initial situation
In many digital learning programs, learning begins where the content starts, not where the learners are. New terms, models, or processes are introduced as if all prerequisites were equally distributed. In practice, however, it quickly becomes apparent that some learners feel underchallenged, while others feel overwhelmed. This is not because the material is too difficult per se, but because it remains unclear how it connects to existing knowledge.
This problem is particularly evident in self-directed learning formats. Without diagnostic introductions or conscious activation of existing knowledge, a gap emerges between what learners already know and what they are supposed to learn. Learning then starts from scratch—at least in terms of perception.
Basic idea
The eLearning tactic Build on What’s There is based on a simple but powerful assumption: new knowledge can only be effectively built upon if existing knowledge is activated. Learning does not mean filling empty memory banks, but rather expanding, restructuring, or refining existing knowledge structures. Building on what is already known reduces cognitive load, increases the likelihood that learners will develop a deep understanding, and strengthens their sense of competence.
Theoretical reference
The approach of basing learning on existing knowledge is deeply rooted in education, learning, and cognitive research. It is based on the assumption that knowledge is not stored in isolation, but is organized in mental schemata. These schemata structure the perception, interpretation, and memory of new information.
Research in cognitive science and educational psychology consistently shows that new learning is particularly successful when learners can activate relevant knowledge structures. Prior knowledge reduces the load on working memory because familiar concepts serve as anchors. This frees up cognitive resources that can be used for deeper understanding, integration, and transfer.
Activated prior knowledge influences not only retention, but also the quality of understanding. Learners with activated prior knowledge recognize connections more quickly, can structure information better, and are more likely to be able to apply new content flexibly.
At the same time, research points to a crucial distinction: prior knowledge does not automatically promote learning. Misconceptions, simplified everyday assumptions, or fragmented knowledge can distort learning processes. Conceptual change research therefore emphasizes that learning often requires a restructuring of existing ideas, not just their expansion. Prior knowledge must be made visible in order to develop or correct it in a targeted manner.
From an educational science perspective, a clear conclusion can be drawn from this: learning opportunities that ignore prior knowledge risk being too demanding or superficial. Learning opportunities that systematically activate, diagnose, and process prior knowledge, on the other hand, create the basis for sustainable, understanding-oriented learning. Activating prior knowledge, diagnosing the level of knowledge, and deliberately disrupting existing knowledge structures are not optional didactic extras, but direct consequences of well-established findings in learning research.
Implementation in detail
These theoretical considerations give rise to specific design decisions for digital learning offerings:
- Diagnostic introductions: Short questions, tests, or prompts for reflection reveal what learners already know—both for themselves and for the system.
- Advance Organizer: Overviews help to classify new content within an existing knowledge landscape before details follow.
- Conceptual anchors: Key concepts are introduced early on and explicitly linked to familiar terms.
- Conscious handling of misconceptions: Tasks or examples can deliberately cause confusion in order to uncover and correct false assumptions.
The transition from theory to practice is fluid: each of these measures aims not to circumvent existing patterns, but to actively incorporate them into the learning process.
Practical example
In an online course on project work, learners start with a brief self-assessment: Which phases of a project are you already familiar with, and where have you encountered difficulties so far? The subsequent content picks up on these answers, builds on familiar experiences, and systematically differentiates them further. New knowledge is thus not presented in isolation, but as an extension of existing practice.
Implementation in Moodle
Moodle supports this approach on several levels:
- Entry tests or feedback activities to activate prior knowledge
- Advance organizers as pages, labels, or short videos
- adaptive learning paths based on test results
- Reflection forums in which learners contribute their own experiences
It is crucial that these elements are designed not as a means of control, but as a learning aid.
Challenges
Activating prior knowledge is not a sure-fire success. Incorrect self-assessments can lead to under- or over-challenging students. There is also a risk of spending too much time on introductions without creating any clear added value. This approach is only effective if prior knowledge is used in a targeted, focused, and learning-goal-oriented manner.
Conclusion
The eLearning tactic "Build on What's There" makes it clear that effective digital learning does not start with content, but with the existing knowledge structures of the learners. Learning does not begin with the first content module, but with the knowledge, skills, and previous experience already present in the "learner" system. Digital learning offerings that take this starting point seriously increase the connectivity of new content and promote sustainable learning.
- Chi, M. T. (2009). Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in cognitive science, 1(1), 73-105.
- Treagust, D. F., & Duit, R. (2008). Conceptual change: A discussion of theoretical, methodological, and practical challenges for science education. Cultural Studies of Science Education, 3(2), 297–328.
AI transparency notice: The basic structure of this text was created using generative AI and revised by a human expert. The text is continuously revised and updated.
