assess student submissions in model-dr...

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Semantic Scholar 27 Citations

Paper Information Summary
Entity Passport
Registry ID	0038069fee6b7a4421dc5ddad8c80a8d5533902e
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{0038069fee6b7a4421dc5ddad8c80a8d5533902e,
  author = {Unknown},
  title = {Automatic assessment of students' software models using a simple heuristic and machine learning Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/0038069fee6b7a4421dc5ddad8c80a8d5533902e}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Automatic assessment of students' software models using a simple heuristic and machine learning [Paper]. Free2AITools. https://api.semanticscholar.org/0038069fee6b7a4421dc5ddad8c80a8d5533902e

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 81

Popularity (P) 57

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Automatic assessment of students' software models using a simple heuristic and machine learning: Authority (A:81), Popularity (P:57), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"Software models are increasingly popular. To educate the next generation of software engineers, it is important that they learn how to model software systems well, so that they can design them effectively in industry. It is also important that instructors have the tools that can help them assess students' models more effectively. In this paper, we investigate how a tool that combines a simple heuristic with machine learning techniques can be used to help assess student submissions in model-dr..."

❝ Cite Node

@article{Unknown2026Automatic,
  title={Automatic assessment of students' software models using a simple heuristic and machine learning},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📈27CitationsSemantic Scholar

🏛️81AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️automation workflowField

🏷️ Research Topics

instruction tuning

📦Data Source: semantic_scholar

🔄 Updated daily

Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: null
forks: null
citations: 27

Data indexed from public sources. Updated daily.