Journal of Educational Media, Vol. 29, No. 3, October 2004

Learner motivation and E-learning
design: a multinationally validated
process
John M. Kellera* and Katsuaki SuzukibaFlorida State University, USA; bIwate Prefectural University, Japan

A general model for motivational design of instruction is described and reviewed in terms of its
application to E-learning contexts. Following a description of what is meant by E-learning
environments and an overview of the four category model and design process known as the ARCS
model, a variety of studies are summarized. The ARCS model is based on a synthesis of
motivational concepts and a problem-solving approach to design, rather than the application of
specific motivational solutions that are advocated without regard to the specific characteristics ofa given situation. The first group of reviewed studies illustrates the results of testing the
motivational design process in several different E-learning settings, in relation to learner self-regulation and in terms of the interaction of personality characteristics and motivational strategies. The
second group of studies includes tests of the validity of a simplified motivational design processthat has been used in diverse types of E-learning settings, including multiple countries andcultures. Overall, the results of these empirical studies have confirmed the validity of this modelfor the systematic design of motivationally enhanced instruction in E-learning settings with regard
to lowering drop-out rates and other positive motivational outcomes.

Introduction
Technology offers many innovative features that can be used to make instruction
more appealing to learners. However, many of these features are interesting only
because they are novel and may lose their appeal as learners become accustomed tothem. Problems with regard to stimulating and sustaining learner motivation are
well documented in the literature of E-learning and the broader context of distancelearning (Zvacek, 1991; Rowntree, 1992; Visser L., 1998), especially when learners
are working independently at a distance. Overcoming these motivational challenges
can be difficult because of the complexity of human motivation and the vast numberof motivational concepts and theories that exist. Frequently, specific motivational
concepts become ‘popular’ and are included in research studies of learner motiv*Corresponding author. Department of Educational, Psychology and Learning Systems, Florida
State University, 305G Stone Building, Tallahassee, FL 32306-4453, USA. Email:jkeller@mailer.fsu.edu
ISSN 1358-1651 (print)/ISSN 1469–9443 (online)/04/030229-11©2004 Taylor & Francis Ltd
DOI: 10.1080/1358165042000283084

230 J. M. Keller and K. Suzuki
ation in relation to interest, attrition or other dependent variables. Such was the casewith ‘locus of control’ in times past and currently with ‘self-efficacy’ and ‘self-regulation’. These are important components of motivation, but they are not sufficient
to explain it, nor do these motivational concepts provide guidance in and ofthemselves as to how to design motivational E-learning.

Motivation and E-learning
One of the first issues to consider with regard to E-learning is ‘What is it?’ The term
has become very popular, but like the phrase ‘distance learning’, it can refer to rather
dramatically different kinds of learning environments. In the present paper we use
the term broadly to refer to almost any learning environment in which electronic
media, such as computers, are used as a component of an instructional delivery
system. These can range from the use of Email to supplement print-based materials
distributed at a distance to courses that are delivered entirely by means of technology such as computers or the World Wide Web.
There are similarities in motivational problems in all of these settings, even though
there are specific motivational challenges within each major system. For example,
drop-out rates tend to be higher than in face-to-face settings, learners often feel
isolated and levels of learning interactivity are often trivial and do not approach therichness of case studies and projects in face-to-face settings (Moore & Kearsley,1996). There are notable exceptions in some Web-based learning systems that are
instructor-led and in which virtual groups work collaboratively, but even in thesesettings there are motivational challenges with regard to effective delivery of instruction and methods of managing the virtual learning environment (Joung & Keller,
2004).
It is one thing to document motivational challenges in these settings, but itis something else to determine what to do about it. For several years Keller(1987a,b) has been developing and testing a model to assist educators in a systematic process for analyzing learner motivation and designing motivational tactics
that are keyed to specific areas of motivational problems and integrated withteaching/learning strategies. This process was derived from a comprehensive review
and synthesis of motivational literature that classifies the major motivational
concepts and theories into four categories depending on whether their primaryarea of influence is on gaining learner attention, establishing the relevance of the
instruction to learner goals and learning styles, building confidence with regard to
realistic expectations and personal responsibility for outcomes and making the
instruction satisfying by managing learners’ intrinsic and extrinsic outcomes. This
process is called the ARCS model based on its acronym (attention, relevance,
confidence and satisfaction). Following a description of this model, we will describe
some of the findings with regard to improving motivation in E-learning environments.

Learner motivation and E-learning design

231

Characteristics of the ARCS modelFirst, a lesson must gain and sustain the learner’s attention. Research on curiosity,
arousal, and boredom (Berlyne, 1965; Kopp, 1982) illustrates the importance ofincorporating a variety of tactics to gain learner attention by the use of interestinggraphics, animation or any kind of event that introduces incongruity or conflict. A
second level of curiosity is aroused by using mystery, unresolved problems and othertechniques to stimulate a sense of inquiry in the learner. An additional importantcomponent of attention is variability. No matter how interesting a given tactic is,people will adapt to it and lose interest over time. Thus, it is important to vary one’s
approaches and introduce changes of pace.
The second requirement is to build relevance. Attention and curiosity are necessary, but not sufficient, conditions for motivation. It is also necessary for learners toperceive the instructional requirements to be consistent with their goals, compatible
with their learning styles and connected to their past experiences. Having clear goals
is a key component of relevance. Learner goals can be extrinsic to the learning inthat it is necessary to pass a course to be eligible for a desired opportunity, but astronger level of motivation to learn is achieved when the learner experiences
intrinsic goal orientation, i.e. when the learner is engaged in actions that are
personally interesting and freely chosen. This condition of intrinsic motivation is an
example of self-determination (Deci & Ryan, 1985) that leads to sustained goal-oriented behavior. Thus, relevance results from connecting the content of instruction
to the learners’ future job or academic requirements or to intrinsically interesting
topics. For example, secondary school children enjoy reading stories with themes ofstigma, popularity and isolation, because these are important issues at that time of
their lives. In recent years it has been popular to refer to these aspects of relevanceas ‘authentic’ learning experiences, which is a concept from constructivist literature(Duffy et al., 1993). Other motivational concepts that help explain relevance are
motives such as the needs for achievement, affiliation and power (McClelland,1984), competence (White, 1959) and flow (Csikszentmihalyi, 1990).
The third condition required for motivation is confidence. This is accomplished byhelping students establish positive expectancies for success and to then experience
success under conditions where they attribute their successes to their own abilities
and efforts rather than to luck or the task being too easy or difficult (Weiner, 1974).
Even a successful accomplishment is not likely to increase one’s confidence if theperson believes that the only reason success occurred was because of luck. This
category of confidence includes some of the most currently popular areas ofmotivational research, two of which are self-efficacy (Bandura, 1977) and attribution
theory (Weiner, 1974).
The first three conditions are necessary to establish the motivation to learn andthe fourth, satisfaction, is necessary in order for learners to have positive feelings
about their learning experiences. This means that extrinsic reinforcements, such as
positive rewards and recognition, must be used in accordance with establishedprinciples of behavior management (Skinner, 1968) and must not have a detrimental
effect on intrinsic motivation (Condry, 1977). Such things as opportunities to apply

232 J. M. Keller and K. Suzuki
what one has learned coupled with personal recognition support intrinsic feelings ofsatisfaction. Finally, a sense of equity or fairness is important (Adams, 1965).
Students must feel that the amount of work required by the course was appropriate,that there was internal consistency between objectives, content and tests and that
there was no favoritism in grading.
If all of these conditions are met, then students are likely to not only have a highlevel of motivation to learn in the immediate setting, but to also have a continuing
motivation to learn, which is defined by Maehr as voluntary engagement in continuing to learn more about a given topic (Maehr, 1976). However, these categories do
not in and of themselves explain what motivational tactics to use or when to use
them. For this it is helpful to use a systematic motivational design process thatprovides guidance in creating motivational tactics that match student characteristics
and needs (Keller, 1987a).
The ARCS model contains a ten-step design process (Figure 1) for the development of motivational systems in work and learning settings. The first two steps,
which are parts of the overall analysis components of the process, produce information about the status quo and provide the basis for analyzing gaps and theircauses, which are done in the third and fourth steps. Based on these analyses, in thefifth step one prepares objectives for the performance improvement project andspecifies how they will be assessed. There are then two steps in design: brainstorming within each motivational category to generate a rich list of potential solutions;selection of the final tactics, which is a more critical and analytical process forselecting tactics that best fit the time, resources and other constraining factors in thesituation. The final steps include both development and evaluation and are similar
to any other development model. Numerous reports and studies have described andconfirmed the validity of this model with respect to its conceptual foundation (see,for example, Visser & Keller, 1990; Small & Gluck, 1994; Means et al., 1997).
Systematic improvement of motivation in E-learning
The ARCS model has also been validated multinationally as a means of improving
learner motivation in E-learning. In a recent study Chyung et al. (1999) used the
ARCS model in combination with a systematic needs assessment process to design
and implement interventions that would decrease the drop-out rate in a distancelearning program. There are frequent citations in the literature to the symptoms
associated with drop-out (Moore & Kearsley, 1996; Visser, 1998), but the incorporation by Chyung et al. (1999) of a needs assessment process assisted them in
identifying the causes of the problem. These included such things as learners having
doubts about their online communication skills, lack of confidence in using the DE
software, feelings of being overwhelmed and other problems with confidence andrelevance. Based on these results, which were combined with an ARCS model
analysis and design process, the investigators developed a list of targeted interventions. These were implemented over a period of three semesters (spring, summer
and autumn). The results indicated that there were improvements in both learningand motivational reactions in all four motivational categories (attention, relevance,

Learner motivation and E-learning design

Figure 1. ARCS motivational design process233

234 J. M. Keller and K. Suzuki
confidence and satisfaction). Also, there was a significant reduction in the drop-out
rate, which decreased from 44 to 22%.
In a study that focused primarily on the relevance dimension of motivation,
Chang and Lehman (2001) used the ARCS model to guide the development of a setof tactics to improve motivation and performance in a distance learning class.
Several of their tactics were designed to facilitate easy scanning of online text, reduce
the word count on a screen compared with printed text, improve the quality ofquizzes as a motivational tool and incorporate more interactive features. Most ofthese tactics are also consistent with cognitive load theory (Pass et al., 2003), which
has implications for learner motivation. The investigators found a significant improvement in learner perceptions of motivation and in scores on a comprehension
test.
In addition to the direct effects that can result from applying the ARCS model,
personality characteristics can interact with the tactics that are used. Bellon andOates (2002) used the Jung Typology Test to measure learners and correlate theresults with their motivational reactions to various features of the Web-based coursethat were designed in accordance with the ARCS model. They found no differencesin relation to some aspects of the course, but they did find interactions in thatcertain personality types had higher levels of motivation for posting Emails and forvarious types of course materials.
Astleitner and Hufnagl (2003) found an aptitude–treatment interaction between
ARCS designed tactics and situation–outcome–expectancies (SOE) with regard toself-regulated learning in a Web lecture course. SOE (Rheinberg et al., 2000) refers
to a person’s belief that a given situation will lead to predictable outcomes more orless automatically. An example of high SOE in relation to the critical thinking task
in this study was ‘I am excellent in critical thinking, even when I do not prepare forit’. In contrast, a low SOE person would express an opinion such as ‘If I do not do
at least five additional tasks, I will not be able to finish my final examination’.
Astleitner and Hufnagl found that participants who had low SOE had higher
motivation and higher levels of achievement in the motivationally enhanced condition based on ARCS design principles, while the high SOE learners did not. There
were no differences in the motivationally unenhanced condition, and they concluded
that motivationally designed instruction had a positive effect on self-regulation for
low SOE learners. These investigators also provided a further confirmation of apreviously validated finding (Suzuki & Keller, 1996) that the ARCS design process
helps one include essential motivational tactics and avoid having excessive tactics
that might in fact annoy the learners.
Validation of a simplified motivational design process in E-learning
One of the challenges in using the full 10-step process for motivational design is thatit can be time consuming and works best for large-scale projects. As a means offacilitating systematic motivational design, Suzuki (Suzuki & Keller, 1996; Keller,
1997) created a simplified approach and tested its effectiveness in a project with 25
teachers in eight subject areas at Sendai Daichi Junior High School in Sendai, Japan.

Learner motivation and E-learning design

235

These teachers had been developing computer application and E-learning projects
for several years as part of a demonstration project sponsored by the Japanesenational government. During the final 2 years of the project they were asked toincorporate systematic motivational design into their process. The goal of thesimplified approach was to develop in a simple matrix format a condensation of thesteps from the larger model. It was designed to ensure that the teachers would
identify key motivational characteristics of the learners, the content area to be taught
and the hardware or software to be used. The teachers then evaluated this information and prescribed tactics based on identified motivational problems.
To facilitate their identification of motivational tactics, Suzuki provided checklists
and tables of potential tactics (Keller & Suzuki, 1988; Keller & Burkman, 1993).
This process helped ensure that teachers avoided the inclusion of excessive numbersof tactics or tactics derived from their own preferred areas of interest without regard
to the characteristics of the students and the situation. An evaluation of theeffectiveness of this motivational design process (Suzuki & Keller, 1996) verified
that the teachers were able to use the matrix accurately with only a few entries not
being placed appropriately and more than two-thirds felt that it definitely helped
them produce a more effective motivational design. Some teachers had difficulties
with the analysis phase, which indicates that this is a critical area to address intraining people to use the process.
This simplified design process was modified (Keller, 1997) and used in two
subsequent projects. The first of these was to develop and test a prototype ofmotivationally adaptive computer-based instruction. In the first, Song (Song &Keller, 2001), building on the work of Astleitner and Keller (1995) and del Soldato& du Boulay (1995), designed and tested an approach to motivationally adaptive
instruction. He built checkpoints into an instructional program on genetics forjunior high school students. At predetermined points students in the primary
treatment group received a screen asking several questions about their motivational
attitudes. Based on the responses, which were compared to actual performancelevels, students would receive motivational tactics designed to improve attention,
relevance or confidence. The simplified ARCS model design process was used tocreate specifications for tactics to be included in the adaptive treatment, which wascompared with a full-featured treatment containing all of the motivational tactics
and a minimalist treatment. The results indicated that both the adaptive andfull-featured treatments were superior to the minimalist treatment and, in most
instances, the adaptive treatment was superior to the full-featured one.
The second extension of the simplified design process (Visser L., 1998) was in asomewhat traditional distance learning course in which printed materials and multimedia were posted to students in several different countries who could then use
Email, depending on its availability, to communicate with the tutor. It was notpossible to modify the materials in this study, however L. Visser postulated thatsignificant improvements in retention could result from improvements in student
support activities. She adapted a motivational strategy developed and validated in an
adult education setting in Mozambique (Visser, J. & Keller, 1990). This approach
includes the creation and distribution of ‘motivational messages’ that are sent to236 J. M. Keller and K. Suzuki
students according to two schedules. The first is a set of fixed points based on
predictions of the points during the course when these messages might have thestrongest effect. These messages are the same for everyone. The second scheduleconsists of personal messages sent to students when the instructor, or in L. Visser’s
case the tutor, deems it appropriate. These messages were in the form of greetingcards, which conveyed messages of encouragement, reminders, empathy, advice andother appropriate content. To assess the effectiveness of this intervention, she
compared retention rates in the experimental section of the course to three othersections that did not receive motivational messages and she did a qualitative review
of student responses to various course evaluation and feedback instruments. She did
not ask them directly about the effects of the motivational messages to avoid
stimulating attitudes that may not have been present spontaneously in the students’minds. However, students included a variety of direct and indirect comments thatvalidated the effectiveness of the messages. Also, improved retention rates of
70–80%, which are similar to conventional education, offered clear support for thisapplication of systematic motivational design.
The model has been tested and validated in many different contexts and cultures(Klein & Freitag, 1992; Bohlin et al., 1993; Suzuki et al., 1993), including educational and employee training at virtually all levels and in settings as diverse as Japan,
Austria, Mozambique and Ireland, to mention only a few. Not all of the efforts toapply the model can be assumed to have been successful, but they are not likely tobe published. However, there is an example of a partial study that was conducted byAstleitner and Lintner (2003). They administered a motivationally enhanced treatment over a period of time in which participants were tested three times. The
self-regulated learners performed worse in the motivationally enhanced condition
than in the unenhanced condition on the first test, there were no differences on thesecond test, but they performed better on the third test. It appeared in this situationthat there was a long-term benefit to the motivational enhancements, even for
self-regulated learners, although there was no short-term benefit. The investigators
discussed possible modifications to make in future studies.
Conclusion
The primary conclusions to be drawn from the research to date seem to be that it
is possible to implement systematic approaches to identifying the motivational
requirements of learners in E-learning settings and to design motivational enhancements that will predictably improve learner motivation and performance. In particular, the ARCS model (Keller, 1987a,b) has been proven in numerous studies to beeffective. Based on this research, it is clear that systematic, holistic motivational
analysis of the audience as incorporated in the ARCS model will help lead one to thecreation and selection of motivational tactics that are consistent with the motivational needs of the audience. In contrast, in motivational design projects where theaudience analysis phase is omitted or restricted to an isolated aspect of motivation,
the result can be to include too many motivational tactics or irrelevant ones
(Farmer, 1989; Suzuki & Keller, 1996). The research on motivational design, bothLearner motivation and E-learning design237

in areas that incorporate the ARCS model and in areas that will be incorporated into
the ARCS model, are continuing into new and interesting areas, including studies of
emotion with regard to motivation and learning (Ortony et al., 1988; LeDoux,
1996), the affective and other motivational effects of animated pedagogical agents(Baylor, 1999) and the design of affective elements into computer interfaces (Picard,
2000). All of these efforts are contributing to more systematic and predictably
effective ways of understanding and influencing learner motivation. In closing, it isimportant to note the emphasis on influencing learner motivation, not controlling it.
Ultimately, instructors and machine-based instruction cannot control learner motivation, but on the other hand, they cannot avoid influencing the motivation oflearners, either positively or negatively. Systematic motivational design has beenshown to be effective when used properly and within the boundaries of modifiableinfluences on learners.

Notes on contributors
John M. Keller has a Ph.D. in Instructional Systems Technology and Organizational
Behavior from Indiana University and is a Professor of Instructional Systems
and Educational Psychology at Florida State University. Since earning his
Ph.D. he has focused on the development of approaches to designing motivational systems and is best known for his motivational design process called the‘ARCS model’. He has published many articles and book chapters on the
ARCS model and other aspects of training development and evaluation. Most
recently, he is co-author of Principles of Instructional Design (5th edn), releasedin July 2004.
Katsuaki Suzuki, Ph.D., is a Professor in the Faculty of Software and Information
Science, Iwate Prefectural University, majoring in Educational Technology.
Prof. Suzuki graduated from the International Christian University, Tokyo andearned his Ph.D. in Instructional Systems at Florida State University. Since
then he has been working to promote the utilization of various instructional
design models in practical settings. His research interests include ICT education, teacher training, educational media utilization and instructional designin E-learning settings.

References
Adams, J. S. (1965) Inequity in social exchange, in: L. Berkowitz (Ed.) Advances in experimentalsocial psychology (vol. 2) (New York, Academic Press).
Astleitner, H. & Hufnagl, M. (2003) The effects of situation-outcome-expectancies and of
ARCS-strategies on self-regulated learning with web-lectures, Journal of Educational Multimedia and Hypermedia, 12(4), 361–376.
Astleitner, H. & Keller, J. M. (1995) A model for motivationally adaptive computer-assisted
instruction, Journal of Computing in Education, 27(30), 270–280.

238 J. M. Keller and K. Suzuki
Astleitner, H. & Lintner, P. (2003) The effects of ARCS-strategies on self-regulated learning with
instructional texts, unpublished manuscript, Salzburg, Austria.
Bandura, A. (1977) Self-efficacy: toward a unifying theory of behavioral change, Psychological
Review, 84, 191–215.
Baylor, A. (1999) Intelligent agents as cognitive tools for education, Educational Technology, 39(2),373–382.
Bellon, T. & Oates, R. (2002) Best practices in cyberspace: motivating the online learner.
Available online at: ccenter.uoregon.edu/conferences/necc2002/program/presenter rp pdfs/
bellon.pdf (accessed 28 April 2004).
Berlyne, D. E. (1965) Motivational problems raised by exploratory and epistemic behavior, in: S.
Koch (Ed.) Psychology: a study of a science (vol. 5) (New York, McGraw-Hill).
Bohlin, R. M., Milheim, W. D. & Viechnicki, K. J. (1993) The development of a model for thedesign of motivational adult instruction in higher education, Journal of Educational Technology Systems, 22(1), 3–17.
Chang, M. M. & Lehman, J. (2001) Making web-based instruction more relevant. Available online
at: www.calumet.purdue.edu/todl/proceedings/2001/2001papers/chang.PDF (accessed 28
April 2004).
Chyung Y., Winiecki, D. & Fenner, J. A. (1999) Evaluation of effective interventions to solve thedropout problem in adult distance education. Available online at: coen.boisestate.edu/ychyung/edmedia.htm (accessed 28 April 2004).
Condry, J. (1977) Enemies of exploration: self-initiated versus other-initiated learning, Journal of
Personality and Social Psychology, 35, 459–477.
Csikszentmihalyi, M. (1990) Flow: the psychology of optimal experience (New York, Harper & Row).
Deci, E. L. & Ryan, R. (1985) Intrinsic motivation and self-determination in human behavior (NewYork, Plenum).
del Soldato, T. & du Boulay, B. (1995) Implementation of motivational tactics in tutoring
systems, Journal of Artificial Intelligence, 6(4), 337–338.
Duffy, T. M., Lowyck, J. & Jonassen, D. H. (1993) Designing environments for constructivist learning(New York, Springer Verlag).
Farmer, T. M. (1989) A refinement of the arcs motivational design procedure using a formativeevaluation methodology (Bloomington, IN, Indiana University).
Joung, S. & Keller, J. M. (2004) The effects of high-structure cooperative versus low-structure
collaborative design on decision change, critical thinking, and interaction pattern, unpublishedmanuscript, Tallahassee, FL.
Keller, J. M. (1987a) Strategies for stimulating the motivation to learn, Performance & Instruction,
26(8), 1–7.
Keller, J. M. (1987b) The systematic process of motivational design, Performance & Instruction,
26(9), 1–8.
Keller, J. M. (1997) Motivational design and multimedia: beyond the novelty effect, Strategic
Human Resource Development Review, 1(1), 188–203.
Keller, J. M. & Burkman, E. (1993) Motivation principles, in: M. Fleming & W. H. Levie (Eds)
Instructional message design: principles from the behavioral and cognitive sciences (Englewood
Cliffs, NJ, Educational Technology Press).
Keller, J. M. & Suzuki, K. (1988) Application of the ARCS model to courseware design, in: D.
H. Jonassen (Ed.) Instructional designs for microcomputer courseware design (New York,
Lawrence Erlbaum), 401–434.
Klein, J. D. & Freitag, E. T. (1992) Training students to utilize self-motivational strategies,
Educational Technology, 32(3), 44–48.
Kopp, T. (1982) Designing the boredom out of instruction, NSPI Journal, May, 23–27 and 29.
LeDoux, J. (1996) The emotional brain: the mysterious underpinnings of emotional life (New York,
Touchstone).
Maehr, M. L. (1976) Continuing motivation: an analysis of a seldom considered educational
outcome, Review of Educational Research, 46(3), 443–462.

Learner motivation and E-learning design

239

McClelland, D. C. (1984) Motives, personality, and society: selected papers (New York, Praeger).
Means, T. B., Jonassen, D. H. & Dwyer, R. M. (1997) Enhancing relevance: embedded ARCS
strategies vs. purpose, Educational Technology Research and Development, 45(1), 5–18.
Moore, M. G. & Kearsley, G. (1996) Distance education—a systems view (Belmont, CA,
Wadsworth Publishing Co.).
Ortony A., Clore, G. L. & Collins, A. (1988) The cognitive structure of emotions (Cambridge, UK,
Cambridge University Press).
Pass F., Tuovinen, J. E., Tabbers, H. & van Gerven, P. W. M. (2003) Cognitive load
measurement as a means to advance cognitive load theory, Educational Psychologist, 38(1),63–71.
Picard, R. W. (2000) Affective computing (Cambridge, MA, MIT Press).
Rheinberg F., Vollmeyer, R. & Rollet, W. (2000) Motivation and action in self-regulation, in: M.
Boekaerts, P. R. Pintrich & M. Zeidner (Eds), Handbook of self-regulation (San Diego, CA,
Academic Press).
Rowntree, D. (1992) Exploring open and distance learning (London, Kogan Page).
Skinner, B. F. (1968) The technology of teaching (New York, Appleton-Century-Crofts).
Small, R. V. & Gluck, M. (1994) The relationship of motivational conditions to effective
instructional attributes: a magnitude scaling approach, Educational Technology, 34(8), 33–
40.
Song, S. H. & Keller, J. M. (2001) Effectiveness of motivationally adaptive computer-assisted
instruction on the dynamic aspects of motivation, Educational Technology Research andDevelopment, 49(2), 5–22.
Suzuki, K. & Keller, J. M. (1996) Creation and cultural validation of an ARCS motivational
design matrix, paper presented at the Annual Meeting of the Japanese Association for
Educational Technology, Kanazawa, Japan.
Suzuki K., Sakayauchi, M. & Akahori, K. (1993). A case study of motivational design: multimedia
courseware “The Secret of Aunt Mariko”, paper presented at the Annual Meeting of theAustralian Association for Research in Education, Freemantle, WA, November 22–25.
Visser, L. (1998) The development of motivational communication in distance education support.
Report for the Educational Technology Department (Twente, University of Twente, The
Netherlands).
Visser, J. & Keller, J. M. (1990) The clinical use of motivational messages—an inquiry into thevalidity of the Arcs model of motivational design, Instructional Science, 19(6), 467–500.
Weiner B. (Ed.) (1974) Achievement motivation and attribution theory (Morristown, NJ, General
Learning Press).
White, R. W. (1959) Motivation reconsidered: the concept of competence, Psychological Review,78, 44–57.
Zvacek, S. M. (1991) Effective affective design for distance education, TechTrends, 36(1), 40–43.