How People Learn a Second Language

(Excerpted from my dissertation.)

Learning a second language is an arduous task. Most scholars would agree that it requires a lot of practice (Krashen, 1982; Nation, 2014), language activities that are embedded in realistic tasks (i.e., communicative approach) (Hymes, 1972; McFarlane, Sparrowhawk, & Heald, 2002), plasticity of the brain (Pinker & Bloom, 1990; Ward, 2010), and high levels of motivation (Crystal, 2010; Gardner, 1985). Here are the five stages of second language (L2) learning: preproduction, early production, speech emergence, intermediate fluency, and advanced fluency (Krashen & Terrell, 1983). Progress through these stages depends on level of formal education, family background, time spent in an English-speaking country, and many other variables.

For young children, oral language and literacy development should include support in their native language, sufficient time and support, developmentally and culturally appropriate material, a balanced and meaningful literacy program, and reliable, ongoing, and valid assessments (TESOL, 2010a). For adults, more specialized vocabulary and education on the sociocultural dimensions for the workplace or academic setting are required (TESOL, 2010b). Otherwise, adult L2 instruction is like that of young children, as noted in the vision and action agenda of the National Literacy Summit (2000). For example, they propose that adult learners also have access to native language or bilingual texts and instruction that is based on meaningful contexts.

There’s some disagreement as to the developmental stages of SLA, but most agree that the initial stage includes a silent period in which you understand some of the L2 but may not be able to produce it (Granger, 2004). Scholars disagree as to whether there is a critical period (cut-off time) for learning a second language with native-like fluency (Crystal, 2010). For instance, cognitive neuroscientists prefer the term sensitive period to refer to the limited window of time to learn due to evidence supporting the possibility of extended learning (Ward, 2010).

I agree with Pinker and Bloom’s (1990) idea that the critical period varies with maturation and plasticity of the brain due to natural selection. Hurford (1991), in his evolutionary model, referred to language learning past the critical age as the natural selection pressures activating the trait.  These pressures affect adults who come from around the world with the hope of learning English in order to attend an American university. One way to affect the plasticity of the brain is to play video games. Current research on the brain and its behavior indicate that playing highly arousing, reward-based video games activates brain plasticity (Kilgard & Merzenich, 1998).

Numerous factors affect learning ESL. For one, learning English takes a long time. For beginners, basic interpersonal communication skills can take two years to learn, while cognitive academic language proficiency can take five to seven years (Cummins, 2008). Influential factors include, but are not limited to, native language (L1) writing system, age exposed to English, cognitive ability, and exposure to other languages (National Literacy Summit, 2000). Another important factor is gender (i.e., female, male, other), which is influenced by the gender of the teacher, strategy use (Kiram, Sulaiman, Swanto, & Din, 2014), and conventional norms (Oxford & Nyikos, 1989). There’s no conclusive evidence that one gender is better at learning a L2. Oxford and Nyikos (1989) posit that it has more to do with strategy preferences and conventional norms.

References

Crystal, D. (Ed.). (2010). The Cambridge Encyclopedia of Language, 3rd ed. New York, NY: Cambridge University Press.

Cummins, J. (2008). BICS and CALP: Empirical and theoretical status of distinction. In B. Street & N. H. Hornberger (Eds.), Encyclopedia of Language and Education, Volume 2: Literacy (2nd ed., pp. 71-83). New York, NY: Springer Science + Business Media LLC.

Gardner, R. C. (1985). Social psychology and second language learning: The role of attitudes and motivation.  London, England: Edward Arnold (Publishers) Ltd.

Granger, C. A. (2004). Silence in second language learning: A psychoanalytical reading. Tonawanda, NY: Multilingual Matters, Ltd.

Hurford, J. R. (1991). The evolution of critical period for language acquisition. Cognition, 40, 159–201. doi:10.1016/0010-0277(91)90024-X

Hymes, D. (1972). Models on the interaction of language and social life. In J. J. Gumperz & D. Hymes (Eds.) Directions in sociolinguistics: The ethnography of communication (pp. 35-71). New York, NY: Holt, Rinehart, and Winston.

Kilgard, M. P., & Merzenich, M. M. (1998). Cortical map reorganization enabled by nucleus basalis activity. Science, 279, 1714-1718.

Kiram, J. J., Sulaiman, J., Swanto, S., & Din, W. A. (2014). The relationship between English language learning strategies and gender among pre-university students: An overview of UMS. Proceedings of the 3rd International Conference on Mathematical Sciences, Vol. 1602 (pp. 502-507). Kuala Lumpur, Malaysia: AIP Publishing LLC. doi:10.1063/1.4882532

Krashen, S. (1982). Principles and practices in second language acquisition.  Oxford, England: Pergamon Press.

Krashen, S. D., & Terrell, T. D. (1983). The natural approach: Language acquisition in the classroom. London, England: Prentice Hall Europe.

McFarlane, A., Sparrowhawk, A., & Heald, Y. (2002). Report on the educational use of games. Cambridge, England: TEEM.

Nation, P. (2014). What do you need to know to learn a foreign language? School of Linguistics and Applied Language Studies.  Victoria University of Wellington, New Zealand. Retrieved from http://www.victoria.ac.nz/lals/about/staff/publications/paul-nation/foreign-language_1125.pdf

National Literacy Summit. (2000). Adult ESL language and literacy instruction: A vision and action agenda for the 21st century. Office of Vocational and Adult Education. Washington, DC: U.S. Department of Education.

Oxford, R., & Nyikos, M. (1989). Variables affecting choice of language learning strategies by university students. The Modern Language Journal, 73(3), 291-300. doi:10.1111/j.1540-4781.1989.tb06367.x

Pinker, S., & Bloom, P. (1990). Natural language and natural selection. Behavior and Brain Sciences, 13, 707–784. doi:10.1017/s0140525x00081061

Teachers of English to Speakers of Other Languages. (2010a). Position paper on language and literacy development for young English language learners. Washington, DC: TESOL International Association. Retrieved from https://www.tesol.org/advance-the-field/position-statements

Teachers of English to Speakers of Other Languages. (2010b). Position statement on adult English as a second or additional language program. Washington, DC: TESOL International Association. Retrieved from https://www.tesol.org/advance-the-field/position-statements

Ward, J. (2010). The student’s guide to cognitive neuroscience. New York, NY: Psychological Press.

The Neuroscience of Learning

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Neuroscience has the potential to prove and disprove existing educational learning theories, as well as identify learning disabilities. It will eventually lead to new discoveries and clearer explanations about the internal processes of the brain/mind. Hopefully, this information will make its way into educational textbooks and school curriculum. It already has determined many specific functions of the brain and aspects of human memory from research experiments using electrodes, electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). For example, from the use of implanted electrodes in rats, neuroscientists identified place cells (neurons) that respond to a specific place from a collection of neurons (schemata) when needed (Ward, 2010).

Neuroscience has determined specific learning activities directly related to components of the brain. Neuroscientists are able to measure neuronal activity by observing the spiking rate of neurons as they code information. For example, the hippocampus stores contextual details for recall in a spatial map of the environment (Ward). This was discovered in a research study that planted electrodes in rats that maneuvered a maze; these rats’ neurons exhibited a high spiking rate only when they were in a particular location (O’Keefe, 1976). Later, in a study of humans that maneuvered in a virtual environment, it was determined that humans have place cells that are lateralized to a particular region of the hippocampus (Hartley, Maguire, Spears, & Burgess, 2003). The implications from this research finding suggest that it’s important for learners to discover the routes themselves in order to store this information; otherwise, it may not become a part of the spatial map if provided directly from the instructor.

Another study related to learning and cognition identified the basal ganglia as being responsible for regulating motor skills and skill learning (Ward). This was found through disorders of the basal ganglia. For example, individuals with Parkinson’s disease have damage to the basal ganglia structures and subsequent poverty of movement (hypokinetic). Neuroscience is helping better understand neurodegenerative disorders like Parkinson, but it still has not been able to solve them all.

Moreover, neuroscientists used fMRI to identify mirror neurons in monkeys. These neurons respond specifically to precise actions that are goal-directed but not to mimicked actions without an object (Ward). The neurons were even sensitive to the direction of rotation in mirroring an action. A study with infants showed similar imitation processes that are goal-directed more so than action-oriented. This data was collected from observation, not with the use of fMRI. These studies indicate that the act of imitation requires deeper cognitive processing than mimicry. Neuroscientists are investigating relations with mirror neurons and mirror systems such as empathy. These mirror systems are “…neural resources that disregard the distinction between self and others (Ward).”

There are many limitations to collecting data for neuroscience. For example, it’s difficult for young children to keep still under a scanner, and this disrupts the MR signal. Children are also unlikely to tolerate electrodes from an EEG. Bruning, Schraw, and Norby (2011) noted that even though fMRI shows activity in particular parts of the brain in correlation to specific mental activities, it really does not explain why or how. Additionally, the medical ethics of research on human subjects limits some of the advances of neuroscience. There is also a political debate on the use of animals as subjects of research studies. 

References

Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction. New York, NY: Pearson.

Hartley, T., Maguire, E. A., Spears, H. J., & Burgess, N. (2003). The well-worn route and the path less travelled: Distinct neural base of route following and wayfinding in humans. Neuron, 37, 877-888.

O’Keefe, J. (1976). Place units in the hippocampus of the freely moving rat. Experimental Neurology, 51, 78-109.

Ward, J. (2010). The student’s guide to cognitive neuroscience. New York, NY: Psychological Press.

What Educators Need to Know about Working Memory

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Working memory is a process in the brain where meaning is constructed from information received and potential self-regulation of memory occurs. It also serves as a temporary storage device. Working memory is limited to the amount of information it can hold and the duration it can remember. According to Miller (1956), humans are capable of remembering only seven plus-or-minus two pieces of information in our memory at any given time without the help of learning strategies. If self-regulation of the information is not engaged, working memory is limited to three seconds duration in the auditory registers (Ward, 2010). Ward notes that young children’s ability to remember information is more stringent than that of adults. This age difference and the other limitations should be considered when designing and/or delivering instruction. For example, instruction of content should also include strategies to help students learn (e.g., mnemonics).

Baddeley and Hitch (1974; Baddeley, 1986) developed a model for working memory to explain the internal processing of information. Its main components are sensory register, working memory, and long-term memory. The subcomponents are an executive control system, an articulatory loop, and a visual-spatial sketchpad. The executive control system selects information, plans, and then transfers information to long-term memory. The articulatory loop consists of the auditory and articulatory processes such as rehearsal. The visual-spatial sketchpad consists of the visual and spatial processes, which can also include rehearsal. An important caveat for educators is that some learners don’t intrinsically know to select only the important information for long-term storage.  Therefore, it would be helpful for educators to preview documents and highlight key points prior to assigning the reading.

Numerous factors and self-regulatory processes affect working memory. Self-regulation processes include rehearsal, selection of important information, and metacognitive strategies (e.g., making it meaningful, organizing, visualization, and elaboration). Self-regulation aids working memory by stretching the time the information is held in storage, as well as enhancing transfer to and retrieval from long-term memory. A helpful example of self-regulation would be self-directed speech. Students might not think this is helpful, so an educator should model this behavior or otherwise teach it explicitly. The National Research Council (Bransford, Brown, and Cocking, 1999) defines metacognition as taking “the form of an internal conversation.”

Here are some factors that hinder working memory:

  1. construction of memory requires attribution and inference and therefore can cause distortions as to the correct source,
  2. articulatory suppression can cause forgetting of non-articulated information,
  3. physical impairments can cause faulty encoding of information,
  4. multitasking influences the depth of learning,
  5. merely trying to remember something can conflict with other memories (Ward); and
  6. cognitive overload can occur when information is presented with distracting enhancements like background music or elaborative fonts.

There are different types of memories: declarative (episodic and semantic) and non-declarative memory (implicit) (Ward). Episodic memory refers to a person’s personal events, whereas semantic memory refers to conceptual knowledge. Ward stated that episodic memory is stronger than semantic memory; therefore, it’s imperative to teach students metacognitive strategies for encoding conceptual knowledge into long-term memory. These strategies should be embedded in the curriculum after they’re presented through direct instruction.

Note: For more information on the information processing system as it relates to instructional design see my blog on The Basics.

References

Baddeley, A. D. (1986). Working memory: Theory and practice. London, England: Oxford University Press.

Baddeley, A. D., & Hitch, G. (1974). Working memory. In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 8, pp. 47-90). San Diego, CA: Academic Press.

Bransford, J. D., Brown A. L., & Cocking R. R. (1999). How people learn: Brain, mind,experience, and school. Washington, DC: National Academy Press.

Miller, G. A. (1956). The magical number seven, plus-or-minus two: Some limits on ourcapacity for processing information. Psychological Review, 63, 81-97

Ward, J. (2010). The student’s guide to cognitive neuroscience. New York, NY: Psychological Press.