Quels sont les modèles ou techniques de modélisation de données courants pour les données sur l’éducation ?

The best (imho) notation is the one developed by Richard Barker and Harry Ellis. For many years it was supported by Oracle Corporation as part of its case tools. Alas, they never marketed it, and finally allowed Oracle Designer to wither away. Fortunately, PowerDesigner (originally from Sybase and now owned by SAP), has taken up the notation in a very powerful way. In my book, "Achieving Buzzword Compliance", I show that the Information Engineering technique, developed by James Martin and Clive Finkelstein, and then promoted by the ERWIN people, has been more present. One problem: the notation presupposes that the implementation will be in a relational database. Also those models are more difficult for subject matter experts to read.

The Entity-Relationship (ER) Model forms the foundational framework for organizing and representing educational data, facilitating a clear visualization of the complex relationships inherent in educational settings. While widely acknowledged for its utility in representing entities such as students, courses, and instructors through entities and relationships, the introduction of Contextual Relationships as an extension to the traditional ER model offers an enhanced perspective. These contextual relationships go beyond the standard associations by embedding context—like temporal or spatial information—into the model.

Even before creating the ERD for any business use case you must first perform the business and data analysis for the given business problem and you must first create a glossary of term including the data objects and their relationship then you can convert them to an ERD which is bit later state. You can create ERD on whiteboard , PPT , draw.io etc your objective is to communicate the data requirements clearly.

You should be using the Database Development Lifecycle for the analysis, conceptual data model, logical data model and database implementation. Having a detailed process will always produce better results.

Businesses often get very stuck on selecting an ERD technology. As a recommendation, use a tool you already have in place, such as Visio, just to get something on the page. There are also free and open-source tools that can help, such as Draw.IO.

One thing I've been developing is a "Storybook", where each one-page model is preceded by a one-page description of the model. The idea was suggested by Jonathan Gray when we were working out how to explain data models to business users who had very little data knowledge. I coupled this to my "Mid-Level Model" approach, where the Conceptual Model is presented in three (sometimes four) separate diagrams: Core Concepts, Core Attributes, a "proper" Conceptual model, then an Conceptual Model with M:N relationships resolved and reference data tables added. Each of these is presented with a page describing what the model is for, and how the business can use the model in a practical sense.

here something is missing , the main question in which scenario you should create the STAR Schema also what are the pre requisite to create STARt schema ? . Before jumping on the dimensional model you must first create a proper 3rd normal form data model because that will give you detailed understanding about your categories , classifications etc. Without understanding your data how you can jump on START Schema ?

Fact tables are central tables in a star schema of a data warehouse that stores quantitative information for analysis that is not normalized. Dimension or Look Up Tables helps you to store attributes and dimensions which describe objects that are stored in a fact table.

Here's my Action Items to optimize your analytical insights. 1) Incorporate Behavioral Dimensions: Extend the traditional star schema with dimensions that capture student behaviors and engagement metrics. This can enrich the analysis, providing a more nuanced understanding of student performance and learning experiences. 2) Utilize Calculated Fact Tables: Develop calculated fact tables that aggregate or compute educational metrics, such as average grades or attendance rates, to facilitate complex analytical queries without compromising performance. 3) Adapt Time-Series Analysis: Implement time-series dimensions to track changes in educational outcomes over time, enabling trend analysis and forecasting within the star schema.

This I would consider as insights about the dataset but as a first step you must identify what exactly you want to do with your raw datasets. Identify what exactly you would present in more visual or analytical format.

In education, a useful data modeling technique is the learning analytics model, which is helping a lot in improving teaching and learning. It can be divided into four categories: First: data sources (like online platforms or learning systematic techniques) Second: data processes (methods to clean and analyze data) Third: data products (results like patterns or predictions) Fourth: data actions (decisions or changes based upon the data, such as feedback or adapting lessons). This model helps educators make better data-driven decisions in order to enhance student outcomes, much like we use data in atmospheric research to improve climate models.

In data modeling patterns; I mean how to structure the fields into entities, concepts & relationship; Graph modeling is more general concept that data modeling pattern itself: all data modeling patterns; like star approach or data vault approach; is about way to generate entities and/or relationship with specific purpose to organize the data, so all is about node; or entities, concepts or object; and edge; or relationship.

This is good technique but a lot needs to be done to make it popular . Main issue is performance of the tool when include the actual data values along with graph model. People are still having difficulties in understanding this.

There's not enough 'why' here. Why would you do data modelling? Where does the data modelling go, in the business? Is it in IT, or the business teams? No mention of data warehousing but you may want to link off to another article on the topic.

This article focus too much on technical stuffs and tools instead it should talk about the various data modelling design patterns and what is the difference between generalise data modelling and specialise data modelling. The sections mentioned here are very common and anybody can master but key design principles , patterns and domain driven modelling not been discussed here.

There are other types of schema i.e. Snowflake and Galaxy. Also, the initial concept of Data warehousing is missing in this article.

Responding to Jennifer Absolutely has to be business owned. The minute it is perceived as an IT process / artifact , the model loses legitimacy and perceived value. The model becomes a communication tool between business teams , business and IT and IT teams when there is the trust that the model is current and data “base” changes will originate with the model - paraphrasing @rainer This is a long journey but one worth pursuing. The model should be supplemented with other metadata - business (data) dictionary, ETL lineage , system lineage etc. Let’s build business process footprints - not data models 😊