Friday, November 26, 2010

Margith Strand/ Fielding Graduate University/ Distance Education/November 26, 2010

(4) It should also clarify the mediation of one structure by others, as well as the contradictions between structures and their role in the structurational process.
 
(5) It ultimately should shed light on how social institutions are reproduced or shaped by the process in question. This turns the context issue on its head: how does structuration influence the context itself?

Part of presentation to Fielding Graduate / From article by DeSanctis and Poole

Tuesday, November 23, 2010

"Referents" to Time, Place and Space..is "Context"...in Semiotic Language and Distance Learning and Teaching language/Margith Strand/ Fielding Graduate University

con·text (kntkst)
n.

1. The part of a text or statement that surrounds a particular word or passage and determines its meaning.

2. The circumstances in which an event occurs; a setting.


[Middle English, composition, from Latin contextus, from past participle of contexere, to join together : com-, com- + texere, to weave; see teks- in Indo-European roots.]

The American Heritage® Dictionary of the English Language, Fourth Edition copyright ©2000 by Houghton Mifflin Company. Updated in 2009. Published by Houghton Mifflin Company. All rights reserved.


Place and Space are two of the variables which I have indicated, at least in thought, to comprise the Distance Education world (along with "time"). I feel that "place" and "space" are the definers of the context of the semiotically linguitic features of the course instructional world, also. If one looks at the definition of the term "context," one can see the "conjoinness" of the features which connect the word "meaning" the other definition of "context."

"Determines it meaning"...indicates that there is a time, place and space capacity to the setting...surroundings...of the "word environ"...i.e. meaning of the phrase or sentence.

This is where I will be proposing the idea of the term "code mobility" within the construction of a possible analytical format. Time, space and place variance of the dimensional parameters and conditions of the "contextual expressions" of the discourse of the online courses.

Humanism is "intentionality" and Constructivism is "self.."..connecting these two will be the indicator of the connectivity of the Distance Education world.

Sunday, November 21, 2010

Phenomenology and Distance Education...."Intentionality"....to Humanism

Basically, phenomenology studies the structure of various types of experience ranging from perception, thought, memory, imagination, emotion, desire, and volition to bodily awareness, embodied action, and social activity, including linguistic activity. The structure of these forms of experience typically involves what Husserl called “intentionality”, that is, the directedness of experience toward things in the world, the property of consciousness that it is a consciousness of or about something. According to classical Husserlian phenomenology, our experience is directed toward — represents or “intends” — things only through particular concepts, thoughts, ideas, images, etc. These make up the meaning or content of a given experience, and are distinct from the things they present or mean.[From: Stanford Encyclopedia of Philosophy]

Design for Dissertation at Fielding Graduate University/ Margith A Strand

Saturday, November 20, 2010

MIS Quarterly Vol. 33 No. 3/ September 2009 M. S. Poole [from]

Research on distributed intelligence and cognition suggests that memory is not just in our heads (Salomen 1991), instead the "surround" in which the memories are formed-including the place and the tools we use - play an important role. Educational researcher David Perkins (1992) puts it as follows: Human cognition at its richest almost always occurs in ways that are physically, socially, and symbolically distributed. People think and remember with the help of all sorts of physical aids, and we commonly construct new physical aids to help ourselves yet more (p. 133)

My comments: Relational cognitivistic interactions in Distance Education/
Humanism and Constructivistic Methodology

Friday, November 19, 2010

Consitutive Analysis and Structuration Theory

http://books.google.com/books?hl=en&lr=&id=rEDSNVV2SDAC&oi=fnd&pg=PA103&dq=constitutive+and+structuration+theory&ots=SZws4rKGHf&sig=WbTXVVoaFb-lKKP31MwWrI89iBQ#v=onepage&q=constitutive%20and%20structuration%20theory&f=false

Wednesday, November 17, 2010

Structuration Theory and Information Systems

http://books.google.com/books?id=UPGo_047vu8C&lpg=PA206&ots=6YJFtU0Tm2&dq=Bridge%20across%20structuration%20theory%20and%20grounded%20theor&lr&pg=PA209#v=onepage&q&f=false

Sunday, November 14, 2010

Situational Analysis: Grounded Theory and Concept Mapping- November 14, 2010

http://sts.ucdavis.edu/summer-workshop/worshop-2008-readings/Clarke%202003%20Situational%20analyses.pdf

Monday, November 8, 2010

Constant Comparison Method: Jane F. Dye, Irene M. Schatz, Brian A. Rosenberg, and Susanne T. Coleman

Constant Comparison Method:
A Kaleidoscope of Data
by
Jane F. Dye, Irene M. Schatz, Brian A. Rosenberg, & Susanne T. Coleman+

The Qualitative Report, Volume 4, Numbers 1/2, January, 2000
(http://www.nova.edu/ssss/QR/QR4-1/dye.html)


--------------------------------------------------------------------------------

Abstract
This paper will attempt to illustrate the use of a kaleidoscope metaphor as a template for the organization and analysis of qualitative research data. It will provide a brief overview of the constant comparison method, examining such processes as categorization, comparison, inductive analysis, and refinement of data bits and categories. Graphic representations of our metaphoric kaleidoscope will be strategically interspersed throughout this paper.
Introduction
As novices to qualitative investigation and data analysis, a research class project left us in the midst of simultaneous learning and doing. This created a challenging and sometimes frustrating journey through the mountains of "how to do" qualitative research. Our challenge centers on making the connection between the reading about qualitative research and the hands-on application of qualitative research. We used the constant comparison method to analyze our data and the metaphor of a "kaleidoscope" to guide us through the analysis process. A kaleidoscope, as defined by Webster's New Collegiate Dictionary (Mish, 1990), is "an instrument containing loose bits of colored glass between two plain mirrors and two flat plates so placed that changes of position of the bits of glass are reflected in an endless variety of patterns" (p. 656).

Constant Comparison Method
According to Patton (1990, p. 376), "The first decision to be made in analyzing interviews is whether to begin with case analysis or cross-case analysis." We began with cross-case analysis of three interviews, using the constant comparison method "to group answers . . . to common questions [and] analyze different perspectives on central issues."

Glaser and Strauss (cited in Lincoln & Guba, 1985, p. 339) described the constant comparison method as following four distinct stages:

comparing incidents applicable to each category,
integrating categories and their properties,
delimiting the theory, and
writing the theory. (p. 339)
Our analysis follows these guidelines closely. According to Goetz and LeCompte (1981) this method "combines inductive category coding with a simultaneous comparison of all social incidents observed (p. 58). As social phenomena are recorded and classified, they are also compared across categories. Thus, hypothesis generation (relationship discovery) begins with the analysis of initial observations. This process undergoes continuous refinement throughout the data collection and analysis process, continuously feeding back into the process of category coding. "As events are constantly compared with previous events, new topological dimension, as well as new relationships, may be discovered" (Goetz & LeCompte, p. 58).

Categorizing Data Bits
According to Bruner, Goodnow, and Austin (1972), "To categorize is to render discriminably different things equivalent, to group the objects and events and people around us into classes, and to respond to them in terms of their class membership rather than their uniqueness" (p. 16). The act of categorizing enables us to reduce the complexity of our environment, give direction for activity, identify the objects of the world, reduce the need for constant learning, and allow for ordering and relating classes of events. At the perceptual level, categorizing consists of the process of identification, " a 'fit' between the properties of a stimulus input and the specifications of a category. . . . An object of a certain color, size, shape, and texture is seen as an apple." (Bruner, Goodnow, & Austin, p. 176).

Categories, created when a researcher groups or clusters the data, become the basis for the organization and conceptualization of that data (Dey, 1993). "Categorizing is therefore a crucial element in the process of analysis" (Dey, p. 112). Content analysis, or analyzing the content of interviews and observations, is the process of identifying, coding, and categorizing the primary patterns in the data (Patton, 1990). "The qualitative analyst's effort at uncovering patterns, themes, and categories is a creative process that requires making carefully considered judgments about what is really significant and meaningful in the data (Patton, p. 406).

Inductive analysis (Patton, 1990) means that the patterns, themes, and categories of analysis "emerge out of the data rather than being imposed on them prior to data collection and analysis" (p. 390). According to Dey (1993), a natural creation of categories occurs with "the process of finding a focus for the analysis, and reading and annotating the data" (p. 99). These categories, while related to an appropriate analytic context, must also be rooted in relevant empirical material: "The analyst moves back and forth between the logical construction and the actual data in a search for meaningful patterns" (Patton, p. 411). The meaning of a category is "bound up on the one hand with the bits of data to which it is assigned, and on the other hand with the ideas it expresses" (Dey, p. 102).

Several resources are particularly useful to the process of category generation: "inferences from the data, initial or emergent research questions, substantive, policy and theoretical issues, and imagination, intuition and previous knowledge" (Dey, 1993, p. 100). To utilize those resources optimally, the researcher should become thoroughly familiar with the data, be sensitive to the context of the data, be prepared to extend, change and discard categories, consider connections and avoid needless overlaps, record the criteria on which category decisions are to be taken, and consider alternative ways of categorizing and interpreting data (Dey, p. 100).

According to Lincoln & Guba (1985), the essential task of categorizing is to bring together into temporary categories those data bits that apparently relate to the same content. It is then important to "devise rules that describe category properties and that can, ultimately, be used to justify the inclusion of each data bit that remains assigned to the category as well as to provide a basis for later tests of replicability" (p. 347). The researcher must also render the category set internally consistent.

Comparing Data
Categories must be meaningful both internally, in relation to the data understood in context, and externally, in relation to the data understood through comparison (Dey, 1993). When a particular category is adopted, a comparison is already implied.

To compare observations (Dey, 1993), we must be able to identify bits of data which can be related for the purposes of comparison. In principle, data is organized by grouping like with like: data bits with data bits. After the bits are separated into piles, each bit is compared within each pile. Data requiring further differentiation, will be divided up into separate "sub-piles." We could then compare observations within each pile or sub-pile, looking for similarities or differences within the data. We could also look for patterns or variations in the data by making comparisons between the different piles or sub-piles. However, things are not simply "alike or related - they are alike or related in some respect or another. Distinctions are always conceptual as well as empirical - they reflect some criterion or criteria in terms of which observations are distinguished and compared" (Dey, p. 96).

The researcher uses constant comparative analysis to look for statements and signs of behavior that occur over time during the study (Janesick, 1994). The process of constant comparison "stimulates thought that leads to both descriptive and explanatory categories" (Lincoln & Guba, 1985, p. 341).

Refining Categories
The meaning of the category evolves during the analysis, as more and more decisions are made about which bits of data can or cannot be assigned to the category (Dey, 1993). The fit between data and categories--the process of developing categories--is one of continuous refinement. "Flexibility is required to accommodate fresh observations and new directions in the analysis" (Dey, p. 111).

During the course of the analysis (Dey, 1993), the criteria for including and excluding observations, rather vague in the beginning, become more precise. The research must continually attempt to define and redefine categories by specifying and changing the criteria used for assigning them to the data. In so doing, one must recognize that any definitions developed in the beginning will probably be quite general and contingent in character. "In defining categories, therefore, we have to be both attentive and tentative - attentive to the data, and tentative in our conceptualizations of them" (p. 102).

Kaleidoscope Metaphor
Metaphors, powerful and clever ways of communicating findings, can converge a great deal of meaning in a single phrase (Patton, 1990). "It is important, however, to make sure that the metaphor serves the data and not vice versa" (Patton, p. 402). In using the kaleidoscope as a metaphor for this project, the loose bits of colored glass represented our data bits, the two plain mirrors represented our categories, and the two flat plates represented the overarching category that informed our analysis. The endless variety of patterns in a kaleidoscope represented the constant comparison of our data bits in our unending journey to create category arrays. The following discussion will attempt to explain our category development and the use of the constant comparative method (Lincoln & Guba, 1985), as viewed through the kaleidoscope metaphor. By sharing our step-by-step analysis process, we hope to bridge the gap between the theoretical methodology and actual hands-on data analysis.

Data Bits: The Kaleidoscope's Colored Glass
In actuality, we began the constant comparison during the process of breaking down the data into data bits. After transcribing three interviews, we entered the analysis phase by selecting one transcript and having all of the researchers read it. After reading it, we broke the interview data into data bits, which we likened to the kaleidoscope's colored glass. We used scissors to cut the data bits directly from the transcript. At this point, our kaleidoscope of raw data bits had no particular pattern or sense of connection. We needed to discover the relationship between the various bits of colored glass, so we convened to place the data bits into piles according to their "look alike, feel alike" qualities (Lincoln & Guba, 1985). For a representation of this process see Figure 1.

Figure 1


After creating numerous piles, we looked over them, came up with some preliminary rules of inclusion, and wrote preliminary category names on the back of each data bit. After agreeing on a tentative list, we wrote the rules of inclusion and the tentative category names on sheets of neon-colored, coded paper. After mixing the data bits together, we each placed the data bits into categories based on our preliminary rules of inclusion. We checked to see if there were data bits that were not placed in their previously assigned categories. When this occurred, we compared the categories and agreed on a placement that felt right at the time. We used removable tape to secure the data bits to the neon-colored papers, which were labeled with preliminary category names and rules of inclusion. We placed all of the unassigned data bits into an envelope labeled "miscellaneous." See Figure 2 for a visual representation.

Figure 2


We took the remaining two transcripts and repeated the same process of category assignment. After all three transcripts were broken down into data bits and placed in categories, we viewed the process through the kaleidoscope metaphor. We saw neon-colored bits of glass swirling around with some cursory sense of relatedness and pattern. These bits of colored glass represented our initial category set.

First Refinement: The Kaleidoscope Changes Its Pattern
After careful scrutiny of data bits in each category, we created a tentative list of all categories. In doing so, we discovered that two themes had emerged from the categories containing the data and not solely from the data itself. These themes, based on the "how" of the data and the "why" of the data, allowed for more precise sub-category development.

By combining some of the tentative categories that looked and felt alike, we created some sub-categories and revised our rules of inclusion. The kaleidoscope's pattern was now beginning to take on a definite shape and form. The colored bits of glass now represented categories that were reflected in the kaleidoscope's two large plain mirrors. Two large, neon-colored, triangular glass bits represented our theme-based categories. The smaller individual glass bits were fewer in number, and many of those colored glass bits now contained black dots. Each black dot symbolized a sub-category assigned to a specific category. We had now completed our first major category refinement. See Figure 3 for a representation of this step.

Figure 3


Second and Third Refinements
Category refinement remained an ongoing process throughout the data analysis. When examining the relationship between categories, we found that certain categories were subsumable under others, while some needed to be sub-divided even further. At this point, we began to sift our way through the "miscellaneous" envelope and realized that many of these data bits now seemed to fit into some of our previously established categories. As the refinements became more focused, we found that some of the data bits did not fit a category's rule of inclusion. Sometimes the rule of inclusion needed to be reviewed and modified. When this was done, we examined the category's data bits to insure that they still fit. Finally, we carefully scrutinized every data bit to ascertain its fit with the assigned category's rule of inclusion. The kaleidoscope pattern now consisted of a well-defined pattern of glass bits: fewer in number, but containing more sub-categories--represented by black dots--than were present following the first category refinement. (See Figure 4)

Figure 4


Final Category Array: A Well-Defined Kaleidoscope Pattern
After reading and re-reading the interview transcripts and slicing the data into smaller bits, we established that most of the emerging data related to one overarching theme. The categories, refined categories, and sub-categories informed the overarching theme. The kaleidoscope pattern now included a large rectangular piece of neon-colored glass, which represented the overarching theme, two medium, triangular, neon-colored bits of glass and nine small triangular neon-colored bits of glass, which represented the categories, and fourteen black dots, which represented the sub-categories. These black dots appeared in pairs on seven of the small triangles. (See Figure 5 for final refinement)

Figure 5


The kaleidoscope metaphor became a template for the organization of our analysis. To help us graphically conceptualize our data analysis, we constructed a visual representation of a kaleidoscope and cut and pasted neon-colored shapes to illustrate the development of our final category array.

Conclusion
Although we found qualitative data analysis to be a complex process, the kaleidoscope metaphor became a helpful template, which enabled us to make better sense of the emerging data. By using this metaphor, we learned the importance of allowing categories to fit the data, rather than actively creating categories to fit the data. We used the constant comparison method of analysis to organize our data bits and categories, visually representing this process through the kaleidoscope metaphor: the loose bits of colored glass represented our data bits, the two plain mirrors represented our categories, and the two flat plates represented the overarching category that informed our analysis. This metaphor helped us to conceptualize the process of ongoing category refinement that ultimately led to the development of our final category array.

References
Bruner, J. D., Goodnow, J. J., & Austin, G. A. (1972). Categories and cognition. In J. P. Spradley (Ed.). Culture and cognition (pp. 168-190). New York: Chandler.

Dey, I. (1993). Creating categories. Qualitative data analysis (pp. 94-112). London: Routledge.

Goetz, J. P., & LeCompte, M. D. (1981). Ethnographic research and the problem of data reduction. Anthropology and Education Quarterly, 12, 51-70.

Janesick, V. J. (1994). The dance of qualitative research design: Metaphor, methodology, and meaning. In N. K. Denzin, & Y. S. Lincoln (Eds.), Handbook of qualitative research (pp. 209-219. Thousand Oaks, CA: Sage.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.

Mish, F. C. (Ed.). (1990). Webster's ninth new collegiate dictionary. Springfield, MA: Merriam-Webster, Inc.

Patton, M. Q. (1990). Qualitative evaluation and research methods (2nd ed.). Newbury Park, CA: Sage.

Author Note
+Jane F. Dye, M.S., Irene M. Schatz, M.S.W., Brian A. Rosenberg, M.S., and Susanne T. Coleman, M.S. are all Doctorate Candidates at Nova Southeastern University in Fort Lauderdale, Florida 33317 USA. Please address all correspondence to Brian Rosenberg at rosenb@nsu.acast.nova.edu. The authors would also like to thank Cody Smith for his help in preparing the kaleidoscope figures for online viewing.

Article Citation
Dye, J. F., Schatz, I. M., Rosenberg, B. A., & Coleman, S. T. (2000, January). Constant comparison method: A kaleidoscope of data [24 paragraphs]. The Qualitative Report [On-line serial], 4(1/2). Available: http://www.nova.edu/ssss/QR/QR3-4/dye.html

Jane F. Dye, Irene M. Schatz, Brian A. Rosenberg, & Susanne T. Coleman
2000 copyright


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Project Report by Maggie Lynch

Constructivism in Instructional Design for Distance Education
Project Report

By Maggie McVay Lynch



Background

This project was designed to gain a better understanding of the theories and concepts of constructivism and their potential impact on instructional systems design for distance education. The project proposal�s original goals were threefold:

To increase the writer�s depth of knowledge regarding constructivism and its many evolutions in the literature.
To evaluate specific tools and methods for providing better opportunities for students to construct knowledge throughout the writer�s distance education curriculum.
To gain resources for increasing faculty awareness of constructivist principles and methods.
The writer used two primary resources for the project: a literature review of selected articles relating to the project goals and the distribution of a brief email questionnaire to five well-known researchers in the field of constructivism.
Research
An Introduction to Constructivism

Since learning is equated with behavioral outcomes, behavioral laws (e.g., the work of Skinner) have provided the foundation of technology efforts in Instructional Systems Design (ISD). According to these behavioral laws, learning can be shaped by selective reinforcement. Behaviorists, such as Skinner, were unwilling to acknowledge the existence of covert mental operations or "the act of knowing" because these were not observable.

Unlike behaviorists, who are only concerned with what learners "do", cognitive psychologists were interested in what learners "know" and how they come to acquire it. Fodor (1981) suggested that cognitive activity was embodied in mental states that enable humans to construct mental representations and manipulate them through the use of symbols.

Epistemological Assumption. Knowledge is a function of how the individual creates meaning from his or her experiences; it is not a function of what someone else says is true. Each of us conceives of external reality somewhat differently, based upon our unique set of experiences with the world and our beliefs about them.

Constructivist Belief. Our personal world is constructed in our minds and these personal constructions define our personal realities. The mind is the instrument of thinking which interprets events, objects, and perspectives rather than seeking to remember and comprehend an objective knowledge. The mind filters input from the world in the process of making those interpretations.

Defining the Relationships Between Constructivism and Learning Environments

Crotty (1994) defined the type of environment a constructivist will try to create, where learners "are required to examine thinking and learning processes; collect, record, and analyze data; formulate and test hypotheses; reflect on previous understandings; and construct their own meaning" (p.31)

Jonassen (1992) researched constructivism as the philosophical foundation for situated learning. Later, Jonassen (1994) further defined constructivist learning environments as those that are best designed for "advanced learners" � "constructivistic environments stress situated problem-solving tasks, because those are the nature of tasks that are called on and rewarded in the real world" (p.2).

The Implementation of Constructivist Characteristics in ISD

Duffy and Jonassen (1992) began defining characteristics of constructivistic instructional design by clarifying the contrasting instructional paradigm assumptions of paradigms between traditional learning and constructivist learning environments. Using the tenants of symbolic reasoning and situated learning, Duffy and Jonassen compared six mental processes: 1) knowledge acquisition; 2) learning process; 3) memory configuration; 4) knowledge representation; 5) instruction processes; and 6) the computational model. Table 1 summarizes those contrasting assumptions.



Table 1

Contrasting Assumptions of Paradigms


Symbolic Reasoning Situated Learning
Objective
Independent

Stable

applied
Knowledge
Acquisition Subjective
Contextualized

Relative

Situated in action

Objectivist
Product-oriented

Abstract

Symbolic
Learning
Process Constructivist
Process-oriented

Authentic

Experiential

Stored representations Memory
Configuration
Connections, potential
Functionally equivalent to real world
Replication of expert

Symbolic, generalized
Knowledge Representation Embedded in experience
Personally constructed

Personalized

Top down
Deductive

Application of symbols
Instruction
Processes Bottom up
Inductive

Apprenticeship

Symbolic reasoning
Production rule

Symbol manipulation
Computational
Model Connectionist
Neural network

Probabilistic, embedded



Because constructivism is based in mental models, Jonassen (1994) later proposed operationalizable representations of mental models that could then be used as a basis for assessing what may result from complex interactions within constructivist learning environments. In Table 2, Jonassen presented his rationalized criteria for evaluating mental models by defining eight characteristics and their measures (p.5).



Table2

Rationalized Criteria for Evaluating Mental Models


Characteristic Measure
Coherence Structural knowledge, Think-aloud
Purpose/Personal Relevance Self-report, Cognitive interview
Integration Cognitive simulation
Fidelity with Real World Comparison to expert
Imagery Generating metaphors, analogies
Complexity Structural knowledge
Applicability/Transferability Teach back, think aloud
Inferential/Implicational Ability Running the model


After studying student responses to initial distance education offerings in two courses, Driscoll (1994) also defined five constructivist characteristics that should form the pedagogical foundation for designing learning.

Provide complex learning environments that incorporate authentic activity.
Provide for social negotiation as an integral part of learning.
Juxtapose instructional content and include multiple modes of representation
Nurture reflexivity (reflect on learning)
Emphasize student-centered instruction
By emphasizing teamwork and situated learning, Driscoll (1994) further found that when students worked in teams on major projects they gained skills and experience in leadership, teamwork, communication and organization. Students elected to form organizing committees and subcommittes and carried out tasks to prepare for a conference, including development of a web site, multimedia presentations, used email accounts for communication with peer, the teaching team and others.
Challenges in the Implementation of Constructivist Learning Environments

In an evaluation of the implementation of constructivist characteristics in a distance learning environment, Remmington and Gruba (1997) found the largest challenge was for the instructor or convenors to:

resist taking control of student activities when they appear to be going astray.
become learning facilitators rather than knowledge transfer controllers.
not underestimate the effects of peer pressure.
re-evaluate the grading system to allow higher marks for students who improve their work on the basis of earlier feedback, or to formally negotiate marks with students.
Jonassen (1991) suggests that in order for technology (distance education) to accommodate constructivistic assumptions, changes in instructional design practices would have to occur. Some of these changes would include:
Instructional goals and objectives would be negotiated, not imposed
Task and content analysis would focus less on identifying and prescribing a single, best sequence for learning.
The goal of the systems design process would be less concerned with prescribing specific instructional strategies necessary to lead learners to specific learning behaviors.
Evaluation of learning would become less criterion-referenced.

The Challenge of Finding Constructivist ISD Models

Most of the literature, on constructivist approaches to educational technology, focuses on instructional theory rather than instructional systems design (ISD) models. There is a healthy literature, for example, on anchored instruction, situated cognition, and cognitive flexibility hypertext. Very little, however, has been written on the instructional design process itself.

Most widely-used ISD models are objectivist rather than constructivist. For example the 4D model of Thiagarajan (1974), Dick and Carey�s (1985) model, and Criswell�s (1989) model take a similar approach to common issues. They reflect the "core" of objective-rational thinking on procedural instructional design models. Bagdonnis & Salisbury (1994) define procedural ISD models as ones that "describe how to perform a task and are formulated to simplify and explain a series of complex processes" (p.27).

Traditional ISD Models are viewed by individuals as representing a linear process -- a plan of separate steps that proceed in a linear sequence. Bagdonis & Salisbury (1994) indicated the typical ISD model is divided into five stages: analysis, design, production/development implementation, and maintenance/revision. The five stages consist of an integrated set of components that are sequenced so that each component within the process must be completed before continuing to the next.

Wilson (1997) advocated for changes in the analysis of instruction based on a model of progression of practice environments, derived from Bunderson�s work models. Table 3 presents the Bunderson work model with Wilson�s suggested changes in the instructional analysis (p. 9).



Table 3

Wilson�s Summarization of Changes in Instructional Analysis Based on Bunderson�s Work Models


The Lexical Loop Work Models
Translation to goal statements through goal/job analysis Master performance is documented through multiple media
Translation to objectives list through task analysis Work models are designed of progressively increasing difficulty.
Translation to print-based tests through test item technologies Learning environments simulate real-life environments
Translation to print-based media using text-design principles. Students practice holistic as well as parts skills.
Student expected to transfer text material into skills of the master. Authentic tools are available.
(Actually, negligible transfer occurs to everyday life.) Info can be accessed through job aids, help systems, and other resources.
Coaching, mentoring, and peer consultation is available as needed.
Students complete work models
1�n.

Student demonstrates master�s knowledge/skill in real-life performance environment.


From this work model, Wilson (1997) advocates that the role of the designer is to design a series of "experiences-interactions or environments or products-intended to help students learn effectively." (p. 9). He suggests the design role is less analytical, more holistic, more reliant on the cooperation of teachers and materials and learners to fill the gaps left by the limitations of our analytical tools. And thus the instruction becomes much more integrally connected to the context and the surrounding culture. "ID thus becomes more truly systemic in the sense that it is highly sensitive to the conditions of use." (p.9).



Wilson (1997) does not suggest throwing away the taxonomies, but rather to keep in mind the following during the ISD process:

Admit the tentativeness of any conceptual scheme applied to content
Realize that no matter how thorough the task-analysis net, it doesn�t come close to capturing true expertise
Realize that since content representation is so tentative, designed instruction should offer holistic, information-rich experiences, allowing opportunities for mastery of un-analyzed content.
Always allow for a lack of fit between the conceptual scheme and any given content
Realize that the very points of lack of fit can be the most critical to understanding that content area
Always be on the lookout for those critical points of idiosyncratic content demands. (p.10).
Willis (1995) also suggests that there are possible constructive-interpretive ISD models that share certain perspectives:
The ISD process is recursive, non-linear, and sometimes chaotic
Planning is organic, developmental, reflective, and collaborative
Objectives emerge from design and development work
General ISD experts don�t exist
Instruction emphasizes learning in meaningful contexts (The goal is personal understanding within meaningful contexts)
Formative evaluation is critical
Subjective data may be the most valuable
Responding to a backlash of critics, Dick (1995) also slightly revised his more traditional ISD approach. Some of his suggestions now seem to reflect some of the positions put forth by Willis (1995). Dick�s new revision of his model included:

Extensive formative evaluation
Broad subject matter knowledge, or access to it
Extensive use of learner analysis
Knowledge of the context
Use of various instructional strategies
Results of Email Interviews with Constructivism Researchers
As indicated previously, the writer selected five well-known constructivism researchers to interview. The writer was unable to reach any of the researchers via the phone, so an email survey of three questions was sent to each of the researchers. The writer limited the questions to only three, with the hope that it would not be too time consuming for the respondents. Due to short time limits and travel/vacation plans of the participants, the writer received only three responses. The respondents were Dr. Brent Wilson, Dr. Philip Duchastel, and Dr. Piet Kommers.

The email explained the purpose of the questions, and asked the researchers to answer the questions in regard to providing a constructivist environment for learning on the web. A copy of the individual answers from each respondent is available in Appendix A.

The three questions submitted to the researchers were:
How do you balance fact dissemination with problem-based or case-based teaching?
What determines the level of online participation for a given individual and how does that relate to required course outcomes?
What are the respective value and contribution to learning in synchronous vs. asynchronous interaction models?
In answer to the first question, all three researchers agreed that the preference is to present facts as a part of a problem or case study, not to present them simply as facts for memorization. Kommers added to this mix the suggestion of allowing discussions between the student(s) and teacher regarding prior knowledge (the student�s current context) to enable the problem cases to be more meaningful and reflective of student needs. Wilson suggested making facts available in various information resources � "job aids, helps, references, manuals, etc" and concentrating the instruction on providing a variety of activities or opportunities for the student to construct their own knowledge.
The second question provided a diversity of answers from the respondents. Duchastel related levels of participation to course requirements and raised an interesting additional question: "Would students pursue their learning if there were no grades?" Wilson agreed with Duchastel that some level of participation is required to achieve course outcomes, though he doesn�t like to quantify expectations. However, he also pointed out the necessity of communicating specific expectations for participation, with the reflection that he does prefer students to "join the flock" and participate extensively. Kommers made the point that "from a learning perspective, participation and communication do not guarantee high learning effects." However, he also discussed the need for the experience of the learning community and its own culture. Kommers also warns that concerns for participation should not dominate the concern for gains in knowledge.

Both Duchastel and Kommers found agreement in answering the final question. Both agreed that both synchronous and asynchronous are useful. Kommers particularly contrasted the two types of learners and why the gravitate to one or the other model. The learners who are more communicative or social selecting the synchronous environment compared to the learners needing to nurture mental concentration selecting the asynchronous environment. Interestingly, Kommers reflects that it is important for instructors to provide both options. It is Kommers belief that most students find it easier to join a group process rather than start the confrontation with themselves on how to learn.


Conclusions

Objectivists would argue against constructivism from the pragmatic perspective that any nonobjectivist or nonrealist position is inoperable, that constructivism is antecedent to academic chaos. In fact, within the writer�s institution, math and computer science distance instructors more clearly identify with this objectivist view. The example put forth, to bolster this belief, is the need for foundational knowledge (e.g., basic calculations for Math) that would likely not be attained in an individualistic or primarily constructed design process. Furthermore, not gaining that foundational knowledge would then impact the students ability to progress to higher level courses. This argument would seem to support Jonnassen�s (1994) statement that constructivist environments are for "advanced" learners.

A primary difficulty in supporting the constructivist view as a systems design process is that the epistemology underlying a constructivist approach to ISD does not permit the creation of a single ISD model that represents this theoretical perspective. On the surface, it would seem that this leads us to an ISD model that is less rigid, less prescribed, less confident of decisions, and more than a little fuzzy. As Willis (1995) stated:

"In a recursive, non-linear model, many decisions are made over and over, and developers begin the process of instructional design without a crisp, clear definition of where they are headed�A team that can tolerate a process in which many things remain fluid and changeable, however, will gain opportunities for fine-tuning and artistic enhancement." (p.21)


The key to living with constructivism appears to be in feeling comfortable with the constant "fine-tuning" aspect of the ISD process. We do not need to fear that a loss of objectivism leads us to a relativism that treats every opinion and theory equally. Since we cannot escape our own backgrounds and experiences, we will each individually assign weight to our practice, design, research, and development of ISD processes.
For today, and from this author�s own background and experience, this writer concludes that learning entails both constructivistic and objectivistic activities. The most realistic model of learning lies somewhere on the continuum between these two positions � keeping in mind the continuum that exists between the objectivist approach for foundational knowledge and the constructivist approach for advanced knowledge. However, by striving to continually "step out of the box" in design efforts, one can capitalize on both approaches by building instructional activities with the considered use of both synchronous and asynchronous interaction.

It appears healthy and even constructivistic to not prescribe an ISD theory of constructivism, but rather to consider the implications of constructivism for instructional systems and to reflect upon and articulate conceptions of knowing and learning and adapt methodology accordingly. In the end, when asked to commit to either the objectivist or constructivist camp, the designer will be best served by replying that it depends upon the context.




References



Bagdonis, A. and Salisbury, D. (1994, April). Development and validation of models in instructional design. Educational Technology, 34(4), 26-32.

Criswell, E. (1989). The design of computer-based instruction. New York: Macmillan.

Crotty, T. (1994). Integrating distance learning activities to enhance teacher education toward the constructivist paradigm of teaching and learning. In Distance Learning Research Conference Proceedings, 31-37. College Station, TX: Department of Education and Human Resource Development, Texas A&M University.

Dick, W. (1995, July/August). Instructional design and creativity: A response to the critics. Educational Technology, 35(4), 5-11.

Dick, W. and Carey, L. (1985). The systematic design of instruction. Glenview, IL: Scott, Foresman.

Driscoll, M.P. (1994). Psychology of learning for instruction. Boston: Allyn and Bacon. Available online at http://www.inform.umd.edu/UMS+State/UMD-Projects/MCTP/WWW/Essays.html

Duffy, T. and Jonassen, D. Eds. (1992) Constructivism and Instructional Design. Hillsdale, NJ:Lawrence Erlbaum.

Fodor, J. (1981). Representations: Philosophical essays on the foundations of cognitive science. Cambridge, MA: MIT Press.

Gropper, G. (1987). A lesson based on a behavioral approach to instructional design. In C. Reigelulth (Ed.) Instructional theories in action (pp. 45-112). Hillsdale, NJ: Lawrence Erlbaum.

Jonassen, D. (1992) Objectivism versus constructivism: Do we need a new philosophical paradigm? ETR&D, Vol. 39, No.3, pp.5-14.

Jonassen, D. (1994) Operationalizing mental models: Strategies for assessing mental models to support meaningful learning and design � supportive learning environments. Available online at http://www-csc195.indiana.edu/csc195/jonassen.html

Merrill, M. (1988). Don�t bother me with instructional design � I�m busy programming! Suggestions for more effective educational software. Computers in Human Behavior, 4(1), 37-52.

Reigeluth, C. (1983). Introduction. In C.M. Reigeluth (Ed.), Instructional-design theories and models: The current state of the art. Hillsdale, NJ: Lawrence Erlbaum.

Remmington, B. and Gruba, P. Constructivism in communication and informatics. Edited by Graeme Hard at Melbourne IT. Available online at http://www.edfac.unimelb.edu.au/online-ed/

Willis, J. (1995, November/December) A recursive, reflective instructional design model based on constructivist-interpretivist theory. Educational Technology.

Wilson, B. (1997) Reflections on constructivism and instructional design. Available online July 7, 1998 at http://www..cudenver.edu/~bwilson/construct.html





APPENDIX A
Researchers Email Responses to Questions



Dr. Philip Duchastel

All questions relate to providing a constructivist environment for learning on the web:

1. How do you balance fact dissemination with problem-based or case-based teaching?

Subsume the first under the latter. ONly the latter is contextified.

2. What determines the level of online participation for a given individual and how does that relate to required course outcomes?

Course requirements, like all pedagogy, are a forcing mechanism to keep students going. The question : would students pursue their learning if there were no grades? Interesting, eh?

3. What are the respective value and contribution to learning in synchronous vs. asynchronous interaction models?

Not comparable - just like apples and oranges. Both are useful - they just taste differently.



Dr. Brent Wilson

1. How do you balance fact dissemination with problem-based or case-based teaching?

I think 'balance' is the wrong idea. I rarely present facts and then expect students to remember them as facts. Facts are best presented within the context of a problem or case. Facts need not be presented and expected to be remembered. Rather, it's best to make facts available in various information resources--job aids, helps, references, manuals, etc. Then students can find the facts when they need them.

If by 'facts' you mean the kind of information found in instructional presentations, then you make sure the presentation helps students see the material in a new light, think more clearly. Then students are given opportunities to try out their new understandings through various activities. I don't use cases or problems exclusively; sometimes students read, reflect, and discuss what they're learning, beyond the confines of a specific case.

2. What determines the level of online participation for a given individual and how does that relate to required course outcomes?

Students need a level of participation, or they won't accomplish the course outcomes. I hate to quantify expectations (three times a week, etc.), and only do it when nothing else is working. Even then, once people begin contributing effectively, then I drop the initial requirement.

I do communicate general expectations. Online, you have to be quite specific in helping students get a clear idea of what's expected, because they've usually never done this kind of work before, exactly.

As you say, people participate differently for a number of reasons. Usually a person's low level of participation has more to do with lack of access than resistance to learning. I usually give people the benefit of the doubt initially, but put pressure on to get their problems resolved and "join the flock."

Actually, I do often think of my online classes as a flock of something--geese, sheep, whatever. To establish an effective community, everyone needs to stick together, invest the time, listen to each other and respond back. These are very herd-like, group-oriented behaviors.

The "stray" student who drops out for awhile, or shows resistance--These students need some extra attention and persuasion of various means, to get them to return to the fold.

3. What are the respective value and contribution to learning in synchronous vs. asynchronous interaction models?

That's a question for the literature; probably best to do a quick lit review and report on it yourself. It's kind of boring to me--Sorry.



Dr. Piet Kommers

1. How do you balance fact dissemination with problem-based or case-based teaching?

There is a fundamental new approach to teaching in my mind: Information transfer is essential in the relation between teacher and student as it comes to direct orientation and letting the student know about regulations, other partners in the network, new sources of information etc. But essentially it is meta-information.

The learning process itself is now seen as quite a delicate balance in the student between what (s)he can derive from prior knowledge, intuition, experts etc, and what still has to be mastered, understood and memorized for a smooth and flexible performance in the next future. This very process can be discussed with the teacher of course, and also the teacher can make students aware of the main mechanism. However essentially the student has to learn to handle these decisions.

In this respect I think teachers are far more effective as they convey students in semi-real problem cases as it is then the student who feels a natural tendency to 'consult' the teacher, instead of the teacher who takes the role of a missionary. This saves a lot of needless information overload and the weakness of teachers who try to convince students of the need of certain knowledge.

2. What determines the level of online participation for a given individual and how does that relate to required course outcomes?

Purely seen from a learning perspective, participation and communication do not guarantee high learning effects. One may say that without commitment and participation it is not likely to have learning effects, but more is needed than that; See my reflection in the previous answer).

But there is also another element: Learning communities have their culture in itself; they depend on the participation of its members. This implies that teachers and co-students tend to reward intense participations on top of the perceived learning result. If this added criterion is made explicit as a coarse goal and assessment criterion, students will be eager to demonstrate their participation. However it should never dominate the concern for the gain in knowledge and skills itself.

3. What are the respective value and contribution to learning in synchronous vs. asynchronous interaction models?

Though asynchronous communication has many benefits like the reduction in dispersed attention etc., there are strong indications that synchronous transactions involve a vital personal, emotional and realistic element in both teachers and students. This has to do with the fact that social reality and the 'here and now' principle is the best way 'to open our mind'.

So again, here we have the same distinction between a. the communicative and informational requisites at one side, and b. the learners' need to nurture mental concentration and achieve a higher control on his/her own thinking.

At this moment the theories are still weak how to exactly balance between the two elements. Intuitively I would say that once b becomes more dominant, also a has to be optimized. This is the best condition for learning, both from cognitive and communicative perspectives. Once teachers focus too much on a, then b is likely to become marginal. It is for most of the students generally less endangering to throw oneself into group processes rather than start the confrontation with oneself 'how to learn'.

Copyright, Maggie McVay, 1998

Psychotextual Notations of Constructivistic Distance Education/ Jonassen

Jonassen(1991) notes that many educators and cognitive psychologists have applied constructivism to the development of learning environments. From these applications, he has isolated a number of design principles:

1. Create real-world environments that employ the context in which learning is relevant;

2. Focus on realistic approaches to solving real-world problems;

3. The instructor is a coach and analyzer of the strategies used to solve these problems;

4. Stress conceptual interrelatedness, providing multiple representations or perspectives on the content;

5. Instructional goals and objectives should be negotiated and not imposed;

6. Evaluation should serve as a self-analysis tool;

7. Provide tools and environments that help learners interpret the multiple perspectives of the world;

8. Learning should be internally controlled and mediated by the learner (pp.11-12).

Jonassen (1994) summarizes what he refers to as "the implications of constructivism for instructional design". The following principles illustrate how knowledge construction can be facilitated:

1. Provide multiple representations of reality;

2. Represent the natural complexity of the real world;

3. Focus on knowledge construction, not reproduction;

4. Present authentic tasks (contextualizing rather than abstracting instruction);

5. Provide real-world, case-based learning environments, rather than pre-determined instructional sequences;

6. Foster reflective practice;

7. Enable context-and content dependent knowledge construction;

8. Support collaborative construction of knowledge through social negotiation (p.35).



One of my goals may be to address these comments from the psychocontextual notations of the Humanist list of practitioners.
These steps would elucidate the application processes and possibly, when applied to the context of my work in the course
sections, affirm or dissuade from the possibilities of confirming the integration of the practice of the conjoinedness of
the Humanistic and Constructivistic approaches in Distance Teaching and Learning.

Humanistic Education/ Wikipedia/ November 8, 2010

Humanistic education

From Wikipedia, the free encyclopediaJump to: navigation, search
Not to be confused with liberal arts education or classical education. See liberal arts .

Humanistic education is an alternative approach to education based on the work of humanistic psychologists, most notably Abraham Maslow, who developed a famous hierarchy of needs, Carl Rogers, previous president of the American Psychology Association and Rudolf Steiner, the founder of Waldorf education.[1] In humanistic education, the whole person, not just the intellect, is engaged in the growth and development that are the signs of real learning. The emotions, the social being, the mind, and the skills needed for a career direction are all focuses of humanistic education. "Much of a humanist teacher's effort would be put into developing a child's self-esteem. It would be important for children to feel good about themselves (high self-esteem), and to feel that they can set and achieve appropriate goals (high self-efficacy)." [2]

Contents

1 Principles of Humanistic Education
1.1 Choice or Control
1.2 Felt Concern
1.3 The Whole Person
1.4 Self Evaluation
1.5 Teacher as a Facilitator

2 Environment
3 See also
4 References
5 External links
6 Heading text

Principles of Humanistic Education

Choice or Control

The humanistic approach focuses a great deal on student choice and control over the course of their education. Students are encouraged to make choices that range from day-to-day activities to periodically setting future life goals. This allows for students to focus on a specific subject of interest for any amount of time they choose, within reason. Humanistic teachers believe it is important for students to be motivated and engaged in the material they are learning, and this happens when the topic is something the students need and want to know.

Felt Concern

Humanistic education tends to focus on the felt concerns and interests of the students intertwining with the intellect. It is believed that the overall mood and feeling of the students can either hinder or foster the process of learning.

The Whole Person

Humanistic educators believe that both feelings and knowledge are important to the learning process. Unlike traditional educators, humanistic teachers do not separate the cognitive and affective domains. This aspect also relates to the curriculum in the sense that lessons and activities provided focus on various aspects of the student and not just rote memorization through note taking and lecturing.

Self Evaluation

Humanistic educators believe that grades are irrelevant and that only self-evaluation is meaningful. Grading encourages students to work for a grade and not for intrinsic satisfaction. Humanistic educators disagree with routine testing because they teach students rote memorization as opposed to meaningful learning. They also believe testing doesn't provide sufficient educational feedback to the teacher.

Teacher as a Facilitator

"The tutor or lecturer tends to be more supportive than critical, more understanding than judgmental, more genuine than playing a role." [3] Their job is to foster a engaging environment for the students and ask inquiry based questions that promote meaningful learning.

Environment

The environment in a school which focuses their practice on humanistic education tends to have a very different setting than a traditional school. It consist of both indoor and outdoor environments with a majority of time being spent outdoors. The indoor setting may contain a few tables and chairs, bean bags for quiet reading and relaxation, book shelf's, hide-aways, kitchens, lots of color and art posted on the walls. The outdoor environment is very engaging for students. You might find tree houses, outdoor kitchens, sand boxes, play sets, natural materials, sporting activities etc. The wide range of activities are offered for students allowing for free choices of interest.

See also

Democratic school
Humanistic psychology
Liberal education
Progressive education
Sudbury school
Transpersonal education
Waldorf education
Neo Humanistic Education
Waldorf Education

References

1.^ Earl J. Ogletree, "Rudolf Steiner: Unknown Educator", The Elementary School Journal, Vol. 74, No. 6 (Mar., 1974), pp. 344-351
2.^ Stuart, G. (n.d.). Humanistic approaches to teaching. Retrieved from http://www.garysturt.free-online.co.uk/human.htm
3.^ Rowan, J. (n.d.). Humanistic education. Retrieved from http://www.ahpweb.org/rowan_bibliography/chapter17.htm
[edit] External links
"The New Humanistic education at Gurukul" - possibly an example of new humanistic education
"The New School at Dawson College" - possibly an example of humanistic education at the community college level