Learner & Contextual Analysis

In summary,  leaner and contextual analysis is useful identifying your audience needs in creating instructional material. Morrison, Ross and Kemp (2007) state that, “To serve either a training group or an academic classroom, the instructional designer must obtain information about the capabilities, needs, and interests of the learners.” Some of the learner characteristics the book covers include: general characteristics, specific entry, learning styles, personal and social characteristics, culturally diverse learners, and learners with disabilities. Topics covered related to contextual analysis include: orienting, instruction, transfer, and performing a contextual analysis. The following are highlights of the aforementioned topics on learner and contextual analysis.

Morrison, Ross and Kemp (2007) defer to Heinrich, Molenda, Russell, and Smaldino (1999) and state that designers should begin with the following categories when describing learner characteristics: general characteristics, specific entry, and learning styles. General characteristics are defined as broad, generalized statements about the learners, such as gender, ethnicity, work experience, and education. Specific entry characteristics are skills or knowledge that the learners must have prior to being exposed to the new training. For example, if you are going to offer a computer-based or on-line training class, a perquisite may be ensuring the learner has access to a computer and is comfortable using the internet and basic word processing skills, such as those found in MS Word. The last category Heinrich et al. (1999) mention is learning style. Morrison, Ross, and Kemp (2007) surmise that knowing learning styles is more important if you are providing one-on-one or small group instruction. Other learner characteristics discussed by Morrison, Ross and Kemp include: academic information, personal and social characteristics, cultural diversity of learners, and learners with disabilities.

Lastly, as a designer, the context to deliver the instruction is important to understand. There are three contexts a designer needs to consider: orienting, instructional, and transfer. Orienting context includes understanding what the learner brings with them, such as skills, knowledge, and attitude. Orienting also includes knowing if the learners feel that the course will be useful to them and how or why the learner feels accountable to learning the material. Instructional context a designer should consider includes method to present the materials and factors in the environment to support the delivery method. Some key environmental factors to consider are: lighting, noise, temperature, seating, accommodations, equipment, and transportation. The last type of context is transfer. Context transfer focuses on “creating an environment that promotes the application of the newly learned knowledge and skills” (Morrison, Ross, and Kemp, 2007). In conducting a contextual analysis, surveys, observations, and interviews may be necessary to gather information necessary to analyze.

All in all,  chapter 3 in Designing Effective Instruction includes a great breakdown of items to consider when defining the target learner and context. Much information is easily available regarding potential learners, although not all of the information may be required to begin design on an instructional module. By considering the individuals’ backgrounds and experiences, and the context of the material and environment, a designer can be off to a terrific start in creating meaningful instruction.

Morrison, G. R., Ross, S. M., & Kemp, J. E. (2007). Designing effective instruction (5th ed.). San Francisco: John Wiley and Sons, Inc.

Heinrich, R., Molenda, M., Russell, J., & Smaldino, S. (1999). Instructional media and technologies for learning (6th ed). Englewood Cliffs, NJ: Prentice Hall.

Which Design Model to Use?

A new instructional designer can be faced with a dilemma of which instructional design model should I use.  This entry focuses on the differences and similarities of ADDIE and MRK.  ADDIE is the acronym for Analyze, Design, Develop, Implement, Evaluate, while MRK stands for the model put forth my Morrison, Ross, and Kemp.

Learning Summary

People learn for various reasons and can be motivated by many stimuli.  Just take a look at the various research and theories that exist out there to try to explain learning:  behaviorism, cognitivism, connectionism, constructivism, and social learning to name a few.  I believe Ertmer and Newby’s (1993) statement that people learn by many different styles and theories is applicable to everyone.  There are many variables that come into play with learning:  is it a new topic, what is the previous knowledge of a subject, which intelligence subset does it connect with, why is the individual interested, how does the individual plan to apply the information, and the list of variables goes on.  The one striking item I found in my study of learning theories is how closely everything is related and most of the theories stem from a single social science, cognitive psychology (Ormrod, 2009).  While no single learning theory is sufficient to describe the population, or even an individual, the science as a whole is able to describe a majority of individuals learning experience within a moment of time.

If you think of learning styles, theories, motivations, and technologies as a house with a foundation, two support pillars, and a roof, motivation is the foundation, the pillar to the left is learning theory, the pillar to the right learning style, and the roof is technology use.  Without motivation, be it extrinsic or intrinsic, there will not be knowledge acquisition.  Motivation is the base upon which learning is built on.  The pillars providing strength are theory and style. The roof, technology, is an element that can enhance and better the learning experience but is most effectively applied once learning theories and styles are understood.

Throughout my studies, I’ve learned more information about my personal learning styles.  I’ve learned that I am more of a constructivist who likes to rely on connections to obtain more knowledge and information.  I also tend to elaborate and look for personal connections within new learnings.

As an instructional designer, it is important to know your preference for learning as well as understand the major learning theories and styles.  It is easy to get caught up in designing materials and courses that best suit your own style.  By knowing more about learning, it can help you view the material through another lens and connect with more students who may prefer other learning styles.  The one most important item I learned throughout my studies is the power of ARCS.  Instructional designers should be able to look at their curriculum and assess the material and teaching methods for:  Attention, Relevance, Confidence, and Satisfaction (Keller, 1999).

References

Ertmer, P. A., & Newby, T. J. (1993). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 6(4), 50–71.

Keller, J. M. (1999). Using the ARCS motivational process in computer-based instruction and distance education. New Directions for Teaching and Learning (78).

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

Week 7 - Reflection of my Learning Styles

I was once asked what I thought my learning style was.  My response was that I preferred a constructivist style of learning.  Of course, I also remarked that it was hard to limit one's self to a single learning style, as Ertmer and Newby (1993) pointed out in one of their articles.  Now that my education in various learning styles is coming to a close, I was asked to revisit the questions of: what is my personal learning style, how has it changed, what can further explain my learning preferences, and what role does technology play in my quest for information.

The Six Sigma Master Blackbelt and Lean Sensei typical response is:  "It depends".  My answers depend on a variety of contexts:  what is it I am learning, why am I learning it, where am I learning, etc., etc.  Because I feel learning is unique and personal to each individual and the environment occurring in, my answers regarding personal learning styles may change slightly.  

I believe it is still hard to limit one's self to a single style of learning.  Upon learning more about various styles of learning, I still believe that my preferred style is constructivist but there is also a lot of connectivism in there.  Of course, connectivism as a learning theory versus learning method is highly debatable.  If you also throw in adult learning, which is broad and vast, I fit that definition as well.  For starters, like most adults, I have to be motivated and have a need or desire to learn.  From there, my preference is to apply or construct my current knowledge to fit the application.  Many times I look to my learning networks (aka connectivism) to provide clarification or advanced examples of principles.  I don't believe my initial response was incorrect or changed over time, but is supplemented with other methods such as connectivism.

Technology plays a large role in my quest for knowledge.  The internet, and all of it's glorious connections via blogs, wikis, learning groups, newsletters, and more, helps me learn more about concepts and how to apply them.  It seems like I am always "googling" something I want more information on at least once a day.  Some of the information is work related, some school, and other is purely personal.  This network helps me acquire information as well as share information with others.  More and more companies are moving towards internal wikis and SharePoint sites to help share information.  I use both of them frequently to further my learning at work.

As I come to a close in this entry, I feel I have gained a better understanding of my learning preferences, even though my thought has not drastically changed.  Technology plays a large part in how I obtain knowledge.  Without technology, I doubt I would have been able to learn as much as quickly as I have over the years.

References

Ertmer, P. A., & Newby, T. J. (1993). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 6(4), 50–71.  
 

Davis, C, Edmunds, E, & Kelly-Bateman, V. (2008). Connectivism. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology. Retrieved February 2, 2010 from http://projects.coe.uga.edu/epltt/  

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

Connectivism - Week 5

Connectivism:  is it a theory to be included with learning theories such as behaviorism, constructivism, cognitivism, and others, or is it just a way people inherently learn?  George Siemens and Bill Kerr have opposing viewpoints regarding if it is a theory or a pedagogical view (Davis, 2008). 

Vygotsky’s constructivist theory relies heavily on the Zone of Proximal Development (ZPD) (Ormrod, 2009), which is important for connectivism to be meaningful.  If an individual has a basic understanding and aptitude, their various social interactions and learning interactions will assist them at being successful in their quest for knowledge.  For example, if I were to perform a transmission rebuild for my car, I would use my learning network to gather information and various perspectives and lessons learned from others.  By using the internet to look at blogs and instructions, purchasing a Chilton’s manual, talking to family and friends who have performed this task before, and relying on my mechanical engineering degree, I would be successful in my endeavor.  However, if I wanted to perform a hip replacement, no amount of research or searching with my learning network will help make me successful.  I simply lack the prerequisite knowledge and understanding in the medical field, whereas the transmission rebuild is similar to my mechanical background.

By studying my personal learning network, as observed in the .jpeg image, I agree with Kerr that connectivism is not a learning theory (Davis, 2008), especially when applied in the above context.  While I may not believe it is a theory, I do feel that connectivism is important to learning and can help an individual grow and develop their skills providing there is a foundation to start with.   Typically, learning networks further knowledge or provide a basic understanding of a concept.

Overtime, my learning network has become more “e”-centric:  electronic focused.  I know I’m not alone when I google a term to find out more information, learn different applications of a principle, or browse the internet searching for differing viewpoints on a subject.  But a majority of my internet research is done on subjects I am already familiar with, which supports my belief that connectivism is not a learning theory, but a learning tool.  Rarely do I use the internet to learn a brand new tool or concept.  That specific learning is still done face-to-face, with through the classroom or through a co-worker or subject matter expert, depending on the subject and location.  As my learning network has grown and evolved through the use of technology over the years, there are specific paths I prefer to take depending on whether I am refining my knowledge base or learning something completely new.  

A great blog I came across during my research, and portrays my sentiments about behaviorism (instructivism), constructivism, and connectivism, is authored by Ryan Tracey.  I recommend taking a look at it:  http://ryan2point0.wordpress.com/2009/03/17/instructivism-constructivism-or-connectivism/.  While I didn't reference his blog in this assignment, I  feel he has captured my sentiments on this topic very well and is worth reading.

References

Davis, C, Edmunds, E, & Kelly-Bateman, V. (2008). Connectivism. In M. Orey (Ed.), Emerging perspectives on learning, teaching, and technology. Retrieved February 2, 2010 from http://projects.coe.uga.edu/epltt/ 

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

Click on the image below to see my learning network and ways I obtain various knowledge.  (Created with Text2Mindmap:  http://www.text2mindmap.com/)

Week 2: Learning Theories and Math

Have you ever wondered why some people have the uncanny ability to spit out numbers they've just heard once, or perform simple math operations in their head within record time?  Were they born with a special aptitude, do they learn (process information) differently, or were they taught by different teaching methods?  Recent research in how people learn numbers and math purport that those viewed to succeed in the above areas exhibit the ability to dual code; i.e. the ability to associate numbers with words and images (Bell, 2003).  Much of the information presented below comes from articles and research posted on two websites:  LD OnLine and Ohio State's Ed Laughbaum. 

Gersten and Chard, in their article "Number Sense:  Rethinking Arithmetic Instruction for Students with Mathematical Disabilities" (2001) state that research from neuropsychologists and cognitive psychologists agree that disabilities in math include three items:  high frequency of procedural errors, difficulty in retrieval of arithmetic facts, and inability to symbolically represent or code numerical information for storage in the brain. Looking back to the way I was taught basic mathematics, the methods used were primarily memorization and recitation.  I recall being graded on how many problems I could solve in 60 seconds, which emphasized memorization of the addition and multiplication tables.  This turned basic math into declarative knowledge, which researchers agree that the retrieval of declarative knowledge is often slow and conscious (Ormrod, 2009). This sentiment is echoed in both Gersten & Chard's work as well as Bell’s work.

After all these years, why is the memorization and recitation method in math now being proved as not adequate?  I do not have an answer for that, except that the brain and learning theories is still an emerging topic.  What we know now is that for humans to place items into long-term memory AND be able to recall it quickly, we need to create connections to words, sounds, experiences, and emotions (Ormrod, 2009).  Laughbaum (2010) posits this theory by stating, "Teachers must create connections to improve the memory of mathematics taught."  He goes on to explain that processes need to be connected by relationships to personal experiences and emotions.  The more connections, or associations, you have to the data, the easier it becomes to recall, even if you don't have all the data upfront.   

If you agree with the current research and theories, what remains is to establish is the best way to teach math to create the most connections possible.  Laughbaum has done extensive research on this topic and has information available on his website: http://www.math.ohio-state.edu/~elaughba/ .  He has several presentations and published papers on his site that explain methods used to help retain math data and procedures, including pattern building, generalization, and technology such as software.  What is important to note is the order he recommends using the methods.  He generally recommends letting students generalize and formulate their own processes and procedures before demonstrating a preferred method to students (Laughbaum, 2008). 

For me, drilling basic math through memorization wasn’t the most effective, in part because I am a very visual and relational type of person.  Current research in the field recommends associating words, colors, patterns, and experiences to numbers, which is something I was never taught and never formulated on my own.  Did it hold me back in school or my profession?  Not at all!  Could it have the potential to hold others back?  I believe so, since those who may be slow to recall math information may be incorrectly labeled as “slow” and lacking understanding to move on to higher forms of math.  Can being slower to recall basic math operations help create test anxiety, causing students to “blank out” during exams and being incorrectly labeled?  You bet, and research by Higbee (1999) validates this theory by correlating the time to recall information with test anxiety.  
 

The authors, researchers, and websites cited in this article are making valuable contributions to the world of information processing and retention in a often overlooked discipline - math.  As an instructional designer, whether within education or corporate training, the theories and methods presented will help you create and present material that will be retained long after the students have left your class.  LD OnLine (http://www.ldonline.org/) has a wealth of information on how learning theories relate to memory retention, especially for those with learning disabilities.  For more information on how the brain processes and retains math, Laughbaum has an extensive work published on his website at http://www.math.ohio-state.edu/~elaughba/.   

I’d like to open this blog to readers to share experiences of how they learned or teach math; what methods work and which didn’t? How do you form connections to be able to recall the information later? How does research in cognitive factors help or change the way math is being taught?

References

Bell, N. and Tuley, K. (2003). Imagery:  The sensory-cognitive connection for math.  Retrieved January 16, 2010 from: http://www.ldonline.org/article/Imagery%3A_The_Sensory-Cognitive_Connection_for_Math

Gersten, R. and Chard, D. (2001).  Number sense: Rethinking arithmetic instruction for students with mathematical disabilities.  Retrieved January 16, 2010 from: http://www.ldonline.org/article/5838

Higbee, J. & Thomas, P. (1999).  Affective and cognitive factors related to mathematics achievement.  Journal of Developmental Education, 23(1), 8.  Retrieved January 16, 2010 from Education Research Complete Database.  Permalink: http://ezp.waldenulibrary.org/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=2256674&site=ehost-live&scope=site

Laughbaum, E.D.  (2010).  The neuroscience of connections, generalizations, visualizations, and meaning.  Retrieved January 16, 2010 from: http://www.math.ohio-state.edu/~elaughba/

Laughbaum, E.D.  (2009).  Generalizing patterns in algebra for long-term memory understanding.  California Math Council, ComMuniCator, (34, 2), December, 2009.   Retrieved January 16, 2010 from: http://www.math.ohio-state.edu/~elaughba/

Laughbaum, E.D. (2008).  Implications of neuroscientific research on teaching algebra, Int. J.

            Continuing Engineering Education and Life-Long Learning, Vol. 18, Nos. 5/6,

            pp.585–597.   Retrieved January 16, 2010 from: http://www.math.ohio-state.edu/~elaughba/ 

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

Week 1

After scouring the internet for interesting blogs dealing with instructional design and technology (IDT), I've found 3 that I believe will help me in my journey, Langevin Learning Services Blog (http://www.langevin.com/blog/), The Instructional Design & Development Blog at DePaul University (http://www.iddblog.org), and John Shook's Lean Management Column (http://www.lean.org/shook/).

Langevin Learning Services presents helpful and informative information on engaging students. They have various categories such as: adding excitement to your class, advanced instructional techniques, how adults learn, and presentation skills. All of the categories present a well-rounded view on the "softer" topics near and dear to a corporate trainer. Their last few posts focused on "difficult learners" and offered advice on engaging these types of students in your classes. This topic is approached positively and insightfully and can assist many trainers in understanding and overcoming this type of learner.

The Instructional Design & Development Blog at DePaul University (IDD) compliments the Langevin blog. This blog appears to focus more on the technological aspects of IDT. Recently, their blogs have focused on Learning Management Systems (LMS), on-line learning, and presentation of materials, to name a few. As my company moves more towards on-line, e-learning modules, I believe IDD will help me by providing tips on constructing more effective learning materials that take advantage of technology. IDD also lists links to 3 additional resources in the realm of digital media. I haven't had time to check out the links yet, but they sound like a great resource for the future.

The last blog doesn't focus on IDT, but it does focus within my specialty of training: lean. I have been following and using the Lean Enterprise Institute (LEI) for quite a few years now. John Shook's Lean Management Column offers insight and examples on various lean implementations and thoughts. Shook has published several books, most recently Managing to Learn. He promotes learning organizations and culture changes, which are important aspects for an IDT professional to consider when creating classes and curricula. Over the years, I have found myself reading his newsletters and using his information in classes I've taught to help explain and/or provide additional examples outside of my industry to help students grasp the concepts taught. Shook states in his last column:

Lean management is very much about asking questions and trying things, or encouraging others to try things. Lean management itself is not much about providing the right answer but it is very much about asking the right question.

This quote is how I view effective instruction and teaching. It's using the Socratic method in helping change culture. You learn by formulating your own ideas and doing, not being told what to do.

I wish you all good luck in your IDT journeys. I hope to be able to share my thoughts, insights, and resources with others and help foster discussion on various learning theories and methods.

Have fun, learn, and make a difference!