The Student Who Attends but Doesn't Learn
When Attendance Hides the Real Problem.
A student is in class every day, but their grades keep falling. Nothing in the attendance register raises an alarm. If attendance looks good, how do we know when a student is actually at risk?
Read the case → 
CASE STUDY 02
Educational Management & Learning Design
The Case
Maya attends almost every class.
She arrives on time, sits through the lectures, takes notes and completes the attendance requirements. From the lecturer's perspective, Maya appears to be a good student.
Her early quiz scores are reasonable because the questions mainly test definitions and basic concepts.
But when students are asked to:
- design a simple database,
- decide which tables are needed,
- identify relationships,
- explain why a particular design is appropriate,
Maya struggles.
She can recognise the correct answer when it is presented to her, but has difficulty producing or applying the knowledge independently.
The lecturer initially thinks:
"She attends every class. Perhaps she just needs to study more."
But that explanation doesn't quite fit.
- Maya is attending.
- She is taking notes.
She is completing the required activities.
So the question becomes:
If a student is physically present and appears engaged, why might meaningful learning still not be happening?
The Problem
Maya wasn't an attendance problem. She was a learning-risk problem.
Her attendance record looked healthy, but attendance alone could not tell us whether she was keeping up with the course.
The warning signs were elsewhere: declining performance, incomplete work, and changes in engagement. Individually, each signal might be easy to overlook. Together, they could tell a very different story.
The challenge, therefore, was not simply to track whether students were attending class. It was to identify patterns early enough to understand who might be struggling—and intervene before the situation became critical.
-----------------------------------------------------------------------------------Instead of treating attendance as the only indicator, I looked at three types of signals that can reveal when a student may be struggling.
A - Attendance
Is the student attending regularly, or are absences becoming a pattern?
B - Behaviour
Are there changes in participation, engagement, interaction, or submission behaviour?
C - Course Performance
Are grades falling? Are assignments incomplete? Is the student repeatedly struggling with assessments?
Any one of these signals can be misleading on its own. Together, they can reveal a pattern.
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The Early Warning Framework
The framework connects the available student data to a simple intervention process.
Rather than treating a single low score or missed class as a crisis, it looks for patterns across multiple signals and gives educators an opportunity to investigate what may be happening.
The process:
Student Data → ABC Signals → Risk Pattern → Educator Review → Early Intervention
The important step is Educator Review.
The system can flag a student who may be at risk, but it should not decide what that student needs. That requires human judgement and, ideally, a conversation with the student.
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What Would This Look Like for Maya?
Let's bring the framework back to Maya.
Her attendance by itself would not have raised an immediate concern. She was still showing up for class. But when the other signals were considered alongside attendance, a different picture began to emerge:
Attendance
Regular - no obvious warning from attendance alone.
Behaviour
Participation and submission activity were beginning to decline.
Course Performance
Assessment results were falling, and difficulties were becoming more consistent.
Risk Pattern
Several signals were beginning to appear together.
Educator Action
Rather than waiting for Maya to fail, the educator could review the pattern, speak with her, and find out what was actually getting in the way of her learning.
The important point:
Maya's attendance didn't trigger the warning. The pattern did.
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What Happens After the Alert?
An alert is not the intervention.
It is a signal to look closer.
Once a student is flagged, the educator reviews the available information and, where appropriate, speaks with the student to understand what may be behind the pattern.
Different problems may require different responses:
Academic difficulty
→ Targeted tutoring, additional learning resources, or academic support.
Low engagement
→ A conversation with the student, mentoring, or a closer check-in.
Attendance concerns
→ Explore possible barriers before assuming a lack of motivation.
Multiple concerns
→ A coordinated support plan involving the relevant academic or student-support team.
From Detection to Support
The educator helps determinewhat kind of attention is appropriate?
- Low engagement
- Attendance concerns
- Multiple concerns
The objective is not to automatically assign a solution based on a data point. It is to use the data as a starting point for a human conversation.
The complete process therefore becomes:
Detect → Review → Understand → Support → Follow Up
The system helps identify
- who may need attention
The educator helps determine
- what kind of attention is appropriate
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The Intervention Doesn't End Here
Identifying a student at risk and offering support is only the beginning.
After the intervention, the educator needs to look again at the same signals that triggered the concern.
- Has Maya's performance improved?
- Is she submitting work more consistently?
- Has her engagement changed?
- Is she still showing signs that additional support may be needed?
This creates a simple feedback loop:
Detect → Review → Understand → Support → Follow Up → Reassess
If the indicators improve, the student can gradually return to normal monitoring.
If they don't, the educator can revisit the situation and consider whether a different or more intensive form of support is needed.
The aim is not to monitor students indefinitely. It is to create a timely cycle of support, reflection and follow-up before a temporary learning difficulty becomes a much larger problem.
An early warning system should not simply identify students. It should create an opportunity to respond, learn from the response, and adjust the support.
What I Designed
For this case, I designed an Early-Intervention Framework for Student Retention that brings together three commonly available sources of student information:
Attendance
- Patterns of absence and attendance.
Behaviour
- Changes in participation, engagement and submission activity.
Course Performance
- Assessment results, grades and evidence of learning difficulties.
The educator remains at the centre of the process—reviewing the evidence, understanding the student's circumstances, selecting an appropriate intervention, and following up on the outcome.
The Design Principle
- People understand the context.
- Intervention provides the support.
- Follow-up checks whether it helped.
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What This Case Taught Me
The case reinforced an important principle in student support:
A student can look fine on the surface and still be struggling underneath.
Attendance, grades, submissions and engagement each tell only part of the story. The value comes from looking at them together and asking what they might mean in the student's actual context.
It also reminded me that data should support professional judgement, not replace it.
An early warning is not a diagnosis. It is an invitation to look closer, talk to the student, understand the situation and decide what kind of support may be appropriate.
For me, this is where educational data and human-centred intervention come together:
The purpose of identifying risk is not to label a student, but to create an opportunity to help them succeed.
Research Behind This Approach
The approach in this case is informed by research on early-warning systems and student support in higher education.
Research shows that institutional data can be used to identify students who may be at risk of poor academic outcomes. However, identifying risk is only the first step. The information needs to be interpreted in context and followed by appropriate support.
Plak, S., Cornelisz, I., Meeter, M., & van Klaveren, C. (2022).
Early warning systems for more effective student counselling in higher education: Evidence from a Dutch field experiment. Higher Education Quarterly, 76(1), 131–152.
Course signals at Purdue: Using learning analytics to increase student success. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge.
Read the full open-access article →
The Connection to This Case
This study examined an early-warning system used at Vrije Universiteit Amsterdam. The system successfully identified students at risk, but simply providing risk information to counsellors did not by itself improve academic performance or reduce dropout. The authors highlight the importance of actionable feedback and appropriate counselling practices.
This supports an important principle in the present case: an alert should start a conversation, not end the process.
A second open-access source provides further support for using multiple forms of student information when identifying academic risk:
Arnold, K. E., & Pistilli, M. D. (2012).
The study describes Purdue University's Course Signals system, which combined different indicators of student progress to identify students who might benefit from additional support. The approach illustrates how learning analytics can be used as an early signal for human intervention, rather than simply as a mechanism for reporting student performance. Read the open-access paper →
Use available data to detect patterns → review the context → understand the concern → provide appropriate support → follow up.
The purpose of an early-warning system is therefore not to label students as at risk, but to help educators recognise when a student may need attention early enough for meaningful support to be offered.
What do you think? 👇
I'd be interested to hear from teachers, educators, lecturers, academic leaders and other education professionals who have experience with these issues. If you have a different perspective, a classroom experience, or an approach that has worked for you, please feel free to share it in the comments.
Thoughtful disagreement is welcome too. The aim is to learn from one another and keep the conversation going. Thank you!
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