Using a Familiar Package to Demonstrate a Difficult Concept: Using an Excel Spreadsheet Model to Explain the Concepts of Neural Networks to Undergraduates. William Fone Staffordshire University Stoke on Trent, ST4 2AZ United Kingdom Telephone +44 1782 294284 E-mail
[email protected] some students maintained that it assisted them to understand aspects of programming taught elsewhere on the course.
ABSTRACT A course introducing neural networks to second year undergraduates with mixed disciplinary backgrounds needed a tool to reduce the overheads of simplifying the complex mathematical and programming skills normally associated with the subject. An Excel model was produced that had the added benefit of reducing anxiety, as all students taking the course are competent with Excel spreadsheets.
2. C O M P U T E R I S E D T O O L S A N D C O U R S E DESIGN Constructivist rather than behaviourist learning paradigms are considered to be more beneficial to learning. Particularly in higher education where worth is placed on deep learning. Introducing computer based teaching resources can undermine the constructivist approach.
Categories and Subject Descriptors
In considering aspects of testing and assessment, it is easier to implement psychometric tests as a computer program than other forms of assessment [4]. The presentation of a body of knowledge for the student to commit to memory is now considered dated and unsatisfactory by most disciplines. Herrington & Standen, [5] consider many of the multimedia based instructional packages tend to present material in an 'instructivist' manner placing the learner in a passive role. They argue that learning should be placed into an authentic setting to provide a constmctivist learning environment.
[The Practice of Teaching Computer Science]: Electronic Forms of Class Support, Innovative Instructional Methods, Instructional Technology.
General Terms Human Factors, Design, Performance.
Keywords Computer anxiety, CAL Tools, Constructivist, Neural Networks.
The topic of neural networks is vast and complex, yet a growing range of every day applications is being continually introduced. When introducing a topic area that contains many concepts and a broad knowledge domain, many opportunities exist to employ exploratory learning methods.
1. I N T R O D U C T I O N In the first presentation of a course designed to introduce the topic of neural networks to undergraduates with mixed disciplinary backgrounds, it became apparent that a flexible tool was needed to help them visualize a network. Over the following two presentations of the course, several models were made available to the students. Many of the models available allowed experimentation but they all needed a quantity of prior knowledge to understand the principles they demonstrated. The first presentation relied heavily upon packages to deliver background knowledge. In the fourth presentation of the course a model of a neural network was produced using a familiar spreadsheet package. It was found to be an effective aid to teaching and learning and
Exploratory learning where the learner may explore unfamiliar concepts and knowledge is an effective way to learn ( Kashihara et. al. [10] cite Carroll et. al. [2] ). When a learner explores a problem within a domain containing concepts and knowledge unfamiliar to the learner, it is essentially a self-directed exercise likely to promote constructive learning. If the learning space is not constrained the learner may experience considerable cognitive overload and that in turn may well be counter productive. Kashihara et. al. [10] propose there needs to be a limit set upon the learning space in order to control the cognitive load placed upon the learner. This is achieved by reducing the amount of choice and restricting the available information.
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Within the first presentation of the introductory neural network course students tended to explore the World Wide Web for additional information, this often resulted in overload and confusion. The course used a feedback questionnaire to gauge student satisfaction and this included a field for them to offer
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comment. Students requested concise models to aid understanding
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in the course feedback questionnaires (26%).
The lasting effect of prior negative experience is less motivation leading to lower realizations of the learning objectives. A negative computer experience has correlation with an increase in computer anxiety.
Figure 1 The model representing a simple four-neuron network able to solve the XOR problem (the full example may be found at http://gawain.soc.staffs.ac.uk/-cmtwf/publications.htm)
Performance is adversely affected by computer anxiety [1]. However increasing experience in the use of computers is not the answer to reducing computer anxiety.
The advances made in computing technology have profoundly effected course design. The opportunities provided by computer aided teaching systems promise freedom and flexibility to allow learners to study at a rate and time to suit them.
Anderson [1] has identified four predictors of computer anxiety, they are,
Tait [13] has suggested that the changes being introduced by this technology give us need to consider course design applied to computer based courses and courseware. He argues that principles of educational theory employed in a conventional teaching and learning environment should be employed within a technologybased system.
severity,
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recency,
•
frequency.
perceived knowledge o f software,
,
personal computer experience,
,
overall knowledge of software,
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programming experience.
Gender was not a factor. Anderson cites Howard, Murphy, & Thomas [9] in defining computer anxiety as "the fear of the impending interaction with a computer that is disproportionate to the actual threat presented by the computer". Tseng, Macleod & Wright [14] have measured the correlation of mood swings upon computer anxiety and concluded that negative mood swings are heightened by computer anxiety. This will then provide a negative affect-provoking stimulus, influencing the subjects' perception o f self-competence.
3. Computer a n x i e t y Prior negative experiences of computers have an impact upon a learner's willingness to engage in activities related to the negative aspects. Holt & Crocker [7] have studied the effects of prior negative experience in students studying a computer course, which introduced computer software packages. Their findings indicate that there are three factors that motivate an individual to avoid activities they associate with negative experience: •
,
It has been demonstrated that higher levels o f computer anxiety occur amongst older learner's [11]. Computer anxiety effects in older learners have been linked to increases in decision time. The 'introduction to neural networks' is a course aimed at providing basic understanding of the capabilities and applications
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of neural networks. It is intended to inform people who will use or specify them rather than design or manufacture them. Many of the undergraduates are pursuing a joint honours programme. Studying computing combined with a science or business discipline.
5. O B S E R V A T I O N S A N D R E S U L T S The model was used for the first time with a cohort of 21 students studying the course. Of the 21 students 10 were bridging from a Business and Information Technology award and another was a direct entrant to the second year of the three-year programme. The bridging students, and the direct entrant were all studying three programming modules. All the students involved in the neural networks module had elected to study it as an elective.
A sizeable proportion may be considered as mature learners. Confidence in mathematics or programming skills is sometimes an issue amongst this population but when apprehensions are overcome they normally demonstrate adequate competence. All of the students who attempt this course have some background in mathematics.
The success of using the model can be gauged from two perspectives.
4. U S I N G A S P R E A D S H E E T T O M O D E L A NEURAL NETWORK. Using spreadsheets to make models simply to act as a teaching aid is not new, tutor lead examples in subjects such as mathematics are very useful [3].
•
The benefits to the student, derived by providing an efficient learning aid.
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The benefits to the presenter by providing an efficient tool to offer explanation and demonstration.
5.1 Benefits from the Students Perspective As with previous presentations of the course an anonymous questionnaire was used in the final week. Unlike previous groups there were no comments suggesting the need for better models. However five comments typified by the statement, "I found the Excel example particularly helpful, not only in helping me understand the networks but with aspects of other modules" were of particular interest.
Artificial neural networks simulate mammal brain function, they consist of a number of parallel computational elements sometimes arrange into rows. Each element or neuron performs a function. When considered holistically the network converges upon a solution in a problem space using algorithms that have a statistical structure. Much of the theoretical background is presented using computer aided instruction packages. These presentation packs have been generated with authoring tools such as Macromedias' Authorware for example. Computerised tools can improve the efficiency when transferring knowledge from teacher to learner. However it must be remembered that although computer are quite powerful at providing a media for the delivery of learning resources, there are many educational technologies that can be applied to the learning environment [ 12].
Following an appeal for more information, all of the bridging students, the direct entrant and three other students consented to be interviewed. The interviews reveled that the students felt that the model helped them to picture the operation of a network. Four felt it made the mathematics easier to understand. Six of the bridging students were initially experiencing difficulties with a Visual Basic module. They reported that the model helped them to better understand Visual Basic and arrays.
5.2 Benefits from the Tutors Perspective
The mathematical notations associated with statistics are often unwelcome to undergraduates who associate them with difficult mathematical problems requiring proof. Once designed the algorithms used within a neural network become generic and they are trained using empirical example data.
Using the model proved interesting, it allowed for truly interactive demonstration. Answers to questions could be demonstrated in a flexible way. Points could be reiterated using different perspectives.
It has proved beneficial to demonstrate the mechanics of a neuron and simple neural network, prior to, or along side the introduction of the mathematical notations. This was achieved using the model implemented using an Excel spreadsheet.
The complicated macros could be expanded when needed and hidden when not required. On some occasions students used the model to help themselves to articulate their questions or comments. This proved a useful way of reaching a common understanding or recognizing misconceptions and misunderstandings.
Neural networks consist of several elements acting in parallel, complex interactions occur between the elements. Interactions are occurring between neurons and between layers of neurons as they interact with input and output patterns.
5.3 General Observations
To the learner this is a complicated system to visualize or relate to the mathematical representations or program listings. Healey [6] discusses the difficulties humans have in the perceptual visualization of large multidimensional data sets. By using tools to offload the analysis tasks allows users to perform rapid and accurate exploratory visualization of the data.
The increased competence achieved in the assignments tended to bear out the perceptions of the students. The assignment work involved peer collaboration. They worked in teams to produce a set of posters each demonstrating an aspect of neural networks. Peer tutoring systems have proven beneficial in mathematical modeling courses where self-selected groups could work upon tasks and report to peers. [8]. In this course the assessment which required analysing or using information remained individual but the tasks of gathering information was shared. Students were observed using and discussing the basic tool, experimenting with it and attempting to enhance it, outside of normal. Three students wrote network simulators using the Visual Basic Language as
The Excel model provides a visual representation that may be viewed from a number of perspectives. This enables the learner to observe the operation of the network as it functions. The mathematical formulae are presented in a familiar format and arrays are presented as tables that are easily visualized. More importantly the dynamic interactions may be observed. The learner controls the observation and may experiment.
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their topic rather than producing the theoretical poster. The rest made much more progress in researching and describing networks than previous courses and they all experimented with the model. 6. C O N C L U S I O N The students were all familiar with Excel spreadsheets. Using an Excel spreadsheet as a vehicle to present information normally expressed mathematically or as a computer program reduced anxiety for most members of the group. On past experience students will have doubts regarding their ability in either mathematics or programming, often both. The model facilitates visualization of the complex mechanisms and allows them to be viewed interactively. Learning was accelerated in comparison to other equivalent groups who had not used this tool and this is attributed to reduced anxiety and the promotion of exploration and experiment.
[7] Holt, D . T . & Crocker, M. (2000) Prior negative experiences: their impact on computer training outcomes. Computers & Education. 35 pp. 295-308 [8] Houston, S. K. & Lazenbatt, A. (1999) Peer tutoring in a modelling course. Innovations in Education and Training International, 36(1) pp. 71-79. [9] Howard, G. S., Murphy, C. M., & Thomas, G. E. (1986) Computer anxiety considerations for the design of introductory computer courses. In Proceedings of the I986 Annual Meeting of the Decision Science Institute (pp. 630632). Atlanta, GA: Decision Science Institute p. 630** [10] Kashihara, A. Oppermann, K. R. Rashev, R. & Simm, H. (2000) A cognitive load reduction approach to exploratory learning and its application to an interactive simulationbased laerning system. Journal of Educational Multimedia and Hypermedia. 9(3), pp. 253-276 [11] Lguna, K. & Babcock, R. L., (1997) Computer anxiety in young and old adults: implications for Human-Computer interactions in older populations. Computers in Human Behavior. 13(3) pp. 317-326
7. R E F E R E N C E S [1] Anderson, A. A. (1996) Predictors of computer anxiety and performance in information systems. Computers in Human Behavior 12(1) pp. 61-77
[12] Patel, A. & Kinshuk, K. (1997) intelligent tutoring tools in a computer-integrated learning environment for introductory numeric disciplines. Innovations in Education and Training International, 34(3) pp. 200-207.
[2] Carroll, J., Mack, R., Lewis, C., Girschkowsky, N., & Robertson, S. (1985) Exploring, exploring a word processor. Journal of Human-Computer Interaction. 1, pp. 283-307 ** [3] Emery, D., (1996) Modelling the spread of disease. Spreadsheet User, Sheffield Hallam University 3(1) pp.ll14.
[13] Tait, W. (1997) Object Orientation in education software. Innovations in Education and Training International, 34(3) pp. 167-187.
[4] Gipps, C. V. (1994) Beyond Testing: Towards a Theory of Educational Assessment. (London, Falmer Press)
[14] Tseng, H., Macleod, H. A. & Wright, P. (1997) Computer anxiety and measurement of mood change Computers in Human Behavior. 13(3) pp. 305-316
[5] Herrington, J. & Standen. P. (2000) Moving from an Instructivist to a Construtivist Leaming Environment, Journal of Educational Multimedia and Hypermedia. 9(3) , pp. 195-205
**Reference not seen by author
[6] Healey, C. G. (2000) Building a perceptual visualization architecture. Behaviour & Information Technology. 19(5) pp. 349-366.
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