Vol. 16 : No. 3< >
Editor's Note: This paper distills research on distance learning for students from a variety of cultures, age groups, learning styles, and backgrounds. It recognizes the role of assessment, instructional design, and interactivity to provide learning environments that support success. "Cultural orientations for heterogenous populations may be evidenced by conflicts in values, interpersonal interactions, communication patterns, time orientation and scheduling, rules of activity and engagement, cognitive processes, and processes of problem solving . . . Consideration of learner orientations can inform the designer of unique approaches to learning that may better support multiple cultures and facilitate successful completion of a course. The findings apply to learning in the workplace and to academic learning.
Web-based Learning Design: Planning for Diversity
The increasingly prevalence of distance learning in the work and learning place
requires attention to assumptions about Web-based learning environments and how
they support a variety of learners. Fluent technological skills do not insure
success in online learning. This paper examines issues of culture and learning
orientation as they may relate to approaches to design.
The distinction between
workplace training and university learning is beginning to blur (Canter, 2000;
Potashnik & Capper, 1998). Increasingly businesses are pressed to offer
training at advanced levels in what may soon replace or supplant degrees offered
at universities. The demand for just-in-time  rather than just-in-case  on
the job training requires flexible scheduling and self-paced courses that meet
the needs of individual learners (Fjortoft, 1995) at reasonable costs (Aldrich,
2001). The trend toward computer-based training (CBT) via multimedia and
distance learning in the business world is mirrored in higher education (Pasquinelli,
1998). A study conducted by the National Center for Education Statistics (1999)
indicates that almost half of all higher education institutions offer
distance-learning courses. E-learning is an increasingly popular solution to
training needs in military and corporate workplaces (Aldrich, 2001; NCES, 1999;
Salopek, 1998) where learners do not want to take time off from work to complete
a degree (Campbell, 2001; Rivera & Kostopolous, 2001) and are better
supported by distributed education  (Ross & Powell, 1990) as industry and
higher education realize the need for providing lifelong learning opportunities
(Gartner Group, 2001). As this trends appears to be accelerating and distance
learning technology rapidly evolves, the transfer of traditional training and
development to a digital medium becomes a challenge in that assumptions about
teaching and learning in a traditional classroom do not hold true in a virtual
one. Anytime anywhere learning does not come without transformation on the part
of the institution, the instructional designer, the instructor, and the student.
The proliferation of
distance learning programs might suggest that transfer of content and
instruction from a face-to-face to a virtual environment is a seamless process.
Those who have designed, taken classes, or taught in both environments realize
that this is not the case (Diaz & Cartnel, 1999). Whatever the motivations
for offering online learning, student success is the desired outcome. However,
attrition rates remain higher than in campus-based courses (Phipps &
Merisotis, 1999;Kelman, 1997; Naidu, 1994; Garland, 1993), and tend to be higher
for first-time distance learners (Morgan, 2000). In those cases where attrition
rates are low, explanations are suspect because there is little evidence that
success in a distance-learning course is nothing more than a matter of learner
characteristics. Even for the experienced distance learner there is no guarantee
that the context, interactions, or conceptualization of content will resemble
previous experiences. Most distance learning programs attempt to provide
services that support the distance learner. These include embedded study
strategies (Morgan, Dingsdag, & Saenger, 1998), prior knowledge assessments
(Portier & Wagemans, 1995), print and electronic resources for information
retrieval and problem solving (Oliver, 1999), tutorials, and advising services
(Wright, 1991). A common pre-course service is a self-assessment tool that
either allows the student to measure his or her preparedness for taking a
distance-learning course or serves as an anticipatory set  by intimating the
nature of a distance-learning course. Such self-assessment tools are typically
in the form of a 10-15 question survey in which the respondent answers ‘yes’
or ‘no’ to a series of learner behaviors attributes or competencies such as
those identified by Rowntree (1995): computer skills, literacy/discussion
skills, time management skills, and interactive skills. Some self-assessment
tools include a sum score that indicates whether or not the learner will be
successful in the course. These types of self-assessment instruments assume that
the learner will (a) complete the survey, (b) reflect upon and honestly respond
to the queries, and (c) take in consideration the analysis when determining to
take a course.
providers do not use self-assessment data to screen for course registration.
Evidently it is assumed that through completing the survey the student will
determine whether or not they will be successful and, if they do not meet the
criteria for success, it is assumed they will not enroll in a course. Such an
approach is grounded in the notion that only those with certain attributes will
or should take distance-learning courses, a faulty assumption that is exclusive
and discriminatory. More importantly, few contingencies or supports exist that
can aid a potential distance learner in acquiring skills or knowledge necessary
to succeed in an electronic learning environment. Without these supports novice
distance learners may be at risk of failure (Dille & Mezack, 1991).
Additionally, there is no evidence that accommodations for a range of abilities,
skill levels, or learning styles are part of distance learning course design. It
is a one-size fits all approach assuming that distance learners are a homogenous
group. Yet the notion of distance learner as “static” rather than dynamic is
increasingly questioned and believed to be invalid (Thompson, 1998; Holmberg,
The purpose of this
paper is to examine aspects of distance learning that are often overlooked in
the design and development of Web-based learning environments as these relate to
the learner. The ideas presented here draw from the following set of
assumptions. First, there is no evidence that self-assessment tools are
correlated with distance learning success. Second, although there may be
specific characteristics that are correlated with success in distance learning,
all learners should learn in environments that support their needs. Third, in
order to learn, one must be actively engaged in the learner process. Fourth,
there are key concepts that are relevant to all distance learners. It is the
author’s premise that the more prepared and informed a learner is about the
distance learning experience the more likely they are to complete a course and
be more successful.
The narrowly defined
attributes of the successful distance learner suggests that there is a need for
mechanisms that will do more than identify the areas in which the potential
learner needs to improve or change. As true of preparing to use tools and
resources of any learning environment, users should be aware of the context of
Web-based learning. In this way, when learners make practical decisions and
choices requiring higher order thinking skills, meta-cognition, and
self-analysis, they will be best supported for academic success.
A further consideration
must be given to the increasingly similar nature of workplace and
campus-initiated learning, particularly as distance learning becomes more
commonplace in both contexts.
motivations vary between work-situated training and university learning, learner
needs must be considered when designing instruction. The literature reviewed in
this section includes several ways of looking at the college-age learner:
learning differences, Web design and culture, and the Net generation.
Models of distance
learning indicate that the content to be delivered and the learning outcomes
should in part determine the delivery medium. We know that some forms of
distance learning are designed with limited peer-instructor or peer-interface
interaction. Computer-based training (CBT) typically only provides multimedia
and interface interaction. Limited forms of interaction, especially in CBT, may
not support all learning needs. Instruction in general and distance learning in
particular should start with an understanding of the population to be served
(Granger & Benke, 1998). The trend in online learning design has been to
develop a static interface that is developed by an instructional designer or a
course instructor for a homogenous population. There is a growing body of
evidence that indicates a need for course and interface design that addresses
individual learner characteristics to provide a more learner-centered experience
intends to address the needs of a wide range of learning needs across a variety
of content areas (Jones, Greer, Mandicah, du Boulay, & Goodyear, 1992). Much
development in adaptive learning has been for intelligent learning systems that
are designed to transfer knowledge from the computer to the learner (du Boulay
& Goodyear, 1992; McCalla, 1992). Increasingly there is a shift away from
knowledge transmission to knowledge constructions (Derry, 1992; Jones, Greer,
Mandicah, du Boulay, & Goodyear, 1992), and requiring support for cognitive
processes (Woolf, 1992). In this approach, the computer guides the learner
toward understanding, soliciting metacognitive reflection about what they know
and understand. The system then can more authentically respond to the unique and
individual needs of the learner (Laurillard, 1992).
Bruner (1960) believes
that culture mediates a learner’s cognitive development as represented by
three modes through which knowledge is acquired: enactive, iconic, and symbolic.
In the enactive representation, an individual learns by doing and by recalling
past events. Iconic representations are internally constructed through
visualized and other sensory organizations. Symbolic representations are
manifested through languages, both verbal and numerical. The learner’s social
and cultural context, according to Bruner (1986, 1990), influences how, when,
and what learning becomes knowledge. Cultural influences, however, are not
necessarily conscious to the individual. Since an instructional designer’s
knowledge of enactive, iconic, and symbolic representations may differ from that
of the intended learner, Bruner recommends that all instruction begin with the learner’s
experiences and contexts.
In many learning
environments, the designer of instruction enters into curriculum development
with assumptions and beliefs that may be, consciously or unconsciously, at odds
with the diversity of targeted learners. Such incompatibilities may sabotage
attempts to adapt learning activities in that cultural predispositions may be
overlooked. Although not an issue in a homogenous learning environment, cultural
orientations for heterogeneous populations may be evidenced by conflicts in
values, interpersonal interactions, communication patterns, time orientation and
scheduling, rules of activity and engagement, cognitive processes, and processes
of problem solving (Boggs, Watson-Gegeo, & McMillen, 1985; Kochman, 1981;
Shade, 1981, 1989). Consideration of learner orientations can inform the
designer of unique approaches to learning that may better support multiple
cultures and facilitate successful completion of a course (Coggins, 1988).
There is much
literature that clearly indicates that learning is best facilitated when
individual needs of the learner are being met, but, as noted by Carrier and
Jonassen (1988), there is a great deal of variance in how differences are
described. The definitive common element among the learner characteristic
literature is that working at one’s own pace supports a variety of needs. If
instructional designers attempt to design computer-based training (CBT) to
address specific learning they may be quickly overwhelmed and under-prepared to
deal with the extent of differences addressed in learning psychology, as
illustrated in Table 1.
Learner Characteristics Typologies (Carrier & Jonassen, 1988, p. 205)
Carrier and Jonassen
recommend identifying a general set of learner characteristics among a target
population and then base the design on the most relevant characteristics for the
intended learning objectives. For example, if the objective is learning a
sequential procedural skill, presentation of steps in appropriate modes as
indicated by cognitive style (visual, auditory, text) may be more relevant that
providing a context that allows the learner to construct their own autonomously
derived knowledge as indicated more specifically by motivation type.
Consideration of learner orientations becomes critical in a Web-based learning
environment in which the learner works autonomously and independently of others
(Charp, 1994). Whether instructor led or computer-based, the learning
environments must adapt to the unique needs of the individual learner.
Web Design and
One common trait that
all people share is culture. To illustrate the complexity and nature of creating
adaptive learning for a generalized population, this paper focuses on the needs
of the second fastest growing ethnic/racial group in the US, the Hispanic/Latino
population which grew 40% from 1990 to 2000, increasing from 9.0 to 11.5 percent
of the US population (US Census, 2000). This group is more likely than any other
group to have limited access to technology outside of work or university
resources. A recent report on Americans' access to technology tools finds that
Anglos (50.3%) continue to be the most likely to use the Internet, followed by
Asian American/Pacific Islanders (49.4%), African-Americans (29.3%), and
Hispanics (23.7%) (Becht, Taglang, & Wilhelm,1999). Hence, there is less
likelihood that Hispanic/Latinos come to the workplace or university with the
technology skills and understandings, which would predict their success in
recommends that a distance-learning course should provide connections among the
learner’s prior experiences that relate to course content. This not only
includes conceptual knowledge but also a consideration of the entering cultural
beliefs and entry level skills which may shape and influences meaning and
ability to connect prior learning and new learning.
The linguistically and
culturally diverse population of the Hispanic/Latino culture is often at odds
with the typically Westernized approach to university teaching and learning
which focuses on knowledge transmission by an expert rather than the culturally
preferred active knowledge construction. Therefore a Web-based course, design in
the didactic, instructor-driven tradition may handicap some populations’
adaptation to the online learning experience.
The body of knowledge
about cultural orientations is well substantiated. However, how cultural
elements and characteristics are interpreted and manifested in Web environments
is still unclear. Marcus and Gould (2001) analyzed international Web sites using
Hofstede’s cross-cultural theory (1997) in an attempt to identify cultural
aspects of user-interface. Hofstede identified five cultural dimensions which
Marcus and Gould believe can serve as a guide to Web designers. As an initial
attempt to consider the influences of Hispanic/Latino, index scores from the
three cultures most closely identified with the Hispanic/Latino culture are
summarized (see Table 2, Culture Indexes by Country). It is important to keep in
mind that Hofstede’s rankings indicate that there is no universal consensus
among cultural inclinations. Cultural influences in the US are even more
multi-faceted. It is not possible to reflect the influence of American culture
in the analysis that follows but it represents an attempt to consider design
elements that more accurately reflect cultural traits as derived from cultural
Cultural Indexes by Country
The five cultural dimensions identified by Hofstede and analyzed in Web design by Marcus and Gould (2000) are Power Distance, Collectivist/Individualist, Masculine/Feminine, Uncertainty Avoidance, and Time Orientation. Although the countries depicted in Table 1 vary in their cultural predispositions, we can assume that of the three, Mexico is the country of origin for the greatest population of Hispanic/Latino in the US. Therefore, in the summary below, Marcus and Gould’s Web design recommendations come from Mexico indexes.
Power Distance (PD).
“The extent to which less powerful members expect and accept unequal power
distribution within a culture” (Marcus & Gould, 2001, p. 5). Cultures with
high PD have more centralized power structures, disparate salary rewards,
acceptance of inequities, and centralized authority. Low PD cultures have less
hierarchical difference in authority, more equitable salaries, and equity is
desirable. Interface implications for a high PD country such as Mexico include:
structured and expert information presentation, strong use of cultural values
and corresponding symbols, emphasis on leader and expert rather than user, focus
on security and restricted access, and information access determined by social
This index refers to the degree to which an individual relates to society or
values their own achievement and status In general members of collectivist
cultures are more intrinsically motivated. (see Table 3)
Collectivist and Individualistic Indexes
Implications for Web
design for collectivist societies include: minimal emphasis on individual
achievement, success manifested in terms of socio-political ideals,
nationalistic slogans and gross generalizations, authority and experience
respected and valued, relationships are determinant of moral actions, and
personal information is kept private.
Hofstede generalizes about gender roles in societies, acknowledging that roles
may vary in cultures that have similar MAS indicators. In general, feminine
cultures tend to allow cross-gender behaviors while masculine cultures are more
likely to maintain strictly defined gender roles. Traditional masculine cultures
value wealth, challenge, promotion, and recognition of achievements. Feminine
cultures value good relations with co-workers, pleasant and congenial home and
workplace, and job security. Marcus and Gould suggest the following interface
implications for high feminine cultures: interchangeable roles, cooperation and
collaboration, and aesthetic expression of values.
“Cultures vary in their avoidance of uncertainty, creating different rituals
and having different values regarding formality, punctuality, legal-religious
requirements, and tolerance for ambiguity” (Marcus & Gould, 2000, p. 20).
High UA cultures: tend to have higher rates of suicide, accidents, additive
disorders and prisoners; are more tactical than strategic in business, expecting
long-term commitments from employees; have a more expressive populace that have
expectations of structure and predictable rules and norms; see teachers are seen
as experts and authorities to be respected; and, see what is out of the norm as
deviant and unacceptable. Cultures with low UA: have higher intakes of caffeine
and more psychosis; business cultures are more informal and strategic; appear
easy-going although the general population is not overly emotive; accept that
teachers may not know all the answers and learning is more open-ended; and see
out of the normal phenomenon as a curiosity. Marcus and Gould suggest that Web
design for high UA should consist of: simple, straight forward design with
minimal choices and concise information, intimation of consequences of actions
before user makes decisions, clear and unambiguous navigation, “mental models
and help systems that help users from becoming lost” (p. 20), and consistent
and repetitive visual cues.
Short-Term Time Orientation (LTO).
Hofstede found that countries with long-term Time Orientation believe that
stability requires hierarchical relations, view the family as the model for all
organizations with elders and males having most authority, believe that
virtuosity does not result in equitable treatment, and see that virtuosity means
working hard to improve oneself, at least in the workplace. Short-term Time
Orientation cultures: emphasize the individual and equitable relationships,
personal fulfillment through self-actualization. Although there is not data
available for Mexico for this index, there is evidence that the orientation is
long-term (Hall, 1989).
Although Marcus and
Gould’s analysis is limited in its scope, it does reveals inherently different
ways of looking at the world as reflected in culturally situated Web sites. Such
perspectives may operate at subconscious levels in the instructional designers
as they create learning experiences for Web-based learning environments.
Consideration of these unapparent preferences may reduce cognitive load and
stress for the learner, thereby contributing to a positive course outcome.
Cultural Learning Style
Another area of
research that can inform the design of Web-based learning environments is that
of learning styles. Consideration of learning preferences speaks to the issue of
adapting instruction to the learner, a commonplace event in traditional
instruction. Adaptive learning in Web-based environments is more challenging
because the mediating technology controls and limits the type and amount of
information known about a learner and the speed with which interaction occurs.
Also, integrating learner choice and path  requires more development time and
energy. However, an adaptive approach may result in lower attrition rates and
higher levels of success. Although the concept of learning preference is used to
define a wide range of typologies and theories, most theories fall into one of
the following groups: learning preference, learning strategy, learning style,
cognitive strategy, or cognitive style (McLoughlin, 1999).
It is important to note
that within a culture, individuals have different styles so using one approach,
however culturally relevant it may be, is not necessarily appropriate or
effective for an entire group. One solution to the challenge of diversity is to
provide multiple paths that learners may take, each of which is designed to
support a specific learning preference. The content should remain consistent
across the site but the interface through which the learner interacts can be
designed to complement learning preference.
The research on
learning preference by culture in Web-based learning environments is limited but
does reflect some of the tenets suggested by Hofstede and Marcus and Gould.
Sanchez (1996) examined US adult Hispanic learning styles and subsequent
implications for Web-based learning. She examined motivation maintenance level,
task engagement level, and cognitive processing level of 240 adult learners. She
found that Hispanic learners preferred evaluative feedback, active
participation, collaboration, and concrete and practical material. Learners
tended: to retain facts well, use elaborative processing, have a positive
attitude about learning, exhibit self-discipline and diligence, attend closely
to tasks at hand, use “imagery, verbal elaboration, comprehension monitoring
and reasoning” (p.58), identify the main idea, apply effective test-taking
strategies and reflect on accuracy of information. The Hispanic learners
preferred active experimentation and tended to use judgment (thinking of
feeling) when interacting with others. Herz and Merz (1998) found that
face-to-face simulation supports Kolb’s concept of active experimentation, a
learning preference identified by Sanchez and Gunawardena (1998). Sanchez and
Gunawardena (1998) make the following recommendations for distance learning for
Hispanic adults, cautioning that they are not intended to perpetuate stereotypes
or disallow for factors that might vary cultural traits but rather as a strategy
to consider different options in course design:
The nature of distance
learning as it is now conceptualized may not be supportive of collectivist
cultures. Anakwe, Kessler, and Christensen (1999) found motives and
communication patterns of learners from individualist cultures were supported in
a distance learning environment more so than learners from a collectivist
culture. The key areas that were not conducive to the collectivist learning were
the very characteristics of distance learning that are touted as the greatest
benefits: learner’s self-reliance and independence. The authors believe that
this may reflect a cultural predisposition toward technology. When used as a
medium to work alone and compete against others it may appeal to individualistic
learners but when technology is used to communicate and collaborate it may
appeal more to collectivist learners.
A generalized cultural
learning orientation in a Web-based learning environment can help the learner
draw upon what they know and are familiar with as they are assisted in
transferring their skills and knowledge acquired in traditional learning
environments to online learning. Some types of CBT may be better suited to
cultural orientations than other. For example, a simulation-game can support
much of the Hispanic/Latino style preferences in that it can be highly
interactive, can engage the learner at higher levels of reasoning, and can adapt
to the learner’s entry level of skill.
for adaptive learning is the age of the target population. There is a growing
body of evidence that suggests that design needs and preferences may vary among
age groups. The Net generation  has grown up with a variety of electronic
media that are unique and which research suggests has influenced their
perspectives and preferences (Tapscott, 1999). This generation::
Most of the Net
Generation has had access to computers and the Internet at home (Grunwald,
2000). This is not true of many minority groups, including the Hispanic/Latino
population (NTIA, 1999). It is not clear that one population has a advantage
over another when confronted with digital learning environments, however, it can
be safely assumed that familiarity with technology reduces the cognitive load
when the learner engages in CBT training or learning.
instructional designers should take heed of these characteristics that may not
be reflected in traditional development processes. When designing instruction
for this generation within a technology-based environment the following factors
must be considered: gender differences in the use and application of technology,
the preponderance of digital play as opposed to the work ethic of older
generations, the influence of global information and relationships, and the
decreased reliance on a teacher for guided learning. Clearly, the attributes of
this generation suggest a need for self-directed learning within an environment
that allows exploration and problem solving.
Research indicates that
simulation and games can support higher order thinking and problem solving
(Hamel & Bishop, unpublished). Although here is little current research that
indicates members of the Net generation have better cognitive skills that their
counterparts from other generations, their tendency toward autonomy and
independence suggests that these learners may be better problem solvers who can
analyze, synthesize, and evaluate effectively (Grunwald, 2000;Tapscott, 1999;
McKenzie, 1998; Bloom, 1956).
As distance learning
appears to be on the brink of becoming a preferred mode of learning in the
workplace, the generation that will be most effected is the one now entering the
workforce or completing an undergraduate education. The autonomous and
independent nature of Web-based learning necessitates problem solving and higher
order thinking for the learner who primarily interacts with a computer rather
than receiving individual feedback from an instructor. Simulation-games are by
definition dependent upon higher order thinking and require the player to make
independent judgments and thus provide a remarkably well suited environment for
a novice distance learner to test the waters of Web-based learning.
Conclusions and Recommendations
Globalization and the
increasing prevalence of the Internet in homes, workplace, and public
institutions require that instructional designers and educators look beyond
generalized approaches to learning and focus on multiple paths for acquiring
knowledge. Although many distance learning programs currently survey potential
students about their probably success, there are few systems that provide
training to insure success. Shifts from broadcasting to narrow-casting and from
large group learning to individualized learning indicate that in order to meet
the growing demands of just-in-time and just-in-need learning, Web-based
learning must be flexible and adaptable to the learner, not just the content.
The design and development of Web-based learning environments should include:
Analysis of target
population(s) preferences in Web design, interaction as well as entry technical
and communication level skills. This is essential to the instructional designer
who may enter into the design process with an unconscious predilection for
certain interface designs and pedagogical approaches that limit breadth of
enactive, iconic, and symbolic representations (Bruner, 1960).
Review of content by subject matter and culture experts for design
integrity and cultural relevancy. As
the work and learning place is gradually subsumed by what we now call the Net
generation, considerations described here may become superfluous. Until then, it
is the responsibility of trainers, educators, and instructional designers to
make conscious and informed decisions about how the unique needs of a learner
can be best supported in Web-based learning environments.
Aldrich, C. (2001).
Strategic e-learning: Trends and observations. (In K. Mantyla, and J. Woods
(eds.) The 2001/2002 ASTD Distance Learning yearbook, pp. 3-29. New York:
Anakwe, U. A., Kessler,
E. H., Christensen, E. W. (1999). Distance learning and cultural diversity:
Potential users’ perspective. The International Journal of Organizational
Analysis, 7 (3), 224-243.
Becht, D., Taglang, K.,
& Wilhelm, A. (1999). The Digital Divide and the US Hispanic Population. The
Digital Beat, 1 (13). Retrieved on June 2, 2001, from http://www.benton.org/DigitalBeat/db080699.html
Bloom, B. S., Englehard,
M., Furst, E., Hill, W., & Krathwohl, D. (1956). Taxonomy of educational
objectives: The classification of educational goals. Handbook I: Cognitive.
Boggs, S.T., Watson-Gregeo,
K., & McMillen, G. (1985). Speaking, relating, and learning: A study of
Hawaiian children at home and at school. Norwood, NJ: Abex Publishing Corp.
Bruner, J. (1960). The
Process of Education. Cambridge, MA: Harvard University Press.
Bruner, J. (1986). Actual
Minds, Possible Worlds. Cambridge, MA: Harvard University Press.
Bruner, J. (1991). Acts
of Meaning. Cambridge, MA: Harvard University Press.
Campbell, M. D. (2001).
Episodic learning: Experiences with distributed education. In K. Mantyla &
J. Woods (eds), The 2001/2002 ASTD Distance Learning Yearbook (pp.70-73).
NY: McGraw Hill.
Canter, J. A. (2000). Higher
education outside of the academy. ERIC Digests. ED446724. Retrieved on July
1, 2001 from http://www.ed.gov/databases/ERIC_Digests/ed446724.html
Carrier, C. A., &
Jonassen, D. H. (1988). Adaptive courseware to accommodate individual
differences. In D. H. Jonassen (ed.) Instructional Designs for Microcomputer
Courseware, pp. 203-225). Hillsdale, NJ: Lawrence Erlbaum Associates,
Charp, S. (1994).
Viewpoint. The On-line Chronicle of Distance Education and Communication, 7(2).
Available Usenet Newsgroup alt.education.distance, May 3, 1994.
Coggins, C. C. (1988)).
Preferred learning styles and their impact on completion of external degree
programs. The American Journal of Distance Education, 2 (1), 25-37.
Derry, S. (1992).
Metacognitive models of learning and instructional systems design. In M. Jones
& P. Winne (eds.) Adaptive Learning Environments: Foundations and
Frontiers (pp. 257-286). Berlin: Springer-Verlag.
du Boulay, B., &
Goodyear, P. (1992). Student-system interactions. In M. Jones, & P. Winne
(eds.) Adaptive Learning Environments: Foundations and Frontiers
(pp.317-324). Berlin: Springer-Verlag.
Diaz, D. P., &
Cartnal, R. B. (1999). Students’ learning styles in two classes: Online
distance learning and equivalent on-campus. College Teaching, 47 (4),
Dille, B., & Mezack,
M. (1991). Identifying predictors of high risk among community college
telecourse students. The American Journal of Distance Education, 5 (1),
Fjortoft, N. F. (1995).
Predicting persistence in distance learning programs. (ERIC Document
Reproduction Service No. ED 387 620).
Garland, M. R. (1993).
Student perceptions of situational, institutional, dispositional, and
epistemological barriers to persistence. Distance Education, 14 (2),
Gartner Group. (2001).
The Gartner higher education distributed learning survey 2001. Retrieved July
17, 2001 from http://www.gartner.com
Granger, D., &
Benke, M. (1998). Supporting learners at a distance from inquiry through
completion. In C.C. Gibson (Ed.) Distance Learners in Higher Education.
(pp. 127-137). Madison, WI: Atwood.
(2000, June). Children, families, and the Internet 2000. San Mateo, CA.
Retrieved July 15, 2001 from http://grunwald.com
Hall, Edward T. (1989).
Beyond culture. New York: Doubleday.
Hamel, C. J., &
Bishop, R. C. (2001). Computer-based simulation games and enhancement of job
performance. Unpublished manuscript.
Herz, B., & Merz,
W. (1998). Experimental learning and the effectiveness of economic simulation
games. Simulation and Gaming, 29 (2), 238-250.
Hofstede, G. (1997).
Cultures and organizations: Software of the wind. New York: McGraw-Hill.
Holmberg, B. (1995). Theory
and practice of distance education. New York: Routledge.
Jones, M., Greer, J.,
Mandinach, E., du Boulay, B., & Goodyear, P. (1992). Synthesizing
instructional and computational science. In M. Jones & P. Winne (eds.) Adaptive
Learning Environments: Foundations and Frontiers (pp. 383-401). Berlin:
(1997).Distance learning at the LSE with virtual tutorials. IT Review, 7
(1). Retrieved from the Internet from http://elj.warwick.ac.uk/jilt/sw/97_1lse/default.htm
Kochman, T. (1981). Black
and White styles in conflict. Chicago: University of Chicago Press.
Laurillard, D. (1992).
Phenomemographic research and the design of diagnostic strategies for adaptive
learning tutoring systems. In M. Jones & P. Winne (eds.) Adaptive
Learning Environments: Foundations and Frontiers (pp. 233-248). Berlin:
Marcus, A., &
Gould, E. W. (2001). Cultural dimensions and global Web user-interface
design: What? So what? Now what? Retrieved on May 28, 2001, from http://www.tri.sbc.com/hfweb/marcus/hfweb00_marcus.html
McCalla, G. (1992). The
search for adaptability, flexibility, and individualization: Approaches to
curriculum in intelligent tutoring systems. In M. Jones, & P. Winne (eds.) Adaptive
learning Environments: Foundations and Frontiers (pp.91-122). Berlin:
McLoughlin, C. (1999).
The implications of the research literature on learning styles for the design of
instructional material. Australian Journal of Educational Technology 15(3),
222-241. Retrieved June 12, 2001 from, http://cleo.murdoch.edu.au/ajet/ajet15/mcloughlin.html.
McKenzie, J. (1998). Grazing
the Net: Raising a generation of free range students. Phi Delta Kappan, 26-31.
Retrieved on June 6, 2001 from http://www.fno.org/text/grazing.html
Morgan, A. (1994). Research
into student learning in distance education. Deakin University.
Morgan, B. M. (2000). Is
distance learning worth it? Helping to determine the costs of online courses. Retrieved
on June 27, 2001 from, http://www.marshall.edu/distance/distancelearning.pdf.
Morgan, C. J., Dingsdag,
D., & Saenger, H. (1998). Learning strategies for distance learners: Do they
help? Distance Education, 19 (1), 142-156.
Naidu, S. (1994).
Applying learning and instructional strategies in open and distance learning. Distance
Education, 15, 23-40.
National Center for
Education Statistics. (1999). Digest of Education Statistics, 1999. Retrieved
June 16, 2001 from http://nces.ed.gov/pubs2000/digest99/
Telecommunications and Information Administration (NTIA). (1999, July). Falling
through the Net: Defining the digital divide. Retrieved on July 7, 2001 from
Oliver, R. (1999).
Exploring strategies for online teaching and learning. Distance Education, 20
Pasquinelli, A. (ed).
(1998). Higher education and information technology: Trends and issues.
Palo Alto, CA: sun Microsystems. Retrieved from http://www.sun.com/products-n-solutions/edu/admin/janeu2.pdf.
Potashnik, M., &
Capper, J. (1998). Distance education: Growth and diversity. Finance &
Development (March). Retrieved on June 17, 2001 from http://www.worldbank.org/fandd/english/0398/articles/0110398.htm.
Portier, S. J., &
Wagemans, L. J. J. M. (1995). The assessment of prior knowledge profiles: A
support for independent learning? Distance Education, 16 (1), 65-87.
Phipps, R. A., &
Merisotis, J. P. (1999). What’s the difference? A review of contemporary
research on the effectiveness of distance education in higher education. Washington,
DC; American Federation of Distance Learning in Higher Education Association.
Kostopolous, (2001). Distance learning trends in higher education. In K. Mantyla
& J. Woods (eds), The 2001/2002 ASTD Distance Learning Yearbook
(pp.64-69). NY: McGraw Hill.
Ross, L. R., &
Powell, R. (1990). Relationships between gender and success in distance
education courses: A preliminary investigation. Research in Distance
Education, 2 (2), 10-11.
Rowntree, D. (1995).
Teaching and learning online. A correspondence education for the 21st century? British
Journal of Educational Technology, 26 (3), 205-215.
Salopek, J. J. (199).
Workstation meets playstation. Training & Development, 52 (8), 26-35.
Sanchez, I. M. (1996). An
analysis of learning style constructs and the development of a profile of
Hispanic adult learners. Unpublished Doctoral dissertation. The University
of New Mexico.
Sanchez, I., &
Gunawardena, C. N. (1998). Understanding and supporting the culturally diverse
distance learner. In C. C. Gibson (ed). Distance Learners in Higher
Education: Institutional Responses for Quality Outcomes (pp. 47-64).
Madison, WI: Atwood Publishing.
Shade, B.J. (Ed.).
(1989). Culture, style, and the educative process. Springfiled, IL:
Charles C Thomas.
Shade, B. S. (1981). Afro-American
cognitive style: A variable in school success? (Report No. ED 21157).
Washington, DC: National Institute of Education.
Taspcott, D. (1999). The
rise of the Net generation: Growing up digital. New York: McGraw Hill.
Thompson, M. M. (1998).
Distance learners in higher education. In C. Gibson (Ed.) Distance learners
in higher education: Institutional responses for quality outcomes. (pp.
24-29). Madison, WI: Atwood.
US Census. (2000). The
Population Profile of the United States: 1999. Retrieved on June 12, 2001,
Woolf, B. (1992).
Towards a computational model of tutoring. In M. Jones & P. Winne (eds.) Adaptive
Learning Environments: Foundations and Frontiers (pp. 209-232). Berlin:
Wright, S. (1991).
Critique of recent research on instructional and learner support in distance
education with suggestions for needed research. Second American Symposium on
Research in Distance Education. University Park, PA: Pennsylvania State
 Acquiring skills or knowledge as it is required.
 Learning or training that is not necessarily required but may be used in the future.
 On demand episodic learning in which the learner determines what they need to learn and who can offer the most appropriate education or training (Campbell, 2001).
 Relates prior experiences to new learning.
 Adaptive learning recommends that learners are given choices or directed to the most appropriate level of learning. One course might be designed to accommodate a learner who has no experience with content or some experience. Entry knowledge would determine the path the learner follows.
 Anyone born after 1979 who has grown up with access to and experience with electronic toys, communication tools, and Internet resources (Tapscott, 1999; McKenzie, 1998).
About the Author:
Patricia McGee, Ph.D.
is assistant professor in the Department of Interdisciplinary Studies and
Curriculum and Instruction at the University of Texas at San Antonio. She has
studied and taught about a variety of topics and issues related to technology
Dr. McGee has been
involved with distance learning programs since 1986 when she taught for and then
managed staff development programming for TI-IN Network. Her varied research
interests include inservice teacher learning with and about technology;
preservice understanding of technology; and Web-based learning and culture.
Currently she is project director for a USDOE "Preparing Tomorrow’s
Teachers to Use Technology" grant. Dr McGee can be reached at firstname.lastname@example.org
or 210 458-7288.