I don’t get it…..yet March 7, 2016Posted by Editor21C in Engaging Learning Environments, Primary Education, Secondary Education, Teacher, Adult and Higher Education.
Tags: curriculum, learning and the brain, learning theories, mathematics education, teacher education
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by Karen McDaid
I love mathematics and not just a little! I really love mathematics, but when I recall my mathematical school experiences, I do so with a fairly dispassionate attitude. Don’t get me wrong, it wasn’t that I disliked school mathematics. On the contrary, I quite enjoyed learning and grasped most mathematical concepts fairly quickly, which meant I met with a small but consistent degree of success in mathematics. I did alright in standardised tests, was about middle in the class, but I was not ‘smart’ in an academic sense, or at least I didn’t think so. In saying that, I was always more than happy to persevere with a challenging problem and wouldn’t let anything get the better of me.
On the other hand, Paula White, who became my friend in Year 4, was my antithesis. I thought Paula was very ‘smart’. She was awarded first in class many times throughout primary school. I admired her greatly and aspired to be as ‘smart’ as her. However, my observations of her as a learner through the years, even to my young self, were puzzling. Although she was top of the class in most of the mathematics tests we undertook, when facing a challenging mathematical problem where the solution was not immediately obvious, often the first words she said were, “I don’t get this” or “This is stupid”. By Year 8 Paula had slipped into a cycle of avoidance and her achievements in primary school were not reflected in high school. It seems to me now that she was so caught up in proving her capabilities and successes that she forgot, or couldn’t embrace, the opportunity to learn. I frequently wondered what made us so different.
Many years later as a teacher I noticed the same traits in several of my Stages 2, 3 and 4 (Years 4 to 8) students in the first few weeks of the year. Some were keen to tackle challenging problems or at least persevere with problems; others used Paula’s mantra to indicate their displeasure. What I found interesting was that there was absolutely no correlation between my primary and high school students’ defeatist attitude and their actual ability in mathematics. I knew they could achieve if only they would try. In more recent years, while teaching Mathematics to primary pre-service teachers at university I often heard Paula’s “I don’t get this” from the adult students with whom I was working. Many also subscribed to society’s misconception that a person is either born with a mathematical ability or they are not. Unfortunately, this misconception has created a culture where it is socially acceptable for someone to openly proclaim that they are ‘no good’ at mathematics and where the belief is that intelligence is fixed and unchangeable (Boaler, 2013).
So began my quest to understand what influences attitudes towards, and self-efficacy in mathematics. My aim was to see if it was possible to develop resilience, motivation and foster positive self-efficacy in my students and in the primary pre-service teachers with whom I work. I became particularly interested in the research of Carol Dweck at Stanford University into fixed and growth mindsets. Dweck (2006) describes a fixed mindset as a significant impediment to learning as it affects the ability of the learner to ‘believe’ in themselves and thus impacts their cognitive development. She also defines mindsets as a set of powerful beliefs that are in the mind and as such are changeable. Dweck argues that those who have a tendency towards a fixed mindset are rarely willing to persevere with challenges for fear they will expose their perceived deficiencies. She believes that this attitude turns people into ‘non-learners’ and an examination of the brain-waves of people with a fixed mindset demonstrated a loss of motivation when faced with challenging problems (Dweck, 2006). On the other hand, people who have a growth mindset are more open to challenges, give up less easily and believe that intelligence is malleable.
I found Dweck’s work fascinating and when reflecting on Paula’s behaviour, I realised that she had exhibited many fixed mindset behaviours as did some of my students. A study into motivation conducted by Blackwell, Trzesniewski and Dweck (2007) followed hundreds of students transitioning to 7th grade. The study found that students who had been identified as having a growth mindset were more motivated and achieved at a higher level than those with a fixed mindset in mathematics and the gap between them continued to increase over the following two years. When a growth mindset intervention was implemented in further studies, Blackwell et al (2007) and Good et al (2003) found that the achievement gap reduced further and in particular that the gap between girls and boys was significantly reduced.
In recent times there has been a lot of talk about brain plasticity, and both Dweck and Boaler acknowledge that intelligence is malleable. My challenge has been to move the immovable from ‘I don’t get it’ to believe that they can ‘get it’. So, how did all this knowledge contribute to my teaching and learning objectives in the mathematics classroom? Well it didn’t, at least not in the beginning. While my teaching philosophy has evolved over a number of years, I have always strived to create a classroom culture where students were learners, not just in name, but really enthusiastic, motivated and driven learners. No doubt this is every teacher’s goal! As such, I set high expectations and wanted students to feel safe to be risk takers. My teaching philosophy mirrored a growth mindset classroom.
So I was working within a growth mindset, unfortunately, that was just it! ‘I’ was working using a growth mindset. While I had taken the time to set up a classroom culture with my school students, I didn’t communicate my philosophy to my university students. I didn’t expect the school children to know what was in my mind; I clearly communicated and worked with them to create a safe learning space. What made me think that my university students would know what was on my mind? They didn’t know about the classroom culture that I was striving to achieve, yet they were part of the classroom community too.
“Just the words “yet’ of “not yet,” we’re finding, give kids greater confidence, give them a path into the future that creates greater persistence”.
(Carol Dweck, 2014)
While teaching time is finite, instead of rushing headlong into content in the first tutorial, I have found that spending twenty minutes setting up our classroom culture has been valuable for student engagement and for students’ self-efficacy in mathematics. I communicate my teaching philosophy and acknowledge that ‘we’ create the culture of the learning space. We discuss how our attitudes can set us up for success and take five minutes in small groups to discuss a time when we learned something well through hard work. We explore the notion of fixed and growth mindset and malleable intelligence. We set high standards for our learning and revisit this notion throughout the semester. No question is ‘dumb’ and mistakes are actively encouraged. I have learned to change my thinking and my language and that praise should be connected to behaviour rather than achievement.
This is my story, which changes according to student dynamics and as I continue to learn and adapt my teaching. I don’t claim that it will work for everyone, but I have seen a marked improvement in the effort and determination with which all students engage with the mathematics activities in class. Students have eagerly embraced replacing the statement ‘I don’t get it’ with ‘I don’t get it yet’. But one of the greatest and most powerful transformations is when you see a student who might have given up in the past, collaborate to work really hard on a mathematical problem and then suddenly they see the value in their effort and shout ‘I get it now!’
Blackwell, L.S., Trzesniewski, K.H., & Dweck, C.S. (2007). Implicit theories of intelligence predict achievement across an adolescent transition: A longitudinal study and an intervention. Child Development, 78. 246-263, Study 1.
Boaler, J. (2013). Ability and Mathematics: the mindset revolution that is reshaping education. FORUM, 55(1), Retrieved from http://www.youcubed.org/wp-content/uploads/14_Boaler_FORUM_55_1_web.pdf on 12th November 2015.
Dweck, C.S. (2006) Mindset: the new psychology of success. New York: Ballantine Books.
Dweck, C. S. (2014). The power of believing that you can improve. [Video/TED talk] Retrieved from https://www.ted.com/talks/carol_dweck_the_power_of_believing_that_you_can_improve/transcript?language=en
Good, C., Aronson, J., & Inzlicht, M. (2003). Improving adolescents’ standardized test performance: An intervention to reduce the effects of stereotype threat. Applied Developmental Psychology, 24, 645-662.
Growth mindset Videos
Growth mindset websites
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The formulation of possible selves through music and singing April 21, 2015Posted by Editor21C in Early Childhood Education, Engaging Learning Environments, Primary Education, Secondary Education.
Tags: arts education, boys' education, creativity, learning and the brain
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from Sarah Powell
There is a range of research now surrounding the connections between music and the brain and the effect of music on learning. For example, in Australia the work of Anita Collins focuses on what happens in the brain when a person plays a musical instrument. From the UK Sounds of Intent is a project that investigated musical development in children with learning difficulties and subsequently produced resources to support educators.
The work of Kate Stevens, Peter Keller and Barbara Tillman from UWS, and Gary McPherson from the Melbourne Conservatorium of Music, University of Melbourne, demonstrates the significant research being undertaken in the area of music and neuroscience. In addition, the recent contribution to this blog from Associate Professor Sue Roffey highlights the reduced emphasis on creativity, critical thinking skills and well being in the new curriculum. Research demonstrates that music (and other arts) has a definite impact on the brain, on learning, on memory, on well being and in the case of my research, identity.
I came from a different perspective in my doctoral research. Rather than using numbers to justify the impact of music and singing, I asked individuals to share their personal stories and because of other research themes (masculinity, success) I focused on males who sang in choirs. So I set out with a different agenda to that of the neuroscience underpinning the research identified above and despite my different angle, it became abundantly clear that music and singing has a profound effect on the identity of an individual.
With this is mind I considered the role of identity from the perspective of possible selves (Markus & Nurius, 1986). Possible selves are the formulations or descriptions of a future self or selves. They represent desired, expected, or feared future selves, and sometimes a combination of these. The theory argues that a person’s present or current self is not simply defined by their past, but by their perceptions of the future as well.
Possible selves have been described as what a person wants to become, what they expect to become, or what they want to avoid or fear becoming (Cross & Markus, 1994; Freer, 2009, 2010; Markus & Nurius, 1986; Sica, 2009). The past is remembered as positive or negative experiences and whilst these experiences shape the future they do not determine or restrict it. Whilst past experiences cannot be revisited in a physical sense, the associations that are retained as memories remain potent and regulate a person’s desire to pursue or avoid a perceived end point. Strahan and Wilson (2006) suggest that it is not simply the memory of an event or circumstance that has influence. Rather, it is “how the past was recalled” (p.4).
Amongst other things, participants in my research were asked about their past experience of music, particularly during their school years. All were currently involved in music in various capacities and planned to continue in this way or develop their involvement further, and they all described positive school experiences. They identified music and singing as a normal part of their life at home. They had parents and grandparents who enjoyed singing, playing musical instruments or listening to music.
Participants reported enjoying classroom music at school and having numerous opportunities to be in the band or the choir, and many received instrumental tuition at school. Interestingly many participants attributed their present path to their past and their subsequent aspirations for the future. The sense of music and singing being part of the individual was strong:
Singing is quite an intimate thing. You’re revealing a lot about who you are in a sense (Secondary School Choir, Year 12 student).
This attitude was coupled with a very strong enjoyment of singing, communicated by all participants in some way:
I love singing, it’s my favourite thing to do, anywhere any time (Junior School Choir, Year 5 student).
Without question, the ability to produce some beautiful sounds in performance is rewarding, emotionally satisfying (Community Choir, male aged 50+).
The research demonstrated that the identity of these participants was built on family background and traditions, grounding them in something bigger than themselves but still intimately connected. It contributed to self-confidence and healthy self-perception in the here-and-now and it provided an outlet for personal expression and spirituality. It provided purpose and direction for the future, offering choices and opportunities for career and pleasure. It also gave them meaningful spaces to work collaboratively and creatively and to develop deep friendships.
Not only is neuroscience proving that music impacts the brain and learning in positive ways, but people are revealing that music and singing is an integral part of how they define themselves. It has significant ramifications for the formation of identity as well as personal well being and must be part of a child’s education. I will conclude by mentioning the work of Sir Richard Gill who continues to advocate the necessity of providing quality music education to every child, arguing that the impact of arts education is broader than simply teaching music:
The very things that promote literacy and numeracy are the arts, beginning with serious arts education in the early years. If we want a creative nation, an imaginative nation, a thinking nation and a nation of individuals, then we must increase the time for arts education, especially music education. If we want a nation of non-imaginative robots who can do tests, then we are well on the way to achieving that condition (Richard Gill’s Blog, 2011).
Cross, S. E. & Markus, H. R. (1994). Self-schemas, possible selves, and competent performance. Journal of Educational Psychology, 86(3), 423-438. DOI: 10.1037/0022-06184.108.40.2063
Freer, P. K. (2009). ‘I’ll sing with my buddies’ – Fostering the possible selves of male choral singers. International Journal of Music Education, 27(4), 341-355. DOI: 10.1177/0255761409345918
Freer, P. K. (2010). Two decades of research on possible selves and the ‘missing males’ problem in choral music. International Journal of Music Education, 28(1), 17-30. DOI: 10.1177/0255761409351341
Markus, H. & Nurius, P. (1986). Possible selves. American Psychologist, 41(9), 954-969. DOI: 10.1037/0003-066X.41.9.954
Sica, L. S. (2009). Adolescents in different contexts: The exploration of identity through possible selves. Cognition, Brain, Behavior: An Interdisciplinary Journal, 13(3), 221-252.
Strahan, E. J. & Wilson, E. (2006). Temporal comparisons, identity, and motivation: The relation between past, present, and possible future selves. In C. Dunkel & J. Kerpelman, Possible selves: Theory, research and application (pp.1-15). New York: Nova Science Publishers, Inc.
Sarah Powell is Education Content Manager at Musica Viva Australia. She is also a sessional academic in the School of Education at the University of Western Sydney, and a UWS doctoral candidate whose thesis is currently under examination.
Categorizing knowledge from an evolutionary perspective: The example of speaking versus writing March 11, 2012Posted by Editor21C in Early Childhood Education, Engaging Learning Environments, Primary Education, Secondary Education.
Tags: learning and the brain, learning theories, literacy education
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from Dr José Hanham
Learning to write is a complex and cognitively demanding process that is acquired often with difficulty. Learning to speak is also complex, but appears to happen effortlessly. Why?
Educators have long been interested (and perplexed!) as to why learners are able to acquire some types of knowledge with ease, and other types with great difficulty. We know that the ease or difficulty of acquiring knowledge is likely to be related to factors such as motivation and/or the cognitive complexity of learning tasks. Another factor, recently proposed by evolutionary psychologist David Geary (2008), is whether the category of knowledge to be learnt is one that we have evolved to acquire, known as biologically primary knowledge, or knowledge that is the product of our cultural inheritance, known as biologically secondary knowledge.
This distinction between biologically primary and biologically secondary knowledge is important in several respects. To begin with, there are different demands placed on a learner’s cognitive resources depending on the category of knowledge to be acquired. Acquiring biologically primary knowledge is usually unconscious, effortless and rapid – even when the knowledge to be learnt is complex. In contrast, acquiring biologically secondary knowledge is conscious, effortful and slow – with complex material severely impeding a learner’s ability to acquire knowledge.
With these differences in mind we can perhaps provide an “evolutionary” explanation in regards to why learning to speak is complex, but apparently not cognitively demanding. There is no doubt that learning to speak one’s native tongue is a complex process – one only needs to think of the vast array of cognitive, perceptual and motor skills involved in expressing oneself verbally. Yet, despite this complexity, very few conscious cognitive resources are involved in learning to speak. This is because learning to speak is a biologically primary form of knowledge – to reiterate, knowledge that we have evolved to acquire. Because learning to speak is innate or instinctual (Pinker, 1994), no formal instruction is required when learning to speak a first language. Although in many Western societies we give our pre-school aged children language lessons, this is not necessary for a child to learn how to speak her or his native tongue. In some societies children have very little verbal engagement with their parents or care givers, yet still end up being competent speakers (Harris, 1998). Indeed, children of profoundly deaf couples who do not have the benefit of being able to verbally engage with their parents also turn out to be fluent speakers of their native language (Harris, 1998). In general, the acquisition of biological primary knowledge, such as learning to speak a first language, occurs when we are very young, most often through immersion in a social context.
The differences between biologically primary and biologically secondary knowledge can also provide an explanation as to why learning to write is both complex and cognitively demanding. The complexity involved in learning to write is widely acknowledged by scholars working in the field of composition studies (e.g. Kellogg, 2001; McCutchen, 2000; Torrance & Galbraith, 2006). Writing often involves the learner managing a large number of cognitive processes including planning, generating language, and reviewing (Flower & Hayes, 1980). Unlike speaking, which we have evolved to acquire, writing is a cultural invention that has existed for the last few millennia – “far too short a time to be influenced by biological evolution” (Sweller, Ayres & Kalyuga, 2011 p.6). Because writing is a recent cultural invention, humans have not yet developed the necessary biological mechanisms to learn this cultural knowledge without instruction. This is important because during the 1970s and 1980s there was view espoused by some scholars that the mechanisms involved in learning to write were very similar to those involved in learning to speak. To acquire cultural knowledge, such as writing, learners have to rely on a cognitive architecture comprised of an apparently unlimited long-term memory and a limited working memory that can only process 2 to 4 elements of new information (Cowan, 2005) for 20 seconds without rehearsal (Peterson & Peterson, 1959). Because writing is a conscious process, this means that working memory is actively engaged in managing multiple cognitive demands. The multiple demands involved in learning to write often overwhelm a learner’s working memory, and as a consequence, many school children have difficulty becoming competent writers. Therefore, it is no surprise that researchers have developed research programs that focus on the demands placed on working memory as school-aged children develop their writing capabilities (e.g., Kellogg, 2001; Ransdell, Levy & Kellogg, 2002).
Using speaking and writing as examples, the differences between biologically primary and biologically secondary knowledge have important implications for instruction. Despite what some people may think, learning to speak a first language does not require direct instruction. Even in terms of grammar, many children by the age of three (Pinker, 1994) are able to verbally communicate complex and grammatically correct sentences without any formal instruction. Early childhood approaches to instruction often adopt discovery-learning methods, and perhaps these methods may be well suited for nurturing biologically primary knowledge, such as speaking. However, in terms of acquiring biologically secondary knowledge, teaching approaches that emphasise minimal instructional guidance may be ineffective. Learning to write will require teaching that takes into account the limitations of working memory. It is important to point out that may scholars working within the field of composition already acknowledge the importance of explicit teaching (e.g. Graham, Harris & Macarthur 2006) and the need to design instruction that does not overload working memory as learners develop their writing skills (McCutchen, 2000). The contribution provided by Geary’s (2008) distinction between biologically primary and biologically secondary knowledge is that we now have an evolutionary perspective that adds to weight to claims that formal instruction is not needed when learning to speak, but it is necessary for learning to write.
In my next blog post I will discuss one of my current research projects on the use of worked examples for improving students’ essay writing capabilities. This research is being carried out in conjunction with my colleague Dr Wayne Leahy (Macquarie University).
References: Cowan, N. (2005). Working memory capacity. New York: Psychology Press. Flower, L. S., & Hayes, J. R. (1980). The dynamics of composing: Making plans and juggling constraints. In L. W. Gregg & E. R. Steinberg (Eds.), Cognitive processes in writing (pp. 31-50). Hillsdale, NJ: Erlbaum. Graham, S., Harris, K. R., & Macarthur, C. (2006). Explicitly teaching struggling writers: Strategies for mastering the writing process. Intervention in School and Clinic, 41, 290-294. Geary, D. C. (2008). An evolutionary informed education science. Educational Psychologist, 43, 179-195. Harris, J. R. (1998). The nurture assumption. New York: Free Press. Kellogg, R. T. (2001). Competition for working memory among writing processes. The American Journal of Psychology, 114, 175-191. McCutchen, D. (2006). Cognitive Factors in the Development of Children’s Writing. In C. MacArthur, S. Graham, & J. Fitzgerald (Eds.) Handbook of Writing Research (pp. 115-130). The Guilford Press: London. Peterson, L., & Peterson, M. J. (1959). Short-term retention of individual verbal items. Journal of Experimental Psychology, 58, 193-198. Pinker, S. (1994). The language instinct, London: Penguin. Ransdell, S., Levy, C. M., & Kellogg, R. T. (2002). The structure of writing processes as revealed by secondary task demands. Educational Studies in Language and Literature, 2, 141-163. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York: Springer. Torrance, M., & Galbraith, D. (2006). The processing demands of writing. In C. MacArthur, S. Graham, & J. Fitzgerald (Eds.) Handbook of Writing Research (pp. 67-82). The Guilford Press: London.
Instructional Design and Human Cognitive Architecture February 20, 2011Posted by Editor21C in Engaging Learning Environments, Primary Education, Secondary Education.
Tags: curriculum, learning and the brain, learning theories
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from Dr José Hanham
In this post José Hanham explores contemporary research on the human brain and memory to identify effective pedagogical approaches which enhance the learning of young people.
In my previous blog (see Minimal Guidance and Direct Instruction – July 2010) I mentioned that educational theories, such as constructivism (Bruner, 1961), were developed at a time when we had a very limited understanding of the human brain and human memory. Empirical evidence to support the efficacy of constructivist teaching has never been strong (see Mayer 2004), which, in part, may be attributed to the fact that constructivist theory does not take into account the memory structures that comprise human cognitive architecture.
The term ‘human cognitive architecture’ refers to the memory structures, sensory memory, working (short-term) memory, and long term memory, which have been hypothesized as fundamental to how learners think, learn and solve problems. A key feature of human cognitive architecture is that it comprises a limited working memory (our consciousness), which can only deal with 2 to 3 elements of new information at time, and a long term memory (our unconsciousness), which can hold an unlimited of number of elements (schemas) on a relatively permanent basis (Sweller, 2004). Over the last two decades a number of educational researchers (e.g., Sweller, 1999) have carried out a large number of experimental studies on how best to overcome the limitations of working memory. In the remainder of this blog I am going to share some of the findings that have emerged from research on human cognitive architecture.
In the early 1990s, Chandler and Sweller (1991, 1992) found that when learners were required to split their attention between two related sources of information, that is, two pieces of information that are unintelligible in isolation, this process placed a heavy load on a learner’s already limited working memory resources. Examples of split-attention include learners having to mentally integrate information contained in diagrams, which are placed separately from their associated formulas (see Year 9 Maths textbooks), or a second language learner having to look up a word in a glossary placed in the back of the textbook in order to understand a sentence in an earlier part of the book. Physically integrating information previously placed separately was identified by Chandler and Sweller (1991, 1992), as a superior alternative to split-source instructions.
Another alternative approach to dealing with the split-attention phenomenon is the dual modality approach, which has been shown to be particularly effective in multimedia learning (Mousavi, Low, & Sweller, 1995; Tindall-Ford, Chandler & Sweller, 1997). As mentioned previously, human working memory is limited. The dual modality approach is an instructional technique designed specifically for increasing the effective capacity of working memory. Our working memory contains two partially separate sub-systems (or channels), one for dealing with audio information, and one for processing visual information. Researchers (e.g., Baddeley, 1992) have hypothesized that working memory can process a considerably larger amount of information when information is presented in a dual mode format (i.e., some information is presented in audio form and some information is presented in visual form) than when information presented using in a single mode. However, it is important to note that dual mode instruction is unlikely to be effective if the audio component is too complex or when one source of information is intelligible in isolation and the other source is simply redundant (for example, presenting a visual image of a square to a Year 3 student and having an associated audio message explicitly stating that the image being viewed is in fact a square).
The redundancy effect (Sweller & Chandler, 1994) is another instructional phenomenon identified in research on human cognitive architecture. The redundancy effect usually occurs when two sources of information, which are intelligible in isolation, are presented in slightly different forms. A familiar example would be when an instructor simply reads, word for word, the contents of an overhead or Power-point slide. It is often mistakenly believed that reading from an overhead consolidates student learning. Yet, research (Sweller & Chandler, 1994) has found that this process requires the learner to deal with extraneous information, which places an unnecessary burden on working memory. The most effective way to deal with redundancy is to simply remove the identified redundant information. Please note, some texts such as poems or excerpts of plays need to be visually presented, and read aloud, in order to be effectively understood.
It is important to note that two of the instructional design effects briefly described here, split-attention (integrated instruction) and dual-mode instruction are most effective when instructing novices. Integrated instruction and dual-mode instruction have been shown to be problematic when used on expert learners (Kalyuga, Chandler & Sweller, 2000). In my next blog I will focus on instructional techniques developed specifically to cater for expert learners.
Baddeley, A. (1992). Working Memory, Science, 255, 556-559. Bruner, J. S. (1961). The art of discovery. Harvard Educational Review, 31,21–32. Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8, 293-332. Chandler, P., & Sweller, J. (1992). The split-attention effect as a factor in instructional design. British Journal of Educational Psychology, 62, 233-246. Kalyuga, S., Chandler, P., & Sweller, J. (2000). Incorporating learner experience into the design of multimedia instruction. Journal of Educational Psychology, 92, 126-136. Mayer, R. (2004). Should there be a three-strikes rule against pure discovery learning? The case for guided methods of instruction. American Psychologist, 59, 14–19. Mousavi, S. Y., Low, R., & Sweller, J. (1995). Reducing cognitive load by mixing auditory and visual presentation modes. Journal of Educational Psychology, 87, 319-334. Sweller, J. (1999). Instructional design in technical areas. Camberwell, Australia: ACER Press. Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science, 32, 9–31. Sweller, J. & Chandler, P. (1994). Why some material is difficult to learn. Cognition and Instruction, 12, 185-233. Tindall-Ford, S. Chandler, P., & Sweller, J. (1997). When two sensory modes are better than one. Journal of Experimental Psychology: Applied, 3, 257-287.
José Hanham is a Lecturer in Educational Psychology and Youth Studies at the University of Western Sydney, Australia. He primarily teaches in the secondary education program.