IAFOR Journal of Education

Volume 7 – Issue 1 – Summer 2019

Transfer of Learning and Teaching: A Review of Transfer Theories and Effective
Instructional Practices
Shiva Hajian
Simon Fraser University, Canada

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Abstract
One of the primary goals of education is to ensure that learners can apply their acquired
knowledge in various ways and under different circumstances. However, this expected“transfer” does not always occur and, therefore, the acquired knowledge cannot be flexibly
employed in different contexts. One way to minimize this problem is to understand howtransfer occurs and what learning conditions can improve this process. This review paper
provides major theoretical perspectives and pedagogical practices to explore the mosteffective ways to optimize knowledge acquisition and transfer. The results of the comparisonof the selected theories indicated that transfer was a multi-dimensional process that could
occur at any stage of learning and could be enhanced through coaching, scaffolding,interacting, assessing and reflecting in situated learning environments.
Keywords: transfer of learning and teaching, situated learning

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Many researchers have defined transfer as the productive application of prior learning andexperiences in novel contexts (Gass & Selinker, 1983; Gick & Holyoak, 1987; Roediger,
2007). While the definition of a “novel context” may initially appear ambiguous, mosttransfer studies consider a novel context as a situation that is potentially different from theoriginal situation in which learning has already taken place (McDaniel, 2007). Thus, applying
knowledge to a different domain, aligning it with a new goal and being assessed differently,
can all be considered as novel contexts (Barnett & Ceci, 2002; Pan & Richard, 2018).
A large body of research indicates that students often find transferring knowledge tosituations other than the one in which it was learned highly challenging (Gilbert et al., 2011).
Therefore, many educators and researchers have attempted to identify the factors that
influence later acquisition of knowledge by examining the cognitive and metacognitive
processes that occur during the learning process. The result has been a vast amount ofliterature with various perspectives towards learning and instructional practices along withlarge variability among findings (Blume, Ford, Baldwin, & Huang, 2010). Therefore, the goalof this article is to review some of these theories to identify the consistent factors thatfacilitate knowledge transfer and provide examples of the instructional strategies that can
provide optimal opportunities for learners to apply their acquired knowledge and expertise.
Since discussing all theories from transfer literature is beyond the scope of this review, only a
few traditional and contemporary theoretical perspectives will be provided and discussed.
These theories begin with the traditional cognitive theories of transfer and end with more
contemporary learning transfer theories. However, the selected instructional practices will
predominantly be based on social constructivist approaches to learning such as situatedlearning theory. The reason for this choice is the extensive employment of contextual andinquiry learning methods in science and technology research and the presence of empiricalevidence for successful application of these methods (Aparicio-Ting, Slater, & Kurz, 2019;
Potvin, Hasni, & Sye, 2017). At the end, some of the advantages and disadvantages of thestated theories will be discussed and several pedagogical suggestions will be provided.
Theoretical Models of Transfer
Theory of Identical Elements
Learning transfer has become a significant research topic in educational psychology since
Thorndike and Woodworth developed the theory of identical elements in 1901 (Schunk,
2004; Singley & Anderson, 1989). According to this theory, learning can be transferred fromone activity to another (e.g., training to performance) if the two activities are highly similarand share many common elements. The level of transfer is also determined by the level ofsimilarity between the original context of the training and the target context of theperformance. Thorndike conducted a study on 8,564 high school students and noticed that theart and science courses taken by the students had very little effect on their problem-solving
skills. For example, subjects such as math, physics, chemistry, Latin and French had very
little effect on strategic thinking and problem-solving skills. While the challenging practicesof learning sciences had helped students develop general learning skills and intellectual
abilities, it had not helped them develop efficient problem-solving abilities required to tackle
real life problems. He therefore emphasized the significance of the relationship between thecontent taught in school and the demands of real-life skills. Identical elements theory
challenged the traditional view of teacher-centered pedagogy and became one of thepioneers of active learning theories. This view of learning was followed by Dewey’s
pragmatic view of learning and his idea of inquiry learning classrooms. According to this95

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view, classrooms need to represent real life situations and provide opportunities for students
to flexibly participate in distinct learning activities and in distinct social contexts (Dewey,
1938; Gutek, 2014; Williams, 2017).
Theory of Low and High Road Transfer
In 1989, Perkins and Salomon developed the integrative theory of “low and high roadtransfer”. According to this theory, there are two different, but connected, underlying
mechanisms for transfer, namely, low road and high road. Low road transfer usually occurs
when the target and original activities share a countless number of features. For example, aperson who is driving a small truck for the first time usually finds the experience very similar
to the experience of driving a sedan car. Driving the truck is the same habitual experience
although it seems like a different task (Perkins & Salomon, 1989; Perkins & Salomon, 1992).
These reflexive behaviours or automatic responses in similar activities are the result of
countless hours of practice. However, high road transfer occurs as a result of mindful
abstraction of general principles among different events in different contexts and a deliberate
search for connections among their structures. For example, a person who takes a C++
programming course and is assigned a game development project at the end of the semesterneeds to ensure that he or she knows the programming language well and then deliberately
search for the connections between what has been learned and what needs to be designed in anovel context. Similarly, learning Newton’s Laws in a Physics course and applying them tobuilding an aircraft model needs flexible adaptations of underlying principles to a completely
new environment. High road transfer demands time for exploration, discovery, flexible
adaptation of skills, and asking questions such as what the problems are, what principles need
to be applied and how the final outcomes need to be assessed. In practice the low and highroad theory has offered two ways for learners to increase their chance of applying theirknowledge in different situations flexibly: effective practice and mindful abstraction ofknowledge (Perkins & Salomon, 1988, 1989, 1992).
This theory leads to two broad instructional strategies in fostering transfer: “hugging andbridging” (Perkins & Salomon, 1992, p.10). Hugging is an instructional strategy that directly
guides and engages the learner in the desired target performance and therefore promotes
reflexive transfer. For example, a teacher might give students sample exam questions rather
than just explaining how some particular strategies may help them succeed in the exam.
Similarly, a university job counselor might encourage students to engage in mock interviews
rather than just providing them with some general rules of effective job interviews. This way
the learning experience hugs the target performance and optimizes the likelihood of
automatic performance in similar situations (low road transfer). On the other hand, bridging
employs the high road to transfer. This instructional strategy encourages intentional
abstraction of the general rules by searching for possible connections among various
experiences or examples and applying them to unknown cases. For example, an instructor
who prompts students to adopt an exam strategy based on their previous experiences createsan opportunity for students to analyze and reflect on their strengths and weaknesses andcreate a general strategic plan for their future exams. To maximize high road transfer,
instructional designers need to create generic skills that can be used in various situations andnot uniquely applicable to their original context (Winn, 1993).
Theory of Analogy and Abstraction
Abstraction is one of the most fundamental principles of many cognitive theories of transfer.
As Skemp (1986) defines it, abstraction is “an activity by which we become aware ofsimilarities among our experiences” (p. 21). By identifying the underlying principles of96

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actions, thoughts, perceptions, and operations we can facilitate the transfer of knowledge tocontexts that are fundamentally dissimilar from those that are initially encountered (Gick &Holyoack, 1980; Hayes & Simon, 1977).
There is ample evidence that understanding of the world is based on abstraction rather thansuperficial comprehension of various systems and their relationships. This is particularly truein understanding and discovery of underlying scientific principles. For example, in a biology
course the goal is to understand the genetic laws behind multiple generations of pea plants
and not the particular properties of the plants in isolation. Similarly, in physics the goal is tofind out the fundamental principles of conservation of energy and not watch what happens toa particular spring when it is weighted down (Chi, Feltovich, & Glaser, 1981). In fact, theprogress of science and mathematics is mostly due to the deep conceptual principles sharedby the phenomena that may look dissimilar on the surface but which are defined in terms of
the same categories or models (Goldstone &Sakamoto, 2003) such as the underlying
principles of Golden Ratio in nature or the Fibonacci pattern in reproduction.
One effective mechanism for abstraction is analogy (Gentner & Hoyos, 2017; Hajian, 2018).
Although analogical transfer may seem intuitive and natural, it is based on complex
mechanisms that need to be employed for extraction of commonalities among various casesand discarding the unrelated information (Gentner, 1983; Gick & Holyoak, 1983; Hummel &Holyoak, 2003). For example, when a person knows how to solve a linear equation withunknown variables added together, it is highly possible that the person also knows how tosolve the linear equations with different operations (e.g., subtraction, multiplication, anddivision) using analogical comparison. Analogy and reasoning can lead to efficient problemsolving strategies which are based on four components of retrieval of information, mapping
structures, transferring solutions, and generalization (Gentner, 1997; McFate & Forbus,2016). Among all these components, it seems that retrieval and access to the source analogue
is the most challenging process. And therefore, it can be performed successfully if explicit
scaffolding such as hints and cues are provided for finding a suitable source for analogy(Gentner et al., 1994; Holyoak & Koh, 1987; Schmid, Wirth, & Polkehn, 2003). Evidenceshows that many university students often fail to employ structurally similar analogies
spontaneously (Gick & Holyock, 1980; Hayes & Simon, 1977) although using this strategy
can greatly facilitate the process of creation of new schemas (Gentner & Hoyos, 2017). Given
the critical role of structural similarity and mapping in schema formation and successfultransfer (Holyoak & Koh, 1987; Reed, 1989; Salomon & Perkins, 1989), it is important toinclude analogy as a supportive mechanism of instruction and knowledge assimilation (Chi,Slotta, & De Leeuw, 1994). Additionally, analogies can be employed as exploratory andcreative tools for solving complex relational problems (Duit, 1991; Glynn, Britton, SemrudClikeman, & Muth, 1989). For example, a novice looking at a chessboard in the middle of thegame becomes overwhelmed by the huge amount of information required to be processed
whereas a chess expert can very quickly recognize the structural similarities among various
positions and relations and work with manageable meaningful structural pieces (Chase &Simon, 1973).
Given the power of analogical comparison in knowledge transfer, along with many otherrobust cognitive mechanisms of transfer, such as self-explanation (Chi et al., 1994; RittleJohnson, 2006; VanLehn & Jones, 1992) and test-enhancement strategy (Pan & Richard,
2018; Roediger & Butler, 2011), how can educators incorporate these mechanisms in
instruction to promote expert learning? A number of studies addressed this question in the
1980s and indicated that the possible solution was learning in similar contextual97

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Volume 7 – Issue 1 – Summer 2019environments. It appeared that from practical point of view “contextual similarity” between
various situations plays a higher significant role in determining whether transfer can actuallyoccur (Day & Goldstone, 2011).This view criticized the cognitive learning models employedin schools as decontextualized and individualized pedagogical models that led to independent
possession of knowledge by the learner (Wilson & Myers, 2000). The idea that concepts are
abstract, self-sufficient, and separate from the context does not support the idea that learning
is an active and engaging process (Bonwell & Eison, 1991; Brown et al., 1996). Furthermore,acquiring abstract knowledge does not guarantee transferrable knowledge – this is similar to asituation when a person has quality tools but is unable to use them. The gap created between
knowing and doing prevents learners from using their knowledge within an appropriate
context (Bransford et al., 2000; Brown, Collins, & Duguid, 1989).
Situated Learning Theory
Although some of the major transfer theories developed simultaneously, it was the theory of“situated learning” (Lave, 1988) that integrated most of the separate branches of investigation
into a more complete theory of learning and transfer. In this view, learning and cognition are
situated and developed through purposeful authentic activities in social contexts. Therefore,
learning and transfer occur when learners are given an opportunity to “observe and practicein situ” (Brown et al., 1989, p.34).
This theory is founded on the principle that knowledge is constructed if the learner becomesan active participant of a highly connected community in which knowledge and culture areintegrated. Learning should also be an unintentional process rather than a deliberate one – aprocess that can take place through “legitimate peripheral participation” (Lave & Wenger,
1991).
What Lave (1988) observed in her work with apprentice tailors is a good example of howlearning can occur as a social phenomenon and how novices become experts throughlegitimate peripheral participation (LPP). She noticed that newcomers to the community ofprofessional tailors began their jobs by doing lots of trivial tasks such as running errands,
preparing the materials for the tailors and cleaning the workspace. Then, they helped themasters by finishing the details of the clothes and gradually helped them more and more withadvanced jobs until they became experts themselves. Lave argues that the trivial looking jobs
performed at the initial stage of learning are not trivial at all as they provide an opportunity
for novices to familiarize themselves with the fundamental knowledge required for that
community. The small responsibilities progressively change to more advanced activities suchas cutting, sewing, and designing (Lave & Wenger, 1991). Therefore, it is mostly throughparticipation within a community of practice that learning and transfer can gradually occur.
This is similar to the foreign language learning process which is only possible throughapplication of words and grammar “within” society and not just memorization of isolated
words from the dictionary. For example, in the study conducted by Preston et al (2015), it
was shown how context-aware systems could help learners keep track of their progress
during an activity and how this strategy allows them to take language learning to a level
beyond the traditional environment of the classroom.
Lave and Wenger define community of practice as members of the same group, with sharedinterests, goals, and passions participating in a sociocultural practice on the regular basis.
Cognition is developed in this type of practice (Chaiklin and Lave, 1996; Lave, 1988) andnovices can become experts through moving from peripheral participation to the central roleof the community with more expertise (Lave, 1988; Chaiklin & Lave, 1996). The idea of98

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cognition in practice supports the significance of the instructional cognitive apprenticeshipmodel in learning and transfer of complex skills (Brown et al., 1989). It is important to knowthat the concept of “participation” does not only refer to informal or unplanned activities.
Participation can also be an arranged activity in formal lessons, laboratory experiments,
scientific conversations and workshops within the same community (Wenger, 1999).
The role of cognitive apprenticeship in learning has been demonstrated for various skills suchas mathematics, reading and writing (Brown et al., 1989), genetics (Charney et al., 2007),
chemistry education (Stwart & Lagowski, 2003), learning clinical skills (Wooley & Jarvis,
2007) and web-based problem solving (Kuo, Hwang, Chen, & Chen, 2012). All these studies
provide evidence that learners need to immerse themselves in authentic learning
environments and gain expertise through participation in the community of practice (Brown
et al. 1989; Greeno & Moore, 1993; Herrington and Oliver, 2000; Lave, 1988). For example,
in the study conducted by Preston et al (2015), it was shown how context-aware systems
could help learners keep track of their progress during an activity and how this strategy
allows them to take language learning to a level beyond the traditional environment of theclassroom.
Instructional Practices
One of the most efficient ways to assist learners to adapt to the demands of novel situations isto provide them with appropriate knowledge and skills that are necessary to confront andsolve complex real-life problems (Brown & Duguid, 1993). One effective practical way toimplement this idea is to design a pedagogical environment that can provide an authentic
environment and authentic activities so novices can have access to expert performance,
scaffolding at critical times, sufficient support for collaborative construction of knowledge,
and monitoring within the learning environment (Herrington & Oliver, 1995, 2000).
Although it is difficult to implement a program that integrates all of the above factors, it has
been shown that pedagogical models such as problem-based learning (PBL), community ofpractice (CoP), cognitive apprentice (CA) and game based learning (GBL) can integratemany components of the situated learning theories such as collaboration, coaching, practice,problem solving and reflection. There is ample evidence that these models can be
implemented successfully in educational practices and make significant changes in student
learning and transfer abilities.
Problem Based Learning (PBL)
PBL is founded on four principles of “constructive, self-directed, collaborative andcontextual learning” (Dolmans, De Grave, Wolfhagen, & Van Der Vleuten, 2005, p.732). In
PBL both learning and teaching are problem driven. The ill-structure problems used in PBL
allow learners to create connections between theory and practice to develop the required
ability to handle complex challenges of real-life situations (Hung, 2013; Savery, 2015).
Reflection is one of the most critical components of PBL as learners constantly need tomonitor their conceptual and procedural understanding for effective learning and adaptive
problem-solving strategies. This monitoring process allows refinement of knowledge andsupports learners in discovering the underlying structural relations between concepts andcategories (Hung, Jonassen, & Liu, 2008; Jonassen & Hung, 2012). Self-directed learning(SDL) is another crucial component of PBL that is fundamental to the development of fartransfer ability. SDL requires reasoning skills and analytical approach towards the issues that99

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arise in the process of learning and application. These skills allow the learner to develop
cognitive flexibility and decision-making ability required for tackling unanticipated novel
problems (Nerali, Telang, Telang, & Chakravarthy, 2016; Stolper et al., 2011). PBL has
successfully been implemented in many science courses such as physics (Celik, Onder, &Silay, 2011), mathematics (Ronis, 2007) and medical education (Barrows, 2000).
Communities of Practice (CoP)
Communities of practice are shaped by individuals who have highly similar concerns orinterests and are willing to encounter challenges related to their shared interests or concerns
through collaboration and interaction together. What makes a community of practiceproductive and functional is an effective leadership. A leader (coach or tutor) facilitatescollaboration and fosters participation during the process of learning (Schwarz, 2002). For
example, in the community of practice of the classroom, the teacher takes a leadership
position by facilitating the actions and decisions of a group of students (Shields, 2003;
Wenger, 2000). This collaboration can also occur in a virtual context such as a multiuser
online discussion board or a virtual lab. Although the physical presence is missing in theseenvironments, communities of practice can still be formed in a highly dynamic manner. A
good example of promoting CoP in an online classroom is assigning group projects that canbe performed in collaborative documents such as Google Docs. Using online sharing
document software, learners can discuss their ideas in real-time, evaluate their work together,
edit the document as a team and generate new goals and ideas for their future collaborative
projects. In fact, virtual environments may contain a variety of unique features that cannot befound in physical CoP environments.
Cognitive Apprenticeship (CA)
Cognitive apprenticeship is strongly based on “modeling, coaching, and fading” (Brown etal., 1989, p.39). An expert coach (e.g., a professor) begins to promote learning throughmaking his or her tacit knowledge explicit and model the required strategies in an authentic
activity for students (Brown et al., 1989). Students use the provided knowledge, manipulate it
and discover their own way of managing knowledge. For example, graduate students learn
how to do proper research by working on an authentic research project with their supervisors
as opposed to learning the principles of research from the books and practicing the learned
principles at the library. The supervisor monitors the resources used by the students, guides
them through the process of research and provides them with the opportunities they need totackle authentic complex research problems. The professor also provides coaching andscaffolding along the way if required. In fact, monitoring and providing feedback are highly
pivotal to this process. Unlike the traditional apprenticeship system that is only focused on
concrete physical skills, cognitive apprenticeship emphasizes on all the cognitive andmetacognitive processes that are involved to achieve mastery of the field. As Brown et al.
(1989) argue this model is founded on “learning-through-guided-experience on cognitive andmetacognitive skills and processes” (p. 457).
Many studies have provided evidence on the role of this model in improving student learningtransfer in different areas. For example, in the study conducted by Wedelin and Adawi(2014), in a mathematical modelling course, those who worked with supervision in a
cognitive apprenticeship environment (e.g., in pairs of an expert and a novice) demonstrated
significant changes in their mathematical thinking and modeling in real work projects. The
students also considered the course as one of the most productive and creative courses takenin their entire program.

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Similarly, in another experiment conducted by Chiu, Chou, and Liu (2002), the results
indicated that grade 10 students who learned chemical equilibrium in a cognitive
apprenticeship context gained better understanding of this phenomenon compared to thecontrol group. They also managed to solve more challenging problems than the control
group.
Teaching in a cognitive apprenticeship environment is highly successful in preparing students
for confronting complex problems in the workplace or any authentic context as this
instructional practice is based on modeling, coaching, scaffolding, discovery, articulation,and reflection (Schoenfeld, 1985). Therefore, the implementation of this educational model ishighly recommended in promoting learning transfer and problem solving in science andengineering. One of the challenges of implementing this model is the requirement of a small
teacher to learner ratio, which may not be possible in large classes or groups.
Game Based Learning (GBL) and Simulations
Educational games and simulations can also provide pedagogical contexts in which learning
can effectively take place under particular pedagogical conditions (Clark, Tanner-Smith, &Killingsworth, 2016). These interactive environments allow learners to check theirunderstanding of the real-world phenomena through observing, modelling, comparing,
testing, and reasoning. For example, simulations can provide immersive model-based
environments that allow learners to simplify or challenge the concepts or the principles they
are learning (e.g., the principle of conservation energy or principles of chemical equilibrium)and expose themselves to various dimensions of the learned principles (e.g., the multiple
ways a formula or a concept can be applied). Additionally, the embedded features of thesimulation allow repetitive practice along with real-time feedback on the user’s learning
progress in the environment (Dede, 2012). For instance, the immersive game of Spore(“Spore TM”, 2018) provides an opportunity for learners to repeatedly design creatures that
can transform through numerous stages of evolution and develop into new varieties. Despitesome scientific imprecision, players can directly observe their creatures and their effects on
the ecosystem. This interaction with the system allows players to constantly apply what they
have learned about evolution and continuously modify their designs to ensure what has beenlearned is valid and applicable.
SimCity TM (“Electronic Arts”, 2018) is another simulation that has been used for engaging
learners to build cities that can continuously evolve. Interestingly, this simulation shows theuser how the choices they make in the game can have dramatic consequences on the life ofpeople, businesses, and natural resources. Incorporating such simulations in the curriculum
provides a great opportunity for students to assess the functionality of their constructed cities
in comparison to the similar designs in the real world. It also allows them to analyze theeffects of their construction on the health of the ecosystem, environment, economy, the futureof agriculture, and many other challenges of today’s modern world. In fact, in the recent
study conducted in an urban geography course, the university students who used SimCity toconstruct their own cities had a greater opportunity to apply the theories they had learned tosupport their urban structures. They also needed to use urban geography principles tocritically evaluate the rationale behind their constructed system in the SimCity compared to a
counterpart design in the real-world. This activity greatly advanced students’ creativity,
originality, and imagination and resulted in multiple interesting cities with unique designs.
The findings demonstrated that an immersive educational environment can be a highly
effective tool for promoting learning transfer in geography and civil engineering education(Kim & Shin, 2015).

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Immersive technologies can greatly help educators implement pedagogical approaches thatare aligned with the situated-constructive learning theories. Well-designed simulations andgames provide immersive environments with appropriate tools, content, feedback, andscaffolds that are necessary for meaningful cognitive and metacognitive learning and transfer.
In such environments learners can explore, observe, question, and learn through peer
coaching and legitimate peripheral participation (Dunleavy, Dede, & Mitchell, 2009). That isprobably why one of the fundamental goals of educational researchers is to develop thepedagogical games that can create the same level of motivation, engagement and passion
produced by some popular commercial video games (Lu et al., 2016; Young et al., 2012).
Discussion
Defining transfer and finding its underlying mechanisms is a challenging task. Researchers
are still trying to understand the challenges that learners encounter when they try to extend
their knowledge from one context to the other. Most traditional assumptions about transfer
are primarily founded on the idea that educating people through instruction, guidance andpractice in a particular context can help them gain mastery of a particular task of a domain
and this mastery is all that is needed to utilize knowledge in practice. While the modern
constructivist theories of learning do not underestimate the role of instruction and practice,they view learning as a whole, integrative process in which learners are actively engaged in
the process of their own learning while they receive appropriate instruction, guidance,
feedback and opportunities through social interaction and involvement in authentic
experiences. Therefore, learning is not merely defined in terms of the cognitive processes that
individuals are engaged in. It is also about how individuals evolve in this process and change(Lave & Wenger, 1989; Singley & Anderson, 1989).
This paper reviewed some of the prominent learning and transfer theories such as the theoryof identical elements, low and high road transfer, analogy as well as the situated learningtheory. While each theory employs effective cognitive and metacognitive strategies such ascomparison, assessment, reflection, and generalization, which can significantly improvelearning and transfer, each approach has also some limitations that need to be consideredwhen these approaches are implemented in practice.
For example, while the ultimate goal of the identical theory of transfer has been to improvestudents’ application of knowledge through providing similar tasks in similar contexts (withshared elements and features), this theory was criticized years ago by Hendrickson andSchroeder (1941) when they empirically indicated that the ability of formulization of generalprinciples (e.g., the principle of water refraction) was more effective than the presence ofhighly similar tasks in transfer of training. It was also noted that other factors such asindividual differences such as the habit of verifying a judgment or the ability to generalize aprinciple for oneself highly influenced the amount of skills and knowledge transferred.
Early research on knowledge transfer was mostly directed by the theories that were focusedon the resemblance between “conditions of learning and conditions of transfer” (Bransford etal., 2000, p.51) – a difference that was addressed and questioned by later theories. However,are learning and transfer truly different? In answer to the question, Perkins and Salomon(1992) argued that there is no solid line between learning and transfer. However, transfer
becomes interesting when it occurs beyond the original context of learning and is notconsidered ordinary learning anymore – a phenomenon that they called “the hoped-fortransfer” or desired transfer. For example, a student may indicate very good vocabulary skills

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on the English test (ordinary learning) but not in daily speech (the desired transfer). The
student may be quite comfortable with solving the problems on the worksheet (ordinary
learning) but not similar questions in an authentic problem-solving context (the desiredtransfer). The idea of ordinary versus desired transfer can also be explained in terms of“near” and “far” transfer (Kassai, Futo, Demetrovics, & Takacs, 2019; Perkins & Salomon,
1992). Near transfer refers to application of the same knowledge in vastly similar contexts.
For example, when students encounter problems similar to what they have previously
practiced in their homework or when a mechanic fixes an engine which is similar to the older
designs of the same model, the transfer is near. On the contrary, far transfer is about utilizing
knowledge in remote and dissimilar contexts. For example, a chess player might apply basic
strategic principles of the game such as center control to investment practices or to sports
games or an engineer may apply the trigonometric principles to a modern design. It should be
noted that the definition of near and far in this context is highly intuitive and, therefore, there
is no specific measurement to determine how far or close the events are.
It is important to know that transfer does not always convey a positive concept although bytransfer we usually mean learning in one context and improvement in performance in someother context. Negative transfer may also occur when learning in one context negatively
influences one’s performance in another context (Chen & Daehler, 1989; Cormier &Hagman, 2014; Perkins & Salomon, 1992). For example, when speakers of one language findit easier to learn related foreign languages, the transfer is positive. However, if differences inpronunciation, vocabulary, and grammar create confusion for the learner and hinder theirlearning process, then the transfer is considered negative. While negative transfer can be areal and problematic issue in learning, it does not seem as significant as positive transfer in
education. This is mainly because negative transfer can usually be problematic in the earlystages of learning a new domain and learners often correct their misconceptions when theygain experience in their field of study. Therefore, the main focus of transfer research is toknow when, where, and how the desired positive transfer occurs.
Similarly, in applying transfer strategies such as analogical encoding, one needs to be awareof the potential challenges that may arise later. Empirical research indicates that analogicalencoding – which occurs through comparison among similar examples and cases – can behighly effective in formation of new concepts, producing general rules, and promoting
problem solving strategies (Gentner et al., 2003; Gick & Holyoack, 1983; Hajian, 2018).
However, comparison may also lead to overgeneralization and incorrect conservation ofvariables due to the application of the intuitive rule of “same A – same B” (Ronan, 2018;
Stavy & Tirosh, 2000). For example, as Ronen (2012) argues, when two rectangles with thesame area (but different lengths and widths) are turned into two cylinders, they end up withthe same area but different volume. However, many learners conserve both area and volume
under this transformation (as cited in Ronen, 2018).
One way to confront this challenge is to stimulate students’ awareness to their intuitive
incorrect responses and challenge their reasoning by employing appropriate prompts andscaffolding at appropriate stages of learning (Hajian, 2018; Lazonder & Harmsen, 2016;
Ronan 2018). For example, it is highly effective to prompt students to explain why and how
they have abstracted a general rule and what evidence they can provide to support theirjustification (Hajian, 2018; Holyoak & Thagard, 1996; Hoyos & Gentner, 2017).
There is no doubt that each reviewed theory has its own complexity and challenges in
implementation and practice. These challenges are mainly due to factors such as the level of103

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and expert learners may take advantage from the same training in different ways as theassociation between learning and transfer also depends on the level of knowledge andexpertise that learners have previously acquired in the field (Gentner et al., 2003; Hajian,
2018; Kalyuga, Chandler, Tuovinen, & Sweller, 2001).
Transfer is an active, dynamic process and not the final product of some sequential steps inlearning or a passive end-product approach. This paper provided some practical educationalmodels such as problem-based learning, communities of practice, cognitive apprenticeship,
and game-based learning to indicate that it is possible to efficiently promote learning transfer
in a relatively authentic context – in situ learning. However, there are still many questions
that need to be addressed in future studies. For example, do all skills need to be acquired
within a social context to ensure their successful application? How is situated learning
implemented in online courses and materials? What kinds of instructional support are
required to be provided for learners with different prior knowledge and self-regulatory
behaviours? And, how can in-situ learning model be employed in our traditional school
system, considering a big volume of material needs to be covered in a relatively short periodof time?
In conclusion, learning transfer is a multi-dimensional process that occurs at any stage oflearning and can be enhanced through coaching, scaffolding, interacting, assessing andreflecting in authentic contexts. Therefore, if learning transfer fails, the entire learning systemneeds to be questioned simply because many things might have gone wrong and there are noquick-fix strategies to solve the problem.

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References
Aldrich, C. (2005). Learning by doing: A comprehensive guide to simulations, computer
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Corresponding author: Shiva Hajian
Contact email: shajianm@sfu.ca

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