Making Learning Fun: The Logical Direction of Things
As we continue to march on into the not-so-new millennium, with the information age in full-swing and Y2K just a distant memory, transformation and revolutionization seem to be the what the world’s all abuzz about. Political upheaval, system overhaul, total disruption of the status quo are all phrases which describe the goings-on of the global community. Promises of enlightenment spoken by contemporary sages support our collective expectation of an easier time ahead for us. Perhaps the notion of competition for survival will soon succumb to a new standard of cooperation for the human race. Automation and artificial intelligence bolster visions of a new and improved tomorrow – and if it’s all to be believed, it seems appropriate to expect we will find a way to make school hurt less; and turn our attention to making learning fun!
Recently the perception of education in America has elicited a wide spectrum of emotional response, from parents who are dismayed to know that our students are not on top in the world today, to politicians gung-ho about their curricular overhauls and new standards and measures intended to aid our youngsters on a race to the top. Meanwhile, science and government have invested and continue to invest a good deal of resources into how a quality education is best delivered. A great deal of focus is now placed on the question of how we learn, what can be done to aid the learning process, what are various aspects of learning and how we might impact them. Knowing this we needn’t be surprised that we actually do know quite a bit about how the mind and brain work and how best to teach them.
Technology affects everything we do today.
Why should education be any different? It is expected, then, that much of our research of the last decade or so has had to do with computers, and how we can use various software to create computer aided learning environments, and how such constructs effect learning on different levels. In 2011 in a scholarly article, “Improving Learning” for the American Psychological Association’s Monitor on Psychology, University of Memphis Professor Arthur C. Graesser explores this subject. The article goes as far as identifying characteristics which would define the ideal vision of learning – that it be “broad, deep, fast, precise and practically relevant.” A standard which is perhaps rarely met given our current methods, but only aspired to. Nonetheless, scientific research has given us some empirical observations of learning principals including seven defined in a 2007 Institute of Science Report:
- Learning should be “spaced over time.”
- Alternately show students how to solve a problem by example and have them solve problems themslves.
- Combine different types of mediums of presentation, for example verbal description and graphics.
- “Connect and integrate abstract and concrete representations of concepts.”
- Use testing as part of learning process. (See former Cool-Math-Games post…)
- Help students utilize time effectively.
- “Ask deep, explanatory questions.”
Graesser goes on to elaborate, citing many more examples of learning principals defined by true scientific research including ten of 25 cognitive principles of learning in “Lifelong Learning at Work and at Home,” another scientific article (2007) which he himself co-authored. For more on this check out the APA website, “Improving Learning.”
The point being that outside of public perception and politicians’ pretenses, the field of cognitive science is making strides toward defining how learning happens and what we can do to facilitate it. Together with the computer sciences, cognitive psychologists are now creating computer learning environments in the more specific areas of traditional computer-assessed instruction, multimedia, interactive simulation, hypertext and hypermedia, intelligent tutoring systems, inquiry-based information retrieval, animated pedagogical agents, virtual environments, serious games and computer-supported collaborative learning. (Graesser, 2011)
In a nutshell, intelligent learning environments have been shown to improve learning. Recently digital “teacher” representations and virtual “peers” have been used in some learning environments with success. These “conversational agents” model social interaction and both student and agent take on different roles to help with the teaching process. A great number of these intelligent environments are in the process of being built or have been created and studied, with different methodologies for aiding different types and various principals of learning. Some are geared toward teaching meta-knowledge which is something in which many folks don’t have much background experience. Meta-comprehension, for example, is knowledge about the process of comprehension. Meta-knowledge is important for self-regulated learning to take place.
One major area of concern with computer-based intelligent learning environments is how to motivate learners to stay engaged. The moment a subject becomes bored or frustrated to the point of disengagement (walking away from the computer, for instance) all benefit from the program stops short. When the material is difficult often the student experiences cognitive disequilibrium – discrepancies between the new information and expectations the student may have had about it based on older information previously learned. How do we keep the attention of the student through these situations? Graesser seems to believe that the answer lies within digital gaming. Imagining games as having the unique ability to turn frustrating work into an “engaging state of flow” where sense of time disappears, along with fatigue, and the activity for which formerly existed measurable disdain is converted into enjoyable play.
Overly optimistic? We hope not.