openla-feature-representation 0.1.1a2

A Python module that adds features to OpenLA data to make it easier to use for ML

openla-feature-representation: generate features for EventStream data

Introduction

openla-feature-representation is an open-source Python module that generates features from OpenLA EventStream data, to make the data easier to use for ML.

Installation

This module module is available on PyPI and it can be installed using pip as follows:

pip install openla-feature-representation

Downloading the model

For the E2Vec class, you will need the openla-feature-representation-fastText_1min.bin model . Feel free to dowload it from the OpenLA models download site.

Usage of the E2Vec class

First, import the openla_feature_representation package with an arbitrary name, here lafr.

import openla_feature_representation as lafr

Initializing the class

This is the constructor:

e2Vec = lafr.E2Vec(fT_model_path, info_dir, course_id)

• fT_model_path is the path to a fastText language model trained for this task
• info_dir is the path to a directory with the dataset (see below)
• course_id is a string to identify files for the course to analyze within the info_dir directory (e.g. 'A-2023')

After getting your own e2Vec object, all methods the class provides can be used on it.

Generate sentences for the event log

The fastText model uses an artificial language to express event log entries as sentences. This is how you can generate them:

sentences = e2Vec.get_Sentences(
    sentences_path=sentence_path,
    eventstream_path=eventstream_path,
    info_dir=info_dir,
    course_id=course_id,
)

If you need to select or filter a time span:

sentences = e2Vec.get_Sentences(
    sentences_path=sentence_path,
    mode="select",
    start=0,
    period=90,
    eventstream_path=eventstream_path,
    info_dir=info_dir,
    course_id=course_id,
)

• sentence_path is the path to the directory where you want the sentence files to be written
• eventstream_path is the path to the event stream csv file
• info_dir is the path to a directory with the dataset (see below)
• course_id is a string to identify files for the course to analyze within the info_dir directory
• mode can be either "all" or "select" (optional)
• start is the minute in the data the sentence generation should start (optional)
• period is the number of minutes worth of sentences that should be generated (optional)

This function saves the sentences to a text file and returns a path to it.

Vectorize the sentences

This function returns a pandas DataFrame with the vectors generated from the sentences.

user_vectors = e2Vec.sentences_to_vector(sentences_path, save_path)

• sentences_path is the path to the sentence files generated in the previous step
• save_path needs a string, but it is currently unused (to be removed)

Concatenation

The class has a function to concatenate vectors by time (minutes) or weeks.

This will concatenate the vectors in 10-minute spans.

user_vec_C = e2Vec.get_concat_vectors(
    sentences_path=sentence_path,
    eventstream_path=eventstream_path,
    vector_path="",
    info_dir=eduData,
    course_id=course_id,
    concat_mode="time",
    start=0,
    period=10,
)

This will concatenate the vectors by the week or lesson.

user_vec_C = e2Vec.get_concat_vectors(
    sentences_path=sentence_path,
    eventstream_path=eventstream_path,
    vector_path="",
    info_dir=eduData,
    course_id=course_id,
    concat_mode="week",
    start=0,
)

• sentences_path is the path to the sentence files generated in the previous step
• eventstream_path is the path to the event stream csv file
• vector_path needs a string, but it is currently unused (to be removed)
• info_dir is the path to a directory with the dataset (see below)
• course_id is a string to identify files for the course to analyze within the info_dir directory
• concat_mode needs to be "time" or "week"
• start is the minute in the data the sentence generation should start (optional)
• period is the number of minutes worth of sentences that should be generated each time (optional)

Usage of the ALP (Active Learner Point) functions

ALP is a set of metrics that take BookRoll (ebook) and Moodle activity per lecture into account: attendance, report submissions, course views, slide views, adding markers or memos, and other actions.

First, the aggregate_feature function aggregates the number of times each user took any of the actions above for each lecture, resulting on a DataFrame that we will call features_df on this example. These are the features ALP will work with.

from openla_feature_representation import aggregate_feature
features_df = aggregate_feature(course_id=course_id)

• course_id is an int to identify files for the course to analyze within the Dataset directory

To further ready the data for ML and other analysis, the feature2ALP function returns a DataFrame that we will call alp_df, in which the feature is replaced by a number from 0 to 5 with the following meaning:

• 5: Top 10%, or attending the lecture, or submitting a report
• 4: Top 20%
• 3: Top 30%, or being late to the lecture, or submitting late
• 2: Top 40%
• 1: Top 50%
• 0: Bottom 50%, or not attending, or not submitting

The additional alp_df_normalized DataFrame returned by the function is the same data as alp_df, only normalized to 1.

from openla_feature_representation import feature2ALP
alp_df, alp_df_normalized = feature2ALP(features_df=features_df)

Datasets for OpenLA

This module uses data in the same or a similar format as OpenLA. Please refer to the OpenLA documentation for further information.