Cognitive science is the study of how the mind works using various forms of disciplines (Thagard, 2010). The following post will describe some basic concepts of cognition and how it relates to the education setting I work in. That educational setting is with student employees within a student union. Using learning outcomes we help our students learn skills to not only do the job they are in but transferable skills to other careers after college.
The first concept to discuss is using artificial intelligence within the educational setting. There are many things artificial intelligence can help with such as language learning by responding to someone in conversational setting (Yang, 2007). I don’t believe AI can easily recreate emotion. Emotions are triggered by a core set a values instilled to a person while growing up. Though the TEDx video suggests that artificial intelligence can learn from “birth” emotion can’t be learned through a mechanism (Bock, 2011). That being said, I think AI could be used within the educational context of the student employee program by providing the employee real-time feedback on work performance and achievement of the learning outcomes. It could also create new learning outcomes and challenges as certain outcome are met (Donnelly, 2008). AI systems might be able to help us understand which student employees are understanding the principals we are hoping they pick-up. By using assessments the AI systems can then begin to compute which students might need more assistance and then also provide it. The AI system could deliver life like situations and depending on the responses of the student, it could change its answer to help the students learn how to handle specific situations.
The following ideas – logic, rules and concepts – are some ideas we may have familiarity with. Logic is represented mentally by using past experiences or understandings to come up with an answer or decision. The “logic” people use to come up with answers is information that that person already has with them (Thagard, 2010). Logic is used in my training or teaching environment through various forms. When a student employee is working with a client during an event they must use logic to determine if they can do something or not. For example, a client might start to do something they weren’t allowed to do. After confronting the client the client might tell our staff that they were told they could do that thing by the scheduling office. The student staff, having gone through training that details what things clients try to get away with which will never be approved, knows that the client is not allowed to do that. The logic is wrong and the staff should tell the client not to proceed with what they are doing.
Rules are represented mentally as what that person can or cannot do. This is a more basic concept as the rules are straight forward (Thagard, 2010). In our department this might be more closely tied to policies. One example is showing up on time. The rule is simple here, if a student staff person is late by more than 1 minute, regardless of explanation, the infraction is noted for supervisor follow-up. To put it more simply, if they are late, then they are written up. The staff are made aware of the rules during training and consistently reminded of them. These are also rules that are more transferable to most other jobs after college.
Concepts are a more broad idea or set of ideas. They are represented by a set a learned experiences or skills. Concepts are more abstract in nature leaving some room for interpretation (Thagard, 2010). In our student employee program, we concepts to describe and help the student understand things that are more complex. Through the use of learning outcomes we set our goals for the students to be able to attain to. Learning outcomes, and sometime what they are assessing can be difficult concepts for our students to grasps. They are abstract in the sense that they aren’t always concrete. Sometime we want them to have an understanding of something rather than know exactly where a particular place is on campus.
Learning styles, the final idea present in this blog, relate to brain processing by the way the information is received and then recalled. Like a computer, the brain stores information but as very small pieces (Chulder, 2001). Learning styles will dictate how those bits of information are input, stored and then recalled. The brain, when needing to recall information will trigger back to how the information was learned allowing the learner to access the information. If the learning styles change, the way the brain recalls the info will need to adapt. If a picture was used to learn information, the picture is what will be recalled. However, if text is used to learn information, it will need to be recalled using actual facts. The learner could not try to look for a mental picture without having associated one with the information learned (Shafagh, 2005). This information will impact my own educational setting by allowing me to better understand how a student learns. Within the student employee trainings, we do a lot with power points and video. The videos specifically allow the staff to put a mental image to the direction we are giving them. One example would be our dress code. When they are working event they have to dress a certain way from when they are working a building shift. We create the video to give them a visual example of how each style should be, as well as, what not to wear. This way they can have visual understanding rather than just a text description. I can also use the same Learning Style Inventory with my student employees to help them, as well as myself, understand how we all learn. Trainings can be created that fall in line with the learning style of the employees (if there is a majority). Likewise, other types of materials can be created to suit those employees with different learning styles if more assistance is needed.
A great talk about using video, visual learning, to teach:
Bock, P. (Producer). (2011). TEDxGWU – Emergence of Creativity in Artificial Intelligence Retrieved from https://www.youtube.com/watch?v=CpNfy7AUPl4&feature=youtu.be
Chulder, E. H. (2001). A computer in your head? Retrieved 5-14-14, from http://faculty.washington.edu/chudler/computer.html
Donnelly, M. (2008). Artificial Intellegence in Schools. Research Starters.
Shafagh, J. (2005). The importance of engaging active learning. from http://serendip.brynmawr.edu/bb/neuro/neuro05/web2/jshafagh.html
Thagard, P. (2010). Cognitive Science. Stanford Encyclopedia of Philosophy. from http://plato.stanford.edu/archives/fall2012/entries/cognitive-science/
Yang, S.-h. (2007). Artificial Intelligence for Integrating English Oral Practice and Writing Skills. Sino-US English Teaching, 4(4), 1-6.