Data-driven development — sometimes referred to as DDD or D3 — is an evidence-based approach to decision-making that impacts the design, implementation, and continuous improvement of learning management systems (LMS) and e-learning platforms. It makes the process not only more efficient in terms of costs and cost but also more likely to produce better learning outcomes.
Through systemic analysis and machine learning, data-driven development enables learning systems to collect, process, and interpret various types of data, including user behavior, learning outcomes, engagement metrics, and system performance, to inform the development process and optimize the overall user experience.
This methodology enables developers and training managers to identify trends among learners, uncover insights, and continuously adapt and improve the LMS to best target outcomes.
Let’s explore what makes data-driven development so impactful for e-learning platforms and why training systems should leverage these capabilities.
Through the data-driven development approach, LMS and e-learning platforms have empowered instructors to leverage data insights in a way previously unimaginable to traditional instruction and even early-stage online learning when it first emerged.
According to a 2019 study, data-driven development not only accelerates the creation of LMS systems but also makes an “unprecedented” impact on the ability to collect and evaluate data on learners.
Prior to cloud-based data collection and automated data scraping, educators and training managers could rely solely on surface-level performance scores, intuition, anecdotal evidence, and established best practices when designing and building course instruction and delivering it to students.
Through machine learning, data-driven development is now able to track a multitude of factors simultaneously. This includes:
User interactions — Behaviors such as clicks, navigation patterns, time spent on various activities, and content consumption.
Performance outcomes — Direct input data generated from learners’ progress, test scores, and assignment submissions to measure comprehension and capability of the subject matter.
Engagement behaviors — Interaction trends such as time of day participation, most responsive prompts, and time spent on certain kinds of questions.
Feedback prompts — Qualitative data from surveys, thumbs-up & thumbs-down reactions, forms, and interviews.
System performance — Evaluating technical aspects, such as load times, errors, and uptime, to ensure a smooth user experience.
Training supervisors for a commercial airline were looking to improve the effectiveness of their in-flight training program delivered through an LMS platform. Data-driven development (DDD) helped the training division identify areas for improvement, streamlined decisions, and enhanced learning outcomes for their trainees.
The organization noticed a significant number of trainees struggled to pass certain sections of the course, and overall completion rates were lower than desired. The LMS was able to collect and report data on learning outcomes, engagement metrics, system performance, and feedback from trainees and instructors.
This data revealed trainees spent a disproportionate amount of time on specific complex topics, test scores in those complex areas were consistently lower than the average, and trainee engagement was lower in sections that relied heavily on text-based content.
Based on these insights, both supervisors and the LMS’ AI made a mixture of automated and manual data-driven decisions to improve the training program:
As a result of these data-driven decisions and improvements, the airline’s training division saw performance outcomes grow, higher completion rates, and increased engagement.
Data scaped and curated by machine learning can be used to build algorithms that predict the best decisions for product and service performance. Not only does this mean data-driven development enables module design based on evidence rather than guesswork, but it also makes continual improvement possible. This can happen both with business and audience metrics.
Additionally, companies like Techinnov with in-depth experience in designing e-learning platforms and modules, can provide rich repositories of historical insights that have been generated across the company and clients.
By analyzing data, such as user interactions, developers can identify the most popular features, content types, learning paths, and areas where users may struggle. This information helps developers prioritize the features and functionalities that meet users’ needs and preferences and steer the planning and storyboarding of new content.
The initial benefit of data-driven development comes from quickly and accurately identifying the most effective and relevant learning goals for a specific audience.
This can be determined based on a complex web of factors, such as learner demographics, skill assessments, benchmarking subject matter best practices, and even proprietary historical knowledge.
Machine learning enables developers to quickly synthesize much of this information and matches it with appropriate planning and strategies. This means before any resources are exhausted on product development, evidence, and facts have already helped ensure they are built correctly and intentionally.
In the long term, this saves massive amounts of time and energy by reducing the chances products have to be scrapped and rebuilt.
By leveraging data-driven insights, e-learning content developers can create more targeted, engaging, and effective learning experiences that cater to their audience’s specific needs and preferences.
In Techinnov’s experience, this data often leads development teams to simpler, not more complex, solutions.
For example, data insight reveals learners readily grasp concepts quicker and score higher when compliance training on proper HAZMAT outfitting is presented in a simple series of multiple-choice questions. Based on this insight, developers quickly create a high-impact, concise module and deliver the content to the client, avoiding typical delays in over-development and over-complexity. The module instantly produces anticipated outcomes and assists the client in bringing its workforce into compliance.
Real-time collection and analysis of learner data can help developers gradually improve product and service performance over time, ensuring that once content is delivered, it can be fine-tuned to precisely target key performance indicators (KPIs) and deliver the best learner-friendly experience.
Data-driven development assisted a health and wellness app rebuild its training system into a high-impact instructional program.
Through data analysis, the company discovered users preferred short, bite-sized lessons with a mix of video and interactive content. They also found users were more engaged with topics related to nutrition, exercise routines, and mental well-being and benefitted greatly from personalized recommendations based on their health goals and lifestyle preferences.
Armed with these insights, developers redesigned and curated existing content into shorter, more focused lessons, incorporating multimedia elements like videos, quizzes, and even example animations demonstrating particular movements and workout concepts. They also developed a recommendation algorithm to suggest personalized learning paths based on users’ goals, interests, and app usage patterns.
The app saw significant improvements in user engagement, course completion rates, and overall satisfaction when the new platform was integrated. App users found the content more relevant and engaging, and they were better equipped to plan and accomplish workouts when they used the app.
By leveraging data insights and analytics, Techinnov’s team creates highly personalized, engaging, and effective e-learning modules and courses that cater to the specific needs of each client’s workforce.
Through continuous monitoring and evaluation of performance metrics, user feedback, and market trends, Techinnov ensures training solutions remain relevant, up-to-date, and aligned with organizational goals. Techinnov empowers clients to maximize the potential of their employee training programs, ultimately driving business growth and fostering environments of continuous improvement.
Data-driven development is an evidence-based approach to decision-making that impacts the design, implementation, and continuous improvement of learning management systems (LMS) and e-learning platforms. It leverages data insights to optimize the user experience and improve learning outcomes.
Data-driven development analyzes various types of data, such as user behavior, learning outcomes, engagement metrics, and system performance, to identify trends, uncover insights, and make informed decisions. This enables developers to create personalized, engaging, and effective learning experiences tailored to the needs and preferences of their audience.
By analyzing data on user interactions, developers can identify popular features, content types, learning paths, and areas where users may struggle. This information helps prioritize features and functionalities that meet users’ needs and preferences, steering the planning and storyboarding of new content.
Real-time data collection and analysis can help developers gradually improve product and service performance over time. This ensures that content is fine-tuned to target key performance indicators (KPIs) and deliver the best learner-friendly experience.