Creating Datasets for Dropout Prediction & Retention AI

Building an effective learning support system begins with creating datasets for models predicting student dropouts and retention. The datasets for the models combine academic performance, behavioral activity in educational systems, and contextual factors, allowing the model to detect early signals of risk. A high-quality combination of different data types enables accurate prediction and provides a basis for timely intervention.

The process of forming a dataset involves collecting data from the LMS and student information systems, creating derived features, and defining the target variable — whether a student drops out or its risk. The data is cleaned, normalized, and balanced to make the model robust and generalizable.

Why Engagement-Centered Datasets Drive Retention and Dropout Prediction

Engagement-centered datasets capture learning outcomes and the dynamics of student behavior. Unlike static academic metrics, interactions captured through LMS logs, attendance signals, sentiment surveys, and progression analytics reflect changes in motivation, activity, and engagement long before a student formally leaves school.

Because engagement levels typically decline gradually, models that utilize engagement-centered data can better understand the pace and direction of change in a student’s academic performance. This enables more accurate predictions, timely interventions, and personalized support. A focus on behavioral indicators enables the transformation of predictive systems from reactive to proactive, allowing for effective support when it is most needed.

Auditing Data Sources

Designing a Retention-Focused Data Schema

• Identify key entities, including students, courses, semesters, interactions within the system, and learning outcomes. This forms the basic structure on which the data will be further built.
• Create behavioral data modules, separate tables, or event streams to capture LMS logs, including activity, transitions between modules, and task completion, thereby forming a comprehensive picture of engagement.
• Integrate presence and rhythm: add structures to store attendance signals, including absences, tardiness, and regularity of participation, as these indicators are important early indicators of risk.
• Include an emotional and motivational module: provide a separate schema for sentiment surveys, open-ended responses, and qualitative comments to analyze emotional and motivational factors.
• Form an academic progress layer by creating tables for progression analytics that contain progress rates, checkpoint completion, task results, and progress against plan.
• Establish time slice logic: provide the ability to build semester, weekly, or daily snapshots, which allows for modeling the dynamics of changes in student behavior and learning.
• Add target variables for models: clearly define retention status, dropout label, or risk indicator so that AI models can learn on standardized outcome data.
• Ensure coherence and consistency by using stable keys, referential relationships, and normalization, so that data from different sources (LMS, SIS, surveys) is integrated correctly.
• Introduce a layer of transformations and aggregates by providing tables with calculated features (feature store), where aggregated metrics of engagement, activity, and progress are stored.
• Consider privacy and access issues: separate personal data, pseudonymize student identifiers, and implement a role-based access system, adhering to the principle of data minimization.

Entity and Time-Window Design

• Definition of main entities (Entities). Firstly, a set of key entities is formed, including Student, Course, Enrollment, Assessment, LMS Interaction Event, Survey Response, and Attendance Record. Each entity represents a distinct aspect of the student’s learning path and serves as the basis for further integration of engagement signals.
• A separate entity for behavioral events. To record interactions, an event entity is created based on LMS logs, where each record corresponds to a specific student action in the system. This enables granular analysis and the creation of activity patterns over time.
• An entity for participation and rhythm. The block covering attendance signals is represented by a separate table or events with the structure “student–session–status”. This is important for modeling the regularity and stability of learning.
• An entity for emotional and qualitative data. Student responses in sentiment surveys and open comments are represented by separate entities, each linked to a specific time, course, or experience. This creates a foundation for natural language analysis and uncovering hidden motives.
• Essential for academic progress. Data for progression analytics is presented as structured snapshot tables or event records — e.g., module completion, grade accumulation, and milestone completion.
• Standardization of time windows. To model trends, unified time windows are created, such as weekly, monthly, or semester-long. This allows students to be compared in a single time frame.
• Rolling windows for behavioral shifts. Rolling time windows (e.g., the last 7, 14, or 30 days) are used to detect gradual changes in engagement that often precede expulsion.
• Event-based windows for more precise analytics. In addition to calendar intervals, event windows are used, such as around deadlines or tests, to analyze student responses to critical learning moments.
• Alignment based on learning phases. Time windows can be tied not to the calendar, but to the phases of the course: “beginning”, “middle”, “before the exam”, etc. This is important because engagement patterns have distinct characteristics depending on the stage.
• Formation of a Time-Indexed Feature Store. The collected features from different entities are aggregated into time-indexed tables, enabling the model to view the full chronology, including activity, presence, emotional signals, and progress in each time window.

Feature Engineering from Student Engagement Data

Feature engineering from student engagement data focuses on transforming raw signals into meaningful features that reflect behavioral trends, risks, and learning progress. Data from LMS logs allows the generation of metrics for interaction frequency, time in the course, task completion rate, and structural navigation patterns. Attendance signals are used to generate indicators of participation regularity, absence duration, presence rhythm, and attendance change dynamics.

A separate layer of features is formed from sentiment surveys, where it is important to identify emotional indicators, trends in attitude towards the course or platform, and motivation signals. Data for progression analytics enables the building of features for progress rate, lag, key milestones, and comparisons of progress over time.

Behavioral Signals

• LMS activity – frequency of logins, module and lecture views, participation in tests and assignments (LMS logs).
• Regularity of attendance – missed classes, lateness, stability of participation in seminars and lectures (attendance signals).
• Pace of task completion – speed of course completion, completion of checkpoints, lagging behind the plan (progression analytics).
• Interactions with fellow students and instructors – forum posts, comments, participation in group projects (LMS logs, communication signals).
• Motivational and emotional signals – changes in satisfaction ratings, survey responses, tone of open comments (sentiment surveys).
• Activity at critical moments of the course – behavior before deadlines and exams, a sharp drop in activity or, conversely, a peak the day before submission of work (LMS logs, event-based windows).
• Engagement trends over time – gradual decrease or increase in activity throughout the semester (progression analytics, rolling windows).

Operationalizing Measurement: Dashboards, Thresholds, and Early Alerts

Effective retention strategy implementation begins with transforming collected data into understandable, actionable decision-making tools. The foundation of this process is the creation of interactive dashboards, the identification of critical thresholds, and the setup of early alerts. Data from LMS logs, attendance signals, sentiment surveys, and progression analytics serve as the basis for these tools, enabling leaders and mentors to quickly identify the risks of expulsion and intervene in a timely manner.

Dashboards provide a comprehensive view of student activity in real-time. They can include time series of activity, heat maps of attendance, interactive progress graphs, and aggregated emotional indicators from sentiment surveys. Thanks to such visualization, university or educational platform staff can quickly identify students with low engagement or at risk of falling behind.

Thresholds define specific values ​​​​of indicators at which a student is considered “at-risk”. For example, this could be a certain number of absences, a drop in activity in LMS logs below a given threshold, or negative dynamics in sentiment surveys.

Early alerts integrate information from dashboards and thresholds, generating notifications for mentors, supervisors, or academic support. The system can notify about the risk of expulsion long before the official semester cutoff, allowing for planning interventions: individual consultations, additional assignments, motivation letters, or connections to support resources.

Summary

Successfully predicting student retention and preventing dropouts requires a systems approach to data, where the integration of different types of signals: academic, behavioral, emotional, and progressive, plays a key role. It is essential not only to collect information but also to structure it in a form that is understandable and organized into clear entities and time windows, which enables the modeling of engagement dynamics and academic progress.

A critical component is the transformation of raw data into analytical features through feature engineering, which turns behavioral patterns into informative metrics for models. After this, the data serves as the basis for operationalization, including the creation of interactive dashboards, setting risk thresholds, and establishing early warning systems.

FAQ

What is the role of LMS logs in retention prediction?

LMS logs capture detailed student interactions with the platform, including time spent on courses and task completion. These signals help models detect early disengagement patterns.

How do attendance signals contribute to dropout prediction?

Attendance signals reflect class participation and consistency, providing an early indicator of potential academic risk. Frequent absences often precede declines in performance.

Why are sentiment surveys important in retention analysis?

Sentiment surveys reveal students’ motivation and emotional state. Integrating these responses helps models understand qualitative factors behind engagement or disengagement.

What are progression analytics, and how are they used?

Progression analytics track the pace and completion of course milestones. They allow early detection of students falling behind and inform timely interventions.

What is the purpose of engagement-centered datasets?

These datasets combine behavioral, academic, and emotional signals to provide a complete view of student activity. They improve predictive accuracy for retention and dropout models.

How are thresholds applied in early alert systems?

Thresholds define specific values of activity or progress metrics that trigger alerts. This enables mentors to intervene proactively before students become completely disengaged.

Why is time-window design important for modeling engagement?

Time windows, such as weekly or rolling periods, capture trends in student behavior over time. They help identify gradual declines in engagement that precede dropout.

What role does feature engineering play in retention AI?

Feature engineering transforms raw data from LMS logs, attendance signals, sentiment surveys, and progression analytics into meaningful metrics. These features improve model sensitivity and predictive power.

What is the advantage of integrating multiple data sources?

Combining LMS logs, attendance signals, sentiment surveys, and progression analytics creates a holistic picture of student behavior. It increases the accuracy of predictions and the effectiveness of support strategies.