The effects of situation-outcome-expectancies and of ARCS-strategies on self-regulated learning with web-lectures.

The purpose of this study was to examine the effects of a prototype of a motivationally designed web-lecture-based learning environment on motivation and learning. The foundation for motivational design was provided by the Attention, Relevance, Confidence, and Satisfaction (ARCS)-approach. This approach included instructional strategies to enhance attention, relevance, confidence, and satisfaction. Results showed that the ARCS-strategies led to higher perceived success estimates, higher general motivation and better knowledge acquisition, but only for students with motivationally advantageous expectancies (i.e., low situation-outcome-expectancies). Students with motivationally disadvantageous expectations did not profit from ARCS-strategies. Finally, implications for further research and instructional design are discussed.

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Self-regulated learning remains active when the learner expects that activities will change the result of a learning process (e.g., Astleitner & Keller, 1995). Rheinberg, Vollmeyer, and Rollet (2000) postulated a model of selfregulated learning in which three different types of expectancies were considered. Whereas action-outcome-expectancies (AOE, i.e., the probability that one's action will lead to the desired outcome [probability of success]) and outcome-consequence-expectancies (OCE, i.e., the probability that an outcome will have the desired consequences [instrumentality]) are comprehensively investigated in psychological theory and research, situation-outcome-expectancies (SOE) are usually neglected by educational psychologists (e.g., Heckhausen, 1991). SOE represents the "(subjective) assumption that the just given situation will lead to the desired outcome on its own, without the need to take any action" (Rheinberg, Vollmeyer, & Rollet, 2000, p. 510). These expectancies correspond with the occurrence of certain thoughts. The following examples of such thoughts correlate with a high motivation to act: "I have good skills, there is no problem for mastering the tasks presented today" could stand, for example, for high AOE. "If I can finish the task, the computer will certainly register and report all my progress," stands, for example, for high OCE. "If I do not do at least five other tasks, I will not be able to finish my final examination" represents an example for low SOE.

Generally, it is assumed that, when SOE are low, then there is a significant motivation to undertake goal-orientated activities in self-regulated learning. Bolles (1972) postulated that SOE are relevant for learning, because they can more easily be influenced by instructional contexts than other expectancies (e.g., AOE). However, Mellgren and Olson (1980) and also Schwarze and Rubeling (1985) found that SOE, although expected, did not change during learning and had no significant influence on successful learning. One reason for these findings can be that SOE are less changeable during learning and represent a stable personality characteristic in a sense of generalized and personalized patterns of expectancies. Based on this assumption, SOE should be considered as aptitude- or trait-variable in corresponding research activities. A second reason for these findings might be that SOE relate to learning in a complex way. Duval, Duval, and Mulilis (1992) found that outcome expectancies and their potential to increase task efforts are dependent on levels of self-focus, of self-standard-discrepancy, and of the rate of progress toward discrepancy reduction. Gendolla (1997) found that outcome valence and importance had effects on expectancy formation, on expectancy-disconfirmation, and on emotions related to learning in achievement situations. These findings indicate that SOE can be made useful for learning, especially when instructional interventions trigger different cognitive processes relevant for expectancy formation and evaluation of learning outcomes and processes intensively.

The Attention, Relevance, Confidence and Satisfaction (ARCS)-approach from Keller (1983, 1997, 1999) represents an instructional model, which can influence such processes in a comprehensive and effective way. It assumes that strategies embedded in instructional materials can enhance the learner's attention (by perceptual and inquiry arousal, or variability) and perceptions of relevance (by familiarity, goal orientation, or motive matching), confidence (by influencing expectancy for success, challenge setting, or attribution molding), and satisfaction (by natural and positive consequences, or equity) about learning, which in turn enhances cognitive performance. The ARCS-approach is based on well-founded results of motivational psychology, provides a definition of motivation, a motivational designprocess, and recommendations for motivational strategies, which can be implemented in different forms of instruction (i.e., traditional classroom instruction, multimedia-based learning, or web-based teaching). It combines a descriptive synthesis of concepts and theories of motivation with a systematic approach to motivational design and has stimulated a considerable amount of empirical research.

Means, Jonassen, and Dwyer (1997) found that embedded relevance strategies (ARCS-strategies related to goal-orientation and familiarity) enhanced motivation and improved performance, especially for the learners for whom the materials were not relevant. Visser (1998) used a simplified version of the ARCS-approach to influence the motivation of learners within web-based learning environments by focusing on the student support system. She implemented a program of motivational messages, which were sent to students. These messages were in the form of greeting cards, which included encouragement, reminders, or advice. The design of the messages was adaptive to personal needs and based on predictions concerning students' precourse and midterm attitudes toward web-based learning environments, their reactions to the course content, and characteristics of their support during the course. These predictions and related messages varied according the four ARCS-parameters. For example, for gaining attention, unexpected communication to students from time to time was used as a motivational tactic. For increasing relevance, messages link feedback to learners' work and daily circumstances. For enhancing confidence, messages were designed to make students feel as part of a group in which all were struggling to get it done. For achieving satisfaction, turn-around time for assignments were kept short. Improved retention rates and student comments offered clear support for the motivational messages. Shellnut, Knowlton, and Savage (1999) applied the ARCS-approach to the design of computer-based modules. Evaluators found the ARCS-approach effective in motivating learners, however pointed out, that more focus should be given to dynamic learner interactions, in particular to motivationally relevant personality characteristics. Chyung, Winiecki, and Fenner (1999) reduced significantly dropout rates in adult distance education programs by constantly evaluating and redesigning instruction based on the ARCS-approach and on individual students' interests and performance. Song and Keller (2001) found that motivationally adaptive computer-assisted instruction was superior to motivationally not adaptive instruction for the enhancement of overall motivation and attention. Within motivationally adaptive instruction, students received motivation enhancing strategies only when they indicated motivational deficits on embedded surveys. In a study from Chang and Lehman (2001), video sequences were implemented in web-lectures and designed using relevance-strategies from the ARCS-approach. Results showed that students who used the enhanced relevance version had significantly higher perceptions of motivation and scored higher on a comprehension test.

In general, web-lectures combine linear sequences of speech-recordings with overviews and presentations with the possibility to freely navigate through the material. Web-lectures consist, as a rule, of audio or video recordings, which can be combined with synchronous (or asynchronous) slides. Such slides contain information based on the recordings in text or in graphical form. The slides presented as learning guidance can be seen as "synchronous organizers," which represent a combination of "advance" and "post organizers." They are "advance organizers" when learners use them for preparing learning at the beginning of listening to the recordings, or they are "post organizers" when learners use them to integrate and compare elements after listening to the recordings. Web-lectures represent a promising way of teaching and learning in the near future, because they are (a) simple to design and use (designers and users do not need sophisticated skills or tools other than a suitable multimedia PC, web connection, and adding streaming media to web pages), and (b) flexible (streamed lectures are available everytime and everywhere across the Internet promoting flexible delivery and broad participation). Although, there are many research studies dealing with learning from audio-video recordings (e.g., Cennamo, 1993), there is little well-founded research dealing with learning within video-stream-based web-lectures. For example, Latchman and Kim (1999), Murphy, Dooley, Wickersham, and Parlin (1999), and also Wheeler (2000) discussed technological, methodological, and psychological factors for the successful use of web-delivered streaming lectures in distance learning settings based on some qualitative assessments, but did not deliver any quantitative data about the the effectiveness of different types of web-lectures.

Based on the given research, it is intended in the present study, to test the effect of SOE (low and high) as trait-variable together with the application of the ARCS-approach (web-lectures with embedded ARCS-strategies and without) on motivation and learning. The effects of these two variables are tested on ARCS-related motivation, success estimates, and knowledge acquisition as indicators of learning. ARCS-related motivation concerned the learner's attention, perceptions of relevance, confidence, and satisfaction experienced during a learning sequence. Success estimates were defined as the learners' subjective assumptions about the quality (i.e., success or failure) of the learning process and the learning result. Knowledge acquisition was defined as the learner's ability to remember the learning contents. Success estimates were considered because web-lectures realize self-regulated learning environments in which the learners themselves control the learning activities, and controlling the learning process is mainly based on evaluations of the learning results. ARCS-related motivation and knowledge acquisition represent typical dependent measures in similar studies (e.g., Song & Keller, 2001).

First, it was expected, that the instructional environment with ARCS-strategies influences significantly ARCS-related motivational parameters what represents, on the one hand, a test of an effect on motivation in general, and, on the other hand, an attempt to test the validity of the embedded instructional design elements, because ARCS-strategies should influence ARCS-related motivation. In respect to success estimates, it was expected that learners would have higher success estimates within the ARCS-enhanced version. This version offers additional learning guidance what might reduce problems in selecting, summarizing, or structuring the content presented within the learning environment. It might give learners a feeling of confidence and satisfaction what should produce higher success estimates. Also, knowledge acquisition should be influenced differently from the different types of web-lectures. First, it was expected, that retention can be improved when ARCS-strategies in comparison to no additional motivational strategies were used. Especially, the capability of ARCS-strategies to create attention and relevance is important for retention, which is based on the often boring relearning of concepts, rules, and so forth.

In addition to the main effects of the ARCS-strategies, it was also expected, that ARCS-strategies influence the given dependent variables in interaction with SOE. Recently, Bellon and Oates (2002) found, that ARCS-strategies varied in their effects when considering different personality characteristics (i.e., extroverts vs. introverts, sensing vs. intuitive, feeling vs. thinking, and judging vs. perceiving). Also, within other mentioned research results, there are clear indications that ARCS-strategies varied in their effects for different types of learners or trait-variables (e.g., Means, Jonassen, & Dwyer, 1997; Visser, 1998; Shellnut, Knowlton, & Savage, 1999; Chyung, Winiecki, & Fenner, 1999; Song & Keller, 2001; Chang & Lehman, 2001). Within this study, SOE were considered as personality characteristics (in a sense of stable expectancy patterns) and should influence learning as aptitude-treatment-interaction. It is assumed that SOE have a different influence on learning parameters in different instructional settings. Especially, it was expected that the relation between SOE and the dependent variables should be different when ARCS-strategies are implemented from the condition when they are not: Students with low SOE think that they need effort, activities, and support to reach a given goal. ARCS-strategies stimulate this kind of thinking and additionally offer support in a way that they increase attention, cognitive engagement, and reflection (see the Multimedia learning theory by Astleitner, 2003). Therefore students with low SOE should benefit more from ARCS-strategies than students with high SOE. Students with high SOE are--according to their expectancies--not sensible for instructional offers like the ARCS-strategies, because they are not convinced that learning activities will change significantly learning outcomes.

METHOD

Participants, Design, and Procedures

Eight male and 36 female university students with an average age of 30 years participated in this experiment. All students attended courses in multimedia learning at the University of Salzburg and got reward points for course examinations. Students were randomly assigned to the experimental conditions.

The experiment was based on a one-factorial-covariance-analytic design. As experimental condition, the motivational design of the web-lecture was manipulated. One half of the students (control group, n = 22) was presented a web-lecture without motivational features. The other half of the students (experimental group, n = 22) got a web-lecture designed according to the ARCS-approach. SOE served as covariable. As dependent variables, ARCS-related motivation, success estimates, and knowledge acquisition were measured. As two dependent variables (ARCS-related motivation and success estimates) were correlated significantly (r = 0.43, p < 0.005), a multivariate analysis of covariance was computed for these two variables. For knowledge acquisition, a univariate analysis of covariance--using the General Linear Model with SPSS--was computed (Winer, Brown, & Michels, 1991, p. 739).

At the beginning of the experiment, the subjects were informed about their task (learning with a web-lecture) and the duration of the experiment, that is, about 60 minutes. Then they had to complete a questionnaire including general questions (age, sex, Internet experience, etc.) and questions about their expectancies for learning. After that, they were told to learn as much as possible about the content of the web-lecture, that is, critical thinking (in particular informal errors in arguments). Learning with the web-lecture took about 45 minutes. After a short break, students had to answer questions concerning success estimates, ARCS-related motivation, and the knowledge test. The whole experiment took place at a room with 12 Internet-connected personal computers. Students wore headphones and used a mouse-device for controlling the video-stream.

Materials and Instruments

As learning environments, web-lectures about informal errors in arguments were developed by using the JAVA programming language, MS-PowerPoint, and a digital video stream on a standard personal computer (with MS-Windows). Within the experiment, two different forms of the web-lecture were used. In the version "without ARCS," students were able to start, pause and stop a video stream with synchronous organizers. The content of the web-lecture was divided into 17 different sections. Each section contained the definition of an informal error together with an illustrative example. The resulting video had a duration of about 13 minutes.

The version "with ARCS" had a duration of about 15 minutes and was different from the "without ARCS" version in several ways: (a) the organizers contained color and symbols, especially for highlighting important information (for stimulating attention), (b) examples within the organizers were related to the life and personal experiences of the students (for stimulating relevance), (c) students were able to select freely each subtopic of the content (for stimulating control and confidence), and (d) within the spoken text, students were presented fictitious opportunities for applying their acquired knowledge in daily situations (for stimulating satisfaction) (Figure 1).

[FIGURE 1 OMITTED]

All motivating features were designed as suggested by the ARCS-approach to stimulate the four important motivational components of attention, relevance, confidence, and satisfaction. These four strategies were selected from a list of 12 ARCS-strategies presented from Keller and Kopp (1987, p. 292). The selection of the strategies was based on the technical and conceptual easiness of their implementation within a web-lecture-based learning environment: Only those strategies were implemented which can be implemented in web-lectures with little effort in time and money, but which also should lead to significant positive effects on motivation and learning.

SOE were measured with the related subscale from the Potsdamer Motivations-Inventar (PMI) by Rheinberg and Wendland (2001) (Cronbach's Alpha = 0.80). The following items had to answered on a 5-point-Likert-scale (from "totally true" to "not at all true"): "I do not need any activity for critical thinking because I understand everything at once," "critical thinking is boring to me because I know everything that is presented during instruction," "when I am thinking critically, then everything is clear to me, I do not need any effort," "I am excellent in critical thinking, even when I do not prepare for it," and "I do not need to learn anything for critical thinking, because I can solve relevant tasks automatically."

ARCS-motivation was measured by 12 items (Cronbach's Alpha = 0.74). All items had to be answered on a 5-point-Likert scale (ranging from "totally true" to "not at all true"). The following items were used to measure the components attention, relevance, confidence, and satisfaction: "Learning with this kind of presentation was interesting to me," "I was curious to get the contained information," "the presentation was designed with varying components" (measured attention), "the contents were presented to me in a well-known and vivid way," "I knew the goals of the presentation early," "I realized the contents of the presentation as helpful" (measured relevance), "the presentation was adapted to my personal needs," "I was able to understand the most important contents well during listening and reading," "the presentation was not too difficult or too easy for me" (measured confidence), "I had the impression that I could understand the content of the presentation when I tried hard to accomplish my goals," "I think that I can use the acquired knowledge in many other situations," and "I think that the acquired knowledge will have a lot of positive consequences" (measured satisfaction).

All items of the so far mentioned subscales were shuffled, so that it was not possible to easily identify which items belong to the same subscale. That makes learners to think before answering what should reduce bias in answering.

Success estimates were measured based on two items (Cronbach's Alpha = 0.79). These items were: "This learning experience showed me that I am able to learn with the Internet" (with "yes" or "no" as answer possibilities) and (choosing one of the following two statements:) "I have learned a lot" or "I have learned a little."

Knowledge acquisition was measured by 12 items (Cronbach's Alpha = 0.88). Here are three examples of the test items: (a) "Alcohol causes liver damage. The explanation is, that alcohol contains liver damaging substances. Irrespective of whether this statement is right or wrong: Is this a vicious circle? [yes, no]." (b) "A 'domino-error' is given, when [a] somebody assumes a questionable circumstance which leads through several steps to an inevitable conclusion; [b] an argument is criticized by referring to a questionable circumstance; [c] somebody estimates a statement as being true, because somebody else has proven that the negation of the statement is true; [d] somebody attacks a weak argument concerning a circumstance for which there are strong arguments available." (c) "If you do not finish your homework, then the head of school will be angry about you," says a mother to its child. Which type of non-formal error is given? [error of authority, error of ignorance, error of loaded statement]."

RESULTS

First, multivariate analysis showed a significant effect of the instructional context (p = 0.029) and of an aptitude-treatment-interaction (instructional context * SOE, p = 0.009) on the measured dependent variables concerning ARCS-related motivation and success estimates (see first part of Table 1).

Based on univariate analysis, the instructional context showed a significant influence on success estimates (p=0.009, [R.sup.2]=0.168). However, this main effect has to be related to a significant aptitude-treatment-interaction-effect (p=0.003, [R.sup.2]=0.207): Low SOE (i.e., persons with a strong belief that it takes an effort to be successful in learning) only led to high success estimates within the instructional context based on the ARCS-approach (r=-0.437). Within the instructional context without the consideration of the ARCS-approach, low SOE corresponds with low success estimates (r=0.480, see the first scatterplot in Figure 2). A similar aptitude-treatment-interaction-effect nearly reached significance for ARCS-related motivation (p=.071, [R.sup.2]=0.083): Low SOE only corresponds with high ARCS-related motivation within the instructional context based on the ARCS-approach (r=-0.138). Without ARCS, low SOE leaded to low ARCS-related motivation (r=0.448, see the second scatterplot in Figure 2).

[FIGURE 2 OMITTED]

Also, in respect to knowledge acquisition, the identified aptitude-treatment-interaction-effect nearly reached statistical significance (p=0.079, [R.sup.2]=0.075, see second part of Table 1 and the third scatterplot in Figure 2): Low SOE lead to higher knowledge acquisition (r=0.119) within the weblecture based on ARCS-strategies, and not within the web-lecture without ARCS (r=-0.451). There was a nonsignificant main effect of the ARCS-strategies on knowledge acquisition (F=2.795, p=0.102).

Overall, the results showed that including ARCS-strategies within web-lectures leaded to higher ARCS-related motivation, to higher success estimates, and better knowledge acquisition, but only for students with low SOE. Students with motivationally high SOE did not profit from an instructional context enriched by ARCS-strategies. Students with high SOE showed high success estimates, high ARCS-related motivation, and high knowledge acquisition, but only within the web-lecture in which ARCS-strategies were not implemented.

DISCUSSIONS

The result that the implementation of ARCS-strategies in web-lecture-environments lead to better motivational and cognitive conditions for learning or better learning itself, at least when considering a certain type of learner, is consistent through all measurements of the dependent variables (motivation, perceived success, and knowledge acquisition); that circumstance strengthens the identified aptitude-treatment-interaction-effect of SOE and ARCS-strategies.

When ARCS-strategies are implemented, then students with low SOE profited more for learning than students with high SOE. Students with low SOE dispose of motivationally advantageous expectations: they think that effort is essential for being successful. Students with high SOE think, that effort is not necessary, because success or failure is given for them irrespective of their actions. In general, it is easier to motivate students with low SOE, because they attribute success or failure to an instable and easily changeable component like effort. In contrast, students with high SOE are difficult to motivate, because they attribute success or failure to more stable and less changeable components like talent. In that sense, the ARCS-strategies helped to motivate and to improve learning for the easy-to-motivate students and were not able to improve the situation for the difficult-to-motivate students.

The concept of SOE (patterns) showed to be a relevant personality characteristic when considering the ARCS-approach for instructional design. On the basis of the results, it can be recommended that students should be asked about their SOE before ARCS-strategies are implemented. Students with low SOE can profit from ARCS-strategies, however for students with high SOE, ARCS-strategies do not lead to higher motivation and better learning. However, before this recommendation should be embedded fully in instructional practice, further replications of this study are necessary.

Of course, within the given study, both, not all ARCS-strategies and only one motivationally relevant personality characteristic were considered. In future research activities, more strategies and personality characteristics should be combined and tested within an aptitude-treatment-interaction-research. Different expectancies or more or less stable patterns of expectancies should be considered, because they are related to theoretical models with a long tradition in research on motivation, self-regulation, and instructional design (Heckhausen, 1991).

Especially, for research dealing with the dropout problem in e-learning, the results of the given study should be considered (Lee & Boling, 1999). It can be recommended that every application of motivational strategies must be combined with a corresponding audience analysis, as suggested by the ARCS-approach itself. For some students, additional motivational tactics are helpful, for others, they have no significant or even negative effects.

The findings from this study should stimulate other researchers investigating in more detail learner personality characteristics and their relation to instructional components of the ARCS-approach. This kind of research can build the basis for more sophisticated quality management activities in e-learning emphasizing in particular the capabilities of an instructional environment to adapt to the personal motivation of a learner.

Table 1 Effects of Instructional Context and of SOE on ARCS-Related
Motivation, Success Estimates, and Knowledge Acquisition

ARCS-motivation and success estimates

                             Multivariate Analysis of Covariance
Source                       Lambda  F      df     p

Treatment
Instructional contexts (IC)  0.826   3.902  2, 37  0.029
Aptitude
Situation-outcome-
expectancy (SOE)             0.977   0.434  2, 37  0.651
Aptitude-Treatment-
Interaction (ATI)
IC * SOE                     0.775   5.366  2, 37  0.009

          Univariate Analyses of Variance
Source    ARCS-motivation          Success estimates
            F      p    [R.sup.2]    F      p    [R.sup.2]

IC        1.678  0.203  0.042      7.699  0.009  0.168
IC * SOE  3.443  0.071  0.083      9.947  0.003  0.207

Knowledge acquisition

                    Univariate Analysis of Covariance
Source              SS       df  MS      F      p      [R.sup.2]

Treatment
Instructional
contexts (IC)        16.624   1  16.624  2.795  0.102  0.065
AptitudeSituation-
outcome-
expectancy (SOE)      5.240   1   5.240  0.881  0.354  0.022
Aptitude-
Treatment-
Interaction
IC *SOE              19.344   1  19.344  3.252  0.079  0.075
Error               237.946  40   5.959
[R.sup.2]=0.104

Note

Correspondence concerning this article should be addressed to: Hermann Astleitner, University of Salzburg, Akademiestrasse 26, A-5020 Salzburg, Austria; Email: [e-mail].

This study is related to the Virtual Thinking School-Project which is financed by the Cornelsen-Stiftung fur Lehren und Lernen (Germany) (T066/11261/2001). The related project is situated at the Zentrum fur Lehr/Lernund Bildungsforschung (University of Erfurt, Germany).

References

Astleitner, H. (2003). An integrated model of multimedia learning and motivation. Paper presented at the bi-annual meeting of the European Association for Learning and Instruction (EARLI), Padua (Italy), August 26-31, 2003.

Astleitner, H., & Keller, J. M. (1995). A model for motivationally adaptive computer-assisted instruction. Journal of Research on Computing in Education, 27, 270-280.

Bellon, T., & Oates, R. (2002). Best practices in cyberspace. Motivating the online learner [Online]. Available: http://ccenter.uoregon.edu/conferences/necc2002/program/presenter_rp_pdfs/bellon.pdf

Bolles, R. C. (1972). Reinforcement, expectancy, and learning. Psychological Review, 79, 394-409.

Cennamo, K. S. (1993). Learning from video. Factors influencing learners' preconceptions and invested mental effort. Educational Technology, Research and Development, 41, 33-45.

Chang, M. M., & Lehman, J. (2001). Making web-based instruction more relevant. Lessons from a research study [Online]. Available: http://www.calumet.purdue.edu/todl/proceedings/2001/2001papers/chang.PDF

Chyung, Y., Winiecki, D., & Fenner, J. A. (1999). Evaluation of effective interventions to solve the dropout problem in adult distance education [Online]. Available: http://coen.boisestate.edu/ychyung/edmedia.htm

Duval, T. S., Duval, H. V., & Mulilis, J.-P. (1992). Effects of self-focus, discrepancy between self and standard, and outcome expectancy favorability on the tendency to match self to standard or to withdraw. Journal of Personality and Social Psychology, 62, 340-348.

Gendolla, G. H. E. (1997). Surprise in the context of achievement. The role of outcome valence and importance. Motivation and Emotion, 21, 165-193.

Heckhausen, H. (1991). Motivation and action. New York: Springer.

Hendrix, L. J., Carter, M. W., & Scott, D. T. (1982). Covariance analyses with heterogeneity of slopes in fixed models. Biometrics, 38, 641-650.

Keller, J. M. (1983). Motivational design of instruction. In C. M. Reigeluth (Ed.), Instructional-design theories and models. An overview of their current status (pp. 383-434). Hillsdale, NJ: Erlbaum.

Keller, J. M. (1997). Motivational design and multimedia. Beyond the novelty effect. Strategic Human Resource Development Review, 1, 188-203.

Keller, J. M. (1999). Motivation in cyber learning environments. International Journal of Educational Technology, 1, 7-30.

Keller, J. M., & Kopp, T. W. (1987). An application of the ARCS model of motivational design. In C. M. Reigeluth (Ed.), Instructional design theories in action. Lessons illustrating selected theories and models (pp. 289-320). Hillsdale, NJ: Erlbaum.

Latchman, H., & Kim, J. (1999). Streaming audio and video in ALNs [Online]. Available: http://csc.list.ufl.edu/~kjm/aln.html

Lee, S. H., & Boling, E. (1999). Screen design guidelines for motivation in interactive multimedia instruction. A survey and framework for designers. Educational Technology, 39, 19-26.

Means, T. B., Jonassen, D. H., & Dwyer, F. M. (1997). Enhancing relevance. Embedded ARCS strategies vs. purpose. Educational Technology, Research & Development, 45, 5-17.

Mellgren, R. L., & Olson, M. W. (1980). Associative processes controlling the persistence of operant responding: S-S+ and R-S+. Bulletin of the Psychonomic Society, 16, 279-282.

Murphy, T. H., Dooley, K. E., Wickersham, L., & Parlin, J. (1999). Streaming media as an instructional delivery strategy [Online]. Available: http://agnews.tamu.edu/saas/MURPH.HTM

Rheinberg, F., & Wendland, M. (2001). Das Potsdamer Motivations-Inventar (PMI). Ein Fragebogen zur Erfassung fachspezifischer Lernmotivation. Poster presented at the 35. Jahrestagung der Gesellschaft fur Didaktik der Mathematik, Ludwigsburg, March, 5th-9th, 2001.

Rheinberg, F., Vollmeyer, R., & Rollet, W. (2000). Motivation and action in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 503-529). San Diego, CA: Academic Press.

Schwarze, H., & Rubeling, H. (1985). Erkennen und Verhalten im Lernversuch. Psychologische Beitrage, 27, 434-446.

Shellnut, B., Knowlton, A., & Savage, T. (1999). Applying the ARCS model to the design and development of computer-based modules for manufacturing engineering courses. Educational Technology, Research and Development, 47, 100-110.

Song, S. H., & Keller, J. M. (2001). Effectiveness of motivationally adaptive computer-assisted instruction on the dynamic aspects of motivation. Educational Technology, Research & Development, 49, 5-22.

Visser, L. (1998). The development of motivational communication in distance education support. Unpublished doctoral dissertation, University of Twente, The Netherlands.

Wheeler, S. (2000). Streaming media and webcasting. Evaluation of an interactive distance learning application [Online]. Available: http://rilw.emp.paed.uni-muenchen.de/2000/papers/wheeler_paper.html

Winer, B. J., Brown, D. R., & Michels, K. M. (1991). Statistical principles in experimental design. New York: McGraw-Hill.

HERMANN ASTLEITNER AND MANUELA HUFNAGL

University of Salzburg Austria

[e-mail]

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