MLOps In Educational Data Mining

This article was published as a part of the Data Science Blogathon.

Introduction

Similar to other fields like healthcare, education is an area that is being penetrated by technology and data science. Many fields have evolved, such as Educational Data Mining EDM, which is a field dedicated to finding actionable insights from educational settings. It utilizes the availability of databases in institutions and the advancements in E-learning.

In many parts of the world, huge amounts of educational data are generated from activities such as online admission processes, online examinations and tests, attendances, lecturers’ assignments to courses, etc. EDM is an emerging field in Data Mining that is taking advantage of this available data from an educational perspective. Also, data science is a young scientific discipline that is expanding quickly. Though much has been observed in all these sciences, statistics suggest that it has only just begun.

 

With all the benefits that MLOps and EDM can offer, combining the two is a research topic that will create an excellent learning environment.

The objective of MLOps is to create a general template for carrying out standardized Machine learning activities for robust systems for production, such as student performance prediction. EDM also suffers a variety of issues with non-standardized ML workflow. No universally agreed-upon design governs the entire lifespan of data production and management, from modeling through deployment in the EDM. This has created a weakness in global research unison.

There is, therefore a need to be able to deploy and manage machine learning models for use in educational settings. To do this, it becomes a requirement to utilize comprehensive MLOps. The Data Scientist will need to improve practice by integrating DevOps procedures and enormous volumes of data from educational institutions, developing and monitoring model performance, and providing actionable insights to educational management.

Some Applications of Data Science in EDM

There are some particular areas in DS that have disrupted the educational system. Research has presented a number of them. We will see a hand full of them briefly.

1. Analysis and visualization of data; here, students’ data go through a phase of data reporting after EDA or regular data analysis. As usual, the data is reported with insights in view. Providing feedback for lecturers/instructors.
2. Recommendations for students; through clustering on courses and recommending the courses they should take for electives.
3. Predicting student performance; student academic performance can be predicted in EDM using demographic and pre-education data. The output will be graduating CGPA or next semester’s likely result. This could help students prepare accordingly and know their weaknesses and strength through the correlation of labels or courses with their target output which could be graduating CGPA.
4. Student modeling/Intelligent tutoring system; Each learner is treated uniquely by an intelligent tutoring system that recognizes their individuality. The foundation for the individualized and adaptive tutoring services provided by an ITS is thought to be the student model. The primary objective of a student model is to develop a thorough profile of a student based on his or her knowledge level and characteristics to conclude the kind of learning style or the most preferred teaching methods or strategies that may be appropriate for each individual student.
5. Detecting undesirable student behaviors; students may possess behaviors that do not follow the patterns of those who had good performance in the future but dropped out or had low grades. Such students can be detected and adjusted early.
6. Student segmentation; here, students are grouped according to certain traits and treated accordingly. For instance, the student could be assigned projects based on similar traits.
7. Planning and scheduling lecturers, etc.

The Benefit of MLOps in Education

The working environment of educational institutions has a lot of activities generating lots of variables. The use of databases and online platforms makes this generated data easy to harvest and apply to a machine-learning workflow. The complexities in the system also make it easy to lose good data or surfer noise. MLOps becomes vital in creating a better education data system and making the environment more accurate and robust with Ml.

The promises are endless if MLOps is properly applied to educational data mining. Since graduating results are the goal of an educational system, having a robust system for carrying out results or grade prediction will come in handy and be impactful.

An educational data scientist using MLOps can outperform some of the best educational counselors and academic planers using traditional methods just as AI games have outshined some of the coolest human game grandmasters in the past. This could remove human errors and save costs and time. This presents both preventive and corrective measures for the school system; hence results can be known beforehand and necessary actions are taken.

MLOps Framework for EDM

Let us first see a regular EDM without MLOps to see the difference. The EDM process has four main phases.

1. Problem Definition; Problem definition is the first phase in which a specific problem is translated into a data mining problem. It is an assertion about a problem area, a problem that needs to be fixed, a challenge that needs to be overcome, or a disturbing issue that has been raised in academic research, theory, or practice and calls for thoughtful analysis and examination. The project’s goal, objectives, and primary research questions are all developed during this phase.
2. Data Collection and Preparation; Data quality is a major challenge in data mining. In this phase, source data must be identified, cleaned, and formatted in a prespecified format. Before problem conceptualization, it is the process of cleaning and converting raw data. It is a crucial step and frequently includes reformatting, correcting, and integrating databases to enrich data.
3. Modeling and Evaluation; This is when parameters are set to optimal values, and different modeling techniques are selected and applied. We use various evaluation criteria to analyze a machine learning model’s performance as well as its advantages and disadvantages. The effectiveness of a model must be evaluated in the early stages of research.
4. Deployment; This is the final stage where the machine learning model is put into production. This makes the model’s predictions available to the academic environment for various applications. Graphs and reports may be used to arrange and illustrate the results.

Let us see a Machine learning workflow for educational data using MLOps. It shows how robust the MLOps is when replaced with regular workflow. The model-building pipeline helps achieve the following:

1. Versioning of student data; Most times, environments where data has been harvested still continue to produce data. The school may still produce new data after previous data has been collected. Even after collecting data and starting or finishing the development process, new school activities from new sections could still generate more data. This data is still useful and needs to be added to still be used to improve the correct results. But just introducing this data as in regular Machine learning or ignoring the data entirely may not be the most feasible. This brings a need to tag these different sets of produced data. The dataset has to be handled so that the best procedures are met. Data versioning is used to assist in tracking changes over time.
2. Educational Data validation; Validating the data is crucial to managing the project as a whole. Multiple data sources from admissions, exams, graduations, venue selection, scheduling, etc., are frequently used in educational institutions. It is crucial to guarantee that the data has been put through the necessary steps to ensure data quality and utility. The data must make sense of the goals of the project.
3. Efficient Data preprocessing; In MLOps, there is a need to describe the activity of preparing raw data for further data processing. Even though the data is still being processed for the system, it is prepared so that the next step is improved to make more sense. Data preprocessing ensure the absence of unwanted contents of the data.
4. Effective training of the model and keeping track of the training; After the previous activities are done the model can then be trained to be able to learn from the educational data and be useful for insights. This has to be monitored to ensure the model training parameters are well set. Even during the training, the model needs to be monitored. This is the advantage of MLOps.
5. Analyzing, validating, and fine-tuning; In contrast to typical training and testing, we also verify the model’s validity. If not, it is tweaked to achieve certain goals. The goal of model validation is to determine whether the model’s predictions are plausible. To prevent inaccurate results and improve relevance, data that seems to make sense is carefully examined for coherence.
6. Model Deployment; Here, the model is put into action depending on the basic goals that were established. The educational establishment now has a system that meets its requirements.
7. Deployed Model Scaling; This is where a typical machine-learning workflow would have ended. The MLOps workflow is still used to carry it out. The model has a chance of being incorrectly positioned in the system, leaving room for corrections to be made without necessarily having to restart the cycle. Scaling will enable the model to be modified even more to account for potential new needs that were overlooked. The model is now scalable because of this robustness.
8. Data Recapturing and Model Monitoring; This face is made possible with the initial technique of data versioning. The new data generated from the educational system after deployment is still reused as a loop to continuously improve the model and make the model have newer versions that meet with the continuous change that may come with the data newly generated. This needs proper monitoring to ensure the new data does not have a negative effect on the model but rather makes every future version of the model to be more robust than the previous one.

Using MLOps for Student Academic Performance Prediction

The following image outlines the MLOps model that would streamline the process of student performance prediction:

1. Develop a machine learning model for student performance prediction with robustness in view, preserving a tier of generality across defined varieties of student factors.

2. Incorporate the students’ behaviors such as reading hours, age, gender, etc., and other factors even not seductively figured out into the model. They may significantly influence the independent variable, which may be the graduating CGPA.

4. Employ the student behaviors to produce a dynamic and predictive performance path followed by the observed variables.

5. Concurrently develop a “progress” metric representing weighted variations in the behaviors of the students, which has an impact on the determining change over time, thus defining a solid metric for “outcome.”

6. Measure the impact of the student’s performance by tracking the change that has occurred in the results and documenting the variables that vary over time.

7. Attach cost variables to the dressings and interventions used so that the EHR can concurrently determine clinical outcomes and cost-effectiveness. It can also include a hybrid of these factors for a data-driven treatment recommendation engine that augments physician decision-making.

Conclusion

Since universities operate in a highly competitive and complex environment, the rapid technological developments with the amount of data stored in educational databases increase to provide a strong opportunity to utilize data science hence the field of Educational Data Mining.

Data mining tools and methods allow us to analyze educational data and find hidden patterns and information. It is a new field with great potential for each student involved in the educational process. In order to automatically uncover hidden knowledge and identify patterns in data, data mining techniques were created.

Educational Data Scientists can use MLOps workflow to provide educational solutions that will make the educational environment very controlled and predictable.

key takeaways;

• Huge amounts of educational data are produced across various regions of the world as a result of processes like online admissions, exams, assessments, attendance records, lecturers’ course assignments, etc.
• The goal of MLOps is to develop a universal template for doing standardized Machine learning tasks for reliable production systems, such as predicting student performance and student clustering.
• The possibilities are unlimited if MLOps is correctly applied to educational data mining. A strong system for delivering results or grade prediction will be beneficial and have an influence because graduating results are the aim of an educational institution.

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Mobarak Inuwa

I am an AI Engineer with a deep passion for research, and solving complex problems. I provide AI solutions leveraging Large Language Models (LLMs), GenAI, Transformer Models, and Stable Diffusion.