The term transfer needs to be more clearly described in order for it to be applicable and understandable across disciplines.
Historically, transfer effects were defined as either specific-to-specific skill (Thorndike, 1906) or more holistic (i.e. specific to general, see Judd, 1908). Hargreaves (1986) suggests that the development of the notion of transfer effects can be attributed to Piaget’s philosophy of ‘learn through play’. According to Piaget, it is during the second stage of developmental learning (from the ages of two to seven) that symbolic play becomes more adapted to reality “in its functional pleasure and autotelism” (Piaget & Inhelder, 1969, p. 63, In Hargreaves, 1986). This early notion (of transfer effects) was further developed in the 1970s when music instruction and performance were thought to be able to act as effective reinforcers of social and academic skills. For example, Greer, Randall and Timberlake (1971) suggested that the discriminate use of music listening impacted upon not only vocal acuity, but also on attending behaviour.
Originally, educational psychologists and educators believed that learning via transfer effects in general was dependent on similarity. Ellis (1965) referred to this as the Identical Element Theory. In a contemporary setting, process and efficacy are considered with regard to the extent to which past experiences (i.e. transfer source) affect learning and performance in a new situation (the transfer target, Helfenstein 2005). Salomon and Perkins (1989) suggested that transfer effects can be positive or negative. They introduced the concept of the low road (of transfer), which has a high level of automaticity based on lots of practice. Conversely, the high road (of transfer) requires intentional and mindful abstraction of an idea plus the conscious and intentional application in their theory of learning. Building on this, Bransford, Brown and Cocking, (1999) specified that initial learning must be more than mere exposure or memorisation. They suggest that for learning to occur there must be understanding, which takes time, and which leads to expertise. This learning is then manifested as deep, organisational knowledge that consequently improves transfer. Practice to improve transfer should include students specifying connections across multiple contexts. They noted four key characteristics of learning with regard to transfer effects: 1) the necessity of initial learning, 2) the importance of abstract and contextual knowledge, 3) the conception of learning as an active and dynamic process, not a static product, and 4) the notion that all learning is transfer.
Perkins and Salomon believed that the history of transfer effects is very important to learning theory and educational practice because most of the time, the desired transfer effects do not appear to take place. They suggested that this might be because notions of near and far transfer are “intuitive, [and] resist precise codification” (Perkins & Salomon 1992, p. 3). For example, when computers came into schools, consideration of transfer effects re-emerged as computer programming was thought to develop problem-solving skills. However, most research failed to support this assumption (see e.g. Beard, 1993; Pea & Kurkland, 1984; Salomon & Perkins, 1987). For example, Simon & Hayes (1976) studied problem solving in mathematics. They found that strategies acquired (in problem solving) were not really carried over to other analogous problem solving puzzles unless the connections/relationships were explicitly pointed out. Indeed, as Dweck (1986) persuasively pointed out, measuring performance on a task in itself does not take into account psychological factors (other than ability) that may influence the outcome. She suggests the late twentieth century move towards a social-cognitive approach of learning has shifted the emphasis towards cognitive mediators such as motivational patterns. Dweck showed that, in terms of goal-orientated behaviours, children tended to display patterns of behaviour. Children whose patterns was characterised as ‘mastery-seeking’
were persistent in the face of sought challenge and described as adaptive. Alternatively, when the children’s patterns were characterised as ‘helpless’, their behaviours were described as maladaptive in that they were averse to the challenge displaying low persistence in the face of difficulty. Moreover, in terms of measurement, Dweck writes that her research demonstrates how “a focus on ability judgements can result in a tendency to avoid and withdraw from a challenge, whereas a focus on progress through effort creates a tendency to seek and be energized by a challenge” (Dweck, 1986, p. 1041). Pintrich and Schunk (2002) had also differentiated between achievement behaviours, though their descriptors differed slightly in that mastery goals concerned gaining competence through the development of skills, whereas performance goals emphasised competence in comparison with others. More recently, Pugh and Bergin (2006) found that with regard to motivation, mastery goals were more consistently linked to transfer success than performance goals. Overall, it is important to note that for children, high achievement in test scores (competence) does not predict the children’s confidence for their future scores. Indeed, there also appears to be a sex (or more probably gender) difference. Intelligent girls have shown a tendency toward low expectancies and maladaptive behaviour patterns. It appears that past success did not provide the girls with a strong self-concept of ability, whereas the boys appeared to prefer challenges they could work to overcome (Licht et al., 1984). The mediating influences of motivation and self-concept, not only with regard to transfer effect but also specifically for music education, will be returned to in chapters five, six, eight and nine.
Building on Butterfield and Nelsons’ (1991) distinctions as within-task, across- task and inventive transfer, Haskell (2001) proposed a more gradual scheme of taxonomy based on the similarity between tasks and situations. He distinguishes between non- specific transfer, which he outlined as the constructivist idea that all learning builds on present knowledge, i.e. pedagogy. He describes application transfer as the retrieval and use of knowledge from a previously learned task. In comparison, he suggested context transfer (rather counter intuitively) means the context-free transfer between similar tasks, and displacement or creative transfer suggests an inventive or analytic type of transfer that refers to the creation of a new solution during problem solving as a result of a synthesis of past and current learning experiences. Table 1.1 depicts an overview of types of transfer used in current discourse according to Schunk (2004).
Table 1.1. Types of Transfer, adapted and updated from Schunk (2004). Type of Transfer Manifestation
Near, Literal, Low Road or Within Task
Intact knowledge transfers to another task directly because there is overlap between original source and transfer target. Contexts similar so well established skills transfer
automatically
Positive/Negative What is learned/not learned in one context enhances/hinders or delays another
Vertical, Contextual or Across Task
Previous knowledge essential to acquire new Horizontal or Non-
Specific
Previous knowledge helpful but not essential
Figural or Displacement Some aspects of general knowledge used to think or learn about new problem
Far, Inventive or High Road
Involves deliberate abstraction and conscious formulation of connections between contexts as there is no overlap, contexts are dissimilar.
High Road Creative and Forward Reaching
As above for potential contexts High Road, Application
and Backward Reaching
As above for previous situations
In the music psychology literature, with regard to transfer effects, a paper often cited but rarely discussed is When and Where Do We Apply What We Learn: A Taxonomy
for Far Transfer (Barnett & Ceci, 2002). The authors reviewed the results of 100 years of
academic argument on the topic of learning and concluded that the failure to specify dimensions along which transfer can occur has resulted in dialogues that are at cross- purposes. Specifically they claim that there has been a comparison of “apples and pears” (Barnett & Ceci, 2002, p. 612). Citing Klausmeier’s (1961) assertion that one reason for teaching in school is to enable learning outside school, Barnet and Ceci suggest that investment in education has been based on the assumption that the acquisition of academic skills will enable students to become productive members of society. This suggests that it is both a practical and philosophical aim of society to educate in order to progress via knowledge applied through a good work ethic. They suggest a taxonomic framework would enable rigorous testing of an operationalised definition of far transfer and state,
“If the goal is to encourage transfer from school-based lessons to nonacademic situations in the workplace years later, then something akin to this context must be explored in transfer research if it is to be applicable to the goal in question.” (Barnett &
Ceci, 2002, p. 632).
Their investigative review suggested that transfer might emanate from two potential sources: familiarity with the relevant contextual factors (i.e. the domain in question) and the individuals underlying cognitive skills regarding “encoding,
representing, retrieving, mapping, and transferring prior learning” (Barnett & Ceci,
2002, p. 633). This suggests some aspects impacting on transfer may be heritable, and some trainable. Barnett and Ceci concluded that transfer is multidetermined and that success may be both situationally and contextually dependent.
Regarding continued misunderstandings of the underlying issues of transfer and shared resources, Klingberg more recently stated,
“The effect of training on a particular cortical region using a specific task would only be expected to transfer to other tasks and functions to the extent that the tasks rely on the same neural networks” (Klingberg, 2010, p. 318)
Concerning the misrepresentation of scientific studies with regard to transfer, Bangerter and Heath (2004) investigated the emergence and development of the scientific ‘legend’ of the Mozart Effect. They suggested that ideas propagate because they address the needs and concerns of social groups. In this case, the parents liked and therefore popularised the idea that listening to Mozart would enhance the intellectual development of their children. Rauscher and Hinton (2006) addressed the extraordinary impact of the original Mozart Effect research (Rauscher, Shaw & Ky, 1995). They confirmed music listening is clearly not music instruction and furthermore their paper had not mentioned any mechanisms of transfer. They discuss transfer and returned to Thorndike’s (1913) premise that the similarity of the elements of the domains constrains the amount of transfer possible. They continue that whilst in the late twentieth century transfer became less vague, “transfer is always a function of the relationship between what is learned and
what is tested” (Rauscher & Hinton, 2006, p. 235). They cite Singley and Anderson
(1989) to explain that the overlap between domains is a function of the shared cognitive elements
instance that requires an understanding of developmental trajectories, representational structural adaptations and functional differences as a result of musical learning. In the well-intentioned attempt to encompass multiple disciplines, it may be that field and context specific use of the terms must be faced in order to avoid unintended attributions. To clarify in this thesis, the use of the term ‘near transfer’ is restricted to associations between the musical skill learned and closely related non-musical abilities. Previous research has shown, for example, that learning to play a musical instrument is associated with the development of fine motor skills (Costa-Giomi, 1999; 2005; Lahav, Saltzman & Schlaug, 2007; Schlaug et al., 2005). In contrast, the term ‘far transfer’ effect is used to describe associations between musical learning and extra-musical abilities such as IQ (Schellenberg, 2004). Literature specifically associated with musical training and transfer effects on cognitive systems will be presented in chapter three, on motor and visual systems in chapter four and on socio-emotional behaviours in chapter five.
Changes in policy that require educators to justify music provision have re- ignited interest in the transfer effects of arts based learning (Branscombe, 2012). The final section of this introductory chapter relates to difficulties and issues specifically associated with research and music education.