VIRTUAL LEARNING SYSTEMS Maryam Alavi Goizueta Business School Emory University and Dorothy Leidner Texas Christian University I. II. III. IV. V.

Introduction Virtual Learning Systems: An Overview Literature Review A Cognitive Learning Framework and Implications for VLS Conclusions GLOSSARY

Asynchronous communication (or interaction) A communication mode in which messages are not coordinated in time and are transmitted at different clock rates. Cognitive learning theory A theory that views learning as a process of acquisition of knowledge and change in an individual’s knowledge structures that enhances his/her potential for effective performance Cognitive structure An individual’s memory and internal knowledge representations. Some times also referred to as mental models Constructivist pedagogy Groupware Software systems designed to enable individuals and groups to communicate and interact to share knowledge and information. Hypertext Information units interlinked based on predefined associations. Learning style Videoconferencing A live communication link between two or more locations involving audio, video and textual information exchange. Videodisk An optical disk used for storage and retrieval of videos Synchronous communication (or interaction) A communication mode in which messages are coordinated in time and are transmitted at the same clock rate.

I. INTRODUCTION A key trend in higher education and adult training and development is the use of virtual learning systems. Virtual learning systems (VLS) are information technology-

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based environments, in which the learner’s interactions with learning materials (e.g., assignments and exercises), instructors, and/or peers are mediated through technology. The term “information technology” or simply “technology” is defined as a broad array of computing, communication, and multimedia technologies and their convergence. This study focuses on learning from instruction. “Learning from instruction” has been defined as an environment in which one individual tries to intentionally influence the learning process and outcomes of another individual. Virtual learning systems can be used at all levels of education from elementary through graduate school. However, this paper focuses exclusively on higher and adult education. The increasing interest and growth of VLS can be attributed to two factors: 1-an increase in demand for learning and education from both the non-traditional and traditional sources, and 2- rapid advances in information technologies. Business, scientific, high-tech and professional fields are faced with an explosion of knowledge. The half-life of learning grows shorter and shorter. For example, in 1997 Van Dusen stated that about half of what an engineering student learns in his/her freshman year is almost obsolete by the time that he/she graduates and enters the workforce. In the introductory chapter of his book titled The Knowledge Economy (1998) editor Dale Neef states: "…there is now compelling evidence that the sudden and ever-accelerating burst of growth in high-technology and high-skilled services …may bring about some of the most profound and unexpected changes to the way in which we live and work witnessed since the nineteenth-century transition from an agricultural to an industrial society." This lifelong learning requirement coupled with distance, geo-physical displacement of co-workers, family, and job constraints of the working adults, fuels the growth of VLS particularly in form of distributed learning systems (which we describe later in the paper).

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In addition to an increasing demand for education from non-traditional students (i.e., working adult), the overall growth in the traditional student demand for postsecondary education drives the growth in VLS. According to the U.S. Department of Education statistic, the percentage of high school graduates who enroll in post-secondary education has increased from 49% in 1980 to 65% in 1995. In the meantime, according to a report published by Merrill Lynch in 1999, the education industry overall is faced with the “baby boom echo” an estimated 72 million children of the 76 million baby boomers (Americans born from 1946 to 1964). The growth of VLS is also fueled by a rapid rate of technological progress. The pervasiveness of the Internet, the emerging high-capacity networks augmented by satellite transmission and cellular and radio-frequency communication, and the prevalence of networked personal computers at homes and businesses are creating costeffective options for delivery of educational services to geographically-dispersed participants. Information and communication technologies, which lie at the core of VLS, are advancing at exponential rates. While these technological advances have provided new options for such conventional learning environments as public and private universities, they have also led to a whole new set of competitors like corporate universities. II. VIRTUAL LEARNING SYSTEMS: AN OVERVIEW The variety and flexibility of modern information and communication technologies provide developers of VLS a wide array of choices. For example, one can design virtual learning such that students need not interact with each other or such that synchronous or asynchronous interactions occur. Thus, explicit choices in the design of the virtual

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environment must be made, choices that have likely consequences on learning and student satisfaction. Explicit design choices regarding interactivity and content delivery comprise the basic attributes of virtual learning systems. These are depicted in Figure 1.

Time Interactivity:

Synchronous Asynchronous None

Person Student-Student Instructor-Student

Place

Degree

Onsite Offsite

Required Optional

Nature Structured Unstructured

Figure 1: Attributes of Interactivity in Virtual Learning Systems

In terms of interactivity, courses that involve a single individual obtaining static content from a learning module delivered online constitute one extreme. In this case, the individuals do not interact. Even if there were a single individual, there could be asynchronous communication with an instructor or instructor assistant. Alternatively, multiple individuals can communicate via a list or real-time chat. Such communication can involve student to student, or student to instructor, or student with a mentor (instructor assistant). Synchronous communication can occur onsite (face-to-face) or offsite (via communications technology). Finally, courses vary in degree of interaction and the amount of structuring, which we refer to as the nature of interaction. Students can be required to participate and interact daily, or the interaction can be voluntary. The interaction can be structured with specific topics, or left general. Figure 2 shows the design attributes concerning content delivery. One major attribute concerns media. Media may include text, graphics, video, and audio in some

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combination. Content can be delivered via linear or non-linear modules. In either case, the designer must also determine whether the student will control progression through a module, or answer periodic questions before the content material continues.

Media Content:

Text Audio Video Graphics

Nature

Control

Linear Modules Non-linear Modules

Student controls progress System determines progress Optional

Figure 2: Attributes of Content Delivery in Virtual Learning System

Virtual learning entails many choices among the above attributes. Our objective here is not to provide an exhaustive discussion of all possible approaches to development of VLS but to focus on those that have been identified as common and dominant forms in the literature. Two primary categories of virtual learning systems can be identified: 1- virtual learning systems designed for use in classroom settings (involving onsite synchronous interactions) , and 2- distributed VLS designed for environments in which the learners and instructors are distributed across time and/or geographic distance (involving offsite synchronous and/or asynchronous interactions). Each category is discussed next. A. Virtual Learning Systems in the Classroom The trend in application of virtual learning systems in the classroom takes the form of electronic classrooms. An electronic classroom is a classroom equipped with advanced information technologies, which are used by instructors and/or students to

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store, retrieve, process, and communicate information in support of learning activities. Electronic classrooms have been used in various disciplines including science, engineering, business and management, and languages. Application of information technologies in the electronic classroom takes two primary forms: a means of information presentation and display, and interactive use information technology by students and instructor as a basis for active learning and communication during class. The information presentation and display feature of the electronic classroom aims at enhancing efficiency of learning and teaching processes. Examples include computer display of lecture notes, electronic note-taking by students, and access to and display of on-line databases. Some authors have observed that as a presentation medium only, information technology in the classroom does not fundamentally alter the dynamics of classroom interactions relative to the more traditional tools and mechanisms such as slide and overhead transparency projectors and even blackboards. The interactive use of VLS in the classroom aims at support of student active and exploratory learning during class. For example, the interactive use of computer models and simulations by students can enhance learning through hands-on problem solving and what-if analysis (A specific example of this approach is presented Section IV of the paper.). This approach to use of technology in the electronic classroom is based on the cognitive learning theories that view learning as an active and constructive process. Interactive use of VLS in the classroom in form of networked computers in conjunction with specialized software tools referred to as groupware can greatly enhance communication and discussion. For example, use of these systems allows students and faculty to brainstorm and share ideas, comment on and criticize these ideas, and

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collaborate in solving problems and performing various learning tasks. Research by Alavi in 1994 has shown that students who routinely use interactive groupware in electronic classrooms learn more and are more satisfied with their learning experience relative to students in traditional classroom settings. B. Distributed Virtual Learning Systems Various forms of distributed VLS have been described in the literature. Van Dusen identified three categories of distributed VLS in 1997: the broadcast model, the online model, and the collaborative distributed model. The broadcast VLS model is typically fashioned after a lecture-style classroom environment, in which the instructor and students are located at two or more remote locations. Sound, full-motion video, and presentation material are transmitted from a central location (classroom or studio) to remote locations. Popular examples of this model include courses delivered through videoconferencing, cable, or satellite transmission (e.g., instructional TV). In this VLS model, the instructor is viewed as the primary source of knowledge, controlling content and the rate of information transmission to students. In this distributed VLS model, the predominant pedagogical approach remains the conventional “chalk and talk” method commonly found in more traditional face-to-face classroom environments. The vision for VLS is primarily that of automation and efficiency gains. Information flow (mostly in the form of lectures and presentation materials) between the instructor and the remote students is automated. Efficiency gains involve cost savings in the form of time and resources otherwise spent on traveling (between the remote student locations, to and from the central classroom

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site). Economic gains to the supplier of the course include increased student coverage due to access to a relatively larger marketplace. In some cases, this predominantly one-way broadcast model may be combined with direct synchronous and/or asynchronous communication links between the instructor and each remote student. These links serve to facilitate communication of students’ feedback and questions to the instructor. Information technologies typically used to support direct communication links between students and instructor in this environment can range from telephone or on-line chat facilities and key response pads

(for

synchronous communication), to e-mail (for asynchronous communication). Use of synchronous communication devices by remote students to ask and answer questions creates some degree of interactivity in the virtual learning environment; it provides the instructor with useful feedback to gauge students’ comprehension, and thus allows the instructor to adjust the presentation of material accordingly. Similarly, the use of asynchronous communication devices (e.g., e-mail) between the instructor and students facilitates student feedback and allows instructor to answer questions beyond the scheduled class period. In the online VLS model, remote students (using information and communication technologies) gain access to course content and learning resources such as simulations, computer-based exercises, demonstrations and hypertext-based study guides. Here, the student is largely in charge of his/her learning thus providing great flexibility in choosing the time, pace, frequency and form of learning activities. This approach to virtual learning increases in prevalence as more interactive multi-media learning resources are

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made available by educational publishers via CD and other resources on the World Wide Web. Unlike the broadcast VLS model, which treats learners as passive receivers of pre-packaged information transmitted by the instructor, the online distributed learning model views the students as proactive in interpreting and constructing meaning from information by processing and filtering it through their existing cognitive structures. The role of IT in the online VLS model is to provide learners with the capabilities to access and manipulate learning materials in order to form new understandings and to create new knowledge. For example, many online VLS provide capabilities for analyzing, synthesizing, filtering, and summarizing information through simulation models. In the collaborative distributed VLS model, students create knowledge and understanding primarily through social interactions across time and/or geographic distance through the use of information and communication technologies such as e-mail and on-line chat facilities. In the collaborative distributed VLS, learning occurs from the opportunity of the group members to be exposed to each other’s thinking, opinions, and beliefs, while also obtaining and providing feedback for clarification and comprehension. It is important to note that the three distributed VLS models described here represent the pure forms. It is quite likely that in a distributed learning program, more than one of these models would be used. For example, a program might combine a broadcast model for delivery of lectures in conjunction with the collaborative distributed VLS model to enable remote students to work on group projects. The majority of VLS are designed and deployed in distributed learning environments. A report published in 1999 by the U.S. Department of Education indicated

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that in 1997-1998, about 10% of enrollments in all 2- year and 4-year postsecondary institutions in the United States were in distance (i.e., distributed) learning courses. This amounted to a total of 1.7 million enrollments in postsecondary distance learning courses. In 1997, Gubernick and Ebeling reported that only 93 postsecondary learning institutions were offering online courses in 1993. According to these authors, this number increased to 762 by 1997. The promise of flexibility and reduced downtime and travel expenses is also steadily increasing the use of VLS for employee training in the business world. Regardless of the specific approach, however, learning is anchored in human mind that, when properly implemented, can be supported via various configurations of virtual learning systems. In section IV, we discuss a framework for conceptualizing the potential role of technology in the learning process and outcome. III. Literature Review Research has examined the outcomes of virtual learning systems in a variety of contexts. A relatively small number of studies have focused on electronic classrooms while most of the studies have aimed at investigation of distributed VLS. Research on electronic classrooms has aimed at evaluation of classrooms settings equipped with VLS in terms of the level of student interactions and engagement in class as well as learning and student attitude. For example, studies of electronic classrooms equipped with communication capabilities have shown an increase in the level of participation and interactions among students. In 1988, Horowitz indicated that professionals who were taught in an electronic classroom and responded to instructor questions via the computers were more attentive and engaged with learning than those who were taught by a traditional lecture method. Butler in1990 found that an electronic classroom induced

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student interactions. In that year, Bump observed that the electronic classroom enhanced student participation in the classroom discussions, and Slatin reported that in a class of 18, students generated 100 messages, with approximately 60% of these messages sent to other students in the class rather that to the instructor. Several other studies of electronic classrooms have focused on the impact of electronic classrooms on student learning and attitude. In 1988-1989, Gist et al. evaluated the impact of an electronic classroom equipped with an instructor computer and videodisks on students attitudes toward the instructor and the instructional method. The study indicated that the students in the electronic classroom had more positive attitudes compared to students who were taught in a traditional classroom. Similarly, in another study of an electronic classroom in 1993, Leidner and Jarvenpaa examined the use and outcomes of computer-based instructional technology in the context of graduate business education. Their detailed case studies indicated that the use of teaching methods requiring hands-on use of computers by students in the electronic classroom enhanced exploratory analysis during class and acquisition of technical procedural knowledge by students. In summary, studies of electronic classrooms have focused on the use and outcomes of VLS by instructors and/or students in classroom settings. These studies collectively illustrate the variety of approaches to use of VLS in classroom settings and the positive impact of these systems on classroom dynamics and learning outcomes. A larger number of research studies have focused on the effectiveness of distributed VLS. In 1997, Moore and Thompson conduct a review of distributed virtual learning research, finding that in general, the outcomes of cognitive factors such as amount of learning, academic performance, achievement, and examination and

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assignment grades are generally not significantly different from the outcomes in traditional courses. This finding concurs with Brownson’s review in 2000, which conclude that virtual learning and traditional classroom-based learning are not particularly different. In Moore and Thompson’s study, examinations of other factors such as student satisfaction with the course, comfort, convenience, communication with the instructor, interaction and collaboration between students, interdependence, and perceptions of effectiveness showed mixed results. While the majority of distributed learning studies find that opportunities for interaction between students and instructors seemed negatively affected in the distributed environment, in 1999, Spooner reported that several studies have found that distance learning positively affects collaboration and interdependence among students.. Various factors may influence the degree of interaction virtual learning provides. In 1997, Webster and Hackley found that teaching style is the most important factor influencing student participation and interactions in virtual learning. They also found that students’ comfort with their images on screen, the quality of the technology, and the perceived richness of the communication medium affected student interactions in virtual learning. The fact that the course was taught via distance was less significant than the teaching attitude and style. In 2000, Hill and Chidambaram in summarizing the findings of virtual learning research, highlighted three key findings: (1) the majority of studies have not taken into account individual differences among students (like age, attitudes, and perceptions); (2) research suggests that no single technology is as important in influencing outcomes as learner and instructor characteristics; and (3) while many studies suggest that no significant differences exist

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between traditional and virtual education outcomes, dropout rates have been higher for virtual education. Until the advent of the Web, most virtual courses were highly impersonal, asynchronous, and non-interactive. Students were limited in their choice of courses by their physical location and the time it would take to send and receive materials and assignments to and from the receiving institute. As media have improved, it has become possible to develop and deliver greater interactivity at a distance. The learning in essence has become far less removed in person and time than with traditional forms of virtual education such as correspondence courses or instructional radio. No longer is the instructional package limited to that which can be mailed, or delivered via television or radio. There are now a great variety of alternatives in how courses can be developed and delivered. Early research on virtual learning environments suggests that the learning outcomes of students using computer-based technology at a distance are similar to the learning outcomes of students who participate in conventional classroom instruction. A study published by Ahmed in 2000, compared an online course to the same course taught traditionally by the same instructor. The study indicated no difference in student performance but did find that the students in the virtual environment reported higher levels of self-efficacy and convenience. While virtual environments can offer convenience, flexibility, currency of material, increased retention, lowered costs, increased feedback, and individualized learning possibilities, in 2000 Dyrud reported withdrawal rates are significantly higher in virtual learning than traditional and students

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report that virtual learning requires more time and effort. Elsewhere, satisfaction and effectiveness of virtual learning have been perceived as lower than traditional learning. Research has also considered the factors that influence students to choose virtual courses over their traditional counterparts. In 1992, Richards suggested that such practical considerations as distance from campus, work schedule, and family commitments affect students’ choice to take online versions of courses. While such practical considerations may influence the study choices, there is growing evidence that learning and satisfaction with virtual learning depend as much upon student characteristics as upon any inherent characteristic of the virtual learning course design. In a study published in 1998 by Salomon and Almog, individual differences that were only mildly implicated in learning in traditional settings were likely to become of central importance when computer-mediated communication was involved. One finding in this study comparing traditional classrooms with virtual ones, was that although measures of ability were the best predictors of learning in the former learning environment, measures of students’ disposition to engage mindfully in learning were the best predictors of the latter. According to these authors, volitional, motivated expenditure of mental effort, mindful engagement, and self-monitoring become crucial in virtual learning environments because of the general constructivist pedagogy that is naturally suited to such environments. In 2000, Robb stated virtual learning requires self-discipline for students to succeed: students have to stay on schedule with assignments, log on regularly, and participate in online class discussion, in order to achieve. In a study reported in 1994, Hiltz found that student levels of academic ability, motivation, degree of effort and maturity all correlated positively with outcomes in online courses. Leuthhold in 1999

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correlated the learning styles of forty students in an online course with their preferences for mode of delivery. Students whose learning orientation was sequential as opposed to random preferred online courses to the traditional classroom. It is not surprising then that aside from practical considerations, students tend to enroll in a course whose format is compatible with their attitudes and learning strategies. In fact, some researchers believe that where such a match is not made, learning outcomes are adversely affected. Ching in 1998 showed that the virtual environment may shape student learning styles: the learning style of the same individuals became more field independent after one year in a virtual learning program.

We can thus distinguish

between immediate and lasting benefits of virtual learning. According to Salomon and Almog, lasting benefits include improved self-discipline, improved tendency for selfregulation, or a possible change in learning style. For example, they further suggested that a more lasting cognitive effect of hypermedia content delivery might be an improved ability or disposition to construct logical cognitive structures of knowledge. In 2000, Tallman and Benson found that students who elected to take a virtual course changed their mental models of themselves as learners and their capabilities. In a new online classroom setting, students were forced into changing their patterns of learning from a need for approval for all their actions to building gradually toward self-discovery and independent learning. Students may be better able to construct inter-related networks of knowledge. On the other hand, it is also feasible that the long-term result of using hypermedia based content delivery, is that students may begin to construct rather shallow associationist cognitive structures, indicating incomplete learning.

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A growing body of research investigates the degree of interactivity that can be achieved in virtual learning environments. There is a growing consensus that online classes engage students to a greater degree than traditional classes because students are forced to write more than they talk. However, increasing interaction is insufficient to improve learning:

the dialogue needs to be content focused. In 2000, Hron et al.

suggested that content-related dialogues with minor off-task talk, coherent subject matter discussion with explanation, and equal participation of students can enhance group processes and learning in virtual environments. They studied the extent to which dialogue structuring promotes coherent subject matter discussion and symmetrical interaction in virtual learning groups engaged in synchronous communication.

They found that

structuring, both explicit and implicit, helps students in the virtual environment achieve more coherent subject matter discussion that was obtained in the absence of structuring mechanisms. In 1999, Haythornwaite reported that

virtual learning environments need

not restrict dialogue structuring to a synchronous exchange, but by extending interactions to times outside of class, a more sustained interaction and the creation of closer interpersonal bonds among students, can occur. Thus, while one cannot totally simulate a real classroom where interaction is real-time and feedback from the instructor and other students is immediate, one can offer feedback that is better reflected and participation that is less an immediate reaction to a question and more a thoughtful reflection. Hiltz and colleagues have extensively studied asynchronous virtual learning environments. According to reports published in 1994 and 1997, their basic premise has been that collaborative (group learning) which can be facilitated by anytime/anywhere access to the communication networks and workspace is key to achieving superior

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learning outcomes. The authors suggested that if collaboration rather than individual learning designs were used in an online class, students should be more motivated to actively participate and should as a result of the online social interactions; perceive the medium as relatively friendly and personal. The authors found that the combination of teamwork in the online course resulted in higher perceptions of self-reported learning, whereas individuals working alone online tended to be less motivated, perceive lower levels of learning, and score lower on a test of mastery. Similarly, in 1995 Alavi et al. conducted a longitudinal field study of virtual learning systems in support of collaborative learning. Two types of collaborative learning environments using desktop videoconferencing were considered: one involving local student groups (i.e., nonproximate student teams on the same campus), and the other involved non-proximate students on two separate campuses. The findings indicated that both of the learning environments were equally effective in terms of student learning and satisfaction with the VLS. However, the students in non-proximate teams using the desktop videoconferencing were more committed and attracted to their groups. To summarize: virtual learning research has covered a variety of topics and learning modes ( in-class and distributed learning). Research has considered the outcomes of virtual learning in comparison with traditional learning. Research has focused on the issues that help predict why students would choose a virtual learning to a traditional learning alternative. Student characteristics have been examined as moderators of any relationship between virtual learning mode and outcomes. Finally, a range of research addresses the issue of interactivity in virtual learning. This research considers the ways in which computer-mediated communication can positively enhance

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the learning outcomes in virtual settings. While the literature review reveals that much research focuses on learning outcomes obtainable in virtual environments, the most commonly used measures of learning focus on final task performance (on a test) or on student perception of learning. What is missing is an understanding of how the attributes of virtual learning systems affect, or fail to affect, the underlying cognitive processing that occurs when individuals learn. The next section will present a framework for understanding these cognitive processes of learning and exploring the relationship between cognitive processes and various attributes of VLS.

IV. COGNITIVE LEARNING FRAMEWORK AND IMPLICATIONS FOR VLS Figure 3 depicts an input-process-output model of learning based on cognitive psychology research reported by Ausubel in 1968, Gagne and Briggs in 1979, and Mayer in 1981. This model was developed to identify the relevant variables and discuss the potential impact of technology on learning. According to the work of Reigeluth, Bunderson, and Merrill reported in 1994, the learning process is influenced by three categories of input variables: instructional strategy, learning technology, and situational factors. Instructional strategy refers to methods and approaches for selecting, sequencing, synthesizing and displaying information (subject-matter content) to the learner. Technology refers to means and tools used to access, process, display, and communicate content information to the learner. Situational factors refer to the relevant learner, instructor, and environmental variables such as learner’s interest and motivation, instructor’s experience and structure of subject matter content.

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Learning Process

Input

Instructional Strategy

Cognitive Process Reception

Learning Technology

Output

Memory Short-term

Availability

&

Learning Outcome

Long-term

Situational Activation Factors Mayer defined Learning as the process by which an individual connects new material (information to be learned) with knowledge that already exists in the memory Figure 3. cognitive An input-process-output foravailability, learning through the processes offramework reception, and activation. Learning

involves both short and long-term memory. According to Mayer, short-term memory is a temporary and limited capacity for storing and manipulating information while long-term memory is a permanent and structured store of existing knowledge. According to the assimilation theory of learning set forth in the works of Ausubel and Mayer, new information provided through instructions and delivered through technology must go through three cognitive processing steps in order for learning to occur: 1. Reception. The first step in the learning process requires that the learner pay attention to incoming information (the target of learning) and then transferring it to the short-term memory. 2. Availability. Once the new information is perceived by the learner and transferred to the short-term memory, it needs to be “anchored” in the relevant

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knowledge structures that already exist in the long-term memory. Thus, existence and availability of the prerequisite concepts is a necessary condition for effective learning. 3. Activation. Once the pre-requisite knowledge in long-term memory is located, it needs to be accessed and “remembered” by the learner. The pre-requisite knowledge is transferred to the short-term memory where it is actively processed and combined with the new incoming information. The incoming information appropriately processed through the three steps of reception, availability, and activation becomes the desired learning outcome, which refers to understanding and changes in the knowledge structure of the learner. According to Mayer, understanding in this context is distinct from rote memorization and is defined as the potential for using learned information in performing new tasks that are different from what was explicitly taught. As Figure 3 indicates, technology applications can enhance the learning process and outcome by facilitating the implementation of the instructional strategy and by eliciting and engaging the underlying cognitive processes of learning. The following example illustrates how this was accomplished in a specific learning situation. A 1993 report by White described a virtual learning system, a computer simulation, used for teaching Newtonian mechanics. Students used the simulation to work with models of force and motion to explore relationships between these two entities in order to learn the corresponding laws of physics. The learning outcomes of the students who used the VLS were consistently superior to the learning outcomes of the students who learned in a traditional environment with the same teacher. In the simulated environment, dynamic

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and graphical information display and data processing capabilities of the computer were used to develop rich visual presentations of the information stimulus (i.e., to implement an element of the instructional strategy) otherwise unlikely to be available to learners. The computer displays supported the reception phase of learning by eliciting a high level of learner’s interest and attention. An example was the symbolic and dynamic computer presentation of change in entities such as force vectors that correspond to formal constructs of physics and have no direct, concrete referent in the real world. Furthermore, the simulation models were designed to be progressively more sophisticated and complex. In the early models, the students worked only with motion in a horizontal plane. In the later models, the students worked with a combination of motion in both horizontal and vertical directions. The last model of force and motion incorporated a complex mix of continuous motion and various forms of force including friction and gravity. It can be argued that in this case, the computer simulation was used for sequencing and structuring the subject-matter content (two other aspects of instructional strategy) and supported the second cognitive phase of learning (availability) by providing the prerequisite concepts required for learning the more complex concepts. The students also used the computer simulation to develop the “law” that captured the relationship between the entities of the simulation model. It can be further postulated that the technology provided a forum for learners to generate and test hypothesis, make decisions, and generate responses. These activities enabled the students to explore proactively and link the new information to their existing knowledge and thus facilitated the activation phase of the learning process. To summarize, this section presented a framework that identified the input, process and output variables associated with learning and their inter-relationships. This

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framework facilitates exploring the potential role of information technology in learning and provides a foundation for the design of virtual learning systems. In the next section, the variables of this framework are linked to specific attributes of VLS discussed in Section II of the paper. A. Linking the Attributes of VLS to the Learning Process Instructional strategy as defined in the previous section comprises the methods and approaches for displaying information to a learner. The attributes of virtual learning systems described in section II would each constitute an instructional strategy decision. For example, deciding the nature and structure of interaction, attributes of virtual learning systems, is an important element of instructional strategy. This section will tie the attributes of virtual learning systems to cognitive outcomes. As previously stated, the reception step in cognitive processing involves stimulating learner attention to incoming information. Several of the attributes of virtual learning may serve as reception catalysts. Specifically, designs that force learners to answer questions before continuing, forces the learner to pay attention to material in as much detail as is necessary to answer the questions. Also, using a range of media rather than a single media to deliver information is thought to increase learner attention. Certainly the dynamic and graphical information display for teaching Newtonian mechanics (the VLS example described in the previous section) was able to create greater learner receptivity than was mere talk. If students are given control over media presentation, they might even be more receptive to the incoming information.

For

example, if students can pause a video presentation, rewind and re-listen, or control the

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amount of time they listen before pausing, they might be able to pay greater attention than if they are regarded as passive recipients of the information. The availability phase of cognitive processing requires that existing knowledge structures already in memory be activated and available for interpreting new information. The virtual attributes that might have an impact on availability include the content delivery choices, regarding specifically the linear and non-linear designs and the progression through the content. Systems that allow learners to choose which topics they will study in which order, may fail to ensure that learners have the requisite knowledge to assimilate the new knowledge to which they are exposed in a given learning module. By contrast, systems that are designed in a linearly progressive fashion whereby students must demonstrate mastery of one module before proceeding to the next, might be more successful in ensuring that availability phase of cognitive processing occurs. In the Newtonian mechanics VLS example, the fact that the simulation models were designed to be progressively more sophisticated ensured that learners mastered concepts of increasing complexity as they worked through the online simulation. The activation phase of cognitive learning processes deals with the ability of learners to recall relevant knowledge. Of the attributes of virtual learning systems that might affect activation, it is likely that interaction has a major impact. If interaction is required and is structured to ensure that learners stay on relevant topics, then by forcing learners to compose their knowledge, to read others’ insights, and to respond to those insights, one is forcing learners to recall their own knowledge and assimilate new knowledge into their existing cognitive frameworks, or at least to interpret new knowledge within their existing framework.

It might be that the asynchronous

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arrangement is even more effective than the synchronous in that it enables learners to more thoughtfully reflect on the knowledge rather than responding with the first idea that comes to mind.

Activation might also be enabled by content delivery attributes of

system progression through material. If the system requires students to answer questions periodically during the material delivery, then the system is forcing students to recall information from short-term memory. If the system requires learners to respond to question dealing with material from previous learning modules, then the system forces long-term memory activation. In summary, drawing from Figures 1 and 2, we suggest that interaction influences activation, with asynchronous, required, structured interaction among students and between an instructor and students being most effective at promoting activation. The media choices are expected to influence receptivity, as is student control of delivery. However, system control over delivery and a linear design are expected to influence availability with systems that require evidence of mastery of information before progression to new material is allowed expected to be more effective in ensuring availability than are designs where progression is not tied to evidence of obtained knowledge. As is evident, virtual learning design decisions are very important in that ultimately, they may be affecting the underlying cognitive processing that occurs during learning. V. CONCLUSIONS Dorothy, it would be great if you add 1-1.5 pages to this section and close the paper with a paragraph at the very end of section V. Thanks. With the improved performance-price ratios of computing equipment, continued convergence of computing

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and communication technologies, and the increase in prevalence of the Internet and the World Wide Web, VLS can provide viable and effective educational alternatives. In addition, the increase in demand for learning at all levels and the growth in continuous and part-time learning requirements will fuel the need for development and deployment of various forms of virtual learning systems. However, in most cases, VLS have been developed without explicit consideration of the underlying learning process and contextual factors. In some cases, this approach has resulted in development of state-ofthe-art VLS without clear guidelines on how these systems may be used to achieve learning improvements. Under these circumstances, despite the expenditure of resources, the opportunity is lost to create virtual learning environments that are pedagogically effective and even superior to the traditional teaching modes. Thus, it is important to continue the inquiry and investigation into the design of effective virtual learning systems. Key research questions include: What technical resources should be used in design of VLS? How do learners respond to various characteristics and attributes of VLS? What contextual factors should be explicitly considered in design of VLS and how? Past virtual learning research has largely focused on student learning outcomes in virtual learning environments and student characteristics influencing student motivation to study virtually and student ability to succeed in virtual learning. Both areas of research are critical to our understanding of the effectiveness of virtual learning.

While great

progress has been made in advancing our understanding of the critical enabling role of information technology in virtual learning environments, we would suggest that it is imperative to gain an understanding of how the explicit design choices one makes in

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developing virtual learning systems influences the learning process and outcomes. Without careful attention to these decisions, one might inadvertently develop a learning environment that is internally inconsistent. Such a system might conceivably give the learner great control in hopes of individualizing learning, yet fail to ensure that the learner actually obtains the requisite knowledge. The following section offers some suggestions for researching virtual learning systems. A. Approaches to VLS Research Most studies of virtual learning systems have adopted classical experimental approaches involving an independent variable (technology intervention) and then investigating and measuring its impact on one or more variables associated with learning outcomes (e.g., student achievement or perceived learning). This form of research studies may result in a limited understanding of the virtual learning systems. In 1991, Kozma stated that this is because these forms of classical experimental approaches examine the potential existence of a direct cause-and-effect relationship between technology and learning and not a potential “causal mechanism” -- the underlying, interactive factors that produce events and processes that affect learning. The study of causal mechanisms in VLS is important because of the interconnected nature of various elements associated with learning as depicted in Figure 3 and discussed in section IV. The challenge posed by this attribute of virtual learning systems is that changes made in one element of a learning environment reverberate throughout the environment. This implies that VLS studies may need to expand to include approaches that attempt to capture the underlying processes and interactions among various underlying factors. That is, studies of the changes and events as learners interact with the technology interventions in certain ways under certain

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situational conditions. These processes can be captured through the inclusion of both qualitative and quantitative data. Examples include computer logs reflecting the interactions between the computer and the learners, computer-mediated communications among the learners and the learners and instructor, learner interviews, and asking learners to think out loud while they learn. Thus, inclusion of outcome and process data as well as expanding VLS studies to include both experimental and qualitative approaches, will enhance our understanding of virtual learning systems and inform the design of effective systems.

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