bihungba1101/slam-en-es-knowledge-tracing

SLAM English-Spanish Knowledge Tracing

This repository contains a Hugging Face-compatible conversion of the English-Spanish track from the 2018 Second Language Acquisition Modeling (SLAM) shared task. Each row represents one learner exercise attempt. The source response token, linguistic, and correctness annotations are preserved.

The dataset supports longitudinal knowledge tracing: the same anonymous learner can have many interactions over multiple relative days, with challenge type, session type, response time, prompt text when available, and token-level outcomes.

Splits

Split	Interactions	Learners	Prompt coverage
train	824,012	2,593	72.2%
validation	115,770	2,592	70.7%
test	114,586	2,593	70.2%

The supplied source splits reuse learner IDs across splits. They are not learner-disjoint cold-start splits. Create new user-disjoint splits when evaluating generalization to unseen learners.

Schema

Each token-level list is positionally aligned. For example:

{
    "user_id": "XEinXf5+",
    "interaction_index": 2,
    "exercise_id": "rsAkJBG002",
    "prompt": "Hola, buenas tardes.",
    "country": "CO",
    "client": "web",
    "session": "lesson",
    "format": "reverse_translate",
    "days": 13.693,
    "response_time_seconds": 15.0,
    "token_ids": [
        "rsAkJBG00201",
        "rsAkJBG00202",
        "rsAkJBG00203"
    ],
    "response_tokens": ["Hello", "good", "afternoon"],
    "pos_tags": ["INTJ", "ADJ", "NOUN"],
    "morphological_features": [
        "fPOS=INTJ++UH",
        "Degree=Pos|fPOS=ADJ++JJ",
        "Number=Sing|fPOS=NOUN++NN"
    ],
    "dependency_relations": ["ROOT", "amod", "nmod:tmod"],
    "dependency_heads": [0, 4, 1],
    "token_labels": [1, 1, 1],
    "token_accuracy": 1.0,
    "correct": 1,
    "prompt_available": True
}

Feature	Meaning	Example
user_id	Anonymous learner identifier used to group interactions.	"XEinXf5+"
interaction_index	Sequential attempt number for the learner within the source split.	2
exercise_id	Identifier derived from the response token IDs for this attempt.	"rsAkJBG002"
prompt	Source prompt when available; often absent for listening activities.	"Hola, buenas tardes."
country	Learner country metadata.	"CO"
client	Application or device platform.	"web"
session	Session type, such as lesson or practice.	"lesson"
format	Challenge type, such as listen, reverse_tap, or reverse_translate.	"reverse_translate"
days	Learner-relative elapsed time in days, not a calendar date.	13.693
response_time_seconds	Time spent on the attempt; may be null.	15.0
token_ids	Source IDs for response tokens.	["rsAkJBG00201", ...]
response_tokens	Tokens produced or selected by the learner.	["Hello", "good", "afternoon"]
pos_tags	Universal POS tags aligned with the response tokens.	["INTJ", "ADJ", "NOUN"]
morphological_features	Morphological and fine-grained POS annotations.	`["Degree=Pos
dependency_relations	Syntactic relations such as ROOT, amod, and nmod:tmod.	["ROOT", "amod", "nmod:tmod"]
dependency_heads	One-based syntactic head positions; 0 identifies the root.	[0, 4, 1]
token_labels	Token correctness labels: 1 correct, 0 incorrect.	[1, 1, 1]
token_accuracy	Fraction of response tokens marked correct.	1.0
correct	Strict whole-attempt correctness; 1 only if all tokens are correct.	1
prompt_available	Whether prompt text is available.	True

The days field is learner-relative elapsed time from the beginning of the learner's observed activity timeline. Time gaps can be calculated as days_current - days_previous. A first row in a particular split may have a nonzero value because the learner may have earlier records in another split.

Knowledge-tracing targets

The conversion supports interaction-level KT using correct and token-level KT using token_labels. A typical objective is:

P(correct[t+1] = 1 | learner history through interaction t, target exercise[t+1])

Load

from datasets import load_dataset

dataset = load_dataset("bihungba1101/slam-en-es-knowledge-tracing")

Citation and license

See CITING.md and LICENSE.md. This conversion does not change the source dataset license or citation requirements.

Conversion

The Parquet files were generated by convert_to_hf.py. It groups source token rows into one interaction row, joins development and test labels from their .key files, preserves linguistic annotations, and derives strict interaction-level correctness.