Education
16 columns
200 rows
8 formats

Adaptive Learning Pathways Dataset

This dataset models personalized curriculum progressions for adaptive learning, capturing detailed learner-module interactions, completion statuses, scores, and adaptive recommendations. It enables EdTech startups to analyze and optimize individualized learning pathways, supporting dynamic curriculum design and learner success tracking.

last updated 20 Aug 2025
by GoMask

The brief that made it

Design and analysis of adaptive learning pathways for personalized education

Sample rows

preview · 8 of 200 rows · all 16 columns

learner_idstring	adaptation_reasonstring	scorefloat	curriculum_titlestring	learner_namestring	learner_emailstring	curriculum_idstring	curriculum_levelstring	module_idstring	module_titlestring	module_sequenceinteger	completion_statusstring	completion_datedate	time_spent_minutesfloat	recommended_next_module_idstring	learning_objectivestring
L0001	mastery	93.5	Foundations of Data Literacy	Ava Richardson	ava.richardson@edulearn.com	CUR101	beginner	M101A	Introduction to Data Concepts	1	completed	2024-02-01	115.3	M101B	Understand basic data types and their uses
L0002	pace	78.2	Foundations of Data Literacy	Benjamin Lee	benjamin.lee@edulearn.com	CUR101	beginner	M101B	Data Collection Methods	2	in_progress	blank	63.7	M101C	Identify techniques for reliable data collection
L0003	remediation	56.1	Applied Machine Learning	Chloe Martin	chloe.martin@learnwise.org	CUR102	advanced	M102A	Supervised Learning Overview	1	completed	2024-03-21	212.6	M102B	Explain the principles of supervised algorithms
L0004	mastery	95.7	Foundations of Data Literacy	Daniel White	daniel.white@learnwise.org	CUR101	beginner	M101C	Data Analysis Basics	3	completed	2024-02-15	142.8	M101D	Perform simple analyses on structured datasets
L0005	blank	blank	Intermediate Python Programming	Ella Nguyen	ella.nguyen@edulearn.com	CUR103	intermediate	M103A	Functions and Scope	1	not_started	blank	blank	M103A	Construct modular code using functions
L0006	prerequisite	blank	Intermediate Python Programming	Finley Patel	finley.patel@brightstudy.com	CUR103	intermediate	M103B	Data Structures in Python	2	skipped	blank	0	M103C	Implement lists, dictionaries, and sets in code
L0007	mastery	88	Foundations of Data Literacy	Grace Campbell	grace.campbell@edulearn.com	CUR101	beginner	M101D	Visualizing Data	4	completed	2024-02-23	130	M101E	Create basic charts and graphs from data
L0008	pace	75.9	Statistics for Data Science	Harper Black	harper.black@learnwise.org	CUR104	intermediate	M104A	Descriptive Statistics	1	in_progress	blank	81.7	M104B	Summarize datasets using central tendency measures
L0009	mastery	99.2	Introduction to SQL	Isabella Wright	isabella.wright@brightstudy.com	CUR105	beginner	M105A	Basic Querying	1	completed	2024-01-29	162.9	M105B	Write simple SELECT statements
L0010	mastery	87.4	Statistics for Data Science	Jackson Kim	jackson.kim@learnwise.org	CUR104	intermediate	M104B	Probability Theory	2	completed	2024-03-03	117	M104C	Apply probability concepts to real data problems
L0011	blank	blank	Data Visualization Essentials	Liam Scott	liam.scott@edulearn.com	CUR106	beginner	M106A	Chart Types and Selection	1	not_started	blank	blank	M106A	Choose appropriate chart types for data
L0012	interest	82.4	Data Visualization Essentials	Mia Young	mia.young@brightstudy.com	CUR106	beginner	M106B	Color Theory in Visualization	2	in_progress	blank	72.4	M106C	Use color effectively in graphs and charts
L0013	remediation	48.6	Applied Machine Learning	Noah Evans	noah.evans@learnwise.org	CUR102	advanced	M102B	Unsupervised Learning Techniques	2	in_progress	blank	154.4	M102C	Cluster data using unsupervised algorithms
L0014	mastery	86.7	Introduction to SQL	Olivia Torres	olivia.torres@edulearn.com	CUR105	beginner	M105B	Filtering and Sorting Data	2	completed	2024-02-09	118.2	M105C	Apply WHERE and ORDER BY clauses
L0015	interest	89.5	Intermediate Python Programming	Penelope Green	penelope.green@brightstudy.com	CUR103	intermediate	M103C	Object-Oriented Programming	3	completed	2024-04-02	95.6	M103D	Design classes and objects in Python
L0016	prerequisite	blank	Statistics for Data Science	Quinn Murphy	quinn.murphy@edulearn.com	CUR104	intermediate	M104C	Statistical Inference	3	skipped	blank	0	M104D	Draw conclusions from data samples
L0017	mastery	92	Foundations of Data Literacy	Ryan Foster	ryan.foster@learnwise.org	CUR101	beginner	M101E	Interpreting Visualizations	5	completed	2024-03-01	120.1	M101F	Analyze and interpret data visualizations
L0018	pace	73.9	Statistics for Data Science	Sophia Perry	sophia.perry@brightstudy.com	CUR104	intermediate	M104D	Hypothesis Testing	4	completed	2024-03-10	82.6	M104E	Test hypotheses using sample data
L0019	mastery	90.8	Introduction to SQL	Thomas Hall	thomas.hall@edulearn.com	CUR105	beginner	M105C	Joining Data Tables	3	completed	2024-02-18	110.4	M105D	Combine tables using JOIN statements
L0020	remediation	61	Applied Machine Learning	Uma Brooks	uma.brooks@learnwise.org	CUR102	advanced	M102C	Model Evaluation Metrics	3	in_progress	blank	170.7	M102D	Assess models using accuracy and recall

What the 200 rows show

from the 200-row sample

Remediation (adaptation reason) stands out: mean score is 49.0, against 86.4 for the rest.

84.6median score
3curriculum levels
4completion statuses
9curriculums
3median module_sequence
120.3median time_spent_minutes

Mean score by adaptation_reason163 rows

score163 rows, in bands of 10

Median 84.6, from 0.0 to 100.0.

curriculum_title200 rows · top 10 of 22 values

Advanced Python Programming12
Machine Learning Mastery11
Front-End Essentials11
Machine Learning Foundations11
Web Development Track10
Applied Data Visualization10
Advanced Coding Skills10
Foundations of Data Literacy9
Statistics for Data Science9
Python Fundamentals9

16 columns by typefrom the column list below

string 12
integer 1
float 2
date 1

Columns

16 columns in three groups

blueprint · 16 columns
column	type	description	example
Text 12 columns
`learner_id`	string	Unique identifier for the learnerunique	L0001
`learner_name`	string	Full name of the learner	Ava Richardson
`learner_email`	string	Email address of the learnerunique	ella.nguyen@edulearn.com
`curriculum_id`	string	Unique identifier for the curriculum assigned to the learner9 curriculums	CUR101
`curriculum_title`	string	Title of the curriculum	Applied Machine Learning
`curriculum_level`	string	Difficulty level of the curriculum (e.g., beginner, intermediate, advanced)beginner · intermediate · advanced	beginner
`module_id`	string	Unique identifier for the module within the curriculum	M101A
`module_title`	string	Title of the module	Data Collection Methods
`learning_objective`	string	Primary learning objective of the module	Style web pages with CSS
`completion_status`	string	Status of module completion for the learnernot_started · in_progress · completed · skipped	completed
`recommended_next_module_id`	string	Identifier of the next recommended module for the learner based on adaptive logicoptional	M101B
`adaptation_reason`	string	Reason for recommending the next module (e.g., mastery, prerequisite, remediation)mastery · prerequisite · remediation · interest · pace · optional	mastery
Numbers 3 columns
`module_sequence`	integer	Order of the module within the curriculum1 or more	1
`score`	float	Score achieved by the learner in the module (0-100 scale)0 to 100 · optional	93.5
`time_spent_minutes`	float	Total time spent by the learner on the module in minutes0 or more · optional	115.3
Dates and times 1 column
`completion_date`	date	Date when the learner completed the module (if applicable)optional	2024-02-01

Use it for

An education dashboard
Score by adaptation_reason and a breakdown of curriculum_title. Excel, Power BI or Tableau.
Why do the 31 remediation rows have a mean score of 49.0?
A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
Learners200
L000193.5mastery
L000278.2pace
L000356.1remediat…
A software demo
Believable learners with learner_name, learner_email and curriculum_id to fill a screen in front of a buyer.

This dataset200 rows16 columns

Yours1,000 rows16 columns

blueprint · adaptive-learning-pathways-dataset

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 16 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Track student progression through pathways
Include module completion time
Flag skipped modules
Exclude optional course material

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: adaptive-learning-pathways-dataset

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Use it for

An education dashboard

A root-cause class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Education

What should your data show?