Adaptive Learning Pathway Analysis

This dataset provides granular tracking of student engagement and adaptive content progression on an EdTech platform, including timestamps, performance scores, algorithmic recommendations, and feedback. It enables analysis of personalized learning pathways, effectiveness of adaptive algorithms, and identification of areas for improvement in student outcomes. Ideal for educational data analytics, pathway optimization, and EdTech product evaluation.

  • last updated 18 Dec 2025
  • by GoMask
The brief that made it

Analyzing the effectiveness of adaptive learning algorithms

Sample rows

preview · 8 of 120 rows · all 20 columns
student_idstringcontent_typestringengagement_scorefloatadmin_review_flagbooleanadaptive_algorithm_versionstringstudent_namestringstudent_emailstringcourse_idstringcourse_titlestringcontent_idstringcontent_titlestringengagement_start_timedatetimeengagement_end_timedatetimeengagement_duration_secondsintegercontent_difficulty_levelintegerstudent_performance_scorefloatadaptive_pathway_idstringcontent_completion_statusstringrecommendation_reasonstringfeedback_textstring
stu_0001video0.92falsev1.0.0Lucas Martinlucas.martin@edulearn.comCRS-MATH101Calculus IVID-00101Introduction to Limits2024-03-12T09:14:21Z2024-03-12T09:32:02Z10612blankPATH-MATH-AcompletedreviewThe video was clear and easy to follow.
stu_0002reading0.78falsev1.2.3Amina Patelamina.patel@university.eduCRS-ENG205Modern LiteratureREAD-00201Poetry of the 20th Century2024-03-13T15:47:13Z2024-03-13T16:05:19Z10963blankPATH-ENG-BcompletedreviewEnjoyed reading the poems, insightful.
stu_0003quiz0.88falsev2.1.0Ethan Zhaoethan.zhao@learnspace.frCRS-HIST150World HistoryQUIZ-00301Ancient Civilizations Quiz2024-03-15T13:03:27Z2024-03-15T13:15:40Z733492.5PATH-HIST-XcompletedchallengeQuiz was tough but I learned a lot.
stu_0004simulation0.82falsev2.0.0Isabelle Dupontisabelle.dupont@openlearn.frCRS-CS102Intro to ProgrammingSIM-00401Variables Simulation2024-03-16T10:30:00Z2024-03-16T11:00:15Z18153blankPATH-CS-Bin_progressremediationSimulation is helpful but a bit tricky.
stu_0005video0.95falsev1.1.1Oscar Romerooscar.romero@edulearn.comCRS-PHY101Physics FundamentalsVID-00501Newton's Laws Explained2024-03-19T09:15:45Z2024-03-19T09:38:01Z13362blankPATH-PHY-AcompletedreviewGreat explanations, easy to understand.
stu_0006assignment0.74falsev1.0.0Sophie Williamssophie.williams@campus.eduCRS-MATH101Calculus IASSIGN-00601Limits Assignment2024-03-20T14:04:18Z2024-03-20T15:30:25Z5227478PATH-MATH-BcompletedchallengeAssignment was long but manageable.
stu_0007discussion0.77falsev1.2.3Mohamed Faroukmohamed.farouk@learnspace.frCRS-ENG205Modern LiteratureDISC-00701Discussion: Symbolism in Poetry2024-03-21T17:00:00Z2024-03-21T17:35:22Z21322blankPATH-ENG-AcompletedreviewEnjoyed discussing with classmates.
stu_0008video0.81falsev2.1.0Emily Chenemily.chen@openlearn.frCRS-HIST150World HistoryVID-00801Rise of Roman Empire2024-03-22T08:20:10Z2024-03-22T08:32:47Z7573blankPATH-HIST-YcompletedreviewThe visuals were very helpful.

What the 120 rows show

from the 120-row sample

Other (content type) stands out: 11 of its 11 rows have admin_review_flag = true, against 1 of 109 for the rest.

  • 10%admin_review_flag = true
  • 0.77median engagement_score
  • 3recommendation reasons
  • 4content completion statuses
  • 5courses
  • 12course titles
Admin review flag rate by content_typeadmin_review_flag = true
0%50%100%0%video0 of …4%quiz1 of …0%read…0 of …0%simu…0 of …0%assi…0 of …0%disc…0 of …100%other11 of…
engagement_score119 rows, in bands of 0.1
020404602231934321700.51engagement_score →

Median 0.77, from 0.02 to 0.97.

adaptive_algorithm_version120 rows · top 10 of 14 values
  1. v2.1.018
  2. v3.0.211
  3. v4.2.111
  4. v5.1.311
  5. v1.0.010
  6. v1.2.310
  7. v2.0.010
  8. v1.1.110
  9. v1.9.49
  10. v2.1.34
20 columns by typefrom the column list below
  • string 13
  • integer 2
  • float 2
  • datetime 2
  • boolean 1

Columns

20 columns in four groups
blueprint · 20 columns
columntypedescriptionexample
Text 13 columns
student_idstringUnique identifier for each student on the platformstu_0001
student_namestringFull name of the studentoptionalLucas Martin
student_emailstringEmail address of the studentethan.zhao@learnspace.fr
course_idstringUnique identifier for the course or learning pathway5 coursesCRS-MATH101
course_titlestringTitle of the course or learning pathway12 titles · optionalCalculus I
content_idstringUnique identifier for the adaptive content moduleVID-00101
content_titlestringTitle or name of the adaptive content moduleoptionalIntroduction to Limits
content_typestringType of content (e.g., video, quiz, reading, simulation)7 valuesvideo
adaptive_pathway_idstringUnique identifier for the adaptive pathway assigned to the studentoptionalPATH-MATH-A
adaptive_algorithm_versionstringVersion of the adaptive algorithm used to assign contentoptionalv1.0.0
content_completion_statusstringStatus of content completion by the studentnot_started · in_progress · completed · skippedcompleted
recommendation_reasonstringBrief reason or logic for why this content was recommended (e.g., 'remediation', 'challenge', 'review')3 reasons · optionalreview
feedback_textstringOptional feedback or comments from the student about the contentoptionalStories were engaging.
Numbers 4 columns
engagement_duration_secondsintegerTotal duration (in seconds) the student spent on the content0 or more · optional1061
engagement_scorefloatPlatform-calculated engagement score for this session (0-1 scale)0 to 1 · optional0.92
content_difficulty_levelintegerDifficulty level of the content as assigned by the adaptive algorithm (1-5)1 to 5 · optional2
student_performance_scorefloatStudent's performance score for this content (e.g., quiz score, assignment grade, 0-100)0 to 100 · optional92.5
Dates and times 2 columns
engagement_start_timedatetimeTimestamp when the student started engaging with the content2024-03-12T09:14:21Z
engagement_end_timedatetimeTimestamp when the student ended engagement with the contentoptional2024-03-12T09:32:02Z
True or false 1 column
admin_review_flagbooleanFlag indicating if this engagement record was flagged for admin reviewoptionalfalse

Use it for

  • admin review f…10%12 of 120 rowsmean engagement score…0.84video0.72quiz0.74read…0.77simu…

    An education dashboard

    The admin_review_flag rate, engagement_score by content_type and a breakdown of adaptive_algorithm_version. Excel, Power BI or Tableau.

  • Why do 12 of 120 rows have admin_review_flag = true?

    A root-cause class exercise

    Hand out the rows and one question. The answer is in the data, not in the brief.

  • A software demo

    Believable students with student_name, student_email and course_id to fill a screen in front of a buyer.

Not quite right?

Make it yours.

Same 20 columns, your size and your rules. See 20 rows before you pay.

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This dataset120 rows20 columns
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blueprint · adaptive-learning-pathway-analysis

Behind this dataset

Same schema. As many rows as you need.

These 120 rows came out of a blueprint — 20 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
  • Each row represents a unique student session on the platform.
  • Adaptive module recommendations must be logged with timestamp and completion status.
  • Engagement level scored based on interaction events (e.g., clicks, time-on-task, hints used).
  • Learning pathway adjustments are triggered if engagement falls below a threshold for two consecutive sessions.
  • Outcome metrics (e.g., quiz improvement) must be linked to pathway changes.
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-pathway-analysis

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