Bharatiya Ganita: A way to reduce Cognitive Load

Understanding Cognitive Load

Cognitive load refers to the amount of mental effort required to process information at a given time. According to cognitive science, our working memory has limited capacity—research by John Sweller and others suggests that when information exceeds this capacity, learning becomes difficult. Working memory can typically handle only a few items simultaneously, while long-term memory provides the structures to store and retrieve knowledge effortlessly. The challenge in education, especially in subjects like mathematics, lies in managing this load so that students are not overwhelmed. When cognitive load is excessive, students may disengage, feel anxious, or resort to rote learning without deep understanding.

Mathematics and Cognitive Load in School Students

Mathematics, by nature, adds significantly to cognitive load because it often involves multiple layers of abstraction. For example, solving a simple algebraic equation requires recalling rules of arithmetic, understanding symbolic notation, applying procedural steps, and monitoring progress simultaneously. Each of these sub-tasks occupies working memory. Neuroscience studies show that mathematical reasoning engages the prefrontal cortex (for working memory and executive control) and the parietal lobe (for numerical processing). When students lack automatic recall of basic facts, their working memory becomes overloaded with elementary calculations, leaving little room for higher-order problem solving. This explains why many students find mathematics “difficult” or anxiety-inducing.

Here are some challenges students face when learning mathematics, using insights from cognitive science, neuroscience, and pedagogy.

Working Memory Overload

Mathematics demands the simultaneous juggling of multiple steps: recalling arithmetic facts, applying rules, and monitoring progress. For example, solving 23×4723 \times 4723×47 requires holding partial products in memory while performing new calculations. According to Cognitive Load Theory (Sweller, 1988), working memory has a very limited capacity (often cited as 4±1 chunks of information). If students haven’t automatised basic skills like multiplication tables, their working memory becomes overloaded, leaving little bandwidth for reasoning.

Abstract Symbol Processing

Math heavily relies on symbolic representations (e.g., x,π,x, \pi, \sqrt{}x,π,​). Unlike concrete objects, symbols have no inherent meaning and require the learner to map them to abstract rules. Neuroscience shows that this activates the parietal lobe, particularly the intraparietal sulcus, which is involved in numerical and spatial reasoning. Students who lack strong number sense (the intuitive ability to perceive magnitude and quantity) struggle to make these mappings, leading to errors and slower comprehension.

Sequential and Hierarchical Nature

Mathematical knowledge is cumulative: algebra builds on arithmetic, calculus builds on algebra, etc. Weakness at an earlier stage creates a “cognitive bottleneck.” For instance, students who have not mastered fractions find algebraic ratios nearly impossible. This hierarchical dependency makes mathematics uniquely challenging compared to subjects where partial knowledge may still allow progress.

Math Anxiety and Emotional Interference

Emotions directly impact cognition. Math anxiety is well-documented: the amygdala becomes hyperactive under stress, which disrupts working memory in the prefrontal cortex. Even students with adequate ability may underperform due to this interference. Neuroscience studies using fMRI show that anticipating a math problem can activate pain-related neural circuits, making students avoidant, which further reduces practice and confidence.

Transfer to Real-World Contexts

A technical but crucial challenge is the “transfer problem”: students may learn to apply a formula in class but fail to use it in a novel situation. Cognitive psychology suggests this is because their knowledge is encoded in a “narrow” form (specific to the worksheet or exam pattern) rather than as a flexible schema. Without strong associations to real-world contexts, the brain does not retrieve the information when it encounters a different but related scenario.

How Bharatiya Math - Ganita helps

Bharatiya Gaṇita—the Indian tradition of mathematics—is a deeply integrated system of logical reasoning, computation, geometry, and real-world problem-solving that evolved over millennia. Far from being a mere collection of formulas, it represents a living pedagogy that blends intuition, memorisation, and application. Rooted in Sanskrit and regional texts like Āryabhaṭīya, Līlāvatī, and Gaṇitakaumudī, it combines rhythmic verse (for effortless retention), concrete examples from trade, astronomy, architecture, and agriculture (for contextual understanding), and elegant algorithms (for mental efficiency).

In Bharatiya tradition, algorithms, tables, and formulas were encoded in metrical Sanskrit verses (the entire text of Lilavati is written in metrical form). Verse-based memory uses rhythm, meter, and auditory reinforcement, which strengthen long-term retention via hippocampal encoding. Once committed to memory, these “chunks” reduce the strain on working memory: instead of juggling several steps, the student retrieves the rule as a single, automatic unit. Modern cognitive science confirms that automaticity (e.g., quick recall of multiplication facts) frees up executive resources for problem solving.

A Verse from Lilavati

Abstract numbers and symbols often intimidate students. In Gaṇita, problems were framed in everyday contexts: distribution of grains, sharing of cows, measuring fields (kṣetra-vyavahāra), or astronomical cycles. Such grounding activates embodied cognition — the brain’s system for linking abstract thought to sensory and motor experiences. By tying symbolic reasoning to lived situations, learners form richer mental models. This helps students make the leap from “marks on paper” to “quantities in life,” reducing the cognitive barrier of abstraction.

Modern math often isolates symbolic equations from verbal or visual forms, forcing students to mentally “translate.” In contrast, Gaṇita pedagogy integrated multiple modalities naturally: oral recitation of rules, symbolic manipulation on palm leaves, and geometric constructions (rekhā-gaṇita). This multimodal approach builds stronger cross-references in neural networks, lowering the “switching cost” between representation systems. When a student encounters a problem, retrieval flows more smoothly because knowledge is encoded in multiple overlapping formats.

Where modern pedagogy struggles with “transfer” (students failing to apply classroom math outside exams), Bharatiya Gaṇita begins with context. Bhāskarācārya’s Līlāvatī includes problems about real-world scenarios — directly relatable to life. Cognitive science calls this situated cognition: knowledge tied to authentic contexts transfers better because the brain encodes it as part of a meaningful schema. Thus, students not only learn the formula but also when and why to use it.

Catering to Diverse Learners

A key distinction between modern mathematics and Bharatiya Gaṇita lies in how abstract relationships are represented and understood. Modern math education often privileges algebraic formulations—symbolic equations that express relationships in compact, language-like syntax (e.g., (a+b)2=a2+2ab+b2(a+b)^2 = a^2 + 2ab + b^2(a+b)2=a2+2ab+b2). While efficient for computation, such symbolic representation demands a high level of abstract manipulation and places heavy load on working memory.

Not all students can relate directly to formulae.

When we learn it as formulae when we are young, it takes many years to understand the reasoning behind these formulae and hence applying them to realworld context becomes difficult.

In contrast, Bharatiya Gaṇita frequently expressed the same truths geometrically and visually, through rekhā-gaṇita (line-based mathematics) and spatial reasoning. The same identity, for instance, could be illustrated as the rearrangement of squares and rectangles—showing that the square built on (a+b)(a+b)(a+b) equals the sum of the individual squares plus two rectangles of area ababab. Neuroscience supports this approach: visual-spatial representation activates parietal and occipital regions of the brain, providing intuitive understanding and offloading symbolic processing from the prefrontal cortex. This geometric embodiment transforms abstract algebra into sensory reasoning, lowering cognitive load while strengthening conceptual grasp. Thus, Gaṇita integrates visual and linguistic modes of cognition, allowing learners to “see” mathematical truth rather than merely manipulate symbols.

The goal is not to abandon modern mathematical methods but to harness the cognitive strengths of Bharatiya Gaṇita pedagogy:

• Use verse-based memorisation to build fluency and reduce working memory load.

• Frame problems in culturally and experientially grounded contexts to strengthen abstraction and transfer.

• Reduce math anxiety by making it more relatable.