A typical university class of roughly 400 students meets in a lecture hall for their bi-weekly factual feeding. The professor speaks, the Powerpoint slides are set on a crisp white background with red font, and students are scribbling down notes that are their lifeline for the upcoming exam, the supposed measure of learning. John, a classmate who happens to be a computer is also working hard to prepare for the next exam. John, the computer is in class every day, listening to the professor through recording devices, downloading the slides and the 400-page new edition textbook as required by the class that only cost the all-time low price of $460.00. The day of the exam arrives, and everyone, including John the computer, writes the exam online going through some multiple choice, true or false, short answer and even a few essay questions. A few days later the results come back and John the computer has top marks of 100%, while the next best mark is 91% of a fellow student who is somewhat annoyed that a computer got a better mark with seemingly little effort. Who learned more? Who is more intelligent? It seems like John the computer is smarter and learned more than any else in class because of the 100% on the exam.
This little story may seem odd, and it is odd to think of a computer learning more than a human student, but the measurements we use to convey information to students and measure what they memorized can all be done by a computer. In the workforce, we are seeing a dramatic increase in the use of and applicability of robotics and A.I. technology (West, 2015). Industries for restaurants, delivery, transportation, retail, trading, stock markets, finance, aviation, military, medical, and telecommunication are a few of the areas where technology is now doing the work that humans did before (West, 2015). Regardless of the changes many people still believe that this sort of change is many years away, so we do not need to adapt yet, but this belief is no more than a wish for no change. “Machines will replace people for most of the jobs in the current economy in the not so distant future” (West, 2015), and the reality is that machines can often do the jobs that require the use of concrete cognitive enabler more effectively and efficiently than humans. Could a computer be a better student at the university? In our current education system having a computer be a more effective and efficient student is really not that far-fetched.
What then can separate us from the machines? One thing that humans can do is develop and use abstract cognitive enablers. Once we have concrete cognitive enablers in place with some needed cognitive development, we can develop abstract cognitive enablers, which is sometimes known as higher-order thinking. Abstract cognitive enablers involve critical thinking skills, metacognition, creativity, complex inductive and deductive reasoning. If students started developing abstract cognitive enablers in university we would see skills such as analysis, synthesis, inductive and deductive reasoning being used to solve unfamiliar problems (Budsankom et al., 2015). Anyone who has worked on abstract cognitive enablers will be open-minded for risk-taking, will have curiosity, will be keen on making a discovery, use planning, have and understand thought processes, be rational with evidence, and do frequent self-monitoring (Budsankom et al., 2015). Developing abstract cognitive enablers is in every sense learning how to learn.
To develop these abstract cognitive enablers in the education system we need to see a shift from the dominant traditional teaching for algorithmic, lower-order cognitive skills, textbook-based, rote learning methods to a method that creates an environment to allow students to develop abstract cognitive enablers. The school environment should allow exploration and inquiry-based learning situated in real-world phenomena (Miri et al., 2007). Students need to be exposed to learning experiences that enable them to construct their own knowledge and promote their thinking skills (Miri et al., 2007). In other words, we cannot tell people facts and expect them to magically obtain abstract cognitive enablers, rather they are learned by using the skills associated with abstract cognitive enablers. This means lectures are more than just ineffective and unethical, they do not belong in a university where higher-order education is supposed to take place.
There are specific things that classes can do to promote abstract cognitive enablers. Classes need to allow students to have opportunities to analyze, evaluate, and synthesize materials (Miri et al., 2007). Teaching for the promotion of abstract cognitive enablers will help along with having classes dealing with real-world problems, having open-ended discussion groups, and inquiry-oriented experiments (Miri et al., 2007). Anything that can help develop critical thinking, metacognition, creativity, and complex inductive and deductive reasoning will enhance abstract cognitive enablers.
I will present a plan for universities that I will call the Autonomy Structure for abstract cognitive enablers. The first year for university students will have some focus on developing concrete cognitive enablers along with abstract cognitive enabler promotion classes that have the first year students doing research on abstract cognitive enablers, engaging in group discussions to share research and thought processes (all discussion groups will have 6 or fewer people), and first-year students will write reflective journals online that is like a synthesis of what they learned. All other years (exact amount of years depends on the individual student) will have students choose a real-world case or problem to research, have facilities to run inquiry-based experiments (labs, computers, etc.), and comfortable areas to meet with discussion groups to reason ideas/evidence. The students will come prepared to group discussions by analyzing and evaluating the evidence and their reasoning. During the group discussions, the students will focus on coming to a consensus on plans to pursue in relation to the real-world case or problem of interest. As well, students will write synthesis papers (can be online) from experiments, research, and discussions. Every 3 or 4 months students will choose a new real-world case or problem and repeat the process just laid out. This kind of loose structure would promote the critical thinking skills, metacognition, complex inductive and deductive reasoning, creativity, allow the learning of various subjects and increase intrinsic motivation among students who want to learn how to learn while doing something meaningful.
Now this would be a drastic change to the current education system and it is unlikely, but Socelor is working on helping students develop abstract cognitive enablers that will allow them to use technology rather than be replaced by technology. We allow students the autonomy to research problems of their interest, discuss their findings in groups and strive to reach consensus on plans. We may not have labs because we are trying to keep Socelor strictly focused on facilitating student learning, especially learning how to think. However, the most important thing is that all the necessary elements for learning will be present. For more details about abstract cognitive enablers and the future of work, please check out our website.
West, D., (October, 2015). What happens if robots take the jobs? The impact of emerging technologies on employment and public policy. Center for Technology Innovation at Brookings.Retrieved from: http://www.brookings.edu
Budsankom, P., Sawangboon, T., Damrongpanit, S., and Chuensirimongkol, J., (October 5 2015). Factors affecting higher-order thinking skills of students: a meta-analytic structural equation modeling study. Educational Research and Reviews, 10, 19, pp. 2639-2652.doi:10.5897/ERR2015.2371
Miri, B., David, B.C., and Uri, Z., (January 12 2007). Purposely teaching for the promotion of higher-order thinking skills: a case of critical thinking. Res Sci Educ, 37, pp. 353-369. doi: 10.1007/s11165-006-9029-2