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Although the future is always uncertain, recent trends in technological development and labor markets suggest that the most rewarding jobs of this century will be those that cannot be done by computers. In order to be prepared for these opportunities, students need conceptual thinking skills.
However, so far, improving human capital accumulation in developing countries has proved to be very difficult. Motivating students to learn calls for motivated and well-trained teachers who are prepared to put in the necessary effort to help each one of their different individual students to progress along their own optimal learning path.
Thus, drawing on a body of knowledge gathered from neuroscience, cognitive psychology and economics, I would like to argue in this post that if a way to improve learning in developing countries is to be found, it will likely involve the use of technology.
Neuroscience, Cognitive Psychology and Learning
Neuroscience –i.e., the study of the brain and nervous system– has long held out the potential to help society to improve its members’ learning processes. This potential is discussed at length in the Royal Society’s recent report titled Neuroscience: implications for education and lifelong learning. My reading of this report has, however, convinced me that there is indeed a wide gap between neuroscience and education (see also Della Sala and Anderson (2012)). Still, I believe that, even though what we see in brain scans cannot predict exactly what a strategy or intervention will mean for individual students or how well it will work, that information can provide an important input in the design of education interventions.
Successful applications of principles of neuroscience to learning have so far invariably been based on sound cognitive research that has had clear implications for educational practice. Thus, although neuroscience is an attractive input for an experimental society searching for ways to improve learning, it seems to me that it will be cognitive psychology that will end up doing all the heavy lifting. Indeed, I am convinced that what will ultimately fill the gap between neuroscience and education as it is today is the development of evidence-based education, with the foundation for that education being provided by cognitive psychology and the evidence being gathered through randomized control trials in different environments.
Neuroscience has some valuable inputs to offer. One of its important contributions is the concept of neuroplasticity. The term “neuroplasticity” refers to the concept that our synaptic connections and neuronal pathways change in response to the world around us (Ian Robertson (1999)). The brain has the ability to change throughout an individual’s lifetime.
In order to design better methods for supporting learning, we must first ask ourselves what learning is. Even before birth we are exposed to different stimuli and learn to respond more or less appropriately to them. Some of what we learn during that phase is developmental and is going to happen eventually, no matter what. However, here we are interested in the learning that happens alongside the developmental process and that continues long after. In particular, we are interested in how we learn skills, acquire knowledge and develop behaviors that do not just come into being as part of a normal growth process, though they are fundamentally affected by everything we experience.
Learning is a process by which changes in our brain allow us to behave and respond in particular ways. It creates structural changes in our brain that help us build on what we already know and change our behavior based on what we already do. The process entails a set of physical changes in our brain that involve synthesizing new proteins, releasing neurotransmitters, and forming new neural connections. Consequently, incorporating skills and modifying behavior involve changing the brain’s existing structure. That means that learning often necessitates unlearning. This is also something that we need to take into account when designing and evaluating new approaches to education (see Collins (2015)).
An important engine for learning is motivation. People who are motivated to learn are going to learn faster and more effectively and are going to retain what they have learned for a longer period of time than those who have merely “shown up” in the classroom. One of the important aspects of motivation is persistence. There is now evidence that shows that, when performing tasks that are cognitively interesting on the whole (as learning certainly ought to be), people are more motivated by intrinsic than extrinsic factors. People are more inclined to persist at a task that they are self-motivated to do than at one in which there are external rewards for performance or in which the goals are externally created.
Oftentimes intrinsic and extrinsic motivations complement (or “crowd in” to use the term employed by Frey (1997)) each other, while other times extrinsic motivation crowds out intrinsic motivation. Benabou and Tirole (2003) model the interaction of intrinsic and extrinsic motivation in the context of a principal-agent relationship with asymmetric information and conclude that “Explicit incentives may, but need not, be negative reinforcers. … The ‘crowding-out’ case first requires that the agent be less knowledgeable in some dimension than the principal.” This asymmetry of information is likely to be more important in some settings (such as learning) than in others (relatively standardized jobs). Furthermore, a sorting condition must hold, in that the principal must be more inclined to offer a reward when the agent has limited ability or the task is unattractive. These conditions are likely to be present in the case of remedial education, for example, which suggests that we need to place greater emphasis on the expression of intrinsic incentives over the use of extrinsic ones in order to help motivate students to learn.
Learning is also furthered by curiosity since it activates the caudate, a part of the brain associated with anticipated reward. What is more, dopamine is released when we seek the answer to a question, which is of course rewarding. Hence, educators would do well to find ways to stimulate their students’ curiosity (see Collins (2015)).
Studies of what makes computer games so captivating show that variable challenge, based on the player’s ability, is the key element (Reigeluth and Schwartz, 1989). The most popular computer games take players through increasingly challenging levels. As skills improve, the next challenge motivates practice and persistence because the player feels the challenge is achievable. The degree of challenge for each level is such that players are neither bored nor overwhelmed and frustrated. Practice allows players to improve and thus experience the neurochemical response of pleasure (triggered by increased levels of dopamine (intrinsic reward)) while moving toward the long-term goal of completing the game.
Educators are now using teaching software that incorporates this approach. Most of the games that I have found are related to mathematics or logic problem-solving, and they share a number of elements. The first is that they use graphics that are appealing to kids, such as monsters, dinosaurs, cartoon characters, princesses, ponies and so on. Additionally, some of them have a format that is similar to a well-known game. For example, I tried out a game called “Math Man” which was a variant of the classic “PacMan”. The games are increasingly challenging and complex, and the solutions may involve a combination of logic and learning through trial and error. What is more, they have flexible formats which can be tailored to the needs of children of different ages. Video games may also exploit interactive functions that enable a student to challenge friends or play with them online.
Technology has a very important advantage as well. It allows us to exploit economies of scale in the dissemination process, conceivably providing high-quality, time-varying teaching and training opportunities. One possible intervention that I favor, but that needs to be experimentally evaluated rigorously in different contexts in order to establish both its internal and its external validity, is the use pedagogical games that mimic the advantages of video games. Additionally, however, interventions of this type would have to be implemented for a long enough period of time to allow students to unlearn old methods and learn the new ones in order to fully benefit from them. The experimental evaluation would also need to gauge the long-term effects, since persistence is one of the key outcomes of interest.
Benabou, Roland and Jean Tirole (2003): Intrinsic and Extrinsic Motivation, Review of Economics Studies.
Collins, Stella (2015): Neuroscience for Learning and Development: How to Apply Neuroscience for Learning and Development, KoganPage.
Della Sala, S. and M. Anderson (eds.) (2012): Neuroscience in Education: The Good, the Bad and the Ugly, Oxford University Press.
Frey, B. (1997): Not Just for the Money: An Economic Theory of Personal Motivation, Edward Elgar.
Reigeluth, C. M. and E. Schwartz (1989): An Instructional Theory for the Design of Computer-Based Simulations, Journal of Computer-Based Instruction.
Robinson, Ian (1999): Mind Sculpture: Unlocking your Brain Untapped Potential, Fromm Intl.