Student Records - live preview

Good morning, Marta

Your attendance is 86% and one assignment is due on Friday.

Attendance86%threshold is 85%

Assignments due1Friday 12:00

Average gradeBacross 4 modules

Credits earned72of 120 required

This week

3 items

Attendance dropped below 90%86% - falling under 85% triggers a tutor meetingWatch

Research methods assignment due FridayNot yet submitted - extensions close WednesdayDue

Data analysis result releasedMerit - feedback available from Claire DuboisNew

Timetable

Where you should be, and who is teaching it.

This week

Day	Time	Module	Room	Tutor	Status
Monday	09:00	Research methods	B204	Claire Dubois	Attended
Monday	13:00	Data analysis	C112	Tom Ellery	Attended
Tuesday	10:00	Statistics	B118	Claire Dubois	Absent
Wednesday	09:00	Research methods	B204	Claire Dubois	Attended
Thursday	14:00	Professional practice	A006	Daniel Novak	Scheduled
Friday	11:00	Data analysis	C112	Tom Ellery	Scheduled

A room or tutor change updates your timetable and notifies you the same day.

Attendance

By module, against the threshold that matters for your course.

Research methods94%

16 of 17 sessions

Data analysis88%

15 of 17 sessions

Statistics71%

12 of 17 - below threshold

Recent absences

Date	Module	Session	Reason given	Status
01 Sep 2026	Statistics	Lecture	None recorded	Unauthorised
25 Aug 2026	Statistics	Seminar	Illness	Authorised
18 Aug 2026	Data analysis	Workshop	None recorded	Unauthorised
11 Aug 2026	Statistics	Lecture	Medical appointment	Authorised
04 Aug 2026	Statistics	Seminar	None recorded	Unauthorised

Statistics is the module dragging your overall figure. Three unauthorised absences trigger a tutor meeting.

Results

Released marks with feedback, and what is still outstanding.

Your results

Module	Assessment	Submitted	Mark	Grade	Status
Data analysis	Portfolio 2	22 Aug 2026	68	Merit	Released
Research methods	Essay 1	04 Aug 2026	72	Distinction	Released
Statistics	Test 1	28 Jul 2026	54	Pass	Released
Research methods	Assignment 2	-	-	-	Due Friday
Professional practice	Reflective log	-	-	-	Due 30 Sep

Marks are released by a tutor with feedback attached, never automatically on marking.

My group

Twenty-four students, with the three numbers that predict withdrawal.

Students24in tutor group 3B

Below attendance threshold5under 85%

Assignments overdue7across 4 students

Tutor group 3B

Student	Attendance	Overdue	Average	Last contact	Status
Student 8841	62%	3	41	18 Aug 2026	At risk
Student 8836	71%	2	52	28 Aug 2026	At risk
Student 8829	86%	1	64	01 Sep 2026	Watch
Student 8820	94%	0	72	-	On track
Student 8812	98%	0	78	-	On track
Student 8804	79%	1	58	22 Aug 2026	Watch

Students are shown by reference in this demo. A live build shows names to the assigned tutor only.

At risk

Attendance, submission and grade trend together. One signal is noise; three is a pattern.

Flagged this week

5 flagged

Student 8841 - attendance 62%, 3 overdueGrade trend falling for 6 weeks - no contact since 18 AugUrgent

Student 8836 - attendance 71%, 2 overdueTwo modules affected - contacted 28 Aug, no replyUrgent

Student 8804 - attendance 79%Attendance only - grades holding at 58Watch

Student 8829 - 1 assignment overdueFirst occurrence, attendance fineWatch

Enrolment

Applications, offers and registration, with what is still missing.

Current cycle

Applicant	Course	Applied	Stage	Outstanding	Status
Applicant 9412	Data science, level 4	22 Aug 2026	Offer made	Qualification evidence	Awaiting
Applicant 9408	Data science, level 4	20 Aug 2026	Enrolled	-	Complete
Applicant 9402	Business, level 3	18 Aug 2026	Interview	References	Awaiting
Applicant 9396	Business, level 3	14 Aug 2026	Offer made	Funding confirmation	Awaiting
Applicant 9388	Data science, level 4	11 Aug 2026	Declined	-	Closed
Applicant 9371	Engineering, level 4	04 Aug 2026	Enrolled	-	Complete

A student cannot be enrolled while identity or qualification evidence is outstanding.

Audit log

Every mark release, enrolment and record access. Retained per your regulator.

Events

When	Actor	Action	Target	Source
01 Sep 09:00	System	Flagged 5 students below attendance threshold	Group 3B	scheduler
31 Aug 15:40	Claire Dubois	Released mark with feedback	Student 8829	web
30 Aug 11:20	Claire Dubois	Viewed student record	Student 8841	web
28 Aug 14:00	Lena Brandt	Enrolled applicant	Applicant 9408	web
22 Aug 10:30	Lena Brandt	Recorded authorised absence	Student 8836	web

Record access is logged whether or not anything was changed.

Ivo Petrov

One student: attendance, marks and what is known.

EnrolledAt riskSTU-20841 · Ivo Petrov · year 2, engineering · tutor Hilde Rasmussen

Ivo Petrov

Attendance fell from 94% to 61% over five weeks. Marks have not moved yet, because the assessments that would show it are not due until November.

Attendance61%was 94% in week 1

Average mark68%unchanged since June

Assessments due4first one 14 November

Contact attempts2both email, no reply

Attendance by module

Thermodynamics38% · 9 a.m. MondaysWorst

Materials54% · 9 a.m. ThursdaysPoor

Design studio92% · afternoonsGood

Mathematics88% · afternoonsGood

Both failing modules are morning sessions and both good ones are afternoons. That pattern is a question to ask rather than an answer, and it is not in the average.

What is known

Started workDeclared in October · 24 hours a weekOn record

TravelMoved home in September · 70 minutes each wayOn record

Support referralNoneNever made

AdjustmentNone requestedNone

Both facts were recorded at the time and neither triggered anything. Together they explain the morning pattern exactly.

Contact history

Attendance letter, automatic18 October · no replySent

Attendance letter, automatic1 November · no replySent

Tutorial offeredNeverNot done

Phone callNeverNot done

Two automatic letters to somebody working twenty-four hours a week and commuting seventy minutes. Nobody has spoken to him.

Enrol a student

Who they are, what they are studying, and what they need.

The student

NameProgrammeYear

ModeEntry route

Circumstances

Working hours a weekNoneUnder 1010 to 20Over 20

Travel each wayUnder 30 minutes30 to 60Over 60

Caring responsibilitiesNoneSomeSubstantial

Asked at enrolment and asked again each term, because the answers change and the ones that change are the ones that matter. Over twenty hours and over an hour of travel together predict a nine-o’clock attendance problem better than anything academic.

Adjustments and support

Any declared disability or condition, with the student consent to share it
Timetable constraints that follow from work or caring, before the timetable is built
Assessment adjustments, agreed once and applied automatically
A named contact in student support from week one, not from the first crisis

Circumstances are visible to the tutor and to support, and not to markers.

Thermodynamics, year 2

One module: who is on it and how it is going.

RunningAttendance 64%MOD-2140 · 88 students · Mondays 09:00 · lead Dr Anselm Werner

Enrolled884 withdrew this term

Attendance64%faculty average is 81%

Pass rate last year72%faculty average 86%

At risk now1820% of the cohort

By circumstance

64% attendance

Cohort	Students	Attendance	Predicted pass	Pattern
Lives under 30 min away	41	84%	89%	Fine
Lives 30 to 60 min away	28	61%	74%	Falling
Lives over 60 min away	19	38%	58%	Failing
Works over 20 hrs a week	22	44%	61%	Failing
Both of the last two	11	31%	49%	Failing

The module is not harder for these students. It is at nine in the morning, and that is the only variable that separates the rows.

Moving this session to the afternoon is a timetabling decision that nobody in this system can make. Showing why it matters is the part that can be done here.

Students at risk

Who is slipping, on what evidence, and who has spoken to them.

Flagged at risk1426.2% of students

Attendance only96marks not yet due

Contacted by a person38of 142

Referred to support14of 142

At risk

104 not spoken to

Student	Programme	Attendance	Mark	Known factors	Contacted
Ivo Petrov	Engineering, Y2	61%	68%	Works 24 hrs, 70 min travel	Letters only
Noor Hakimi	Computing, Y1	48%	54%	Caring, substantial	Tutorial held
Sam Oyelaran	Business, Y3	72%	41%	None recorded	Letters only
Ruta Kalnina	Nursing, Y2	58%	77%	Placement travel	Referred
Emre Yilmaz	Engineering, Y1	39%	-	Works 30 hrs	No contact

The known-factors column is filled from what students told us at enrolment. Three of five rows explain themselves and were flagged as if they were a mystery.

An automatic letter is not contact. A hundred and four students have had two each and nothing else, and the ones most likely to ignore a letter are the ones this list is for.

Progression

Who continues, who does not, and when it was decided.

Progressing86%faculty target is 90%

Withdrawn this year1486.4% of students

Withdrew before December9665% of withdrawals

Had a support referral first22of 148

Withdrawals by month

This academic year

October34

November62

December41

January18

February and later12

Two thirds are gone before the first assessment. Whatever decides it happens in the first ten weeks, and no mark exists in that window to see it with.

What withdrawals had in common

Attendance below 60% by week 6112 of 148Visible early

Working over 20 hours78 of 148Known at enrolment

Travel over an hour64 of 148Known at enrolment

Never spoke to a tutor104 of 148Preventable

The first three were all knowable by week six. The fourth is the one that is actually a choice this institution made.

Attendance is the only signal available before the first assessment, and two thirds of withdrawals happen before it. A system that waits for marks to identify risk finds most of these students after they have gone.

Reports: activity

Volume and throughput, by owner and by programme.

Volume over time

12 weeks

Attendance per week across the institution. It falls every term in the same shape, and the fall starts before any assessment exists to explain it.

Enrolled students2,284across 4 faculties

Opened this month2,284against 148 closed

Median age-target is,

Owned by nobody38oldest is 6 weeks

By programme

This quarter

Engineering684

Computing612

Business548

Nursing440

By tutor

This quarter

Tutor	Open	Closed	Median days	Overdue
Hilde Rasmussen	41	38	84	2
Anselm Werner	38	31	76	6
Beatriz Nunes	44	41	88	3
Karim Haddad	39	28	71	9
Unassigned	38	0	-	38

The fourth column is the share of at-risk tutees actually spoken to. Thirty-eight students have no tutor at all, six weeks into term.

Reports: performance

Against target, with the gap named.

Against target86%progressing to the next year

Best tutorNursing91% progression

WorstEngineering81%, and the earliest 9am timetable

Trend-2 ptson last quarter

Against target by programme

100 is the target

Nursing91

Business88

Computing86

Engineering81

Entry grades across the four faculties are within three points of each other. The thing that separates them is when the compulsory sessions are scheduled.

What moved

Rule-derived

Progression down 2 pointsEntirely in students who work over twenty hoursDown

Two thirds of withdrawals before DecemberBefore any assessment existsWatch

Support referrals up 18%Since circumstances were re-asked each termUp

Threshold rules over the figures above, not a model. Each line names the number it came from so you can disagree with it.

Reports: exceptions

What breached, what is late and what nobody owns.

Breached38students with no tutor

At risk142inside 48 hours

Unassigned38no owner at all

Reopened104closed then raised again

Exceptions

38 open

Student	Programme	Attendance	Known at enrolment	Spoken to
Emre Yilmaz	Engineering, Y1	39%	Works 30 hours a week	Never
Ivo Petrov	Engineering, Y2	61%	Works 24 hrs, 70 min travel	Letters only
Sam Oyelaran	Business, Y3	72%	Nothing recorded	Letters only
Noor Hakimi	Computing, Y1	48%	Substantial caring	Tutorial held
Ruta Kalnina	Nursing, Y2	58%	Placement travel	Referred

Four of five had the explanation on file before term started. The system flagged them in week six as though it were a surprise.

A hundred and four at-risk students have received two automatic letters and no conversation. The students least likely to answer a letter are precisely the ones this list exists to find.

People

Who has an account, and what they own.

People

188 accounts

Name	Role	Faculty	Last active	Status
Ottilie Vance	Administrator	Registry	10 minutes ago	Active
Hilde Rasmussen	Tutor	Engineering	30 minutes ago	Active
Anselm Werner	Module lead	Engineering	1 hour ago	Active
Beatriz Nunes	Tutor	Nursing	2 hours ago	Active
Karim Haddad	Tutor	Business	Yesterday	Active
Sinead Kelleher	Student support	Support	45 minutes ago	Active

Deactivating somebody prompts you to reassign what they own rather than doing it silently, because an unowned record is how a register rots.

Roles

What each role is scoped to and what it may approve.

Roles

5 roles

Role	Type	People	Scope	Can approve to	Status
Administrator	Built in	3	Everything	No limit	Active
Registry	Built in	8	All records	Enrols and withdraws	Active
Tutor	Built in	96	Own tutees and modules	Refers to support	Active
Student support	Built in	14	Referred students	Agrees adjustments	Active
Marker	Custom	62	Assessments only	-	Active

A role carries a scope and an approval ceiling. Both are enforced on the server; hiding a button is not access control.

Recent changes

Marker role stopped seeing declared circumstancesOttilie Vance · 2 SeptemberLogged

Tutors given attendance by module rather than overallOttilie Vance · 14 JulyLogged

Role changes sit in the same audit log as record changes, because both change what somebody can do.

Why custom roles exist

A tutor sees why a student is struggling, because that is the conversation
A marker sees the work and not the circumstances, because that is the point of marking
Student support agrees adjustments and registry applies them
Nobody edits a recorded mark; a correction is a second, visible entry

Permissions

Every capability against every role, as enforced.

What each role can do

10 capabilities

Capability	Administrator	Registry	Tutor	Support	Marker
View a student record	Yes	Yes	Own tutees	Referred	No
See declared circumstances	Yes	Yes	Own tutees	Yes	No
See attendance	Yes	Yes	Own modules	Referred	No
Record a mark	Yes	Yes	Own modules	No	Yes
Edit a recorded mark	No	No	No	No	No
Refer to student support	Yes	Yes	Yes	Yes	No
Agree an adjustment	Yes	No	No	Yes	No
Enrol or withdraw	Yes	Yes	No	No	No
Export student data	Yes	Yes	No	No	No
Read the audit log	Yes	Yes	Own	Yes	No

Enforced per query rather than by hiding controls. A scoped "yes" means the row exists but is limited, which is why some actions need a second person.

A marker cannot see declared circumstances and a tutor can. Marking should not know why the work is late; the conversation about it should.

Notifications

What the system sends, to whom, and when.

What is sent

Attendance below 60% for 2 weeksTo the tutor, with the known factorsOn

Attendance falls 20 points in a termTo the tutor, before any mark is dueOn

At-risk student not contacted in 14 daysTo the tutor and the programme leadOn

Circumstances updatedTo the tutorOn

Student has no tutorTo registry, weeklyOn

Every message names the record it is about, so a reply lands somewhere.

Quiet hours

No routine notifications20:00 to 07:00, local to the personOn

Urgent overridesA safeguarding concern, at any hourOn

Weekly digestMonday 08:00On

A notification that waits until morning is one that did not work, so the override is explicit rather than a setting nobody finds.

Notification rules are yours to set. What is here is a starting point drawn from how the queue actually behaves.

Integrations

Connected systems and their sync history.

Connected

4 connected

TimetablingWhen sessions run, and whereConnected

Attendance captureRegisters from the roomConnected

Virtual learningSubmissions and engagementConnected

Student supportReferrals and agreed adjustmentsConnected

FinanceFee status and hardship fundsNot connected

What breaks without them

Without attendance capture the only early signal disappears and risk waits for marks, which arrive after two thirds of withdrawals
Without timetabling the nine-o’clock pattern cannot be seen at all
Without finance a student in hardship looks identical to one who stopped caring
Records still work; the early warning is what goes

Sync history

Last 24 hours

When	Source	Records	Result
Continuous	Attendance capture	2,284 students	Live
06:00 today	Timetabling	412 sessions	Current
Hourly	Virtual learning	2,284 students	Live
06:00 today	Student support	142 referrals	Current

A sync that half-succeeded is listed as such rather than as a failure, because the records that did land still changed something.

Import and export

Bulk load and extract, with per-row validation.

Import

WhatIf a row already exists

Validated before anything is written. A file with one bad row imports the rest and tells you which line failed and why.

Export

WhatFormat

RangeEverythingThis yearFiltered view

Recent imports and exports

Last 30 days

When	What	Rows	Result	By
1 Sep 07:00	Import	641	Complete	Ottilie Vance
1 Sep 18:00	Export	2,284	Complete	Ottilie Vance
26 Aug 09:00	Import	412	Complete	Ottilie Vance

Every import is kept with its source file, so a bad load can be traced rather than argued about.