Firstly, just to mention the three types of cognitive load (Garner):

• Intrinsic cognitive load is determined by the mental demands of the task (Chandler & Sweller, 1996); programming is a domain with a high intrinsic cognitive load and this needs to be taking into account when teaching it. The intrinsic cognitive load cannot be reduced, however something can be done about the extraneous cognitive load.
• Extraneous cognitive load is generated by how the new knowledge is taught; poor lesson planning leads to a high extraneous cognitive load. If a high extraneous cognitive load is combined with a high intrinsic cognitive load then this can lead to working memory overload. This is often what happens with novice programmers when the lesson is not designed appropriately.
• Germane Cognitive Load is when the way the information is presented is optimum and the demand of the concept is not too high, then there is some working memory available to use for conscious processing. An example is the use of partially completed solutions in the learning of problem solving.

The evidence and theory from cognitive science says that presenting new material in small steps is an effective strategy to use with all students to avoid cognitive load. Sweller et al. (2011) summarised this research on cognitive load and the applications of this within education. Cognitive Load Theory builds on the multi-store model of human memory (Atkinson and Shiffrin, 1968) where the working memory receives, rehearse and retrieves information from either the sensory memory or the long-term memory. Long-Term Memory (LTM) holds a permanent and massive body of knowledge and skills in what is called schema. Working memory is the part of the brain that receives the immediate information through our senses. Working memory allows us to think creatively and solve problems, however, it has two limitations. Firstly, it is short-lived. Secondly, it is only capable of processing up to approximately seven things at a time (Miller, 1956), meaning that if too much new materials are presented, it will be overloaded.

It is well known that experts employ automated retrieval processes to recall information from highly organized schema in LTM; During recalling activities, knowledge is retrieved from Long-Term Memory into working memory, therefore, regular recalling and retrieving practice improves the efficiency and storage of knowledge within schema that will be needed by working memory when solving a problem. This process gradually become automated so novices can progress towards expert status. Consequently, there are two main strategies to alleviate the limits of working memory; schema acquisition, which allows us to chunk information into meaningful units, and automation of procedural knowledge (Wilson & Cole, 1996).

Finally, just to share some specific strategies that could reduce cognitive load in programming:

• Use of Worked Examples and ask the students to annotate them.
• Repeated practice using partially completed coded solutions for students to complete using the previous worked examples. Provide coding scaffolding or cheat-sheets when needed.
• Remove gradually scaffolding or cheat-sheets from practice tasks. This ensures that less information is gradually required and the novice becomes an expert eventually.
• Repeated practice, individually and in pairs (Pair programming)
• Extensive repeated practice with problems that require mixed solutions, for example, a subroutine that requires date to be written using an external file. This strategy supports to build the schema even further by strengthening associations.
• Chunking up the new programming techniques presented into small segments, for example, introduce selection with IF…ELSE with a lot of practice afterwards, then move on to present IF…ELSE IF..ELSE.
• Present coding strategies used to solve problems, for example, if a block of code has to be repeated a specific number of times, that requires a counter-controlled loop.
• Link new programming concepts with prior learning as much as possible
• Use models in order to ensure students connect new knowledge to familiar scenarios, for example, use the analogy of a house when explaining OOP paradigm.
• Provide tasks that are open-ended in terms of coded solution (Creativity).
• Integration of text and graphics, such as diagrams, when presenting new concepts.