Welcome to a web site that serves as a free source of animated e-learning tools describing basic
concepts in the plastics/polymer science, engineering and technology areas.

Why use this site?
•    It’s easier to learn if you can first obtain an overview or  'framework' for a new area.
     Animation forces more mental interaction with the material and helps develop an overview.
•    E-learning tools are Flash® scientific animations (Modules) and/or virtual instruments (VIs);
     both are so-called Reusable Learning Objects (RLOs).
•   Basic concepts are presented using animation with minimal text or complex equations.
•   Comprehension is approximately junior/senior high school science level, and suitable for
     students and personnel in plastics and related academic or industrial areas.
•   Newly added materials from PREP provide additional ‘one shot’ animations and an
     extensive library of illustrations

From the Main Menu, click on the "List of Modules and VIs" to show sub-groupings for all
available Plastics e-Learning RLOs.  Click on a group and use links within the group to go directly
to the module or VI of interest. Navigation within the RLO is by arrows at top left of the embedded
RLO.  The 'up' arrow returns to the RLO introductory screen showing general topics covered and  
this material’s position in the overall organization. The [Back] button returns to the previous screen;
in this case the starting group of RLOs.

Recently added PREP Materials are divided into ‘Illustrations’ and ‘Animations’ with links to further
topic sub-divisions that consist of groups of related illustrations or animations respectively.

The role that animation has in learning is a complex state of affairs that has been studied for a
number of years and not surprisingly has produced conflicting ideas. In reviewing this topic,
particularly drawing from a recent paper by Lai (1), I’ll focus on those areas where there appears
to be a general consensus. Nevertheless, it might be wise to acknowledge the influence that the
common quotation "a picture is worth a thousand words" has on those of us in the animation
area. Naturally, the implication is that if a single picture is worth that many words, how much more
can we convey with a movie or animation. We should remember though that the expression is
attributed to Fred Barnard, an advertising manager in the early 1920s; he had an axe to grind, he
was selling advertising space. In reality Barnard's claim was not about information content, but
about the affect of content, namely, when used in advertising, a picture draws attention more than text.

Where does animation stand with respect to learning theory? Simplistically, it helps if learners can
easily obtain an overview of the area in question, a mental ‘Cliff’s Notes’, and animation helps to
do this. As stated by Ausubel’s (2) theory of meaningful learning, if a learner already has a clear
mental picture of a concept or discipline this conceptual model can provide a framework or
‘anchor’ to organize and incorporate new material (3). This begs the question how does a novice
learner obtain a clear model to start with. Well, animated pictures can present a variety of
different ‘views’ of a subject and therefore provide more information to a learner and force more
mental interaction than static pictures (4 - 6). Computer-generated animation offers a potentially
powerful medium that helps learners build mental models to aid comprehension (7,8). The mental
model theory proposes that learners are helped to form such a model by showing the various
states and relationships within the concept or idea (9 - 11).

To further help the learner it has been suggested (12) that breaking training into appropriate
‘chunks’ reduces cognative overload. As appealing as this idea may be, it appears as though it’s
better to give the complete picture as fully as possible and at one time (6). There has also been
discussion regarding program control; should learners follow a more or less prescribed path
through the information or be allowed to wander through the material at will (13 - 16). First we
need to appreciate that there is a limited amount of working memory and it’s tough to focus on
both content and program control (17 - 19). If the student spends too much time controlling the
learning program the effectiveness of learning the material is reduced (20). Whatever the
situation, control of the program by the learner has to be as painless as possible, we need
students to spend time learning the material not how to navigate through it (21).

Time is clearly an important consideration in instructional design (20). Learners require adequate
time to learn (22) since understanding new material involves integrating new information with prior
knowledge and that takes time if it’s to be performed effectively (23). The more time spent
interacting with instructional elements or questions, the better the learner will move information
into long-term memory for storage and future retrieval (24, 25). Providing sufficient processing
time for visuals with realistic details is also important (26). Richly detailed visuals may require
learners to search for learning cues. Therefore, when animation is used to provide a conceptual
model, the ‘prescribed path’ approach is to be favored over ‘free roaming’ since the former will
help maintain attention on the relevant information and at the same time help learners build
connections between abstract and concrete (1). Presumably, simpler cartoon type animations
that sacrifice realistic detail for clarity of concept are also to be preferred. While novice learners
are better served by ‘prescribed path’ programs (21, 27, 28) students with higher abilities and/or
better background may prefer their own route through new material (27). Intuitively, allowing
learners control over their instruction has a certain appeal (29); being able to exert control over
any environment is generally a pleasing experience. Essentially, it is assumed that individual
learners know their own needs best and are qualified to control their own learning (30 - 33).
Indeed, there is some support for the idea that higher-ability students prefer more control and
know their needs best (34).

Building an interactive computer based learning environment that is truly engaging is a difficult
task. The quality of multimedia assets such as images, sounds, and animations is a key factor
(stimuli) in getting users actively involved in the system. It appears that initially a ‘prescribed path’
to learning is favored. However, as students become more grounded in background material they
should perhaps be permitted to exert more control over their learning environment.

References
1. Lai, S-L. (2001) Controlling the display of animation for better understanding, Journal of
Research on Technology in Education, 33(5), http://www.iste.org/jrte/33/5/lai.html
2. Ausubel, D. P. (1968). Education psychology: A cognitive view. New York: Holt, Rinehart, &
Winston.
3. Mayer, R. E. (1976). Some conditions of meaningful learning for computer programming:
Advance organizers and subject control of frame sequencing. Journal of Educational
Psychology, 68, 143–150.
4. Rieber, L. P. (1990). Animation in computer-based instruction. Educational Technology
Research and Development, 38(1), 77–86.
5. Rieber, L. P. (1995). A historical review of visualization in human cognition. Educational
Technology Research and Development, 43(1), 45–56.
6. Schnotz, W., & Grzondziel, H. (1996, April). Knowledge acquisition with static and animated
pictures in computer-based learning. Paper presented at the annual meeting of the American
Educational Research Association, New York City. (ERIC No. ED 401 878)
7. Mayer, R. E., & Gallini, J. K. (1990). When is an illustration worth ten thousand words? Journal
of Educational Psychology, 82(4), 715–726.
8. Shih, Y. F., & Alessi, S. M. (1994). Mental models and transfer of learning in computer
programming. Journal of Research on Computing in Education, 26(2), 155–175.
9. Borgman, C. L. (1986). The user’s mental model of an information retrieval system: An
experiment on a prototype online search. International Journal of Man-Machine Studies, 24,
47–64.
10. Gentner, D., & Stevens, A. L. (Eds.). (1983). Mental models. Hillsdale, NJ: Lawrence Erlbaum
Associates.
11. Payne, S. J. (1988). Methods and mental models in theories of cognitive skill. In J. Self (Ed.),
Artificial intelligence and human learning (pp. 69–87). London: Chapman & Hall.
12. Clark, R. C., & Taylor, D. (1994). The causes and cures of learner overload. Training, 31(7),
40–43.
13. Morrison, G. R. (1992). Learner control of context and instructional support in learning
elementary school mathematics. Educational Technology Research and Development, 40(1),
5–13.
14. Pollock, J., & Sullivan, H. J. (1990, April). Learner control, achievement, and continuing
motivation in computer-based instruction. Paper presented at the annual meeting of the American
Educational Research Association, New Orleans, LA.
15. Ross, S., & Rakow, E. (1981). Learner control versus program control as adaptive strategies
for selection of instructional support on math rules. Journal of Educational Psychology, 73(5),
645–653.
16. Steinberg, E. R. (1977). Review of student control in computer-assisted instruction. Journal of
Computer-Based Instruction, 3(3), 84–90.
17. Park, O. (1992). Instructional applications of hypermedia: Functional features, limitations, and
research issues. Computers in Human Behavior, 8, 259–272.
18. Stoney, S., & Wild, M. (1998). Motivation and interface design: Maximizing learning
opportunities. Journal of Computer Assisted Learning, 14(1), 40–50.
19. Tsai, C. (1989). The effects of cognitive load of learning and prior achievement in the
hypertext environment. Unpublished doctoral dissertation, Florida State University, Tallahassee.
20. Spotts, J., & Dwyer, F. (1996). The effects of computer-generated animation on student
achievement of different types of educational objectives. International Journal of Instructional
Media, 23(4), 365–375.
21. Cho, Y. (1995). Learner control, cognitive processes, and hypertext learning environments.
(ERIC No. ED 392 439)
22. Block, J. H. (1971). Mastery learning: Theory and practice. New York: Holt, Rinehart, &
Winston.
23. Garhart, C. & Hannafin, M. (1986). The accuracy of cognitive monitoring during
computer-based instruction. Journal of Computer-Based Instruction, 13(3), 88–93.
24. Craik, F., & Lockhart, R. (1972). Levels of processing: A framework for memory research.
Journal of Verbal Learning and Verbal Behavior, 11, 761–784.
25. Slater, R. B., & Dwyer, F. (1996). The effect of varied interactive questioning strategies in
complementing visualized instruction. International Journal of Instructional Media, 23(3), 273–280.
26. Dwyer, F. M. (1978). Strategies for improving visual learning. State College, PA: Learning
Service.
27. Chung, J., & Reigeluth, C. M. (1992). Instructional prescriptions for learner control.
Educational Technology, 32(10), 14–20.
28. Clark, R. C., & Taylor, D. (1994). The causes and cures of learner overload. Training, 31(7),
40–43.
29. Csikszentmihalyi, M. (1997). Flow and education. NAMTA Journal, 22(2), 2–35.
30. Freitag, E. T., & Sullivan, H. J. (1995). Matching learner preference to amount of instruction:
An alternative form of learner control. Educational Technology Research and Development,
43(2), 5–14.
31. Mager, R. F. (1964). Learner-controlled instruction—1958–1964. Programmed Instruction,
4(2), 1, 8, 10–12.
32. Merrill, M. D. (1975). Learner control: Beyond aptitude-treatment interactions. AV
Communications Review, 23, 217–226.
33. Merrill, M. D. (1980). Learner control in computer based learning. Computers and Education,
4, 77–95.
34. Morrison, G. R. (1992). Learner control of context and instructional support in learning
elementary school mathematics. Educational Technology Research and Development, 40(1),
5–13.