To improve student performance, according to Edelenbosch et al (2015) all teachers need to understand the brain evidence base that informs and helps improve their practice. This suggestion is keeping with Gathercole & Alloway (2008) who suggest that the ability for any student to learn new information is directly related to the amount of information being presented to an individual over a time frame. One theory that has looked at the amount of information that is presented within a given time frame is the Cognitive Load Theory (Sweller, 2010). The Cognitive Load Theory has been proposed by Gerjets, Scheiter & Cierniak (2009), to be built upon two commonly accepted ideas. The first is that there is a limit to how much new information the human brain can process at one time. The second is that there are no known limits to how much-stored information can be processed at one time. These two assumptions highlight that memory can be divided into working memory, the memory system where small amounts of information are stored for a very short duration (Clark, Kirschner and Sweller, 2012) and long-term memory and that processing new information results in ‘cognitive load’ on working memory (Sweller, 2010). According to Clark, Kirschner and Sweller (2012), working memory relates to what one is conscious of at any one time and has limited mental space available, in-fact research by Cowan (2001) suggests that the average person can only hold about four pieces plus or minus two pieces of information in their working memory at one time. Following this lack of space available in the working memory Mayer et al (1996) makes mention that learners do not learn effectively when their limited working memory is directed to unnecessary or redundant information, termed the Redundancy Effect. The Redundancy Effect is said to occur when learners are presented with additional information that is not directly relevant to learning with any irrelevant inhibiting learning because it overloads working memory (Torcasio & Sweller, 2010). Research by Yun, Krystal and Mathalon (2010) stated that overloading the working memory system was associated with varying degrees of the subsequent decline in performance accuracy and reduced activation of brain regions central to task performance. The degree of performance decline was independently predicted by three separate factors operating during the overload condition: the degree of task failure, the degree of amygdala activation, and the degree of inverse coupling between the amygdala and dorsolateral prefrontal cortex. These findings suggest that vulnerability to overload effects in cognitive functioning may be mediated by reduced amygdala suppression and subsequent amygdala-prefrontal interaction, the reduction in cognitive flexibility and enhancement in cognitive fatigue. According to Kilpatrick and Bressloff (2010), there is a finite amount of fuel being provided to the brain in the form of glucose or neurotransmitters that through transmitting synapses relay information to different regions of the brain requires constant assembly, uptake, passage, usage, and breakdown of the neurotransmitter. Shail (2009) suggest that the neurotransmitter’s, adrenaline, noradrenaline, serotonin, dopamine, gamma-aminobutyric acid (GABA), acetylcholine, glutamate, and endorphins via enzymes, engulfed by voltage-gated calcium channels, and processed by synaptic vesicles when there is an over-stimulated brain, a cognitive overload. If the brain is not given time to rest and recalibrate its neurotransmitter and synaptic vesicle stockpile, the neurons temporary fail to fire and cannot transmit an input signal, leading to synaptic fatigue or short-term synaptic depression (Kilpatrick and Bressloff (2010) which negative contributes to an imbalance long-term memory neuronal connection (Murre and Dros, 2015).
Cognitive fatigue is seen as difficulty maintaining attention, concentration and focus, poor endurance for tasks involving thinking, lack of motivation, increased boredom, lack of participation, needing more sleep than usual, behavioural difficulties including hyperactivity, irritability, tearfulness, distant and feeling miserable as well feelings of low self-esteem, worry, depression and anxiety over how they will perform (Slimani et al, 2018). One way to reduce Cognitive Overload and the Redundancy Effect seen in many classrooms today and which are major barriers facing many learners today is through facilitating a microlearning environment. According to Korstange et al (2020), a microlearning environment reduces information volume into bite-sized, small learning units with just the necessary amount of information to help learners achieve the learning goal. Research by Kapp et al (2015) postulated that the short bursts of content in microlearning improved retention of information by 22% over traditional learning. Interestingly, Gutierrez (2018) reported that bite-sized chunks can increase efficiency and transfer of learning by 17%. Research suggests that there are several benefits of using microlearning including (1) better retention of concepts (Shail, 2019), (2) better engagement for learners (Nikou, 2019;), (3) improving learners’ motivation (Nikou and Economdies,2018), (4) engaging in collaborative learning (Reinhardt and Elwood, 2019;) (5) improving learning ability and performance (Mohammed et al., 2018) by the hypotheses that microlearning uses the conceptual model of neuronal regulation and advocates preventing over stimulations or cognitive exhaustion via multiple time-spaced lessons (Shail, 2009).
According to Hug (2006), there are seven dimensions of microlearning: time, content, curriculum, form, process, modality, and learning type. They describe mainly the design aspect of microlearning. While the consensus is that the duration of microlearning should be less than 15 minutes (Kapp & Defelice, 2018). Microlearning processes often derive from interaction with micro-content or otherwise known as learning nuggets which are mini learning activity, usually less than 5 minutes in length and are supplied through a stream of intermittent nuggets that involves a variety of learning-related events (Conole & Fill, 2005) situated among a spaced learning effect (Appleton-Knapp, Bjork, and Wickens,2005). Spaced learning according to highly condensed learning content over a short period, a teaching nugget/microlearning environment repeated after breaks (microbreaks) of 10-minute breaks during which respite activities like such as physical activities/socialising by playing games are performed by the students (Hug,2006). Spaced learning is based on the temporal pattern of stimuli for transferring short term memory from brain regions (the pre-frontal cortex being strongly involved) to long-term memory regions for indefinite storage (Tyng, Amin, Saad and Malik, 2017), providing the foundation for positive neuroplasticity and not negative neuroplasticity seen when the brain is overloaded with information (McGaugh, Cahill and Roozendaal, 1996).
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