Peer Tutoring Session Analytics
This dataset provides detailed analytics on peer tutoring sessions within academic institutions, including session frequency, participant feedback, session outcomes, and academic improvement indicators. It enables institutions to evaluate and optimize their peer tutoring programs by analyzing session effectiveness, attendance, and student progress.
Sample rows
preview · 8 of 200 rows · all 16 columns| session_idstring | session_typestring | tutor_feedback_scorefloat | attendance_confirmedboolean | subjectstring | student_idstring | tutor_idstring | session_datedate | session_start_timedatetime | session_end_timedatetime | duration_minutesinteger | student_feedback_scorefloat | session_outcomestring | academic_improvement_indicatorboolean | institution_idstring | notesstring |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| S00001 | one-on-one | 4.7 | true | Mathematics | STU1001 | TUT2001 | 2023-02-07 | 2023-02-07T15:00:00 | 2023-02-07T16:00:00 | 60 | 4.8 | completed | true | INST01 | Covered calculus basics, student demonstrated strong improvement. |
| S00002 | online | 1.8 | true | Physics | STU1002 | TUT2002 | 2023-03-15 | 2023-03-15T09:00:00 | 2023-03-15T10:00:00 | 60 | 2 | completed | false | INST04 | Student appeared tired, low engagement throughout session. |
| S00003 | group | 2.9 | true | Computer Science | STU1003 | TUT2003 | 2023-04-22 | 2023-04-22T17:30:00 | 2023-04-22T19:30:00 | 120 | 2.7 | completed | false | INST03 | Group struggled with coding exercises; distractions noted. |
| S00004 | group | 2.5 | true | History | STU1004 | TUT2001 | 2023-05-10 | 2023-05-10T14:00:00 | 2023-05-10T14:30:00 | 30 | 2.3 | completed | false | INST03 | Low participation, students distracted by other activities. |
| S00005 | in-person | 5 | true | Mathematics | STU1005 | TUT2004 | 2023-06-02 | 2023-06-02T20:00:00 | 2023-06-02T21:00:00 | 60 | 4.9 | completed | true | INST01 | Worked on algebra, student showed marked progress. |
| S00006 | online | 1 | true | English Literature | STU1006 | TUT2005 | 2023-09-17 | 2023-09-17T07:00:00 | 2023-09-17T07:30:00 | 30 | 1.5 | completed | false | INST06 | Early morning session, student was not focused. |
| S00007 | in-person | 4.1 | true | Physics | STU1007 | TUT2002 | 2023-10-05 | 2023-10-05T13:00:00 | 2023-10-05T15:00:00 | 120 | 3.9 | completed | true | INST04 | Prepared for upcoming exam, student made good progress. |
| S00008 | group | 3 | true | French | STU1008 | TUT2003 | 2023-11-21 | 2023-11-21T11:00:00 | 2023-11-21T12:15:00 | 75 | 2.8 | completed | false | INST06 | Group practice session, some improvement noted. |
| S00009 | in-person | 4.4 | true | Spanish | STU1009 | TUT2005 | 2023-12-10 | 2023-12-10T16:30:00 | 2023-12-10T18:30:00 | 120 | 4.2 | completed | true | INST02 | Student practiced conversational skills and progressed steadily. |
| S00010 | online | blank | false | Mathematics | STU1010 | TUT2006 | 2024-01-28 | 2024-01-28T08:30:00 | 2024-01-28T08:45:00 | 15 | blank | cancelled | blank | INST01 | Session cancelled due to technical issue. |
| S00011 | online | 2 | true | Computer Science | STU1011 | TUT2007 | 2024-02-15 | 2024-02-15T23:45:00 | 2024-02-16T00:45:00 | 60 | 1.2 | completed | false | INST03 | Late night session, student struggled to stay engaged. |
| S00012 | one-on-one | 4.8 | true | Mathematics | STU1012 | TUT2008 | 2024-03-02 | 2024-03-02T15:00:00 | 2024-03-02T16:00:00 | 60 | 5 | completed | true | INST01 | Student excelled in geometry, rapid improvement shown. |
| S00013 | group | blank | false | History | STU1013 | TUT2004 | 2024-04-10 | 2024-04-10T10:00:00 | 2024-04-10T10:01:00 | 1 | blank | cancelled | blank | INST03 | Session cancelled due to group absence. |
| S00014 | online | 2.2 | true | Computer Science | STU1014 | TUT2009 | 2024-05-12 | 2024-05-12T23:30:00 | 2024-05-13T00:30:00 | 60 | 1.4 | completed | false | INST03 | Session crossed midnight, student showed little improvement. |
| S00015 | group | 3.3 | true | Mathematics | STU1015 | TUT2010 | 2024-06-18 | 2024-06-18T12:00:00 | 2024-06-18T13:30:00 | 90 | 3 | completed | blank | INST01 | Group solved equations, moderate engagement. |
| S00016 | in-person | 4 | true | Physics | STU1016 | TUT2009 | 2024-07-07 | 2024-07-07T16:00:00 | 2024-07-07T18:00:00 | 120 | 4.1 | completed | true | INST04 | Student reviewed mechanics, good progress before exam. |
| S00017 | group | 2.7 | true | Computer Science | STU1017 | TUT2007 | 2024-08-09 | 2024-08-09T09:00:00 | 2024-08-09T11:00:00 | 120 | 3.2 | completed | blank | INST03 | Group coding session, some students participated actively. |
| S00018 | in-person | 3.1 | true | French | STU1018 | TUT2008 | 2024-09-04 | 2024-09-04T18:00:00 | 2024-09-04T21:00:00 | 180 | 2.6 | completed | blank | INST06 | Intensive session for French exam preparation. |
| S00019 | one-on-one | 4.6 | true | Mathematics | STU1019 | TUT2011 | 2024-10-11 | 2024-10-11T10:30:00 | 2024-10-11T12:00:00 | 90 | 4.7 | completed | true | INST01 | Student excelled at statistics, high engagement. |
| S00020 | online | blank | false | English Literature | STU1020 | TUT2012 | 2024-11-22 | 2024-11-22T13:00:00 | 2024-11-22T13:02:00 | 2 | blank | cancelled | blank | INST06 | Session cancelled due to student illness. |
What the 200 rows show
from the 200-row sampleOnline (session type) stands out: mean tutor_
- 86%attendance_
confirmed = true - 3.7median tutor_
feedback_ score - 3session outcomes
- 6institutions
- 60median duration_
minutes - 3.5median student_
feedback_ score
Median 3.7, from 1.0 to 5.0.
- string 8
- integer 1
- float 2
- date 1
- datetime 2
- boolean 2
Columns
16 columns in four groups| column | type | description | example |
|---|---|---|---|
| Text 8 columns | |||
session_id | string | Unique identifier for each peer tutoring sessionunique | S00001 |
student_id | string | Unique identifier for the student receiving tutoring | STU1001 |
tutor_id | string | Unique identifier for the peer tutor conducting the session | TUT2001 |
subject | string | Academic subject or topic covered during the session8 subjects | Mathematics |
session_type | string | Type of session (e.g., one-on-one, group, online, in-person)one-on-one · group · online · in-person | one-on-one |
session_outcome | string | Outcome of the session (e.g., completed, cancelled, rescheduled)completed · cancelled · rescheduled | completed |
notes | string | Additional notes or comments about the sessionoptional | Covered calculus basics, … |
institution_id | string | Unique identifier for the academic institution6 institutions | INST01 |
| Numbers 3 columns | |||
duration_minutes | integer | Total duration of the session in minutes1 or more | 60 |
student_feedback_score | float | Feedback score provided by the student (e.g., 1.0 to 5.0)1 to 5 · optional | 4.8 |
tutor_feedback_score | float | Feedback score provided by the tutor (e.g., 1.0 to 5.0)1 to 5 · optional | 4.7 |
| Dates and times 3 columns | |||
session_date | date | Date when the tutoring session occurred | 2023-02-07 |
session_start_time | datetime | Timestamp when the session started | 2023-02-07T15:00:00 |
session_end_time | datetime | Timestamp when the session ended | 2023-02-07T16:00:00 |
| True or false 2 columns | |||
attendance_confirmed | boolean | Indicates if both tutor and student attended the session | true |
academic_improvement_indicator | boolean | Indicates if the student showed academic improvement after the sessionoptional | true |
Use it for
An education dashboard
The attendance_
confirmed rate, tutor_ feedback_ score by session_ type and a breakdown of subject. Excel, Power BI or Tableau. Why do the 22 online rows have a mean tutor_
feedback_ score of 2.0? A root-cause class exercise
Hand out the rows and one question. The answer is in the data, not in the brief.
- Sessions200S000014.7one-on-o…S000021.8onlineS00010online
A software demo
Believable sessions with student_
id, tutor_ id and session_ date to fill a screen in front of a buyer.
This dataset200 rows16 columns
Yours1,000 rows16 columns
blueprint · peer-tutoring-session-analytics
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
- Record session date and participant IDs
- Track session length and subject focus
- Flag repeated tutoring pairs
- Exclude canceled sessions
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
- peer-tutoring-session-analytics