llama-cli 1.0.6

Llama CLI fetches and preprocesses learning data

Llama CLI

A utility program that fetches and preprocesses learning data from supported learning tools. Educators and researches have important usecases for accessing the raw data that is generated while learners are using digital learning tools and environments. These stakeholders can aim to e.g. analyse and improve teaching materials, methods, and activities.

The aim of Llama CLI is to support and ease the steps of

1. connecting to the supported learning data sources
2. excluding persons and unwanted data tables or columns
3. fetching partial and complete data sets
4. anonymizing data before research activities
5. standardizing or transforming data
6. sampling and selecing data for analysis/ML

Currently supported data sources are

• A-plus https://apluslms.github.io/

Etymology

The name for the project comes from ~ la lumière à Montagne analytique. Pardon my French for ~ light on the mountain of analytics. Also LA is an acronym, that the package author may have used in his thesis more than a decent number of times, and that stands for Learning Analytics which is a research field in education technologies. Llamas are also known from a controversial programming exercise for computer science majors at Aalto University.

Installation

Llama CLI is available at PyPI. It has a number of automatically installed dependencies, most notably pandas, numpy, and requests.

  % python3 -m pip install llama-cli
  % llama

OR contained in a virtual environment (directory)

  % python3 -m venv .venv && .venv/bin/pip install llama-cli
  % .venv/bin/llama

Instructions

Llama CLI operates on the current working directory. The configurations and data will be stored in that directory – little bit like when working with git repositories. One work directory can connect with multiple data sources and one should select the sources that the current research or analysis project requires.

  % llama
  Llama CLI fetches and preprocesses learning data

  usage: llama <cmd> [<args>]

     status      Show the working tree status
     source      Manage sources for learning data
     list        List available data tables and columns
     privacy     Configure privacy (default: pseudoanonymous)
     exclude     Exclude selected tables, columns, or persons at fetch
     fetch       Fetch data from sources
     anonymize   Export anonymized data
     shell       Open python REPL with 'llama' instance to fetched data

1. Use llama source add to interactively connect with data sources. The required addresses and keys will be prompted when required.
2. Use llama list to fetch the available data tables.
3. Time to consider excluding some uninteresting data or persons who have not consent to the research at hand. See llama exclude for examples.
4. Use llama fetch rows to download data tables. Depending on the project it may be necessary to also llama fetch files and/or llama fetch meta. This step has a delay between internet requests and it may take a long time to complete. The rows can be fetched again to append new data if supported by the data source.
5. The data in fetched directory is pseudoanonymized by default. The pseudo identifiers are required to complete fetching of depended data. Use llama anonymize to produce export directory that can be e.g. stored in research repository when the security measures and research consent allow it.

Output

The Python class from llama import Llama offers some programmatic accessors and samplers to the data for further processing and analysis, as well as interactive testing via llama shell.

Naturally, the raw CSV and other files are available in export directory.

TODO

• Some R scripts for fast access and general measures & visualizations could be added.