What tools and technologies are used for LMS data visualization?

Tools that are used for LMS Data Visualization are the following: 1. Google Charts 2. Visually 3. MonkeyLearn 4. Sisense There are many others not mentioned that are very useful in providing Data visualization.

When exploring Learning Management System (LMS) data sources, it's important to recognize the potential integration of external data, such as web analytics, social media, or third-party applications. Understanding the native visualization capabilities within the LMS and considering embedded analytics tools is crucial. His insight emphasizes not only functional aspects like report creation and permissions but also the significance of aligning technical architecture with specific goals. In this context, LMS data sources encompass user, course, interaction, assessment, content, engagement, and progress data.

I find the key sources that you need to have when running an education program are these: - User data: this ties everything together with names, emails, etc. - Assessment data: this will allow you to see things like pass rates, average scores, and total passes. - Activity: you can see things like course progress and drop-off rates - Feedback: Course NPS, ratings, and reviews are key to having quality content. Most of these items are going to be native to an LMS that you buy but you can also use BI tools like Looker, Tableau, or Sigma to use a visualize the data.

La visualización de datos en el contexto de los Sistemas de Gestión del Aprendizaje (LMS) es fundamental para comprender y comunicar información relevante de manera efectiva. Las Frameworks y bibliotecas de programación son tecnologías permiten construir aplicaciones y servicios basados en datos. HTML5 (Canvas) SVG (Scalable Vector Graphics) y WebGL seguro que os suenan. Plataformas de visualización que permiten crear cuadros de mando y visualizaciones interactivas completas, como Tableau (Business Intelligence y análisis de datos) Qlik o Tabulae. Las herramientas de publicación como CKAN, Socrata En resumen, la visualización de datos en LMS es crucial para transformar información en conocimiento y tomar decisiones informadas.

Visualization Libraries: Libraries like D3.js, Chart.js, Plotly, and Highchart Dashboard Frameworks: Frameworks such as Dash (built on top of Plotly), Shiny (for R programming language), and Bokeh (for Python) enable the creation of interactive dashboards with LMS data. Business Intelligence (BI) Tools: BI tools like Tableau, Power BI, and QlikView offer powerful data visualization capabilities along with advanced analytics and reporting features. Programming Languages: Languages such as Python and R are commonly used for data analysis and visualization. Python libraries like Matplotlib, Seaborn, and ggplot2 in R provide extensive support for creating static and interactive visualizations. SQL and Database Technologies

The employment of Open AI technologies will play a pivotal role in the process of LMS data extraction. Such technologies can be employed for more accuracy as well as smooth data collection aside from human errors. Moreover, utilising technologies is work- saving and labour-saving.

Typically, LMS systems are the start of the data pipeline for learning teams. A lot of teams want to use learning data to feed into their BI tools like PowerBI. Here, the LMS extracts learning data and sends it to your data lake, where you can copy it over to your BI tool. This can be done natively by utilising pre-built data pipeline products like Docebo Learn Data, or use open APIs and developer resources to connect your LMS with your data lake.

LMS data extraction involves retrieving relevant data from the learning management system (LMS) for analysis or reporting purposes. It typically entails accessing the LMS database or using built-in reporting tools to extract user activity, course progress, assessment results, and other pertinent information. This extracted data can then be analyzed to gain insights into learner performance, course effectiveness, and overall learning trends.

Tracking Learning Activities Dashboards can track student learning progress, ongoing learning programs, and project what needs to be done to improve performance.

A melhor ferramenta é o Power Bi da Microsoft, com ela é possível realizar qualquer tipo de analise! Sem contar na facilidade de utiliza??o!

Every LMS is different here and so is the strategy deployed by the specific learning team. Some want it all done inside of the LMS and if so, the system needs the capability to take learning data and transfer it into meaningful reports. These can be visual or more “excel-style reports”. A lot of learning teams wants to measure Kirkpatrick 1-4 levels so here a mix of quantitative and qualitative data measured is key. When we get to a more mature data strategy, the LMS needs to give quick “grab and go reports” - Kirkpatrick 1-3, but if you want to mix internal and external data sources (i.e learning correlation to attrition of employees), you need to do that in your BI tool. The LMS exports data to your data lake and then inti your BI tool.

LMS data analysis involves examining the data collected from the learning management system (LMS) to uncover patterns, trends, and insights that can inform decision-making and improve learning outcomes. This analysis may include identifying learner engagement levels, assessing course effectiveness, measuring learning progress, and evaluating the impact of instructional strategies. By analyzing LMS data, educators and administrators can make data-driven decisions to optimize teaching and learning processes, personalize instruction, and enhance overall educational experiences.

The visualisation can be a combination of qualitative and quantitative data. Typically, Kirkpatrick 1-3 levels are qualitative and level four is more quantitative. To get a gauge of how learning is performing, LMS visualised reports are great. If you need to provide the causation of how learning impacts other areas like HR/revenue/productivity, it is best done in a BI tool where the LMS feeds data into your data lake and then you can copy it over into your BI tool.

Visualizing student data based on varying parameters is helpful for different teams to interpret the data. In addition, users having the ability to reorganize the data for their purposes is important. For example, a student success team could be interested in the overall change in test scores whereas a grade level team could be analyzing only the top scores to determine A.P. placement. Whatever the case, having the raw data and giving educators the ability to organize specific and targeted data to suit their research is essential with LMS data visualization.

The visualisation can be a combination of qualitative and quantitative data. Typically, Kirkpatrick 1-3 levels are qualitative and level four is more quantitative. To get a gauge of how learning is performing, LMS visualised reports are great. If you need to provide the causation of how learning impacts other areas like HR/revenue/productivity, it is best done in a BI tool where the LMS feeds data into your data lake and then you can copy it over into your BI tool.

As a teacher, when it comes to data visualization for LMS data analysis, it's essential to leverage tools that suit your preferences and needs. Python (I prefer Google Colab) and R are excellent choices due to their versatility and extensive libraries. However, it's also beneficial to consider user-friendly platforms like Tableau, Power BI, or Google Data Studio for creating interactive dashboards and sharing insights with stakeholders. These tools offer a range of visualization options and features for data manipulation, analysis, and presentation, catering to different skill levels and requirements.

As an educator, when crafting LMS data visualizations, adhering to principles such as signaling is paramount. Use clear visual cues like color coding to highlight important information, guiding learners' attention effectively. Additionally, consider principles like coherence, ensuring visualizations are logically organized and easy to follow. By integrating these principles with multimedia learning principles, we can create data visualizations that enhance understanding and engagement among learners. Always have a checklist of multimedia learning principles with you.

Learning Management Systems usually produce large datasets that need to be translated in a way that managers can make the right decisions. The keypoint is first to understand the intended audience of the visualizations: the simpler they are, the higher the likelihood of success. On a substantive level, it is important to have the right indicators selected which are presented in the appropriate type of graph. Finally, the interactivity and security of the data must be ensured.

I stress the importance of principles like signaling, using clear visual cues to highlight key information, and coherence, ensuring logical organization for easy comprehension. These principles, when integrated with multimedia learning principles, contribute to creating data visualizations that enhance understanding and engagement among learners. Additionally, concrete examples of LMS data visualizations, such as course progress dashboards and learner engagement maps, offer practical ways to leverage visualization for effective online learning scenarios.

??? Integrating gamification is a must for students of all ages. Consider a course progress dashboard transforming completion rates and grades into a game-like adventure, where learners conquer modules as if they were levels. The learner engagement map becomes an interactive game board, turning geographic exploration and preferences into a thrilling quest. I have yet to find (and efficiently use in the curriculum) such a tool.

Power BI can be utilized as an external tool to built a dashboard. It is an interactive data visualization software product developed by Microsoft.

?????? Beyond tools and tech, it's about teachers' ingenuity, students' resilience, and the genuine tales that add a human touch to data. So, amidst data sources, extraction, analysis, and visualization, let's not forget the unpredictable beauty found in the uncharted territories of education.

O LMS se torna o grande ponto de integra??o de dados educacionais, porém, geralmente, n?o apresenta ferramentas interessantes de visualiza??o de dados. Por isso é necessário utilizar outra ferramentas para gerar visualiza??es mais funcionais, como o power BI e a Qstione (no caso da aplica??o de avalia??es via integra??o com LMS), por exemplo.