e-Learning: A Programmatic Research Construct for the Future

e-Learning: A Research construct: of 29

RUNNING HEAD: e-LEARNING: A RESEARCH CONSTRUCT

e-Learning: A Programmatic Research Construct for the Future

Edward L. Meyen

Ron Aust

John M. Gauch

H. Scott Hinton

Robert E. Isaacson

Sean J. Smith

Meng Yew Tee

University of Kansas


Author Info

Edward L. Meyen is Professor in the Department of Special Education, The University of

Kansas. Ron Aust is Associate Professor in the Department of Teaching and Leadership, The

University of Kansas. John M. Gauch is Associate Professor in the Department of Electrical

Engineering and Computer Science, The University of Kansas. H. Scott Hinton is Professor and

Chair in the Department of Electrical Engineering and Computer Science, The University of

Kansas. Robert E. Isaacson is a Program Associate in the e-Learning Design Lab, The

University of Kansas. Sean J. Smith is Assistant Professor in the Department of Special

Education, The University of Kansas. Meng Yew Tee is a Graduate Research Assistant in the e-

Learning Design Lab, The University of Kansas. All authors are members of the e-Learning

Design Lab, The University of Kansas.

Correspondence should be addressed to Sean Smith, 1122 West Campus Road,

Joseph R. Pearson Hall, Room 406, University of Kansas, Lawrence, KS, 66045.

Email to: [email protected].


Introduction

Building on the capabilities of the Internet, educational institutions as well as

industry at all levels have moved quickly to exploit this new technology for instructional

purposes. According to some industry estimates, the number of users worldwide will

pass the one billion mark by 2005 (United States Internet Council, 2000). In the United

States, 97% of full-time faculty and staff at two- and four-year institutions of higher

education have access to the Internet, and 40% use Web sites to post course-related

information (U.S. Department of Education, 2001). Virtual universities, with no prior

education histories have come into being, attracting large enrollments. Two- and four-

year institutions have quickly responded by placing courses and degrees online. In

addition, a growing number of universities are attempting to enter the corporate-

dominated distance learning market through venture startups and partnerships with

technology companies including, Columbia University, the London School of Economics

and Political Science, Stanford University, Duke University, Harvard University, and the

University of Pennsylvania's Wharton School of Business (DeBellis, 2000).

As a further testimony to the exponential growth of online education, The Online

Academy, a project funded by the Office of Special Education Programs (OSEP), has

produced 22 online modules for teacher education that were adopted by over 160

universities (Meyen, 2000). Nearly 710,000 students in 1998 were enrolled in at least

one online course, and that figure is predicted to reach 2.2 million in 2002 (Meister,

2000). Colleges and universities are now encountering competition from private industry

in the form of companies that not only have developed the technology to deliver e-

learning, but also have the capacity and resources to produce and market content. The


corporate e-learning market is expected to surpass the $23 billion mark by 2004, up

from $2 billion in 1999 (IDC, 2001). Lou Gerstner, chairman and CEO of IBM, referred

to email as the Internet’s first killer application. John Chambers, president and CEO of

Cisco Systems, said that e-learning will be Internet’s next "killer application."

Companies have been formed for the purpose of creating online offerings for institutions

of higher education and training for industry. Examples include Blackboard, Web CT,

eCollege, SmartForce, KnowledgeNet, Click2Learn and DigitalThink.

The Internet and its applications in education and industry have significantly

influenced how we teach and learn. This has all occurred as a consequence of

emerging technologies and the demands for online instruction by consumers. In the

midst of this environment of rapid growth, a new form of pedagogy has emerged.

However, much of it is not the result of research.

Problem

What has not evolved during the rapid emergence of e-learning as a popular

mode of instruction and learning is a systematic approach to researching the pedagogy

of online instruction, interface designs, the application of technologies to e-learning, and

the framing of constructs to guide needed research. Much of the existing educational

research on print materials, telephone, radio, television, video, and computer can, in

one way or another, carry over to the Internet. However, since all of these media

converge on the Internet, a new and challenging area of educational research is

becoming more apparent (Moursund & Smith, 1999).

In discussing the critical research challenges of the future, Hinrichs and Kelly

(2001) suggest that research over the next few years can be expected to result in

practical products for post secondary education. The authors go on to identify areas in


which major advances need to be made for this to occur (e.g., the learning sciences,

learning technology tools, and evaluation and assessment).

Over the last four years an interdisciplinary group of researchers at the University

of Kansas has been engaged in the design, development and evaluation of tools to

develop and deliver online instruction. This experience has resulted in the production

and dissemination of extensive online instructional programs. It has also lead to the

creation of the e-Learning Design Lab (eDL), a partnership between the Center for

Research on Learning (CRL) (affiliated with the Department of Special Education) and

the Information, Technology and Telecommunications Center (ITTC). The faculty, staff

and students affiliated with the Lab come from the fields of electrical and computer

engineering, computer science, instructional design, education, special education, and

design. Central to the mission of the Lab is the development of a program of research

focused on the pedagogy of e-learning from a technology and learning perspective. As

an underpinning of this mission, participating researchers have begun to address the

issue of creating a research construct as a context both for structuring their own

research and as a framework for stimulating research on e-learning among potential

collaborators (see Figure 1). The construct is viewed as a work in progress and

presented here for the purpose of inviting discourse on how to shape it. The construct is

not intended as an all-inclusive research model on learning with the aid of technology.

Instead, an attempt was made to ascertain the language used in the literature to define

e-learning.


Defining e-Learning

A number of terms are used in the literature to say basically the same thing. For

example, terms that are often used include e-learning, online learning, computer-based

training (CBT), Web-based learning, distributed learning, and electronically enabled

distance learning (Cramer, Krasinski, Crutchfield, Sackmary, & Scalia, 2000)). For the

purpose of this article and the construct proposed we use the term e-learning. We

believe that setting parameters on the definition will enhance the refinement of the

research construct.

An Internet survey found that among professionals working for institutions of

higher education and vendors that offer course material, 40% prefer to use the term e-

learning when asked what they would use to describe learning with technology

(Carnevale, 2000). Thirteen percent used the term online training, 10% used Web-

based training while the remainder used terms such as technology-based training,

distance learning, and computer-based training. On the other hand, 21% of the learners

used computer-based training, 19% Web-based training, 18% online learning, and 10%

each for distance learning, training, and e-learning.


Carnevale (2000) sums up his findings by quoting a training industry

professional, who said that different groups attach different meanings to the same

terms and concepts. Urdan and Weggen (2000) seem to concur, stating that: “Terms

like e-learning, technology-based learning, and Web-based learning are defined and

used differently by different organizations and user groups” (p.4). Urdan and Weggen

go on to define e-learning as the delivery of content via all electronic media, including

the Internet, intranets, extranets, satellite broadcast, audio/video tape, interactive TV,

and CD-ROM. The term e-learning is also used synonymously with technology-based

learning. It is also worth noting that Urdan and Weggen consider e-learning as a subset

of distance learning, online learning a subset of e-learning and computer-based learning

as a subset of online learning.

In another study, Wentling, et al. (2000) reviewed terms such as computer-based

training (CBT), online training and e-learning, referring to several sources dating to 1994

(Gotschall, 2000; Hall, 1997; Karon, 2000; Porter, 1997; Schreiber & Berge, 1998;

Urdan & Weggen, 2000; Willis, 1994; Zahm, 2000). Ultimately, they arrived at their own

definition of e-learning.

E-learning is the acquisition and use of knowledge distributed and facilitated

primarily by electronic means. This form of learning currently depends on

networks and computers but will likely evolve into systems consisting of a variety

of channels (e.g., wireless, satellite), and technologies (e.g., cellular phones,

PDA’s) as they are developed and adopted. E-learning can take the form of

courses as well as modules and smaller learning objects. E-learning may

incorporate synchronous or asynchronous access and may be distributed

geographically with varied limits of time. (p.5)

https://www.researchgate.net/publication/248409388_Web-Based_Training_Cookbook?el=1_x_8&enrichId=rgreq-9e6fbf13b44a68f6539a3b414bad50d1-XXX&enrichSource=Y292ZXJQYWdlOzIzOTU0OTg3NDtBUzoxMTUxOTQ3MDEyOTE1MjBAMTQwNDQ3NTk2NjM3Mw==

https://www.researchgate.net/publication/243779686_Creating_the_Virtual_Classroom_Distance_Learning_with_the_Internet?el=1_x_8&enrichId=rgreq-9e6fbf13b44a68f6539a3b414bad50d1-XXX&enrichSource=Y292ZXJQYWdlOzIzOTU0OTg3NDtBUzoxMTUxOTQ3MDEyOTE1MjBAMTQwNDQ3NTk2NjM3Mw==


This definition of e-learning represents the definition adopted as the basis for the

construct illustrated in Figure 1.

An Overview Perspective of e-Learning

For purposes of the research construct, e-learning must be viewed within the

larger realm of life-long learning, target audiences, and the elements that comprise

learning environments. Figure 1 illustrates the relationships between target audiences

and the elements of e-learning that evolve once learners are formally engaged in e-

learning as an instructional program. Target groups can be further defined from the

perspective of life- long learners; for example, adult learners may be refined to focus on

a particular industry or professionals with retraining needs. Conditions characterizing

learning environments may generalize or be unique to a particular target population of

learners. As will be discussed in defining the features of the research construct

illustrated in Figure 1 the learner attributes within target audiences (e.g., those with

disabilities) will also vary and result in sub-classifications of learners. This variability,

along with the purpose of engaging in e-learning, helps to define the learning

environment(s). For example, a secondary student may be engaged in an e-learning

experience designed to teach paraphrasing strategies and be studying independently at

a workstation in the classroom; a special education teacher may be pursuing a staff

development program online and working in the privacy of her home or in a lab setting.

Thus, the learning environment becomes defined by (a) the nature of the e-learning

program, (b) the attributes of the learner, and (c) the conditions preferred by or available

to the learner.

As e-learning programs are implemented and the engagement of learners

contemplated, a number of elements come into play as noted in the right-hand circle of


Figure 2. While their substance may vary, the need for each one generalizes across

formal instructional organizations.

Learner Populations and Learning Environments

K-12. E-learning opportunities for K-12 schools are currently limited primarily to

middle school and above. However, with emerging technologies and as teachers gain

more experience with Web-based instruction, independent forms of e-learning

applicable to all levels will evolve, especially engaging learning environments to meet

the needs of students with disabilities. As this occurs, the K-12 population of learners

will become the focus of a full array of e-learning designs.

Higher education learners. This learner population includes those individuals who

are enrolled in post-secondary education. Included are those enrolled in all forms of

public and private higher education such as community colleges, 4-year colleges,

universities, technical colleges and trade schools.

Adult learners. This group of postsecondary learners ranges from young adults

who opted to enter the workforce rather than pursuing postsecondary education to mid-

career adults (i.e., teachers related service providers) seeking instruction to develop

new skills or preparation for career shifts. Their sources for e-learning may be

institutions of higher education, professional associations, for-profit educational

organizations, or online experiences resulting from personal initiation. The latter

category may well increase, as URL sites become more information-based and better

structured for learning.

Preschool learners. The computer games industry has already demonstrated that

preschool learners can effectively learn via e-learning experiences. Yet, e-learning

instruction is just beginning to gain a presence in preschools, in part due to the

instructional focus on group experience and a concern for the development of social


skills. As research is carried out to determine ways to employ e-learning experiences

that complement and enhance interpersonal interactions and goals associated with the

social development of young children, more attention will likely be given to designing e-

learning experiences for preschoolers.

Elements of e-Learning Environments and Technology Infrastructure

Pedagogy. Principles of teaching are well established in traditional methods of

instruction. These principles may generalize to e-learning, however, limited data-based

research has been conducted to systematically assess the application of teaching

principles to online instruction. Pedagogy includes teaching methods related to the

presentation of experiences, engagement of learners, reinforcement, motivation,

organization of teaching tasks, feedback, evaluation, and curriculum integration.

Assessment. In traditional forms of instruction, teachers collect anecdotal and

other forms of evaluative data in addition to the results of formal assessments when

making judgments on students’ progress. In e-learning, informal assessments are

difficult to employ, as assessments must be planned and precisely designed to evaluate

performance, to provide feedback, and to be aggregated for grading purposes.

Assessment in e-learning is more essential than in traditional instruction due to the

nature of the limited opportunities for traditional informal assessment and related face-

to-face interaction that traditional instruction offers. It may also be more critical to

assess frequently and to target different levels of instruction (e.g., pre- and post-

assessments tasks associated with activities, instruction, units, midterm and final

exams, and task-specific assessments (Dereshiwsky, 2001).

Content. Theoretically, the content of instruction should not differ across

instructional methodology and delivery systems. However, some content may lend itself

more readily to instruction via e-learning (e.g., introductory material). Traditional forms


of content need to be researched from the perspective of structure to determine if a

broader base of content can be taught as effectively via e-learning.

Instruction delivery. Electronic delivery is the one feature that clearly

differentiates e-learning from other forms of instruction. Too often, e-learning is viewed

primarily as an instructional delivery system. In reality the delivery element of online

instruction makes possible the pedagogy, that is, the methodology of e-learning. It is the

pedagogy that is most significant in e-learning.

Instructional management. Two aspects of instructional management become

more important in e-learning than in traditional forms of instruction. These include

resources and systems (a) to facilitate student management of resources, feedback,

and assessment data; and (b) to enhance instructional management of student

performance, resources, formative data or course design, student work, and anecdotal

information..

Standards and policies. Prior to the advent of the Internet and e-learning,

academic policies and standards in educational institutions were based on traditional

forms of instruction, that is, classroom delivery models. A similar situation of training

programs within industry also existed. The features of accessibility, flexibility,

asynchronous learning and structure that characterize e-learning result in

circumstances that alter the conditions under which instruction and training occur. This

changing condition calls for different policies and standards.

While neither designs nor models of e-learning will be discussed in this article, it

is important to note that no e-learning designs or models at this time have been

validated via research to be the most effective. Not only is an absence of research on e-

learning designs and model, there is also a paucity of research on the effectiveness of

e-learning. Yet, the unprecedented growth in e-learning offerings at the postsecondary


level indicates a strong learner preference for the instructional features that characterize

e-learning. Although some would argue that learners’ responsiveness to e-learning as

reflected in growing enrollments is sufficient evidence of its effectiveness, it can be

argued that programmatic approach to research will maximize the effectiveness of this

new form of pedagogy and result in more efficient and effective uses of technology.

Additionally, there is much to be learned about translating sound instructional principles

that have benefited from research on traditional instructional environments to e-learning

environments.

Programmatic Research Construct

In building the research construct, a major decision involved determining the

primary dimensions or families of variables for the construct. From prior development

and evaluation work, much was known about the specific variables. We wanted to

create a construct that was expandable and that was most likely to enable researchers

with a wide array of interests to relate their research to it. The latter was important, as

we wanted to subject the construct to input from others who share an interest in building

a construct that might both stimulate research on e-learning and bring some order to

addressing the most significant research questions as a way of improving the power of

the pedagogy.

Three categories of variables (i.e., outcome variables, in situ variables and

independent variables) were selected from a number of options, because we believed

they offered meaning to researchers and developers as well as to instructors or trainers.

Each of these groups has much to offer in the framing of research. In contrast to

researching areas where the practices, beliefs or knowledge are well established, e-

learning represents an area of study where there is very little experience on the part of

learners, instructors, developers or researchers. This calls for a sharing of what is


known as research agendas are formed toward building the needed knowledge base for

optimizing the pedagogy of online instruction.

Outcome Variables

In defining outcome variables, attention was given to the array of conditions,

other than learner performance, that might be impacted by the implementation of e-

learning programs and/or the engagement of learners in e-learning experiences. Given

the newness of e-learning as a pedagogy, and from a programmatic perspective, it was

considered important to extend the array of outcomes beyond those specific to learner

performance. Research is needed to bring about solutions for those who design,

develop, administer, and create policies for e-learning programs. For these reasons, we

defined outcome variables as the variables associated with the consequence of

implementing or engaging in e-learning that can be measured. They are analogous to

dependent variables. They translate into results such as competencies achieved, rates

of gain, costs, need for new policies, data on instructional effectiveness, and so on.

Academic policy implications. Policies are framed in academic institutions,

professional organizations, and industry to enhance and govern the education, training,


and professional development they provide. Policies must be responsive to emerging

conditions such as those brought about by e-learning.

Pedagogical effectiveness. E-Learning is a form of pedagogy, and the

effectiveness of pedagogical practices can be measured. A knowledge base of

pedagogical practices can evolve over time. Examples include practices related to

communication, assessment, instructional design and mediation.

Economic implications. This involves measures of economic elements, including

time needed, human resource needs, and cost of equipment.

Technology implications. This variable focuses on all technologies and

infrastructure requirements essential to facilitate e-learning.

Learner performance. Instructional programs influence the performance of

learners in a number of ways (e.g., achievement, demonstrated competencies,

attainment of student objectives). This variable involves validating interventions and

practices that enhance learner performance in the realm of e-learning.

In Situ Variables

A significant concern in researching e-learning relates to the conditions

surrounding the educational training experience. In contrast to traditional instruction,

which has tended to be place bound, e-learning has few parameters related to access.

This in turn alters the context of the e-learning experience and broadens the conditions

under which learning occurs. In addition, it transfers much of the control for where

learning occurs to the learner.

Most important, a new aspect of learning is introduced with the emergence of e-

learning—the design of the instruction itself. Whereas, traditionally, instructors structure

how they teach and may employ selected strategies in the engagement of all learners,

e-learning is designed instruction, specific to the individual learner. The design is


consistent within the instructional experience although designs vary. The use of

technology requires specific design features for the instruction to be effective for each

learner. Yet, e-learning still occurs in the larger context of traditional learning

environments (e.g., meeting the needs of all learners) and content structures which

largely remain unchanged. Thus, in researching e-learning it is important to know the

stable conditions under which e-learning is occurring. While they are not easily

controlled or manipulated, they may be researched in terms of their impact. Examples

include age, gender, disability, ethnicity, content of subject matter, school or program

organization, the composition of classes, and available technology resources. It is

difficult, if not impossible, to research e-learning without taking into consideration these

in situ variables.

Learner attributes. Basic characteristics including age, gender, disability, area of

origin and residence, ethnicity, race, first language, socio-economic status, learner’s

ability, subject matter experience, learner’s perception, and educational history.

Learning environments. This includes the settings and natural environments in

which teaching and learning occur. All elements of teaching and learning are involved,

including social and motivational factors.

Nature of content. Content varies in form and how it is configured for instruction.

The accommodation of varied forms of content interacts with how it is delivered. E-

learning is one form of content delivery that remains to be assessed as to its capacity to

accommodate the varied forms of content.

Technology infrastructure. This refers to the configuration and adequacy of

technology (software, hardware, and bandwidth) within a learning environment.

Independent Variables


From a research perspective this is the primary dimension because these are

variables that can be manipulated for purposes of research. They are also the variables

that are impacted significantly by enabling technology such as bandwidth,

computational power, sensor and mobile technology. In reviewing them it is important to

view them reference to their interaction with in situ and outcome variables.

Levels or types of interaction. This includes interaction between students and

instructors, interaction among students, as well as the interaction with the e-learning

mode. It also includes the technology required to accommodate types, modes and

frequency of interactivity.

Instructional design. Instructional designs vary in content and media richness,

structure, instructor dependency, and technology requirements, including interactivity,

computational power, bandwidth, sensors and wireless capabilities.

Learner interface. This refers to non-human elements that learners interface with

in order to perform e-learning activities. This can include the phone, desktop computers,

handheld computers, and so on.

Instructional environments. This includes the settings in which learners engage in

e-learning, that is, the elements essential to implementing e-learning experiences.

These may take the form of an office or home setting, a laboratory, classroom or setting

where learners access connectivity or utilize wireless technology for instructional

purposes.

Expansion and Application of Construct

The present status of the construct as illustrated in Figure 2 contains 100 cells,

with each cell defined by three major descriptors: the dimensions of outcome, in situ,

and independent variables. These categories of variables provide the three primary

parameters, however minimal, for each cell.


The next step is to further define each cell as subsets from the perspective of

research questions characterizing the work to be done in the respective cells. The goal

is to engage researchers and developers nationally in this process. The variables

defining each cell will also be subjected to review. As variables in the three dimensions

are added, the number of cells will increase proportionately. The intent of the construct

is to provide researchers a structure for framing and categorizing research questions.

As research questions are generated across the 100 cells, it may become necessary to

expand the construct or to revise the three primary dimensions of the variables within

each dimension.

To illustrate one strategy for bringing definition to each cell, three examples of

preliminary expansion will be presented. Each falls within the interests of the eDL, but

collectively do not fully represent the interests of the Lab. The intent of the examples is

to illustrate that each cell can be further defined and contribute in a generative way to

the development of research agenda. The outcome of this process across all cells may

be the beginnings of a base for prioritizing research needs as a way of maximizing the

effectiveness of e-learning designs in meeting the needs of all learners. The ultimate

goal is not to replace face-to-face instruction but to ensure that when e-learning

solutions are developed, it is research-based and maximally effective.


Figure 3 illustrates the cell excerpt that focuses on the impact various technology

infrastructures will have on the learner interfaces that can be developed for future e-

learning environments. The technology infrastructures for this cell could include a matrix

of technological capabilities, including bandwidth, client-side computational power,

server-side computational power, display size, multimedia capabilities (e.g., video,

sound, pen-based input, voice input), wireless or fixed connectivity, and internet security

requirements.

This technology infrastructure will provide the main limitations for the type of

learner interfaces that can and will be developed. These learner interfaces vary in

complexity from a simple text-based Web page running on a network browser to

interactive voice- and pen-based input that includes intelligent agents to manage the

entire learning process. The outcome of these studies of the interrelationships between

technology infrastructure and learner interfaces will result in identification of the

technology requirements for each of the different types of learner interfaces. These

technology implications can then be used to help school districts, universities, corporate

training centers, and so on, understand the type of infrastructures they need to provide

to meet their e-learning objectives.

Research questions. Some of the research questions bound by the dimensions

of this cell include:

1. What are the technological requirements and societal implications of intelligent

agent-based learning environments that automatically adapt the learning process

to the student’s learning profile?

2. What are the technological requirements for interactive multimedia learning

environments?


3. What are the technological requirements for wireless audio-based learning

environments?

4. What types of learner interfaces are possible with 56Kb/s modem-based learning

environments?

5. What are the security risks for school districts, universities, and corporate training

centers by providing server-side e-learning environments?

6. What learner interfaces can be created to take advantage of heads-up displays?

In Figure 4, the cell focuses on the interaction between learner attributes and e-

learning instructional designs in the context of assessing pedagogical effectiveness.

Learner attributes for this cell may include a variety of learner characteristics such as

age, gender, area of origin, ethnicity, race, learning styles, first language,

socioeconomic status, intellectual ability, previous educational experience, as well as

learning challenges associated with disability (e.g., processing difficulties, reading

comprehension, organization).


Selected learning attributes may influence the performance of learners in e-

learning environments. For example, individuals with a learning disability may face

significant challenges interacting with text-based content that does not offer design

component options designed to accommodate their disability. The current knowledge

base on students with learning disabilities suggests that instructional design features

should offer a variety of options to accommodate this student population. In generalizing

to e-learning environments, one might assume this would apply; that is, content-rich,

multimedia Web pages offering user-interactive illustrations to anchor their

comprehension for future applications of what they are learning. However, there is still

no research base supporting this in e-learning environments. This is true of learner

attributes across all age levels. Many learner attributes impact the ability to learn

efficiently. However, we do not know how these attributes interact with the features of e-

learning. The outcome of studies in this cell that explore the interrelationships between

learner attributes and instructional design could result in identification of e-learning

pedagogical and design requirements that will effectively accommodate varying

learners’ attributes. This in turn could influence the pedagogical practices of teachers,

teacher educators, designers and developers who engage in creating e-learning

experiences.

Research questions. Examples of research questions specific to this cell include

the following:

1. What e-learning instructional design features lead to effective pedagogical

practices that accommodate particular learner attributes?

2. To what extent does matching the needs of learners with accommodating

instructional design features influence learner performance in e-learning

environments?


3. What learning characteristics most significantly enhance performance in e-

learning environments?

4. What are the instructional design features that influence effective instruction and

the accommodation of learners with disabilities?

5. What types of pedagogical e-learning practices are required to meet the

instructional needs of students with language comprehension problems?

6. What are the instructional design limitations of e-learning for individuals with

learning challenges?

7. What learner attributes most significantly affect the instructional design of e-

learning environments?

8. Which learner attributes have the greatest influence on the performance of

learners in e-learning environments?

9. Do specific learner attributes influence the ability to navigate e-learning

environments more than others?

The cell in Figure 5 illustrates how instructional design factors that are mediated by

learner attributes interact with learner performance. The instructional design umbrella

covers a broad range of topics that impact e-learning, including message design (e.g.,


learning object design, richness of media, message redundancy, and feedback),

instructional strategies (e.g., cooperative learning and knowledge structures), and

instructional systems (e.g., pacing, advice mechanisms and personalized learning

systems). The degree to which these instructional design components affect learner

performance is often influenced by the learner attributes such as age, gender, learning

styles, previous knowledge, and cognitive abilities. For example, visual learners might

benefit more from the animated illustrations than verbal learners; adult learners might

benefit more from time-and-place shifting than traditional students; or learners with an

internal locus of control might benefit more from advice systems.

Research questions: Research questions that may be explored within the perimeter

of this cell include:

1. Do learners in an online course with interactive animated equations obtain

significantly higher scores in solving equations than students in an online course

that presents equations as text only?

2. Is there any interaction between an animated illustration treatment and

visual/verbal learning styles on problem-solving ability?

3. Do learners in an online foreign language course who engage in target language

live discussions anytime and anyplace achieve significant higher pronunciation

and vocabulary scores than learners who have only limited access to live

discussions?

4. Is there an interaction between access to online discussions and learner level

(adult professionals vs. university students) on pronunciation and vocabulary

performance?


5. Do students who receive integrated advice in how to sequence and pace the use

of learning objects perform better in recalling facts and problem solving than

those who receive the same learning objects without integrated advice?

6. Is there an interaction between the use of advice systems and locus of control on

recall of facts and problem solving?

Summary

The research construct presented in this article represents the early efforts of

researchers affiliated with the e-Learning Design Lab to frame a programmatic research

initiative intended to guide the generation and categorization of research questions on

the design and pedagogy of e-learning. The ultimate goal is to stimulate the

development of a systematic approach to researching the many technical and

pedagogical elements of effective e-learning instructional environments for all learners,

especially those with disabilities. The construct presented has evolved from five years

of R&D work in e-learning by the participating researchers. In addition, the Lab is

currently carrying out six externally funded projects, involving target audiences such as

preservice students in teacher education, practicing teachers, secondary students, and

leadership personnel in the military.

The largest project completed to date is the Online Academy (Meyen et al.,

2001). The Online Academy was funded by OSEP in 1996 to develop online modules

for pre-service teacher education in reading, positive behaviors supports and

technology in education. The 23 modules developed and tested by the Academy were

content-rich and employed streaming media. They were designed to be completed as

self-contained online independent instructional units or as interactive online instructional

units with the involvement of an instructor. Table 1 illustrates the extensiveness of the

modules. They were beta tested by 32 institutions of higher learning and have been


adopted by 161 institutions for implementation. Development included the use of

content experts as judges in moving research to practice through the development of

content, the creation of a template-based tool to automate transitioning content to the

online module format and the development and validation of production processes. New

generations of module designs and processes have been developed. Research projects

are underway and in proposal stage.

Researchers currently affiliated with the Lab come from the disciplines of

electrical and computer engineering, instructional technology and education. Affiliation

will continue to expand in numbers a well as disciplines. While all affiliate researchers

are engaged in sponsored projects, that is not a requirement. Figure 6 illustrates the

areas of R&D interests that characterize participating faculty, students and staff.

The core activity of the e-Learning Design Laboratory is focused on research,

development, and understanding of enhanced learning environments. Thus, it is the

intent of the Lab not only to contribute to the research of the new e-learning

environments but also to participate in their development.


Research

The first research focus will be e-learning technologies. This work will include

exploring both the hardware and the software technologies required to create the

technology-enhanced learning environments needed to support the e-learning of

tomorrow. The second research focus is e-learning pedagogy, including the exploration

and development of teaching methodologies that are effective for e-learning

environments. The third research area of focus is e-learning policy. Initially, this effort

will focus on participation on e-learning standards committees. The fourth research area

of focus is centered on understanding the economic impact of e-learning. This area is

not only important to business, but also to the entire international university system.

Finally, we also need to understand the social impact of e-learning.

Development

The first of the developmental areas of focus is demonstration prototypes. This

area focuses on the construction of research prototypes designed to demonstrate the

ideas and technologies developed by the research arm of the Lab. It is important that

eDL develops a strong relationship in an effort with industry to allow them to complete

the final product development. The second developmental area is content development.

Here the Lab wants to work with faculty and industry to create new content for

enhanced learning environments using the tools and techniques developed by the Lab.

The third developmental area is evaluation services. Specifically, the Lab wants to

establish partnerships with industry to participate in the testing and evaluation of new e-

learning products. The final developmental area of focus is technology transfer. Here

the Lab hopes to successfully transfer e-lab intellectual property to the marketplace.

Through the involvement of national and regional advisory boards, a council of

principal investigators and a management team, the Lab is being operationalized. The


infrastructure is in place, and individual projects are being shaped into research

programs. Further, partnerships with researchers and developers in private industry are

being pursued, as are formal relationships with labs having similar missions. While most

of the work is project-specific, a series of professional initiatives such as the

development of the research construct serve as the common theme for the professional

growth of participating researchers.

TABLE 1: Scope of Academy Modules Production

Modules................................................................................ 23Lessons................................................................................. 92Interactive Open Response Items* ................................... 1,200Unique Glossary Terms* ................................................. 1,380Interactive Multiple Choice Items* .................................. 1,400Interactive Glossary Links* ............................................. 3,600Streaming Media Files* ................................................... 4,600Graphics* ...................................................................... 13,800Unique Files* ................................................................ 50,000Words*......................................................................1,600,000Total Size of all modules (zip. compressed)* .......... 1 Gigabyte

* estimates

The aspiration of the Lab is to attract researchers and developers nationally who are

inclined to engage in collaborative work with lab researchers. The outcome may be

collaboratively developed research projects, joint work in refining the research

construct, and/or the establishment of a community of individuals who share a common

commitment to inquiry centered on e-learning environments.


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e-Learning: A Programmatic Research Construct for the Future

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