ALP Tutorial

ALP (Active Learner Point) 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.

from openla_feature_representation import Alp
alp = Alp(course_id="114")

• course_id is a string to identify files for the course to analyze within the Dataset directory

The Alp class constructor above makes three DataFrames available as properties of the returned Alp object:

• features_df: aggregated totals of how many times each user took any of the relevant actions for each lecture

• alp_df: the features above replaced by a number from 0 to 5 following the criteria below

• alp_df_normalized: same as above, only the 0 to 5 numbers are normalized between 0 and 1

Criteria for the ALP 0-to-5 scale

value	description
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

Examples for the class’s methods and instance variables

alp.features_df       # These are the aggregated features
alp.alp_df            # These are the ALP 0-to-5 values
alp.alp_df_normalized # These are the normalized values

# The following will write CSV files for the relevant DataFrame
# Paths and filenames can be specified, but there are default
# filenames and the paths default to the present directory
alp.write_features_csv()
alp.write_alp_csv()
alp.write_alp_normalized_csv()