The Student Who Stops Submitting

When Non-Submission Is a Symptom, Not the Problem.

A first-year IT student begins missing assignments. Is the problem motivation — or is something breaking down in the learning experience?

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CASE STUDY 01
Educational Management & Learning Design

The Case

Meet Arjun!

Arjun is a first-year student in a Bachelor of IT programme. He is taking an introductory programming course delivered through a combination of face-to-face classes and an institutional learning management system (LMS).
There are about 45 students in the class.
During the first four weeks of the semester, there is nothing particularly unusual about Arjun's performance. He attends most classes, follows the programming demonstrations and submits the first two small exercises. His marks are around the class average.
Then his pattern begins to change.
In Week 5, one assignment is submitted two days late.
In Week 6, there is no submission.
In Week 7, Arjun attends the lecture but does not attend the practical session. He also stops asking questions in class.
By Week 8, another assignment has not been submitted.
The LMS now shows three consecutive missed or late submissions.
At this point, a lecturer might reasonably describe what they are seeing as a motivation problem.

Perhaps Arjun has lost interest in the course.
Perhaps he has become disengaged.
Perhaps he simply does not want to do the work.

But there is an important problem with these explanations.
They describe the behaviour without necessarily explaining the cause.
Arjun's examination performance has not yet collapsed. There is therefore no clear evidence that he has stopped learning altogether.
What has changed is his learning behaviour. So before deciding what intervention Arjun needs, we need to ask a more fundamental question:

What is actually happening between the moment an assignment is given and the moment Arjun fails to submit it?

This case explores that question.

What We Observe

At first glance, Arjun's behaviour appears to show a gradual decline in engagement. However, before interpreting that behaviour, it is useful to separate the observable evidence from assumptions about its cause.

The available evidence shows:
Arjun attended most classes during the first four weeks.
He completed and submitted the first two programming exercises.
His marks on those exercises were around the class average.
In Week 5, he submitted an assignment two days late.
In Week 6, he did not submit the assignment.
In Week 7, he attended the lecture but did not attend the practical session.
He stopped asking questions in class.
In Week 8, another assignment was not submitted.
The LMS records three consecutive missed or late submissions.
There is no clear evidence at this stage of a major decline in examination performance.
These observations tell us that something has changed in Arjun's learning behaviour.
They do not, by themselves, tell us why.
For example, the same pattern of missed submissions could potentially arise from very different circumstances:
Arjun may have lost confidence in his programming ability.
He may have gaps in prerequisite knowledge.
He may be struggling to plan and manage larger assignments.
The assignment itself may be creating excessive cognitive load.
He may not understand what is expected of him.
Previous feedback may not have helped him identify how to improve.
There may be personal, financial, employment, language or other circumstances affecting his ability to study.
At this stage, each of these remains a hypothesis rather than a diagnosis. The important distinction is:

We have evidence of a change in behaviour. We do not yet have evidence of its cause.

That distinction changes what we do next. Instead of immediately asking,

“How do we motivate Arjun?”,

we need to ask:

“What evidence would help us understand why Arjun has stopped submitting?”

What Might Be Going Wrong?
Once we separate what we can observe from what we assume, several possible explanations emerge.

1. Motivation

The most obvious explanation is that Arjun has become less motivated. Perhaps he has lost interest in programming, does not see the value of the course, or has other priorities competing for his attention.
This is possible.
But motivation is only one hypothesis. The behaviour itself does not provide enough evidence to establish that it is the cause.

2. Self-Efficacy

Arjun may no longer believe that he is capable of completing the programming tasks successfully.
A student who repeatedly struggles may begin to avoid tasks that appear increasingly difficult. What looks like “I don't want to do this” from the outside may sometimes be closer to “I don't think I can do this.”
This raises an important question:
Has Arjun's confidence in his ability to succeed changed?

3. Self-Regulated Learning

Arjun may understand the programming concepts reasonably well but struggle to manage the process of completing larger assignments.
For example, he may have difficulty:
planning the work;
breaking a large task into smaller steps;
managing his time;
monitoring his progress;
recognising when he is stuck;
seeking help at the right time; or
using feedback to change his approach.
In this case, the problem may not simply be whether Arjun can do the work, but whether he can effectively manage the process of doing it.

4. Prerequisite Knowledge

The assignment may depend on knowledge or skills that Arjun has not yet mastered.
A student can appear disengaged when, in reality, the task has exposed a gap in earlier learning.
For example, difficulty with a programming assignment may actually originate from weaknesses in variables, conditional logic, functions or problem decomposition.

5. Cognitive Load

The assignment may simply be demanding too much at once.
Arjun may be expected to understand the programming concepts, interpret the instructions, plan a solution, write code, debug errors and produce the required submission simultaneously.
If the task places too much demand on working memory, avoidance or delayed submission may become an observable consequence.

6. Assessment Design

The problem may lie partly in the assignment itself.
Are the instructions clear?
Is the task appropriately challenging for a first-year student?
Are the requirements broken into manageable stages?
Does Arjun understand how his work will be assessed?
A poorly designed or insufficiently scaffolded assessment can create difficulties that may initially appear to be student-related.

7. Feedback

Perhaps Arjun received feedback on his earlier work but did not know how to use it.
Feedback only becomes useful when the learner can understand it, connect it to the task, and use it to improve subsequent performance.
If that process is not working, repeated mistakes may gradually become discouraging.

8. External Factors

Finally, there may be factors outside the immediate learning environment.
Employment, financial pressure, family responsibilities, commuting difficulties, language-related challenges, social circumstances or other personal issues could affect Arjun's ability to complete his work.
These possibilities should not be assumed either.
They need to be explored sensitively and, where appropriate, through existing student-support processes.

From Assumption to Investigation

At this point, we have several plausible explanations, but no confirmed diagnosis.
That is intentional.

The purpose of the investigation is not to find the most interesting explanation. It is to find the explanation that is best supported by evidence.

The process therefore becomes:
Observed behaviour → Possible explanations → Evidence collection → Diagnosis → Intervention → Evaluation
The next question is therefore not: “Which explanation is correct?”

It is: “What does the evidence tell us?”

What Does the Evidence Say?

At this point, we have a student who has stopped submitting work.

We also have several possible explanations. But which ones deserve closer attention?

This is where research can help - not by giving us a diagnosis, but by helping us ask better questions.

A student can stop trying for very different reasons

One useful starting point is self-efficacy: a student's belief in their own ability to successfully perform a task.

A 2025 systematic review examining the relationship between self-efficacy and university dropout found that higher self-efficacy was generally associated with lower dropout intentions or dropout-related outcomes. Across the studies reviewed, self-efficacy also interacted with factors such as motivation, engagement, academic performance, self-regulation and satisfaction.

That does not tell us that Arjun has low self-efficacy.

But it gives us a question worth investigating:

Has Arjun's experience of struggling with programming begun to change what he believes he is capable of doing?

There is an important difference between:

“I don't want to do this.”

and

“I don't think I can do this.”

From outside the classroom, both can look exactly the same.

But confidence isn't the whole story

A second systematic review looked at interventions designed to support self-regulated learning (SRL) among higher-education students in distance and digital learning environments.

SRL involves much more than motivation. It includes how learners plan, monitor and evaluate their learning, as well as how they manage cognitive, motivational and emotional demands.

The review found that existing interventions tended to focus heavily on metacognitive regulation during the performance stage, while areas such as emotion regulation, initial goal-setting and later reflection received less attention.

For Arjun, this raises another set of questions:

Does he know how to break a large programming assignment into manageable steps?
Does he recognise early enough that he is stuck?
Does he know when and how to seek help?
Can he use feedback to change his approach?
Does he monitor his progress, or only discover that he is behind when the deadline arrives?

Again, these are questions, not conclusions.

The learning environment matters too

There is another important implication. If we look only at Arjun, we may conclude that the problem is inside the student.

But learning does not happen in isolation.

The assignment itself may be unclear. The jump in difficulty may be too large. The instructions may assume prerequisite knowledge that some students do not yet have. Feedback may arrive too late to influence the next attempt.

Even a capable student can struggle when the learning environment does not provide the right level of structure and support.

This is why educational diagnosis should look in two directions:

What is happening with the learner?

and

What is happening around the learner?

So, what do we know about Arjun?

Not as much as we might initially think. The research gives us several plausible pathways:

Self-efficacy
→ Does Arjun believe he can succeed?

Self-regulated learning
→ Can Arjun manage the process of succeeding?

Knowledge and cognitive load
→ Does he have the knowledge and mental capacity needed for the task?

Assessment and feedback
→ Is the learning environment helping him understand what to do and how to improve?

External circumstances
→ Is something outside the course interfering with his ability to participate?

The evidence therefore changes the nature of the problem.

We are no longer simply looking at a student who has stopped submitting assignments.

We are looking at a learning system in which something may have stopped working.

And that leads to a more useful question:

Before we design an intervention for Arjun, what evidence should we collect to find out where the breakdown is occurring?

Designing the Intervention

Suppose we have now collected enough evidence to understand what is happening.

Perhaps we discover that Arjun understands the basic programming concepts, but struggles when an assignment becomes larger and less structured. He knows how to write individual pieces of code, but has difficulty deciding where to begin, breaking the problem into smaller tasks and recognising when he needs help.

That changes the intervention completely.

The answer is not simply:

“Tell Arjun to work harder.”

Nor is it necessarily:

“Give Arjun more programming content.”

Instead, the learning experience itself may need to provide more structure.

Step 1: Break the assignment into milestones

Rather than giving Arjun one large assignment with a deadline several weeks away, the task could be divided into smaller stages:

Milestone 1: Understand the problem
Milestone 2: Design the solution
Milestone 3: Write a basic version
Milestone 4: Test and debug
Milestone 5: Submit the completed solution

Each milestone creates an opportunity to check progress before the student reaches the final deadline.

Step 2: Build in early feedback

A short submission at each stage could receive brief formative feedback. The purpose would not be to grade every small activity heavily. It would be to answer a much more useful question:

“Am I heading in the right direction?”

For a student who is uncertain about his ability, early evidence of progress may also be more useful than discovering a problem after the entire assignment has been completed — or not completed.

Step 3: Make help-seeking part of the design

Students do not always ask for help when they need it.

Instead of waiting for Arjun to approach the lecturer, the course could provide clear points at which help is expected.

For example:

Stuck for more than 20 minutes?

Try the troubleshooting guide.

Still stuck?

Post your question or attend the support session.

This changes help-seeking from something that may feel like failure into a normal part of the learning process.

Step 4: Provide targeted support

If the evidence shows a prerequisite knowledge gap, Arjun should not simply be given the entire programming curriculum again.

A short diagnostic activity could identify the specific gap.

He might then receive a targeted micro-learning activity followed by a small practice task.

If the problem is different — for example, planning and self-regulation — the support should address that problem instead.

Step 5: Monitor the response

The intervention should not end when support is provided. We need to look for evidence of change.

Has Arjun:

started submitting the milestones?
begun attending practical sessions again?
asked for help earlier?
used feedback in subsequent work?
shown improvement in the areas identified by the diagnosis?

If the answer is no, the intervention itself needs to be questioned.

Perhaps we misunderstood the problem.
Perhaps the support is insufficient.
Or perhaps there is another factor that has not yet been identified.

The important principle

The intervention should follow the diagnosis.

If the problem is a knowledge gap, provide targeted learning support.
If the problem is self-regulation, provide structure, planning support and opportunities for monitoring.
If the problem is self-efficacy, create achievable success experiences and useful formative feedback.
If the problem is the assessment design, redesign the learning task.

And if the evidence points to circumstances outside the course, connect the student with the appropriate support rather than trying to solve everything through teaching.

The goal is therefore not simply to get Arjun to submit the assignment.

The goal is to identify and address whatever is preventing him from participating successfully in the learning process.

That distinction matters.

A student who submits one assignment because a lecturer repeatedly reminds him may look like a success in the LMS.

A student who gradually learns how to plan, monitor, seek help and complete the work independently represents a very different outcome.

Measuring Whether It Worked

An intervention is only useful if we can determine whether it actually changed something.

That sounds obvious.

Yet in education, we often introduce a support strategy and then judge its success using the final examination result.

For Arjun, that would be too late.

If the problem is developing over several weeks, we need indicators that tell us whether the situation is improving before the final outcome arrives.

Look for early signs of change

The first indicators might be behavioural:

Is Arjun submitting the smaller milestones?
Has the number of missed deadlines decreased?
Is he attending practical sessions?
Is he accessing the learning resources?
Is he asking for help earlier?
Is he responding to feedback?

These indicators do not prove that Arjun has learned more.

But they can tell us whether his engagement with the learning process is changing.

Then look at learning

The next question is whether the intervention is improving actual learning.

We might examine:

performance on targeted programming tasks;
reduction in recurring errors;
ability to complete tasks independently;
quality of submitted work;
performance on short formative assessments.

This is where the distinction between participation and learning becomes important.

A student can start submitting assignments again without actually understanding the material.

So submission alone cannot be our definition of success.

Finally, look at the larger outcome

Over time, we can examine whether the changes are sustained.

For example:

Behaviour

→ fewer missed submissions

Engagement

→ greater participation and earlier help-seeking

Learning

→ improved performance and fewer recurring errors

Independence

→ greater ability to plan and complete tasks without intensive lecturer intervention

Outcome

→ successful completion of the course

The indicators can therefore form a simple progression:

Behaviour → Engagement → Learning → Independence → Outcome

What if nothing changes?

This is perhaps the most important part of evaluation.

If Arjun receives additional support but continues to miss milestones, the conclusion should not automatically be:

“Arjun is not trying.”

The intervention itself needs to be examined.

Did we identify the right problem?
Was the support appropriate?
Was it introduced early enough?
Was the task itself still creating unnecessary difficulty?
Was there another factor we had not considered?

Evaluation therefore becomes a feedback loop rather than a final judgement.

Intervention → Observe → Evaluate → Adjust

The purpose is not simply to prove that the intervention worked. It is to learn enough from the evidence to decide what should happen next.

And that may be the most important lesson in Arjun's case:

When an intervention fails, the student is not necessarily the failed component of the system.

Practitioner Reflection

A student who stops submitting assignments is easy to notice. Understanding why it happens is much harder.

In a busy semester, it is tempting to interpret repeated non-submission as a lack of motivation, poor attitude or declining commitment. Sometimes that interpretation may even be correct.

But we should not make the diagnosis before examining the evidence.

Arjun's case reminds us that the same observable behaviour can have very different causes.

A student may be struggling with confidence.
They may lack prerequisite knowledge.
They may not yet know how to manage a complex learning task.
The assessment itself may be poorly scaffolded.
Feedback may not be reaching the learner at the point when it can make a difference.
Or circumstances outside the classroom may be affecting participation.

The educator's role is therefore not simply to respond to the behaviour.

It is to investigate what the behaviour might be telling us.

That requires a shift in mindset:

Don't diagnose the learner before analysing the learning environment.

This does not mean removing responsibility from the student. Students remain active participants in their own learning.

It means recognising that learning is produced through an interaction between the learner, the task, the learning environment, the support system and the circumstances in which learning takes place.

For educators and academic managers, this also has a wider implication.

A missed assignment may be a small event. Three consecutive missed assignments may be a signal.

If an institution can identify such signals early, investigate them systematically and respond with appropriate support, intervention can happen before a temporary learning difficulty becomes academic failure or withdrawal.

That is where instructional design and educational management meet.

The question is no longer simply:

“Why isn't this student doing the work?”

It becomes:

“What is the learning system telling us, and what can we do about it?”

Perhaps that is the more useful question to ask when a student stops submitting.

Research Behind This Case

This case draws on research concerning self-efficacy, self-regulated learning and student persistence. The following open-access systematic reviews were particularly useful in developing the analysis and intervention ideas presented above.
Bernardo, A. B., García-Gutiérrez, V., Esteban, M., & Maluenda-Albornoz, J. (2025).
Relationship between self-efficacy and university dropout: a systematic review.
Read the full systematic review
The review analysed 16 studies examining the relationship between self-efficacy and university dropout. It found that self-efficacy was generally associated with dropout-related outcomes and interacted with factors including engagement, performance, self-regulation, motivation and satisfaction. The authors also note that most of the underlying studies were cross-sectional, limiting conclusions about causality.
Edisherashvili, N., Saks, K., Pedaste, M., & Leijen, Ä. (2022).
Supporting Self-Regulated Learning in Distance Learning Contexts at Higher Education Level: Systematic Literature Review.
Read the full systematic review
This systematic review examined 38 studies of interventions supporting self-regulated learning in higher-education distance-learning environments. It considered cognitive, metacognitive, motivational and emotional regulation across the preparatory, performance and appraisal phases of learning.

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!