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Teaching and learning abstract physical science concepts in a computer based multimedia environment

Robert Loss, Mario Zadnik and David Treagust
Curtin University of Technology, Perth, Western Australia
Despite the extensive application of computers in the physical sciences for instrumental control, data acquisition and computational purposes, their effectiveness as educational tools in these disciplines has been substantially below predictions (Salinger, 1991). Among the factors contributing to this has been the relative difficulty of using computers to represent and interact with complex, abstract physical science concepts. The current generation of computer graphical user interfaces combined with interactive multimedia (IMM) potentially provides an environment more attuned to the needy of instructors and learners of the physical sciences. To explore these possibilities, in combination with other educational issues associated with physical science learning, the authors have been developing an IMM physics instructional program. A major outcome of this project has been the recognition that most commonly used IMM instructional design bases have limited applicability in teaching and learning physical science. One reason for this is the abstract and counter intuitive nature of many (even elementary) physical science concepts. The limitations of these design bases are unlikely to be resolved by application of more advanced hardware or software tools but rather require that significant research be directed into instructional designs appropriate to the physical sciences and to the development of effective learning interactions.


Demand for introductory and for more specialised physical science instruction at Curtin University has increased dramatically in recent years placing greater pressures on physics teaching staff. The backgrounds and abilities of students wishing (or required) to undertake physics units have also broadened along with the demand for distance education in this area. Traditional modes of physics instruction are impossible to pursue effectively given the current pressures of tertiary student numbers and funding, and cannot readily take into account the wider range of abilities and backgrounds. In addition, recent science education research has demonstrated that the teaching of science concepts solely in abstract terms is unsuited to most students and that contextual and constructivist approaches are more effective. To cater for these and other educational demands (eg Zadnik and Treagust, 1992), the authors have examined ways in which computer technology could be utilised in the undergraduate physics teaching program at Curtin.

One of the first steps in this process was to assess the potential of existing physics educational software and IMM courseware to teach introductory university physics to approximately 500 students per year (Low, 1992). About one third of these students have little or no previous physics instruction and most required physics content specific to their major discipline. While several useful programs were identified and were subsequently incorporated into other physics units, none of the programs were found to be suitable for our major requirements. The only program attempting to provide comprehensive learning resources and interactions in an IMM format was the Comprehensive Unified Physics Learning Environment (CUPLE) (Wilson & Redish, 1992a, 1992b; Kagan, 1992) which is currently only available on the IBM PC. The difficulties involved in developing IMM for the physical sciences are highlighted by the fact that even though CUPLE involves several dozen contributors only a small percentage of the overall package has been completed. Most of the current content of CUPLE is also too advanced for many Curtin University physics service students and would have required substantial modification to meet their needs.

Interactive multimedia instruction

By IMM we refer to any computer application which incorporates a range of media including text, graphics and high quality digital video and audio (DiVA) within a user controlled interactive framework or shell (Wolff, 1993). Despite the growing literature on IMM, the nature, potentials and problems associated with this rapidly expanding phenomenon, particularly in physical science education, remains poorly understood. Relatively few studies have been performed to measure the efficacy of IMM instruction compared with traditional teaching methods. This is especially the case for computer based physics instruction where many developers appear to focus on the media and the subject content, and have neglected important educational, cultural and gender issues (Redish, 1993).

To minimise hardware and software complications the authors initial developments were based around the Macintosh platform and SuperCard software environment. Approximately three person months were devoted to the instructional design including navigation and provision of user resources. Once an initial framework or shell had been established the authors began to experiment with the incorporation of physics content but it rapidly became apparent that mathematical expressions and physics concepts such as vectors and fields posed difficulties well beyond initial expectations. A number of other issues were also identified (Loss et al, 1992; Loss et al, 1993) including:

After several iterations it was found that these could not be considered in isolation from the overall instructional design of the project. The significance of these issues in general IMM instruction have only recently being discussed in the literature (eg, Preece 1993; Romiszowski, 1993). The effect of these and other related issues on teaching and learning abstract physical science concepts using IMM instruction are examined in the following sections.

Presentation of complex concepts

The presentation of complex and abstract concepts and the design of effective user interactions using IMM is still in its infancy. Although concepts such as 2 dimensional vector quantities have been reasonably consistently represented in texts this has not made them any easier to understand. More complex concepts such as fields in three dimensions and particularly changes in fields have never been satisfactorily represented in texts. The representation of these concepts using IMM have the potential to assist understanding well beyond that currently available in texts but few attempts have gone beyond that of simply transferring existing 21) images from texts onto computer screens. Because IMM instruction itself is such a new and very complex field there do not appear to be any well defined design rules. General graphical and computer interface design principles (eg, Tufte, 1990; Apple Computers, 1987) can provide some useful guidelines but many of the graphical presentational problems are unique to this medium. While advances in hardware and software may eventually reduce these limitations to some extent, there is significant room for. improvement through careful consideration of interactions, screen and navigational design.

While IMM can be considered in terms of the management and presentation of information, there is a considerable difference between the presentation of information and the provision of effective learning opportunities. Effective communicators and instructors (be they scientists, shareholders, union officials, lawyers or teachers) all need to consider not only what they are presenting but how they are presenting it and to whom. Information by itself does not provide for learning any more than a library (or for that matter a lecture) does. Many initial attempts at IMM instruction are no more than the transfer of inexpensive and highly portable text books onto considerably more expensive and much less portable computers. Effective instruction considers the background of the learners and attempts where possible to provide multiple learning pathways and interactions under the control of the learner, and to suit the learners individual learning style. These well established learning requirements (although not always offered in practice in conventional university physical science instruction) are especially significant where the instruction involves highly complex and abstract information and concepts. An even more important distinction between presentation and instruction is that the latter should provide substantially more sophisticated levels of interactive feedback to enable the learner and instructor to assess the level of understanding achieved.

IMM instructional design bases and learning interactions

There are many different instructional design bases and learning interactions being trialled and implemented in IMM instruction. In many cases these are usually adaptations or extensions of existing computer or text based instructional designs and interactions which take advantage of the information storage capacity and the ease with which computers manipulate text and graphical data. Most current IMM packages assume the student has the necessary motivation to work through the material and learning is assumed to occur almost incidentally. A potentially more effective use of this type of material is its integration into a more structured learning environment, where the instructor provides or specifies other learning interactions (eg formal tutorials, discussion groups etc) to provide for higher levels of learning. Irrespective of how IMM instructional packages are used, few currently offer the interactions which can assist deep or high level learning. Irrespective of the medium, interactions to assist deep learning are rarely single events but rather a dialogue or sequence of interactions between the instructor and the learner (Romiszowski, 1993). A further complication arises where deep learning and complex concepts are involved since most deep learning interactions will require careful integration with navigational and/or other information manipulation interactions.

In the following section some of the more common IMM instructional design bases that go beyond the usual electronic encyclopedia and are being or could be used in physical science instruction and are described. Many IMM instructional packages incorporate several of these bases in the one package and some may also include other interactions specific to the subject content and the overall objectives of the project. In each case we will examine their potential at delivering deep learning interactions and their overall applicability in the instruction of complex and abstract concepts such as those encountered in physical science instruction.

Tutorial base

IMM which focuses on the provision of practice questions for learners and provides feedback to the learner is known as a "tutorial base". These responses and other interactions can be stored for subsequent analysis by human instructors. Computers have been used to present questions (over a wide range of learning levels) in this way for many years. Early user interfaces usually restricted the way questions could be asked to those involving numerical or multiple choice answers in a text form. Furthermore, the analysis of learner responses and learner/instructor feedback was restricted to whether the answer was correct or not. The need for numerical answers reinforced traditional algorithmic learning rather than the deeper understanding of physics concepts which often requires qualitative thinking or explanation. These limitations were especially significant in physics instruction, where algebraic expressions and graphical input /output are often utilised in question presentation and learner responses. Nevertheless, a large number of physics multiple choice question banks and other computer based physics tutorial materials have been developed. The effectiveness of the computer tutor mode has been assessed as comparable to formal physics tutorials (Ellis, 1993) but this mode has not been used widely to introduce new concepts.

The development of new graphical user interfaces and IMM has enabled complex symbolic and graphical information to be incorporated into questions. Although this has improved the way questions can be asked, the ability of computer programs to analyse learners responses has not changed significantly. Free form text, and graphical and algebraic responses to questions remain awkward if not impossible for current computers to grade. These types of questions are even more difficult to analyse for the provision of feedback to learners and human instructors. This problem is applicable to all other instructional design bases which attempt to tackle deep learning.

Data base

Another common IMM learning interaction involves the student interrogating text or graphic data bases in either a structured (eg specific question and answer) or an unstructured (eg discovery) format. Instructional designs centred around data bases which use relevant content and a flexible navigational system can provide a highly stimulating, context based, deep learning environment. This instructional design is especially suited to the social and biological sciences where a significant level of comprehension by the learner of the individual elements within the data base may be assumed. This is not always possible in the physical sciences where many of the concepts are new and often counter intuitive. However, an example of how this type of learning interaction could be used in the physical sciences is the use of periodic table data to examine aspects of atomic physics. Other data bases such as properties of materials, properties of nuclides and astronomical data bases could be used in a similar way.

Case study

This design refers to a specific, multiple step problem solving exercise in which learners combine their own skills and knowledge with some initial information or boundary conditions to make decisions and obtain further information. Case studies can be thought of as highly structured simulations which can involve a significant amount of qualitative information. This method of presenting complex information combined with the provision for learning has been well developed in other media forms and has the potential to be even more effective in an IMM format. Most case studies can be designed to take advantage of the benefits of hypermedia and can incorporate a degree of randomness to inject an element of unpredictability into the cases. This approach has been used to provide the health professional student (eg Edwards and Fox, 1992) with practice in diagnosis. It can also provide learners with a wide variety of highly realistic exposures to complex decision making processes.

Well constructed case studies using a contextual approach can also offer a highly stimulating and deep learning environment, but can be a nightmare to program, particularly where multiple possibilities am allowed. Conventional and IMM based case studies in physics education are rare probably because once again these types of interactions assume a certain level of understanding of the material and do not appear to be well suited to the introduction of complex physical concepts. However, some examples of where this could be applied in physics are in areas such as electrical or mechanical fault diagnosis and for consolidation or revision purposes.


Computer simulations are commonly used to explore complex phenomena in the physical world. Examples of these in physics education include The Physics Academic Software Library, (North Carolina State University) and Wings for Learning (Scotts Valley, CA) software packages.

Of the 48 papers presented at a recent conference on the use of computers in physics education at universities (OzCUPE1, 1993), 32% had as their main theme the development and/or use of computer simulation in physics instruction. The main reason is that simulation is an already well established in physical science research so the extension of this tool into education is understandable and even desirable. Most advanced physics students at university routinely use simulators to study complex phenomena such as atomic and crystalline structures, digital and analog circuits, optical and acoustical design and chaotic systems.

Because the results can be expressed in quantitative terms, very sophisticated computer based simulations have been developed and used for many years to analyse complex phenomena in industry, the social sciences and commerce. Many simulators also integrate aspects of IMM including exploratory learning facilities suited to deep learning interactions. Simulators handling multi-dimensional input information and providing output in multiple media formats are often difficult to distinguish from visualisation or virtual reality (VR) applications.

Although simulation has the capacity to promote deep learning in physics, our evaluation of most stand alone physics simulators including those designed for novice physics, shows that they are unsuited to the introduction of elementary physics concepts. The main problem with most existing simulators is that the investment in time required by students to become familiar with these appears to outweigh any educational benefits. This is in part due to the complexity of the concepts being investigated, and the added complexity of the simulation process and the levels of abstraction or simplification required to produce physically meaningful results. Although products such as Interactive Physics (Knowledge Revolution, San Francisco, CA) are considerable improvements over earlier simulators, their practical use in introducing students to physics concepts is limited. Simulation may be more effective for novice students when integrated in small doses into a more structured learning environment such as a IMM or for more advanced students who already have some familiarity with the specific concepts.


Visualisation is the use of IMM to represent complex phenomena or information derived from a combination of measured, calculated or simulated data. Complex dynamic processes can be demonstrated together with other integrated information or images (eg, movie clips, time sequences or animations) and with audio into short (eg ten second) digital video clips. The digital nature of the information enables these movies to be played in slow motion, backwards, paused on any frame or used to provide data for analysis (Wilson & Redish, 1992). Perhaps the most spectacular examples of visualisation are in atmospheric physics and fluid dynamics, where a multitude of fluid flow parameters can be presented simultaneously on three dimensional images. This ability to integrate such a wide range of complex and abstract information is one of the most potentially useful features of this new medium. Its teaching effectiveness has not been demonstrated.

While there are still some practical hardware problems with using IMM (eg computer speed, memory and storage restrictions), recent developments should soon provide more than ample performance. An excellent set of examples of this interaction are displayed and discussed by Wolff and Yaeger (1993). Virtual reality (VR) devices have also been used to visualise highly abstract physical concepts such as the curvature of space-time in general relativity (Bryson, 1992). The addition of new tactile feedback devices to VR will add a useful kinaesthetic dimension which may assist in understanding physical concepts.

Specific learning interactions

There are many occasions in IMM development where specific learning interactions must be developed to provide the depth of learning experience required and are appropriate to the concepts and content but cannot be satisfied by any of the design bases described above. Some examples of this in physics instruction are: computer control and data acquisition from scientific apparatus; the utilisation of specific video or image analysis tools (Wilson & Redish, 1992a), and animated mathematical derivation of a specific physical relationship (Loss et al, 1993). All of these interactions are extremely difficult and time consuming to design because one cannot draw on generic interactions or existing examples as a starting point. As more IMM developers experiment with and evaluate different types of learning interactions this should assist other developers including those working other disciplines.

Improved levels of help and feedback

Irrespective of the combinations of and typos of instructional interactions used in IMM instruction, future users should be provided with more than contextual help while using such applications. Ideally, help with navigation and control should appear indistinguishable from instructional feedback. One of the most potentially valuable features of IMM instruction is the potential for endless practice. However, practice without feedback becomes a much less effective instructional technique. IMM users may not even realise they have a problem or misunderstanding, hence applications should be developed which monitor learner interactions and, where necessary, provide diagnostic support. To some extent, this level of feedback can readily be provided if IMM developers are prepared specifically to code the interaction but more generic IMM software tools need to be developed.

An example of this level of feedback or user sensitive software is already appearing in some current non-IMM applications in which the software or system attempts to assist the user . The development of software tools to provide IMM developers with the ability to provide improved real time feedback from users of their application would be a major breakthrough in increasing the learning depth potential of this medium.

Research in learning interactions

Calls for research into the effectiveness of this medium (Reeves, 1993; Redish, 1993) are being poorly answered due in part to the lack of fully developed applications and the ever changing capability of hardware and software. The significance of learning interactions and their incorporation into IMM instructional design are largely underestimated by IMM developers as pointed out by Romiszowski (1993). Most IMM instructional interactions operate at the superficial learning level (eg recall, comprehension and application) while few exploit the potential of IMM to provide the variety of learner interactions over a range of learning levels such as provided in human learning environments. To achieve this will require research into the nature of instructional interactions along with significant improvements in artificial intelligence (AI) and software development tools.

A major attraction of IMM to educational and training administrators is the possibility of economic savings in both campus based and distance education and training. Our experience over the past three years indicates that many current IMM instruction strategies are not sufficiently well developed to teach even many elementary physical science concepts. Because of the complexity of many physical science concepts, the authors recommend that IMM research in physical science education concentrate on the development of small scale learning interactions and exploration of the learning depth dimension. A major problem in funding these aspects of IMM instructional projects is that sponsors and funding agencies do not recognise the significance and time consuming nature of this research and tend to favour projects which are little more than electronic encyclopedia.

Improvements in hardware and software may solve some of the presentational problems described above but it is unlikely that technology will ever automate the development or design of effective deep learning interactions. In the case of stand alone products perhaps IMM developers should assess the reintroduction of a more human element into IMM. There may well be cases where an abstract concept is best described by a short video (or sound) clip of an instructor explaining the phenomena judiciously combined with other DiVA or graphics. What makes this different from a lecture other than the replay facility? What makes the digital replay facility different from a video tape recording? The flexibility, complementary information, interactions and potential learning depth, which after all is what IMM instruction is all about.


Assistance with educational, technological and programming aspects of this project by staff from the Curtin University Educational Computing Support Group (Curtin University Computing Centre), Ms Daphne Sands (Department of Applied Physics, Curtin University); Ms Sue Stocklmeyer (SMEC, Curtin University) and the Curtin University Teaching Learning Group, is gratefully acknowledged. This work has also been supported by funding from DEET (including the Committee for Advancement of University Teaching, CAUT), the Curtin University Vice Chancellor's Discretionary Reserve, the Curtin University Minifellowship scheme and an Apple Computer University Development Grant.


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Authors: Dr Robert D. Loss, Lecturer, Department of Applied Physics, Curtin University of Technology, PO Box U1987 Perth WA 6001. Tel: 351 7747 Fax: 351 2377. Email: riossrd@cccurrin.edu.au

Dr Mario G. Zadnik, Lecturer, Department of Applied Physics, Curtin University of Technology, GPO Box U1987, Perth WA 6001

Associate Professor David F. Treagust, Science and Mathematics Education Centre, Curtin University of Technology, GPO Box U1987 Perth WA 6001

Please cite as: Loss, R., Zadnik, M. and Treagust, D. (1994). Teaching and learning abstract physical science concepts in a computer based multimedia environment. In C. McBeath and R. Atkinson (Eds), Proceedings of the Second International Interactive Multimedia Symposium, 311-316. Perth, Western Australia, 23-28 January. Promaco Conventions. http://www.aset.org.au/confs/iims/1994/km/loss.html

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