All datasets / SEACrowd

SEACrowd/id_short_answer_grading

40 downloads in 30 days1 likesLast modified 24 Jun 2024View on Hugging Face

Unclear

The checks could not identify a recognised licence ID in the dataset's metadata.

Declared licence: Not declared in the tag or the card (see datasets with no declared licence)Evidence: README.md at a9a4c93
Licence files: LICENSE, licence not identifiedEvidence: LICENSE at a9a4c93
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

Each cell links to what was checked: a file, a file list or a Hub record.

Tasks

short-answer-grading

Citation text from the card

@article{
    JLK,
    author = {Muh Haidir and Ayu Purwarianti},
    title = { Short Answer Grading Using Contextual Word Embedding and Linear Regression},
    journal = {Jurnal Linguistik Komputasional},
    volume = {3},
    number = {2},
    year = {2020},
    keywords = {},
    abstract = {Abstract—One of the obstacles in an efficient MOOC is the evaluation of student answers, including the short answer grading which requires large effort from instructors to conduct it manually.
                Thus, NLP research in short answer grading has been conducted in order to support the automation, using several techniques such as rule
                and machine learning based. Here, we’ve conducted experiments on deep learning based short answer grading to compare the answer
                representation and answer assessment method. In the answer representation, we compared word embedding and sentence embedding models
                such as BERT, and its modification. In the answer assessment method, we use linear regression. There are 2 datasets that we used, available
                English short answer grading dataset with 80 questions and 2442 to get the best configuration for model and Indonesian short answer grading
                dataset with 36 questions and 9165 short answers as testing data. Here, we’ve collected Indonesian short answers for Biology and Geography
                subjects from 534 respondents where the answer grading was done by 7 experts. The be

All datasets marked Unclear | JSON

The same facts for your agent

A free MCP server at mcp.datalicense.uk/mcp. Connect it from an MCP client such as Claude or Cursor.

dataset_licence({ "id": "SEACrowd/id_short_answer_grading" })
signal: "unclear"
stated_licence: null
sources: 0