hina.individual

Tutorial

The individual module provides functions for computing node-level measures related to the participation of individuals in learning processes. The module focuses on analyzing the quantity and diversity of interactions based on the connections between individuals and tasks. This module provides node-level measures for understanding individual participation patterns.

Currently, the module contains the following Python files:

quantity.py: Provides functions to calculate the quantity of individual interactions with tasks.
diversity.py: Provides functions to calculate the diversity of individual interactions based on task distributions.

Functions
Function	Description
quantity(B, attr=None, group=None, return_type=’all’)	Computes various quantities and normalized quantities for student nodes in a bipartite graph.
diversity(B, attr=None)	Computes the diversity value of individual nodes in a bipartite graph based on a specified attribute or the object nodeset.

Reference

function quantity(B, attr=None, group=None, return_type='all') [source]

Description: Computes various quantities and normalized quantities for student nodes in a bipartite graph, gauging the quantity of individual involvement in the studied objects.

Parameters:

(B, attr=None, group=None, return_type='all')

B: A bipartite graph with weighted edges. Nodes are expected to have attributes if attr or group is provided.
attr: The name of the object node attribute used to categorize the connected object nodes. If provided, the function calculates quantity by category. Default is None.
group: The name of the student node attribute used to group nodes. If provided, the function calculates normalized quantity by group. Default is None.
return_type: Specifies the type of results to return. Options are:
- 'all': Returns all computed quantities (default).
- 'quantity': Returns only the raw quantity for each node.
- 'quantity_by_category': Returns only the quantity partitioned by category.
- 'normalized_quantity': Returns only the normalized quantity for each node.
- 'normalized_quantity_by_group': Returns only the normalized quantity by group.

Returns:

dict: A dictionary containing the computed quantities based on the return_type parameter. Possible keys include:
- 'quantity': A dictionary mapping each node to its total edge weight.
- 'normalized_quantity': A dictionary mapping each node to its normalized edge weight.
- 'quantity_by_category': A dictionary mapping (node, category) tuples to their total edge weight.
- 'normalized_quantity_by_group': A dictionary mapping each node to its normalized edge weight within its group.
dataframe: A dataframe containing the diversity value of each student node.

function diversity(B, attr=None) [source]

Description: Computes the diversity value of individual nodes in a bipartite graph based on a specified attribute or the object nodeset.

Parameters:

(B, attr=None)

B: A bipartite graph. Nodes are expected to have a 'bipartite' attribute indicating their partition.
attr: The column name of the attribute related to the studied objects. If provided, diversity is calculated based on the categories of the specified attribute. Default is None.

Returns:

dict: A dictionary where keys are nodes and values are their diversity values, indicating how evenly their connections are distributed across different categories.
dataframe: A dataframe containing the diversity value of each student node.

Demo

Example Code

This example demonstrates how to use the quantity and diversity functions to compute node-level measures.

Step 1: Import necessary libraries

import pandas as pd
from hina.individual import quantity, diversity
from hina.construction import get_bipartite

Step 2: Define the dataset

A dataset containing student-task interactions:

df = pd.DataFrame({
     'student': ['Alice', 'Bob', 'Alice', 'Charlie'],
     'object1': ['ask questions', 'answer questions', 'evaluating', 'monitoring'],
     'object2': ['tilt head', 'shake head', 'nod head', 'nod head'],
     'group': ['A', 'B', 'A', 'B'],
     'attr': ['cognitive', 'cognitive', 'metacognitive', 'metacognitive']
 })

Step 3: Construct the bipartite network representation

We create a bipartite network representation of the interactions between students and objects in the ‘object1’ category, adding the additional attribute ‘attr’ storing object codes.

B = get_bipartite(df,student_col='student', object_col='object1', attr_col='attr', group_col='group')

Step 4a: Compute quantity measures

We calculate the quantity of interactions for each student relative to the object.

quantities = quantity(B, attr='attr', group='group', return_type='all')
print("Quantities for bipartite network:\n", quantities[0])

Step 4b: Compute diversity measures

We calculate the diversity of interactions for each student relative to the object.

diversities = diversity(B, attr='attr')
print("Diversities for Tripartite network:\n", diversities[0])

Example Output

Quantities for bipartite network:
{'quantity': {'Alice': 2, 'Bob': 1, 'Charlie': 1},
'normalized_quantity': {'Alice': 0.5, 'Bob': 0.25, 'Charlie': 0.25},
'quantity_by_category': defaultdict(<class 'float'>, {('Alice', 'cognitive'): 1.0, ('Alice', 'metacognitive'): 1.0, ('Bob', 'cognitive'): 1.0, ('Charlie', 'metacognitive'): 1.0}),
'normalized_quantity_by_group': {'Alice': 1.0, 'Bob': 0.5, 'Charlie': 0.5}}

Diversities for Tripartite network:
{'Alice': 1.0, 'Bob': -0.0, 'Charlie': -0.0}

Paper Source

If you use this function in your work, please cite:

Feng, S., Gibson, D., & Gasevic, D. (2025). Analyzing students’ emerging roles based on quantity and heterogeneity of individual contributions in small group online collaborative learning using bipartite network analysis. Journal of Learning Analytics, 12(1), 253–270.