R ESPONSABILIDAD L EGISLATIVA Y J UDICIAL

M‑learning has its roots in the instructional design methodologies proposed by the likes of Benjamin Bloom and Robert Gagné. Over the first half of the 20th _{century, educational} practice was informed by the behaviourist school of psychology. Behaviourism has its origins in the animal research of Russian physician Ivan Pavlov (1927) and is a perspective found in both psychology and in learning and teaching. The educational theory of

behaviourism holds that the learner is a passive recipient of knowledge. In philosophical tradition, a metaphor of a blank slate or tabula rasa (erased wax tablet) is often used to represent the fact that the learner has no pre-existing knowledge - or more precisely lacks a particular desirable behaviour that the teacher wishes to instil (Aristotle, 2008). Behaviourism takes a positivist stance and was advocated by early practitioners such as J.B. Watson (1931) and B.F. Skinner (1988) as a backlash to introspectionism. Their rationale was based on the premise that data derived from self-analysis of mental processes could not be empirically measured, whereas changes in behaviour could be objectively observed and quantified.

The behaviourist approach was prevalent up until the end of the 1950s but the following decade saw a rejection of behaviourism in favour of an emerging theory known as cognitivism. The so-called cognitivist revolution of the 1950s (Baars, 1986) was driven by the emerging understanding that the human learning process involves more than reflex responses to external stimuli. In the pre-Socratic Greek philosophical origins of learning, there was a theory, in what is known as the rationalist tradition, that knowledge is a

priori, meaning that humans have an innate knowledge that exists before the event of

learning (Plato, 1973). This theory assumes that knowledge pre-exists in the mind of the learner waiting to be illuminated by the process of education. Behaviourist methods of classic conditioning, (and to a lesser degree operant conditioning) required the formation of mental links between innate responses and the required behaviour to be learnt

(Skinner, 1988). However, modern neuro-scientific methods such as functional magnetic resonance imaging (fMRI) can provide new information about how learning occurs. Neuroscience tends to support a cognitivist a posteriori model where knowledge is not considered to be innate, and learning requires sensory input. New information is stored in

short-term and long-term memory after the event of learning (Moscovitch 1992, 1994; Mather, Cacioppo and Kanwisher, 2013).

The area of the brain that controls autonomic responses such as salivation (as researched by Pavlov) is known as the hypothalamus, but this is not the same area of the brain that is required when learning (for example) how to play a musical instrument or how to talk. The hypothalamus is part of a structure known as the diencephalon. Its anatomical position is deep inside the brain, and it performs all of the autonomic functions required for an organism to survive, but the higher cognitive and motor functions required to learn a language, or how to play a musical instrument involve other parts of the brain,

especially the outer cortex and sub-cortex (Carpenter and Reddi, 2012). There is now convincing evidence from neuro-imaging studies, that learning results in changes to brain plasticity, i.e. “changes in structure and function of the brain that affect behaviour and are related to experience or training” (Herholz and Zatorre, 2012, p.486) suggesting that knowledge does not pre-exist, but is (physiologically) constructed during the learning process. This concept has recently been confirmed in animal studies by Ramirez, et al., (2013) who were able to isolate memories relating to learning (external fear stimulus) in a small number of individual neurones (cells) in the brain. These studies convincingly

discredit the Cartesian, rationalist view of a priori knowledge.

Cognitivism, therefore, considers the brain (metaphorically) as a computer requiring an input of data that must then be processed, stored in memory and then used to inform future actions. There is also a parallel strand of cognitivism known as computationalism, a theory, first established by philosopher Hilary Putnam in 1961. Computationalism

acknowledges the similarities between a human brain and a computer. These include the inputting of information, the step-by-step algorithms required to process that

information in random access memory (RAM), and importantly the ability to correlate one set of data with another.

Figure 1-2: A computational model of cognitive processing (after Putnam (1961), Carpenter and Reddi (2012) and Neisser (1967)

The theory was further developed by Ulric Neisser (1967), the founder of cognitive psychology and neuroscientist David Courtney-Marr. Being able to correlate one set of data with another is crucial to learning the higher-level skills in Bloom’s cognitive domain. Neuroscientists, such as Ramirez (2013), call these organised patterns memory engrams, but psychologists describe them as schemata (singular schema). This term was introduced by cognitivist educator and child-development researcher Jean Piaget (2001) and is key to cognitive learning. Piaget was one of the first educators to recognise the importance of Cognitivism in education. His interest was piqued during research into child development during which Piaget noted that some children gave strikingly illogical answers to simple questions. His subsequent research lead to a theory of cognitive development that recognised that the biological development of the brain plays an important role in learning and that learning strategy must take cognitive development into account.

Piaget organised learning into stages, namely:

• Sensori-Motor (birth-2 yrs.) • Pre-Operational (2-7 yrs.)

• Concrete Operational (7-11 yrs.) and • _{Formal Operational (11 yrs. onwards)}

Piaget’s view differed from those held by many of his contemporaries, in that he proposed that these stages of cognitive constructivism are related to physiological maturity. This is certainly the case when considering the myelination of the brain in very young infants (Paus, 2005). Piaget also placed importance on learning as being created by personal experience and interaction with the environment rather than being reliant upon socio-cultural interactions and language – the theory favoured by Vygotsky (2012). Modern thinking has refuted some of Piaget’s ideas. Bruner (1960) for example, argued that individuals have the capacity to learn fairly complex concepts at any stage in their development and that matching the level of learning activities to biological maturity may be of lesser consequence than Piaget believed. These differences aside, Piaget’s schemata theory is still widely regarded in the field of cognitive educational-psychology. It provides a useful representation of mental activity that can be used in the design of teaching materials and assessing the cognitive load imposed on the learner. It also allows knowledge to be quantified because schemata are considered to be “chunks” of information. Carpenter and Reddi (2012) concur with Ramirez et al. (2013), in

acknowledging that this process is mediated by the neurones of the brain and that this involves converting patterns of stimulation into patterns of response. This process relies on the fact that the human brain is structured in layers, each layer consisting of a network of neurones. Each neurone acts as a miniature computer in that it can respond to

particular patterns of activity occurring the adjacent layers. The incoming sensory pattern generated by a stimulus is therefore modulated as the activity passes through the layers resulting in a very different pattern at the output (response). This process is echoed in the neural networks found in computing and is thought to be the underlying mechanism behind cognition.

In distance learning, one of the main challenges for an educator is the engagement of the learner at a location that is remote from the classroom. This physical separation is a potential pitfall in learning and teaching because cognitivist learning theory recognises the need for learner engagement from the outset of any learning activity. To foster engagement in the mobile learner, educational materials must be presented in a way that can be easily understood and assimilated (Sweller, 1994; Mayer, 2009; van Merriënboer and Sweller, 2010). This basic need relates strongly to how the human brain receives and processes information and therefore falls into alignment with what is known as the cognitive domain of learning. This domain was first identified by educational psychologist Benjamin Bloom (1956), whose interest centred around the design of educational

materials, particularly relating to the setting of learning objectives. In Bloom’s philosophy, these objectives should feature measurable outcomes relating to any learning that could be used to inform the student assessment process. With this in mind, Bloom spent several years from 1948 onwards, collaborating with many of the university examiners across North America. In 1956 this collaboration culminated in the publication of his influential work entitled The Taxonomy of Educational Objectives, The Classification of

Educational Goals, Handbook 1, The Cognitive Domain.

The cognitive domain relates to brain-based learning and lies somewhat in opposition to the rationalist and behaviourist approaches that had been favoured for the first half of the 20th _{century. One of the first educators to recognise the importance of this trend} towards a cognitive approach in learning was experimental psychologist Robert Gagné (1985), whose early work was very much in the behaviourist tradition. At the end of the 1950s - during the cognitivist revolution - Gagné switched his attention to the newly developing field of cognitivism. One of his principal interests at the time was instructional design. Using some of Bloom’s themes relating to learning taxonomy and incorporating some of the concepts introduced by behaviourist B.F. Skinner, Gagné published an influential work in 1965 entitled The Conditions of Learning. He categorised learning into five main areas, namely;

Motor Skills relate to the ability to make co-ordinated body movements. Verbal Information relates to the ability to state facts or describe something.

Attitude relates to “acquired internal state that influences the choice of personal action”

(Gagné & Driscoll, 1988, p.58).

Intellectual skills relate to the mental faculty of reasoning.

Cognitive strategy relates to how “learners regulate their own internal processes of

attending, learning, remembering, and thinking” (Gagné, 1985, p. 55).

Areas 4-5 relate to the way that the brain receives and stores information, particularly relating to verbal, intellectual and cognitive skills. These categories are pertinent to m‑learning because they are reflected in the theory and process of designing interfaces required for human/computer interaction and software. Zhang and Galletta (2006) explain the need for HCI to be human-centred and list the human characteristics that are relevant to the interaction with information technology. It can be seen that they echo Gagné’s conditions of learning very closely, they are defined as:

Physical or Motor Skills relating to interaction with the user-interface.

Affective and Motivational Aspects relating to affective state, mood, feelings, emotions

and motivation.

Cognitive Issues relating to perception, attention, memory, knowledge, learning, error

and distributed cognition.

Demographics relating to gender age and culture.

The similarity with Gagné’s educational model is not coincidental, because HCI research evolved in tandem with the cognitivist revolution, and used many of Gagné’s

underpinning theories relating to cognition in its methods. A cognitivist approach is, therefore, particularly suited to the development of m‑learning materials and hardware design, because of the synergy between HCI and Gagné’s theories relating to the

conditions of learning.

More recently, in 1988, psychologist John Sweller made a link between cognitive load and earlier research relating to the capacity of working memory. Sweller’s research in

mathematical problem-solving lead to the formulation of CLT. This model has evolved over the last thirty years and has been adapted to investigate ways in which cognitive load can be measured in various domains including health-professional education (van Merriënboer and Sweller, 2010).