Интеллектуальный анализ образовательных данных (EDM) — это процесс использования методов интеллектуального анализа данных для анализа данных, полученных в образовательных учреждениях, с целью улучшения преподавания и обучения. Он открывает огромные возможности для персонализированного обучения, прогнозирования успехов учащихся, совершенствования учебных программ и повышения успеваемости, но сталкивается с серьезными проблемами, такими как конфиденциальность данных, проблемы с качеством, интеграция сложных данных (текстовых, видео), технические барьеры и сопротивление со стороны преподавателей, а также требует соблюдения этических норм и экспертных знаний в предметной области для достижения значимых результатов.

• Altynbek Zhakurov

Интеллектуальный анализ образовательных данных (EDM) — это процесс использования методов интеллектуального анализа данных для анализа данных, полученных в образовательных учреждениях, с целью улучшения преподавания и обучения. Он открывает огромные возможности для персонализированного обучения, прогнозирования успехов учащихся, совершенствования учебных программ и повышения успеваемости, но сталкивается с серьезными проблемами, такими как конфиденциальность данных, проблемы с качеством, интеграция сложных данных (текстовых, видео), технические барьеры и сопротивление со стороны преподавателей, а также требует соблюдения этических норм и экспертных знаний в предметной области для достижения значимых результатов.

• Altynbek Zhakurov

Интеллектуальный анализ данных в сфере образования открывает широкие возможности для прогнозирования успеваемости, персонализации обучения и совершенствования преподавания. В то же время он сталкивается с проблемами, связанными с качеством данных, контекстом, этическими нормами и интерпретацией. Для эффективного использования интеллектуального анализа данных в сфере образования необходимо сочетать анализ данных с профессиональным суждением и соблюдением этических норм.

• Altynbek Zhakurov

Интеллектуальный анализ данных в сфере образования открывает широкие возможности для прогнозирования успеваемости, персонализации обучения и совершенствования преподавания. В то же время он сталкивается с проблемами, связанными с качеством данных, контекстом, этическими нормами и интерпретацией. Для эффективного использования интеллектуального анализа данных в сфере образования необходимо сочетать анализ данных с профессиональным суждением и соблюдением этических норм.

• Altynbek Zhakurov

Educational Data Mining offers powerful possibilities for predicting performance, personalizing learning, and improving instruction. At the same time, it faces challenges related to data quality, context, ethics, and interpretation. Effective use of EDM requires balancing data insights with professional judgment and ethical safeguards.

• Lady Lyka Sawali

As a science teacher, I see EDM as a powerful tool — especially when we want to support many students, identify those struggling early, and adapt instruction to students’ needs. It can help us notice invisible trends and give support more fairly and promptly.

But I also believe EDM must be used with caution and humility. Data and algorithms are not perfect. They should complement — not replace — teacher observation, student conversations, hands-on labs, and traditional assessments. In science, where understanding is often messy, creative, and experiential, human insight and empathy remain irreplaceable.

• Mark Joey Macariola

ducational data mining (EDM) is the process of using data mining techniques to analyze data from educational settings to improve teaching and learning. It offers huge potential for personalized learning, predicting student success, improving curricula, and boosting retention, but faces big challenges like data privacy, quality issues, integrating complex data (text, video), technical barriers, and resistance from educators, requiring ethical guidelines and domain expertise for meaningful impact.

One useful recent example is Educational data mining: prediction of students' academic performance using machine learning algorithms (2022). In that study, the authors applied a suite of machine-learning techniques to a dataset of undergraduate students’ midterm grades — and used those to predict their final exam grades.

• Shaira Joy Dela Rosa

Educational data mining offers significant possibilities for understanding and improving learning, but it also raises substantial practical, ethical, and equity related challenges.
Possibilities
• Deeper insight into learning and teaching: Mining large educational datasets can reveal hidden patterns in how students study, participate, and succeed, supporting better curriculum design, teaching strategies, and institutional decision making.
• Prediction and early intervention: Models can predict outcomes such as course failure, dropout, or specific misconceptions, allowing educators to identify at risk students early and target support more effectively.
• Personalization and optimization: By modeling individual knowledge, preferences, and behaviors, educational data mining can help tailor content, pacing, and feedback, contributing to more personalized and efficient learning experiences.
• Resource and policy support: Aggregated analytics can inform program evaluation, resource allocation, and policy decisions (for example, which courses need extra support, or which teaching approaches are most effective).
Challenges
• Data quality, generalizability, and bias: Many models are trained on specific institutions or subgroups and may not generalize; biased or incomplete data can produce misleading or inequitable predictions if not carefully checked and validated across diverse learners.
• Privacy, consent, and data ownership: Educational data (including demographics, performance, and behavioral traces) are sensitive; ensuring informed consent, secure storage, clear ownership, and compliance with “right to be forgotten” and other protections is complex and often unresolved.
• Algorithmic opacity and misuse: There is a risk of over trusting data driven models (“data ism”), using them as objective truth for high stakes decisions, or letting opaque algorithms reinforce existing inequalities or stereotypes.
• Capacity and ethical competence: Effective, responsible use requires technical skills, statistical literacy, and ethical awareness among educators and administrators, which many systems currently lack; without this, data mining can become either underused “dashboard wallpaper” or overused surveillance.
Make an Update: Find a piece of research that uses educational data mining as a source of evidence. What kinds of things can educational data mining tell us, or not tell us?
Educational data mining has been used to predict performance, detect problematic behaviors, and guide interventions, but it cannot by itself explain why patterns occur or fully capture complex learning in context.
Example study using educational data mining
One illustrative line of research is Ryan Baker’s work on “gaming the system” in intelligent tutoring systems such as Cognitive Tutor and ASSISTments.
• Researchers gathered detailed log data on student actions (rapid guessing, repeated hint requests, systematic answer trials) and combined it with human labeled examples of “gaming” to train classifiers that detect when a student is trying to exploit the system rather than learn.
• Analyses showed that frequent, concentrated gaming on poorly known skills is associated with significantly lower learning gains, whereas more occasional gaming on easier skills can coexist with reasonable learning, revealing distinct behavioral profiles within the same environment.
• This evidence was then used to design interventions (e.g., agent feedback, extra practice on bypassed skills, changes in lesson design) that reduce gaming and improve learning outcomes.
What educational data mining can tell us
• Predictive signals: It can identify patterns in clicks, timings, errors, and help seeking that predict outcomes like course grades, dropout risk, or low learning gains, often with useful accuracy for early warning and targeting support.
• Behavioral profiles and pathways: Clustering and sequence mining can reveal distinct profiles of engagement (e.g., persistent, strategic, gaming oriented) and common action sequences linked to success or failure, helping educators redesign tasks and supports.
• Associations among factors: Relationship mining can uncover unexpected links (e.g., between certain study habits, forum behaviors, or course combinations and later success), generating hypotheses for further investigation or design changes.
What it cannot (reliably) tell us
• Causal explanations on its own: Data mining alone cannot establish that one behavior or feature causes better or worse learning; causal claims need experimental or strong quasi experimental designs layered on top of mined patterns.
• Deep “why” behind behavior: Log traces show what students do, not their motivations, emotions, or broader life circumstances; interpreting behavior (e.g., gaming vs. strategic skipping) still requires theory, qualitative insight, and contextual knowledge.
• General truths across contexts: Models trained in one system, school, or population often degrade when applied elsewhere; without careful validation, educational data mining cannot guarantee that discovered patterns hold for all learners or settings.
In short, educational data mining is powerful for detecting patterns, making predictions, and informing design and interventions, but its findings must be interpreted cautiously, combined with other methods, and checked for validity and fairness across different learners and contexts.

• Danalyn Piamonte

Educational data mining has a lot of exciting possibilities. It can use data from LMS logs, quizzes, and grades to find patterns in how students learn, who might be at risk of failing or dropping out, and which activities help learning the most. This can support early interventions, more personalized lessons, and better decisions at the classroom and school level.
ScienceDirect

But there are also big challenges. The data can be incomplete or messy, and results can be biased if only some groups of students are well represented. Many models show correlations, not clear causes, so it is easy to over-interpret the numbers. There are also concerns about privacy, consent, and how schools store and share sensitive student data. Finally, teachers and leaders may not always trust or understand complex models, which can limit how useful the findings become in real classrooms.
MDPI.

• Ruby Mae Andres

Possibilities: Educational data mining can reveal learning patterns, predict student performance, support early interventions, personalize instruction, and help schools make better decisions based on real data.
Challenges: It raises issues about privacy, data security, and fairness. Algorithms may misinterpret behavior, reinforce bias, or be used for high-stakes decisions. It also requires technical skills and reliable data, which not all schools have.

• Anna Mari Bermido