Strategic Approach: Understanding How We Learn

Knowledge Acquisition defined is a complex matrix of learning theories, neuroscience, psychology, personal preferences, influenced by motivation, guidance, and technological advancement. Confused, so was I at the beginning of this learning experience. I defined learning, more explicitly designing for learning, a narrow definition where one size fits all. Learning requires the facilitators to use approaches to knowledge acquisition and correlate contextual relevance to maximize positive influence on instruction and motivation. No one method or design is the sole solution for adequate storage in long-term memory, LTE (Kerr, 2007) (Ormrod, Schunk, & Gredler, 2009). 

 Through this course, I often reflected on the role of LTE and working memory, STE, in planning instruction. The influence an instructor has on presenting the stimulus to motivate information processing, in my opinion, is not about LTE. LTE is the product of stored files, saved as experiences and understandings; in my opinion, it cannot change. Once an experience or skill is recalled from LTE, the memory file is then transferred to STE; where it will be processed, organized, and stored as a new LTE file (Ormrod et al., 2009) (Ertmer & Newby, 2013). As an instructional designer, it is the expectation to provide stimulus to STE that expertly guides the re-coding of LTE.

 In a learning environment, the defined learning objectives, behaviors, or skills are small pieces of the learning puzzle. Human beings are only differentiated, in relevance to learning, to other species by cognitively processing and constructing individual interpretations of knowledge for future use (Ormrod et al., 2009). Metaphorically, learning is a human body; everything is connected. The external environment provides humans with direction and stimulus—the internal functions of the body process sensory stimulus to adapt to new environmental changes. Human only can adapt if the human is, in fact, living. Lacking motivation from the learner triggers can be considered the “death” of instruction. Understanding knowledge is only successful when motivation driven learners explore resources in their learning network and learning tools to socially construct understanding. 

 An instructional designer’s primary goal is to present stimuli to learners guiding new experiences to advance an expert level of understanding (Ormrod et al., 2009).  Facilitation, especially in online andragogy, requires an understanding of learning psychology, the environmental stimulus and motivational influence, and cognitive processing, recalling LTE to analyze with current STE. Planning how to transfer knowledge to each individual’s learning preferences has shown no significance in expanding the learner’s zone of potential development (Ormrod et al., 2009). Therefore, learning theories, psychology, and styles provide a framework that matches the knowledge presented with the most efficient approaches to knowledge acquisition. For an instructional designer to successfully transfer knowledge, an understanding of how learners acquire knowledge and what motivates them to learn must be foundational for each learning project. 

References

Ertmer, P. A., & Newby, T. J. (2013). Behaviorism, Cognitivism, Constructivism: Comparing Critical Features from an Instructional Design Perspective. Performance Improvement Quarterly, 26, 43-71

Kerr, B. (2007, January 1). _isms as filter, not blinker. Retrieved May 20, 2020, from      http://billkerr2.blogspot.com/2007/01/isms-as-filter-not-blinker.html

Ormrod, J., Schunk, D., & Gredler, M. (2009). Learning theories and instruction (Laureate custom edition). New York, NY: Pearson.

Learning Reflection

In self-reflection, over the last eight weeks, after a deeper dive into the different approaches, strategic styles, and environmental issues that influence an individual’s ability to acquire new knowledge. The basic of learning is a subjective interpretation of the presented experience. This intrinsic synthesizing of information is still considered ongoing research in search of a direct correlation to the brain’s role in information processing through neuroscience (Eric, n.d.). My understanding of learning before analyzing the psychology of learning deemed learning a singular process in which individuals process stimuli. This, however, is proven incorrect by the influence of the environment in which instruction is facilitated. 

The principles of higher education, corporate learning, and overall adult learning, andragogy rely on guided, self-directed learning to promote information processing compared with prior experience or storage files retrieved from long term memory LTE (Conlan, Grabowski & Smith, 2003). There are four main theoretical approaches to define the learning process. Behavioralism, cognitivism, constructivism, and the newer addition connectivism, describe how learners absorb knowledge through an internal/external scaffolding to demonstrates a baseline understanding (Conlan et al, 2003)(Kerr, 2007). Behaviorism and cognitivism rely on guided instruction to synthesize information through direct information processing to replicate skills obtained from the facilitation. Constructivism and connectivism give a high level of influence to the social environment, personal learning network, PLN, to cognitively process, and a personalized view of knowledge (Cercone, 2008). It is important to note that not one learning theory is the sole answer in planning instruction. Learners effectively sort information in working memory, STE, to organize experiences for recall in LTE using dual-coding methods (Gutierrez, 2016). No one theory is the one-stop-shop for understanding. 

Andragogy requires the intrinsic motivation to navigate learning strategies and styles in the most effective way (Huett, Moller, Young, Bray, & Huett, 2008 ). Most often, the facilitation of material is presented online through interactive eLearning. Technology, in a generalized summary applying to instruction, enhances a learner’s PLN through machine-based learning (Conlan et al, 2003). Misconceptions stemmed from technology defined as computer influenced, or automatic information analysis leaves out the importance of interaction. Through simple technological tools such as infographics, gamification, and multi-media, they result in positive influences from diverse cultural backgrounds to advance an expert level of understanding (Cercone, 2008). Learning styles help define intrinsic motivation through prior confidence in knowledge in LTE recalled, and preferred individually. However, the task of tailoring media, resources, and the social environment to a specific learning style is relevant to the amount of time the instructor has to implement ideas and eliminate strategies that induce cognitive overload.

In conclusion, learning is a complex mixture of neuroscience and psychology. In instructional environments, it is vital to provide a framework for the knowledge presented to ensure that emotional responses from stimulus promote motivation and self-directed learning. 

References:

Cercone, K. (2008). Characteristics of adult learners with implications for online learning design. AACE Journal, 16(2), 137–159. Retrieved from http://www.editlib.org/index.cfm?fuseaction=Reader.ViewAbstract&paper_id=24286

Conlan, J., Grabowski, S., & Smith, K. (2003). Adult learning. In M Orey (Ed.), Emerging perspectives on learning, teaching and technology. Retrieved from http://textbookequity.org/Textbooks/Orey_Emergin_Perspectives_Learning.pdf

Eric P. Jensen: A Fresh Look at Brain-Based Education – Teachers.Net Gazette. (n.d.). Retrieved from http://www.teachers.net/gazette/OCT08/jensen/

Gutierrez, K. (2016, June 21). What are personal learning networks? SH!FT eLearning. Retrieved June 8, 2020, from https://www.shiftelearning.com/blog/personal-learning-networks

Huett, J., Moller, L., Young, J., Bray, M., & Huett, K. (2008). Supporting the distant student: The effect of ARCS-based strategies on confidence and performance. Quarterly Review of Distance Education, 9(2), 113–126.

Kerr, B. (2007, January 1). _isms as filter, not blinker. Retrieved May 20, 2020, from http://billkerr2.blogspot.com/2007/01/isms-as-filter-not-blinker.html

artificial intelligence – technology driven pedagogy

Knowledge acquisition evolves from the environmental and technological influences on the presented information. Artificial intelligence (AI) uses machine-based learning to accomplish tasks and activities that have historically relied on learner’s cognition (Alexander, Ashford-Rowe, Barajas-Murphy, Dobbin, Knott, McCormack, Pomerantz, Seilhamer, & Weber, 2019, p. 27). Like original computer-based learning theories, AI uses computer programming to navigate advanced algorithms to predict and measure human task completions and decision-making (Alexander et al., 2019, p. 27). Learning institutions and change-based organizations have implicated AI into the facilitation of knowledge. AI, a tool to support instructors, has not been well received by the masses, hindered by privacy and ethical concerns (Gimbel, 2018).

AI has increasingly adapted to modern society, with seventy-four percent of Americans’ integration perception being positive (Gimbel, 2018). AI has many promising implications on instructional facilitation, time-constraints, and the ability to scale instruction while maintaining a personalized approach to the Learner (Brooks, 2018). Innovations such as Apple’s iPhone’s FaceID, Amazon’s Alexan, Google Assistant, and many variations of interactive chatbots all exist as commonly used AI advancements (Alexander et al., 2019, p. 27 & Brooks, 2018 ). In correlation to the provided low-educational examples, learner’s engagement level has sparked the need for further exploration or development to validate AI in higher education (McMurtrie, 2018). 

In a poll by Gallup and Northeastern University, seventy-three percent of Americans believe that AI, also referred to as “Robot,” will replace the need for human contributions (Gimbel, 2018). Machine-based learning can personalize instruction using user-influenced learning algorithms and reverse-engineering processes to gain an expert level of understanding through the lens of the learner (Gimbel, 2018). In 2017 the MIT Sloan Management Review found 85% industry professionals agree that AI shows promising competitive advantages to their nitch, however only 20% have shown any indications for planning for AI integration (Alexander et al., 2019, p. 27). Fear of privacy, negativity, and bias algorithms influenced by the learner solidify the argument; AI should be used as a support tool for instructors (Means, 2009). 

AI has demonstrated advanced approaches to feedback based on data analytics, synthesizing unique information processing, and minimizing time concentrating for real-time collaboration (Alexander et al., 2019, p. 27 & Gimbel, 2018). Chatbots are a form of AI widely accepted in learning cultures that have aided in instruction facilitation. In both educational and corporate learning environments, they have a positive influence on the instructor’s ability to challenge learner’s current interpretation, generating a personal learning experience. The learning experience is driven by sorting, assigning, and evaluating the information presented (McMurtrie, 2018). The storage of the AI at an introductory level uses machine-based logic to clone cognitive psychology to sort, organize, and recall information when presented with a stimulus (McMurtrie, 2018).

An illustration of baseline AI integration was demonstrated by Craig Coates, an entomologist at Texas A&M University. Coates faced challenges to facilitate a science course plagued by cheating (McMurtrie, 2018). In this example, Coates used AI to automate the plagiarism check, comparing text strings in a given database of knowledge. This allowed more time for: Coates to inspire an expert level of understanding and learners to process information at their own pace (McMurtrie, 2018). In this example, AI or robot is not the method of instruction but a useful tool when added in an instructional setting. In this context, technology does not demonstrate the driving characteristics to formulate privacy and ethical concerns (McMurtrie, 2018). Instructors use face-to-face interaction or social presence to deliver guidance that is contingent on the learner’s self-efficacy and task value matter (Artino, 2008). In summary, AI effectiveness relies on environmental integration and engagement levels through both the student and instructor lens. 

Facilitating instruction derived from learning theories, strategies, and styles in a blended environment use a combination tailored to the educational content. AI integration focuses on behavioralism, cognitive-constructivism, and connectivism. Behavioralism, as a stimulus-response theory, correlates directly with the AI, as a machine-based learning tool. Through connections, including peers, learning evolves to cognitive constructivism, where learners are considered self-efficient, but open to “construct” their desired understanding level. 

References

Alexander, B., Ashford-Rowe, K., Barajas-Murphy, N., Dobbin, G., Knott, J., McCormack, M., Pomerantz, J., Seilhamer, R., & Weber, N. (2019). Horizon Report 2019 Higher Education Edition. EDUCAUSE. https://library.educause.edu/-/media/files/library/2019/4/2019horizonreport.pdf?la=en&hash=C8E8D444AF372E705FA1BF9D4FF0DD4CC6F0FDD1

Artino Jr. AR. Promoting Academic Motivation and Self-Regulation: Practical Guidelines for Online Instructors. TechTrends: Linking Research & Practice to Improve Learning. 2008;52(3):37-45. doi:10.1007/s11528-008-0153-x.

Brooks, C. (2018, November 2). How artificial intelligence and virtual reality are changing higher ed instruction. Education Dive. https://www.educationdive.com/news/how-artificial-intelligence-and-virtual-reality-are-changing-higher-ed-inst/541247/

Gimbel, E. (2018, August 16). Artificial intelligence is poised to expand in higher education. Technology Solutions That Drive Education. https://edtechmagazine.com/higher/article/2018/08/artificial-intelligence-poised-expand-higher-education

Means, Barbara, 1949-. (2009). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. Washington, DC: U.S. Dept. of Education, Office of Planning, Evaluation and Policy Development, Policy and Program Studies Service. Retrieved from http://eric.ed.gov/?id=eD505824

McMurtrie, B. (2018). How Artificial Intelligence Is Changing Teaching. The Chronicle of Higher Education, 40, 14.

Learning Network

Personal Learning Network

In high school, the time it took to complete a course was a drawn-out perception. Motivation, extrinsically influenced, relies on a facilitator’s role in direct information processing (Cercone, 2008). As a student, I was more internally motivated to get to the next summer/holiday break. The days went by slow, and the ability to cognitively process knowledge limited by forced engagement resulting in generalized storage of the experience. As we age, the perception of time shortens. “Where has all the time gone” a frequently used saying that directly correlates with this belief. As an adult learner, the processing time for knowledge is shortened; As we have little “time” to commit to the learning process and ability to infer internal and external experiences to reduce overwriting previous LTE storage (Cercone, 2008). Knowledge facilitation evolves to become quick, direct factual information that requires self-direction (Conlan, Grabowski & Smith, 2003). The rise of technology allowed advancements in multi-media facilitation in a virtual environment built to expand one’s learning network. 

Personal Learning Networks (PLN), challenge learning interpretations by engaging in a social presence, both physical and virtual. Connectivism, theorized by George Siemens in 2005, hypothesizes continuous learning using personal informational structures is most effective when looking beyond the traditional settings to support an advanced level of understanding (Andriotis, 2017). Developing a network of learning requires a PLN comprising three elements: connection building, connection maintenance, and connection activation (Gutierrez, 2016). As these three components express similarities in personal learning environments (PLE), PLN is rooted in self-directed learners (Gutierrez, 2016). PLNs focus on adding new people or resources, keeping resources fresh, and activating these connections in the relevant context (Gutierrez, 2016). As demonstrated in many different attempts to solve how learning occurs, a PLN is only a portion of one’s PLE. One’s PLE can be comprised of like-minded peers. However, a PLN requires unique constructivist views that challenge fundamental understandings using active engagement through technological advancements in social and educational platforms (Gutierrez, 2016). In summary, PLNs are self-constructed extensions of one’s PLE (Andriotis, 2017).

The time constraints and changing demands, professional and personal, on an adult learner help define the need for a PLN, especially in the workplace. To address sustainability, and relevant both an employer and employee must develop a learning-centric culture starting with one-on-one interaction. As job expectations advance form generalized stimulus-response, cognitive processing is required to remain at the forefront of learning initiatives. The need for “expert” learning is crucial to stay innovative. PLNs answer concerns with implicating traditional learning approaches in adult learning. Formal education can negatively impact the ability to juggle “life,” the time spent constructing interpretation, and financial obligations (Andriotis, 2017).  

When reflecting on your PLN, you need to understand the benefits. PLNs are self-directed/self-constructed. They offer the learners a choice in what they learn and the resources that create active engagement in the learning process, personalizing the learning experience (Lynch, 2017). Formal and informal resources allow insight from multiple backgrounds and or experience, levels of understanding, technological facilitation methods, i.e., blogs, scholarly articles, discussions, social networks, and internal knowledge reflection (Gutierrez, 2016).  

Final Thoughts:

If the ability to absorb knowledge is infinite, only confined by how the brain processes, store, and retrieves knowledge, can a personal learning network be infinite?

Resources:

Andriotis, N. (2017, October 23). Why and how to create successful personal learning networks. TalentLMS Blog. Retrieved June 8, 2020, from https://www.talentlms.com/blog/why-and-how-to-create-a-personal-learning-network-in-the-workplace/

Cercone, K. (2008). Characteristics of adult learners with implications for online learning design. AACE Journal, 16(2), 137–159. Retrieved from http://www.editlib.org/index.cfm?fuseaction=Reader.ViewAbstract&paper_id=24286

Conlan, J., Grabowski, S., & Smith, K. (2003). Adult learning. In M Orey (Ed.), Emerging perspectives on learning, teaching and technology. Retrieved from http://textbookequity.org/Textbooks/Orey_Emergin_Perspectives_Learning.pdf

Gutierrez, K. (2016, June 21). What are personal learning networks? SH!FT eLearning. Retrieved June 8, 2020, from https://www.shiftelearning.com/blog/personal-learning-networks

Lynch, M. (2017, August 3). What is the importance of a personal learning network? The Edvocate. Retrieved June 8, 2020, from https://www.theedadvocate.org/importance-personal-learning-network/

ANDRAGogY: Adult Learning

There are many times we look to relate knowledge we have previously learned to current information presented. I consider this a way for adults to reassure confidence in prior knowledge is still relevant. Previous theories and psychological evaluations of the acquisition of knowledge, and retrieval of knowledge, approach learning as a stimulus-response through a learner’s physical or mental capabilities. Still, these options do not explicitly consider emerging technology implications on an adult’s perception of time. The time needed to process, organize, store, and recall information has decreased, allowing learners to synthesize material and build a more profound understanding in a short time frame (Cercone, 2008). The unique approach to understanding adult learners’ needs and motivation is called andragogy (Conlan, Grabowski & Smith, 2003). Andragogy uses five assumptions to facilitate adult learning effectively (Cercone, 2008). Adult learners are self-directed, internally motivated, problem-centered as it relates to current social roles, and rich in life experiences (Conlan et al, 2003).

Adult learners are often self-directed learners; however, that does not imply facilitation or environmental influences are not needed for absorption. Research has concluded the importance of social interaction as knowledge is presented. Only allowing self-directed learners in one specific environment would not allow the adequate communication needed for the deeper, or expert level of understanding. In expertise theories, adults process information quickly, analyze problems and mistakes more efficiently than novices (Conlan et al, 2003). Experts are assumed after repeated findings maintain and excel learning in their domain (Conlan et al, 2003). In my opinion, expert learners and novice learners can facilitate a better understanding. Novice learners can give different interpreted beliefs that expert levels can explore, thus maximizing both individual’s capacity for learning. However, I feel there is a point in education and or training where a self-directed learning environment would benefit expert level learners, such as a thesis or high-level management training. In these types of situations, the cognitive processing ability is more advanced and requires the application of knowledge concepts in a reflective “self-narrative” where there is no right or wrong.

The best way to reduce juggling “life” and adult education, in my opinion, is the use of online eLearning platforms. Adults are often tight scheduled, and making additional time to continue education is hard. Online learning offers facilitated direction with little face-to-face instruction, opening the pathway for self-driven learning but, more importantly, the internal identification of motivation triggers (Cercone, 2008).

In my opinion, adult learners do cognitively process information the same as children do. If there were no similarities between the two, we would not be able to apply multiple theories to each side. The critical differentiator is going to be in the response. Children are not going to have as many prior experiences to push the organization of memory effectively. In my interpretation of Dr. Anthony Artino’s post concerning the differences in training and education, learning in a child could be referred to as “training” centered education. Responses are often processed as new knowledge, or with little prior situational experience. Adult learners are pushed more into an “educational” centered approach where individuals construct answers to complex problems by applying previous experiences to further cognitive abilities (Artino, 2020). As children, most information learned will be challenged or relevant in future situations generating an implied task time frame. As adult learners, a task can have flourished rapidly; however, the knowledge constructed challenge the “knows” of the presented material. 

In conclusion, In my opinion, there are two main components to consider when designing for andragogy, adult online instruction. The Learner and the means of facilitation coexist to deepen the level of education. The Learner also must be presented with clear, goal-oriented solution that relates to the corresponding social environment (Cercone, 2008). 

Resources

Artino, A. (2020, June 3). Re: Education vs. Training [Comment]. Retrieved from https://class.waldenu.edu/webapps/discussionboard/do/message?action=list_messages&course_id=_16681348_1&nav=discussion_board_entry&conf_id=_3517726_1&forum_id=_7980848_1&message_id=_108663893_1

Cercone, K. (2008). Characteristics of adult learners with implications for online learning design. AACE Journal, 16(2), 137–159. Retrieved from http://www.editlib.org/index.cfm?fuseaction=Reader.ViewAbstract&paper_id=24286

Conlan, J., Grabowski, S., & Smith, K. (2003). Adult learning. In M Orey (Ed.), Emerging perspectives on learning, teaching and technology. Retrieved from http://textbookequity.org/Textbooks/Orey_Emergin_Perspectives_Learning.pdf

Foley, G. (Ed.). 2004. Dimensions of adult learning: Adult education and training in a global era. McGraw-Hill Education.

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Social Enviroments implications on learner

To address if the element of “social” environments, impact on learning environments is the same, you have to understand the role of social behavior and the implications on learning. Constructivism view humans construct knowledge by adapting meaning from a previous stimulus-response in the current relevant context (Jenkins, 2006). The process of knowledge is interpreted as a “personal world” or by “mind’s adaptations” that is all internally driven (Jenkins, 2006). Piaget furthered constructivist reach by describing biological readiness, life experiences, and structures that play a crucial role in self-constructing information (Jenkins, 2006). Social environments offer the transference of an individual’s prior skills or processes, allowing learners to voice their point of view and influence the organization of information.  

In my opinion, online pedagogy can offer the same “social” environment as a traditional classroom setting but hinder the ability to advance to the next level of understanding. Howard Gardner stated there are three types of learners, native, traditional, and expert, and if the misconceptions are not challenged, the level of understanding will remain the same. The zone of proximal development describes the kind of learning environment that enables effective knowledge transfer and cognitive development (Laureate Education, n.d.). In applying both approaches, one can produce an active learning environment that is conducive to both external experiences and cognitive processing (Ormrod, Schunk, & Gredler, 2009). When looking at learning strategies and styles that each _ism favors, it doesn’t hinder the ability to influence learning, just merely suggestions that favor memory storage and organization. In the Constructivist point of view, a learner can construct their understanding based on the social environment (Ormrod, Schunk, & Gredler, 2009) ; the variable to me would be the learner’s level of understanding. Instructional settings typically viewed as social offer groups, activities, feedback, and open dialogue between leaner that foster intuitive thinking. However, emerging technology now gives us the ability to see them still visually and communicate openly in realtime, creating the same atmospheric factors. To help guide instruction, facilitators must demonstrate the ability to create a social presence, or the ability of learners to project personal characteristics, experiences, and influences into a community presenting as if “real people” (Kilgore, 2016).

Resources:

Jenkins, J. (2006). Constructivism. In Encyclopedia of educational leadership and administration. Retrieved from http://knowledge.sagepub.com.ezp.waldenulibrary.org/view/edleadership/n121.xml

Kilgore, W. (2016, November 14). Social Learning in Online Environments – Humanizing Online Teaching and Learning. Retrieved May 28, 2020, from https://humanmooc.pressbooks.com/chapter/social-learning-in-online-environments/ 

Laureate Education (Producer). (n.d.). Theory of social cognitive development [Video file]. Baltimore, MD: Author.

Ormrod, J., Schunk, D., & Gredler, M. (2009). Learning theories and instruction (Laureate custom edition). New York, NY: Pearson.