Learning That Lasts: Merging Cognitive Science, Curriculum, and Gamification

Section 1: The Foundations – Insights from Learning Science & Cognitive Psychology

Understanding the Basics

Proven methodologies from cognitive science—such as spaced repetition, active recall, and metacognition—consistently enhance knowledge retention and deepen understanding. Central to our approach is Cognitive Load Theory (CLT), which optimizes cognitive processing by effectively managing the limited capacity of working memory. Cognitive load is categorized into intrinsic load (the inherent complexity of content), extraneous load (irrelevant or distracting information), and germane load (mental effort directly contributing to learning and schema development).

Optimizing learning by managing cognitive load is particularly crucial for adult learners in the workplace. Adults typically balance multiple demands on their attention, from professional responsibilities to personal commitments. This scenario often results in scarce cognitive resources. Instructional designs that minimize extraneous cognitive load allow adult learners to dedicate their cognitive resources more effectively to germane load, enhancing deeper, more meaningful learning.

Adult learners benefit immensely from instructional approaches that respect cognitive limitations. By carefully sequencing content and utilizing strategies like information chunking and effective multimedia use, we prevent cognitive overload. Optimizing cognitive load management accelerates the formation and automation of schemas, enabling adults to integrate new skills and knowledge efficiently into their professional practices, thereby boosting workplace performance.

Deep Dive into Schema Theory

Schemas are cognitive frameworks that our brains use to organize and interpret information based on previous experiences and knowledge. These mental structures enable us to swiftly process vast amounts of information. For example, upon entering a restaurant, our "restaurant schema" guides our expectations—knowing to wait to be seated, order from a menu, and pay at the end.

Originally introduced by cognitive psychologists Jean Piaget and Frederic Bartlett, schema theory explains how our minds categorize new information. When new information aligns with existing schemas, we easily integrate it—a process known as assimilation. Conversely, information that conflicts with our existing schemas creates cognitive discomfort (cognitive disequilibrium), prompting schema modification or the formation of new schemas, known as accommodation.

Schema automation is another vital component. Initially, applying schemas requires conscious effort, but through repeated practice, these schemas become automatic. Consider driving: initially, each action requires deliberate thought, but with practice, driving becomes effortless and automatic. Automating schemas significantly reduces cognitive load, freeing up cognitive resources for more complex tasks and problem-solving. This highlights why repeated practice is essential in education, directly influencing how easily learners can apply knowledge and skills practically.

Understanding schema theory deeply informs instructional design, enabling the creation of educational experiences that effectively form, adjust, and automate schemas. This empowers learners with enduring knowledge and practical skills.

The Power of Spaced Learning

Building on schema theory, another critical element of our cognitive approach is spaced learning. This technique involves distributing learning sessions across intervals rather than concentrating them into a single prolonged period. Grounded in cognitive research, spaced learning leverages the psychological spacing effect—the phenomenon that learning and memory retention significantly improve when study sessions are spaced out over time.

Spaced learning reduces cognitive overload and enhances information encoding into long-term memory by repeatedly revisiting concepts after strategically placed intervals. This method strengthens memory retrieval pathways, resulting in more effective and durable learning. Our instructional design incorporates spaced learning through scheduled lesson repetitions and gamified elements like "defence boosts," ensuring learners engage with content consistently over optimal intervals. This results in improved knowledge retention, better schema automation, and enhanced application of skills in real-world contexts.

Why It Matters

Together, schema theory and cognitive load theory significantly influence instructional design. These theories guide content sequencing, complexity gradation, and practice methodologies, ensuring effective schema automation. By designing learning experiences aligned with cognitive principles, we facilitate deeper understanding and retention, enabling learners to transfer knowledge effortlessly across various contexts.

Section 2: Translating Science into Curriculum

Our Approach

Translating cognitive science principles into effective instructional design requires deliberate structuring and intentionality. Our instructional designs extensively leverage spaced repetition, active recall exercises, and personalized feedback to optimize cognitive load management. This ensures learners clearly understand, retain, and apply knowledge through deliberate practice.

Curriculum Design Informed by Science

To ensure practical relevance, our curriculum development is firmly rooted in current scientific research across multiple disciplines. Initially, we analyze industry-specific statistics and cybersecurity research to identify areas significantly impacting daily business operations. This rigorous, data-driven approach ensures our curriculum remains highly relevant and immediately applicable.

Once relevant modules are identified, we carefully explore individual learning concepts within each module, leveraging schema theory to guide our instructional design. Learning concepts are intentionally structured to progressively build knowledge, guiding learners systematically from foundational comprehension to real-world application.

Foundational learning forms the essential starting point of our instructional design, creating a robust basis for subsequent learning experiences. After establishing foundational understanding, we gradually introduce higher-level cognitive tasks, allowing learners to expand and refine their schemas.

Ultimately, our curriculum aims to help learners recognize and apply their knowledge practically in everyday cybersecurity interactions. Consistent reinforcement of real-world applicability facilitates schema automation, empowering learners to instinctively apply cybersecurity knowledge and skills.

Section 3: Gamification – Making Theory Tangible

Why Gamification?

Gamification at Prventi is a strategic decision driven by both scientific evidence and positive user experience. Research consistently demonstrates gamification significantly boosts learner motivation and engagement, transforming routine tasks into compelling educational activities.

By effectively capturing and sustaining attention, gamification reduces cognitive fatigue, ensuring active engagement throughout training. Structured progression, clear goals, and immediate feedback in gamified systems further support schema formation and automation.

Positive user feedback consistently highlights gamification's motivational power. Points, badges, and leaderboards create positive reinforcement loops, encouraging continuous participation and deeper learning.

Our Gamified Experience

Our gamification strategies are firmly grounded in cognitive principles. "Defence boosts," strategically scheduled repetitions informed by proprietary algorithms, optimize spaced repetition for effective recall and knowledge retention.

Formative feedback through personalized paths and visual domain graphs motivates learners by highlighting strengths and areas needing improvement. Leaderboards introduce competition, rewarding active engagement and repeated practice. Ultimately, this systematic approach facilitates robust schema automation, significantly enhancing practical cybersecurity skills.

The Defence Meter

An integral part of our gamification approach is the "Defence Meter." This interactive tool visually represents a learner's overall cybersecurity readiness. The Defence Meter tracks awareness training performance, diligence in completing training sessions promptly, and engagement with defence boosts for effective spaced learning. By clearly displaying progress and areas for improvement, the Defence Meter motivates learners to remain consistent and proactive in their cybersecurity training, reinforcing effective schema formation and enhancing practical application skills.

Conclusion

At Prventi, effective learning is about more than transferring information—it's about creating meaningful, lasting behavioral and skill changes. By merging cognitive science principles such as cognitive load theory, schema theory, and spaced learning with engaging gamification strategies, we design experiences that resonate deeply with learners. Our approach ensures knowledge is retained, retrievable, and practically applicable in professional contexts. As we continuously refine our methods, we remain dedicated to empowering learners with enduring skills and heightened cybersecurity awareness.

Sources cited in this article:

Al-Fraihat, Joy, Masadeh, Sinclair. 2019. Evaluating E-Learning Systems Success: An Empirical Study. https://doi.org/10.1016/j.chb.2019.08.004

Joosten, Cusatis. 2020. Online Learning Readiness. https://doi.org/10.1080/08923647.2020.1726167

Kirschner, Sweller, Kirschner, Zambrano. 2018. From Cognitive Load Theory to Collaborative Cognitive Load Theory. https://doi.org/10.1007/s11412-018-9277-y

Lang, Siemens, Wise, Gasevic, Merceron. 2022. Handbook of Learning Analytics. DOI: 10.18608/hla22

Sawyer. 2014. The Cambridge Handbook of the Learning Sciences 2nd Edition. Cambridge University Press.

Sousa. 2017. How The Brain Learns 5th Edition. Corwin.

Sweller, Merrienboer, Paas. 2019. Cognitive Architecture and Instructional Design: 20 Years Later. https://doi.org/10.1007/s10648-019-09465-5

Sweller, Ayres, Kalyuga. 2011. Cognitive Load Theory. Springer.

Tokuhama-Espinosa. 2019. The Learning Sciences Framework in Educational Leadership. doi: 10.3389/feduc.2019.00136

Weinstein, Sumeracki, Caviglioli. 2018. Understanding How We Learn. Taylor & Francis.