Briefly: Simulation and Interaction

"For better or worse, simulation is no mere fad. Indeed, to think of simulation games as mere entertainment or even as teaching tools is to underestimate them. They represent a major addition to the intellectual repertoire that will increasingly shape how we communicate ideas and think through problems."

 

Since the popular adoption of the PC for educational andrecreational purposes, the terms ‘simulation’ and ‘interaction’ haveattributed numerous and diverse meanings and have often been used in contextsindicating beneficial uses of the new technology. When thinking aboutsimulations, people reflexively cite examples such as Simcity and MicrosoftFlight Simulator .

In computer-based learning any system can be simulated,providing that its relevant attributes can be expressed in terms of analgorithmic model. The system being modelled may be derived from the ‘real-world’or based primarily on fantasy. Generally, in the educational setting, attributesof physical, social, economic and political domains are replicated in simulationmodels. For the purposes of this discussion, the following definition will beused:

The term ‘interactive’, by virtue of its literaldefinition: ‘mutually or reciprocally active’ , has been widely usedin sales pitches to describe software ranging from simple productivity tools tolarge multimedia encyclopaedias. For applications dedicated to learning, thisbroad, somewhat jingoistic definition has proven to be totally deficient, and asa result, several taxonomies have been formulated in an attempt to affordpedagogical significance to aspects of interaction. Whether deep or surfacelearning occurs with user's involvement with an application, was addressed byJonassen in framing his five levels of interaction. The learner’s mentalengagement was identified as being crucial within three dimensions ofinteractivity, classified as ‘Levels’, ‘Functions’ and ‘Transactions’. More recently, this mental engagement, has been considered in conjunction withthe learner’s ability to manipulate and navigate through the environment, withcalls for greater attention to be paid to the design of context-basedinteraction .

The ubiquitous presence of game consoles bears testimony tothe popular appeal and acceptance of the simulation as a vehicle for learningand entertainment. The efficacy of adopting simulators in education and traininghas been well studied , and although it is not strongly conclusive whetherconcept learning is improved by the use of simulators as compared to traditionalmethods, there certainly does not appear to be evidence to the contrary.

"Simulated experiences have the potential to become powerful instruments of cognition. They support both experiential and reflective processes: experiential because one can simply sit back and experience the sights, sounds, and motion; reflective because simulators make possible experimentation with and study of actions that would be too expensive to try in real life."

 

On a cognitive level, sensory input (usually visual, auditoryor haptic) is frequently updated in response to user initiated-commands. Thisresults in a feedback loop that is controlled and guided by the learner, throughwhich they are able to observe changes, make inferences, and test emerging ideasabout the model in question. Pedagogically, the process appears ideal, however,concerns remain.

"Tim's approach to ‘SimLife’ is highly functional. He says he learned his style of play from video games... Tim is able to act on an intuitive sense of what will work without understanding the rules that underlie the game's behavior. His response to ‘SimLife’—comfort at play, without much understanding of the model that underlies the game—is precisely why educators worry that students may not be learning much when they use learning software."

 

What is essentially suggested here is that computerizedmodels can be so complex that the user has little to no hope of understandingthe relationships between key underlying attributes. The ‘black box’ conceptof the algorithm controlling the simulation, has been the cause of much concern,where users are unable to query or debate crucial assumptions forming the basisof the program. This criticism highlights the point that designing foreducational impact requires the consideration of several layers, from thealgorithm to the interface, to the nature of the learning task itself.

A model that defines these layers as well as represents theirjuxtaposition in an interactive environment is the essential starting point forany systematic design process.

A ‘Conveyor’ model

Represented in the diagram below is a model where theinterface assumes a central position, with the human and program at opposingpoles.

Diagram1: Conveyor model overview

Beginning the process at the section representing humaninput, the learner usually sees or hears a stimulus from the interface – thisundergoes integration in a cognitive process – the resultant is an output suchas a planned course of action – and so certain elements of the interface aremanipulated – resulting in the definition of the inputs for the underlyingprogram – which are then processed by program algorithms defining the model– creating output data – that the interface represents as feedback to theuser – and so the cycle continues.

Using this framework, the subsequent sections examine thehuman, interface, and program ends, particularly placing emphasis on the designoptions available at each, and their significance within the system.

The Human End

Learning processes

Human sensory input occurs through sight, hearing, touch,taste, smell and vestibular mechanisms. Most commonly the design of a computerinterface is concerned with sight and hearing, with a small but steadily growingindustry dedicated to haptic (touch) feedback. Visual input results from theattention a user gives to the interface. It is needed to guide actions (e.g.hand movements), to make decisions about actions, and most significantly in thiscase, to learn about the behaviour of an underlying model.

A learning process can be defined as "cognitivetransactions of the learner that are meant to transform information intoknowledge" . The definition is largely born out of aninformation-processing approach, which grew from theory that was a result ofresearch within the field of Artificial Intelligence, where much endeavourcentred around creating programs able to simulate human cognitive processes.

The learning theory that is often applied to simulations is‘discovery learning’. Described formally by Bruner , interest in discoverylearning became resurgent after the advent of personal computers in the early1980’s. This was largely as a result of the computer’s data processing powerenabling educators to simulate aspects of an environment that were previouslyunreplicable, and also because of the increasing educational emphasis beingplaced on constructivist approaches . Since then, several variants andelaborations of the basic theme have been proposed .

In discovery learning, one of the ways in which learning cantake place is when the subject reflects on the outcomes of their actions, makesinferences and verifies them with further action. This is implicit within thenotion of a simulation. De Jong & Njoo identify four features for theinstructional use of simulations: the presence of formalised, manipulableunderlying models, the presence of learning goals, the elicitation of specificlearning processes, and the presence of learner activity. The human end isconcerned with the latter two. The learning processes described by those authorsincludes hypothesis generation, prediction and model exploration.

It has been suggested that there are two fundamental ‘problemspaces’ that are searched in the process of discovery learning, the ‘hypothesis’space and the ‘experiment space’. The hypothesis space is essentially a bankof possible hypotheses relating to the problem, which the learner searches inorder to explain what is observed. It may be informed by prior knowledge and theoutcomes of experimentation. The ‘experiment space’ is a bank of possibleexperiments that may be conducted, and may or may not necessarily be guided by arelevant hypothesis. The model proposed by the same authors is called ‘ScientificDiscovery as Dual Search’ (SDDS), and features the key processes of searchingthe hypothesis space, testing the hypothesis and evaluating the evidence.

In relation to computer simulations Reimann suggests aninductive model of learning featuring the following phases:

Diagram 2: Discovery learning cycle

If the environment permits, in the early part of exposure toan interactive system the subject undergoes an orientation, where familiarity isgained with controls, feedback areas and other relevant features of theinterface. After this initial period, the user may start to apply themselves tothe particular task, progressively acquiring ‘domain’ knowledge in theprocess. If the goal of arriving at a hypothesis/set aligned to the programmedmodel is to be achieved, the learner must progressively advance from arudimentary understanding of the system to a refined notion of what itrepresents. Subsequently, as domain knowledge builds and becomes moresophisticated, the learner engages in deeper reflective thought processes, whichmay be manifested in less frequent and more deliberate interactions.

Considering the discovery cycle in the diagram above as beingthat of a ‘top down’ or aerial view of the process, viewing it from the ‘side’shows how the series of iterations may be represented as a function of time.

Diagram 3: Discovery iterations as a function of time

The ‘spring’ is compressed during orientation, followedby the relative infrequency of iterations/coils during hypothesis refinement. Itis over-simplistic to suggest that this is a universal pattern of interaction,as differences in individual learning behaviour account for a diverse range ofapproaches. Klahr and Dunbar suggest that users apply themselves as ‘experimenters’or ‘theorists’, signifying predominant activity within experiment orhypothesis spaces respectively. ‘Experimenters’ would demonstrate a greaternumber of iterative cycles compared to ‘theorists’. Orientation, also, mayor may not be extensive, depending on how familiar the user is with the system,and how intuitive the interface appears to them.

Information processing limitations

Within an interactive simulation the flow of activity takingplace during learning can be considered in terms of an information-processingmodel. Classically, the approach categorizes memory into sensory, short-term,and long-term stores. Information enters initially via the senses (sensorymemory) and then proceeds to short-term memory and subsequently, under certainconditions, to long-term memory .

Over the past fifty years researchers in the field ofArtificial Intelligence (AI) have attempted to simulate aspects of humancognition using computer systems. The models created are based on the ‘declarative’and ‘procedural’ knowledge dichotomies that are allied to similardistinctions in other theories of knowledge and learning. Piaget’s separationof ‘concepts’ and ‘schemes’ , and Schema-theory’s ‘objects’ and‘events’ are examples.

Although popularly adopted as a model of cognition, someauthors have argued that short-term and long-term memory are essentially part ofa single memory system. The ‘levels of processing’ frameworksuggested that information is processed at different levels concurrently,depending on its particular characteristics, and deeper processing results inmore information that can be remembered. These deeper levels require analysis ofmeaning, which could involve thinking of associations, images, and pastexperiences.

Short-term memory, from the Atkinson-Schiffrin model, has twoimportant limitations. Firstly, it can hold at any given time 7 (+/-) 2"chunks" of information . Secondly, its holding ability isapproximately 20 seconds. Such limitations appear to be of limited relevancewithin the context of a simulation based on reasoning, where there is littleneed for the recall of strings of words or numbers. Aspects of greatersignificance can be found in the contemporary cousin of short-term memory, ‘workingmemory’ . This is considered as a dynamic system, active in the execution ofhigher-level cognitive tasks such as learning and reasoning. It does so by beinga system for the temporary storage and manipulation of information via two typesof components: a storage and a central executive. The storagesystem is considered to be passive, and mainly responsible for the transientstorage of verbal and visual information, whilst the central executive, isregarded as being actively involved in encoding, storing, and retrievinginformation. The concept of a central executive was preceded by the comparable supervisoryattentional system , which also was seen as having limited capacity, andactive in tasks involving decision making and problem solving. Demands onresources vary during the learning process; for example, the executive functionis likely to require greater processing power when a subject is presented with anew task or environment, as compared to when they perform familiar routines.

The notion of limited capacity is fundamental when designingactivity flow as represented by the conveyor model. If resources are limited andneed to be shared by the demands of a storage and central executive, then itmust bear consideration that a balance should exist between any new inputs andthe time and resources needed to process them. In a simulation, a prime mediatorfor establishing this balance is the interface.

The Interface

Keyboards, monitors, voice activation, and mouse devices areclassically considered as the interface between man and machine. In the Conveyormodel, rather than focusing on hardware, the interface is viewed as the softwareintermediary between the human and the model.

Graphical metaphors constitute the most common way tocontextualize a simulation for the user, and are often used to implicitly conveya basic paradigm of operation. Most simulations rely on visual representationsto provide the setting, and display feedback and manipulation environments.

Emphasis placed on the realism, or fidelity, of a simulationhas resulted in increasingly rich graphical environments that aim to reproducethe look and behaviour of an alternate system (usually a real-world system). Theterm ‘virtual reality’ conjures up the notion of being totally immersed inan artificial world, where the user moves and acts as they would in ‘realreality’. Fidelity has been categorized as being physical and functional, where physical fidelity refers to how authentic the interface feels throughmanipulation and feedback, whilst functional fidelity is a measure of howfaithfully the system being simulated is represented by the model.

In simulations where skilled operation is a desired outcome,high levels of fidelity will result in the user being able to more readilytransfer learned procedures to the real working environment. High fidelity mayalso be an important factor in learner motivation, and in most cases, where thecosts and technology permit, it is recommended as good design practice .However, as is discussed below, at times it may prove beneficial to sacrificehigh physical fidelity for an interface that promotes planning and reflection.

Manipulation

Manipulation of an interface usually takes the form ofoperating sliders /dials /buttons (using keys or a mouse), or entering numbersand characters within specified fields. The term ‘Direct manipulation’ wascoined by Ben Shneiderman, and essentially features the following three criteria:

1. Continuous representation of the object of interest.

2. Labeled button presses used instead of command line syntax.

3. Operations whose impact on the object of interest is immediately visible.

 

These guidelines, due to their obvious synergy to real lifeexperience, became fundamental parameters in developing any graphical userinterface. Norman identified the ‘gulf of execution’ which refers tothe distance, or difference, between one’s intentions to the actions that mustbe carried out in acting through the interface. Bridging this gulf throughdirect manipulation has been established for several years as a fundamentaltenet of good interface design, and it follows logically that the case should beno different when constructing an educative simulation.

With this assumption in mind, and therefore somewhatcounter-intuitively, findings of recent studies revealed some evidence to thecontrary. Results indicated significant improvements to problem solvingperformance when using less direct forms of manipulation, such as command lineinterfaces, or having to act on alternate representations of the object needingmanipulation.

Researchers attempted to explain these effects in a number ofdifferent ways. Svendsen proposed that the verbalization employed during the useof a command line interface resulted in subjects developing a deeper explicitknowledge of rules, therefore resulting in less moves taken to problemcompletion. Alternate explanations have suggested that increased ‘implementationcosts’ lead to a greater ‘planfulness’ during the problem-solving process.This means that from a perceptual-motor standpoint, the burden to the user ofusing a command line is significantly greater than the click of a mouse, andhence, to avoid the excessive execution of such operations, the user chooses toplan each move more carefully. This corresponds to the ‘rational analysis’of ACT-R theory , where it is proposed there exists a cognitive tradeoff betweenmaximizing goals and minimizing implementation costs. In another study involvingsubjects placing blocks in particular pre-determined configurations, constrainedby visual and memory cost factors, this tradeoff between memory costs andperceptual-motor costs, predicted by Anderson’s rational analysis, was againsupported.

Limiting the number of key presses or moves, as well asproviding set goals for interactions have also shown to improve overall learningof an interface . The studies indicate that if manipulation is unlimited and tooeasy to perform, the user will tend to operate without thinking enough about theprocess. A similar condition may eventuate when learners fail to adequatelyestablish goals for the interaction, and spend excessive amounts of time ‘roaming’the interface. Although some exploration is essential for orientation within theenvironment to take place, aimless interaction beyond a certain point could leadto boredom, frustration and even abandonment of the system.

Some studies have attempted to evaluate concept learning in asimulation within the context of dual-coding theory . Interface designcomplementing the referential processing suggested by this theory was shown tosignificantly enhance the explicit understanding of the physical scientificprinciples under review. Manipulation of the interface varied from visualrepresentations of objects to numeric displays.

Given the implications of these studies, the following itemswould be worthy focus points for discussion when designing interfacemanipulation:

Feedback

The feedback most commonly encountered in learningsimulations is visual feedback, and can be categorized as synchronous orasynchronous. Synchronous feedback is ‘real-time’ in nature, and changesinstantly, in synchrony, with user manipulations of the interface. It isfaithfully representative of our movement in the physical world, as our actionsreveal immediate and visible consequences. Asynchronous feedback, on the otherhand, is feedback that is delayed or modulated in some way. It is instituted byprogrammed time delays or additional operations that the user must perform toreveal the outcome of previous actions.

As stated previously, direct manipulation requires as one ofits conditions ‘Operations whose impact on the object of interest isimmediately visible’, whilst Norman’s second gulf, the ‘gulf ofevaluation’, refers to the difficulty a user has in determining whethergoals have been achieved. These have been well-established guidelines forinterface design for several years.

Thus, at face value, the design decision for feedback insimulations appears to be quite straightforward: provide feedback that mostclosely resembles what happens in the system being simulated. However, again, aswith manipulation parameters, there is evidence to suggest that this does notnecessarily produce optimal learning outcomes.

Some research has demonstrated that synchronous feedback(sometimes termed continuous feedback) can result in the inducement of animplicit learning mode, whilst asynchronous feedback (similarly termeddiscontinuous feedback) may be optimal for the production of declarativeknowledge .

A skill-based simulation has the intention of promotingreflexive actions, experiential processes or procedural skills through implicitlearning . ‘Shoot-em-up’ arcade games are good examples. Synchronousfeedback is desirable in such instances, where skills are mainly developed ‘unconsciously’during the process of interaction. This type of implicit learning is oftenillustrated by a person learning to ride a bicycle, where they respondreflexively, but cannot articulate explicitly what knowledge has been acquired.

Concept-based simulations have an underlying model, informedby observed relationships, and defined by rules, conditions and actions, whichneed to be explored and uncovered by the learner. These types of simulationspromote reflective processes , explicit learning and the formation ofdeclarative knowledge. Asynchronous feedback can thus be effectively employed tocreate enforced delays, encouraging the learner to engage in the deeper thoughtprocesses demanded by the simulation. Modulation of feedback also decreases theconcurrent information being processed by the user. A learner fully focused onplanning and executing a manipulation may be distracted by a continuous streamof feedback, especially if it is visually or audibly intrusive. Reducing theeffects of this interference is another benefit of an asynchronous feedbackloop.

The application of asynchrony in feedback can be achieved inseveral ways. The user entering data into a field may be required to press ‘enter’.In addition, there may be a time delay before the result is made evident.Alternately, whilst dragging a slider control with the mouse, change in outputwould only be displayed upon reaching the ‘mouse-off’ state. Another methodcould be to necessitate the use of a ‘show me’ button, which the user clickson to find out the result of a previous series of manipulations.

Studies, grounded in the framework of information-processingtheory, have primarily sought to explore the effects of the nature and timing offeedback in computer-based instruction (CBI). The type of feedback addressed inCBI studies ranges from that associated with discrete student responses to themore informative feedback used to enlighten the student on their progresstowards a particular goal. It is common to find reference to two forms offeedback , ‘verification’, whether an answer is right or wrong, and ‘elaboration’,the provision of guidance to the learner. Similarly, Overbaugh described foursuch levels of feedback as lying on a continuum of usefulness for learningenhancement.

Although superficially appearing to differ from the types offeedback present in simulations, there are several valuable parallels that canbe drawn. CBI research has shown that immediate feedback, which could be likenedto synchronous feedback, may be more effective for lower-level knowledgeacquisition . Conversely, delayed feedback, allied to asynchronous feedback, hasproven more effective for the comprehension of higher level concepts .

In summary, for a concept-based simulation, options for thedesign of feedback should include:

The Program End

Learning simulations have at their core a set of algorithmsor models that reflect the operation of another system. The distinction betweenthese rules and the supporting software that they are programmed in needs to beclearly made. The model represents the fundamental conceptual basis of theinteraction, whilst the software simply ‘houses’ or facilitates the process.

A model is composed of variables and their mutualrelationships, and it is the discernment of these variables and relations thatis the heart of a concept-based simulation.

In further developing SDDS, attempts were made at describingthe structure of the hypothesis and experiment space as well as the searchwithin these spaces. The hypothesis space was subdivided into spaces for ‘variables’and ‘relations’, variables were ordered from general to specific within ahierarchical tree structure, and relations were similarly represented accordingto their level of precision. The diagram below displays the structure.

Diagram 4: Example of a relation hierarchy, adapted from

In searching the hypothesis space the learner firstconstructs a set of hypotheses by searching both the variable and relationspaces. Subsequent search operations continue to be characterised by activity inboth these spaces as well as those that alter the set itself. The authorsproceed to categorize each type of search operation in a classification that isrepresentative of the various parameters of a hierarchical tree structure, forexample, ‘abstraction of a hypothesis’ is seen as a relation spacesearch operation, and is described as moving from a more to a less preciserelation. This equates to moving ‘up’ within the tree structure.

The experiment space is seen as consisting of ‘value-tuples’,which are sets of variables with corresponding values assigned to them. Thesevalues may be numeric or qualitative in nature. In searching this space thelearner first chooses the variable/s to be altered followed by the allocation ofa value to them.

It is self evident from the limited processing capacityavailable to a human during interaction that the number of variables and thecomplexity of their relationships constitute essential elements in the design ofa model. However, greater complexity of this nature does not automaticallysuggest greater ‘difficulty’ for a learner, as will be discussed whenconsidering the inextricably linked notion of ‘intention’.

Intention

The ‘Tower of Hanoi’, invented by the Frenchmathematician Edouard Lucas in 1883, is a popular puzzle task that requiresusers to transfer discs from the left (Peg 1) to the right (Peg 3).

Diagram 5: The Tower of Hanoi Puzzle

The rules simply state that only one disc can be moved at atime, and a larger disc may not be placed on a smaller one.

Applied through the medium of a computer interface, theintention for which the puzzle is presented significantly determines thelearning outcome for the user. For example, if rules are explicitly statedinitially, and the user is simply asked to complete the puzzle, the learningoutcome may be a rote memorization of sequence. If the rules are not disclosed(i.e. during the session an illegal move results in a beep, and the disc beingreturned to its original position), an additional learning outcome may be thediscovery of the puzzle constraints. If the goal is to complete the puzzle in aminimum number of steps, a learning outcome may be the rote memorization of anideal sequence derived through trial and error, or the formulation of an optimalalgorithm.

The intention of any task or interaction cannot be formulatedin isolation of the developmental and situational contexts of the users.Developmental issues include language and logic sophistication, especially whendesigning interactivity for young children. Situational contexts refer to theassociation of domain-specific knowledge to the problem, as well as to thesetting and environment of the simulation.

Using the example of Simcity , the intent for novice usersmay just be a superficial appreciation of a growing city, whilst students ofeconomics may be required to uncover relationships between unemployment andcrime.

Similarly, advanced computer programming students presentedwith the Tower-of-Hanoi problem during a lesson on algorithm production woulddiffer considerably from primary school children in terms of the scope and depthof understanding puzzle solution paths. The intention may be for them to derivethat the minimum number of steps needed to complete the puzzle equals 2ⁿ- 1, (where n is the number of discs present), whereas the childcould simply be undertaking a recreational exercise in logic.

Largely, task intentions can be categorized as thosepromoting model operation or model conceptualization, which equate to thepreviously discussed skill-based and concept-based simulations respectively. Itmay be argued that in many instances model conceptualization is necessary forskilled model operation, and, less frequently, this could be conversely true.Any single ‘learning simulation’ may contain multiple or combinations oftask intentions. For example, the same flight simulator may be used foroperational training (e.g. sequences for takeoff and landing, and associatedskills), but may be also used for teaching theory (e.g. lift/drag ratioproblems). The former has the intent of model operation, the latter modelconceptualization.

Learners will often attempt to achieve a particular end-goalduring a simulation rather than theorizing about the model . For example, in arocket fuel simulator, they may have a goal of sending the rocket into orbitwithout necessarily understanding the chemical relationships of the fuelmixture. This has been referred to as an ‘engineering approach’, in contrastto a ‘scientific approach’ where users systematically uncover model rulesand relationships . Therefore, a learning simulation may utilize operationaltasks to illustrate particular behaviours of the system, in conjunction withconceptual exercises that encourage hypothesis formation.

The model operation/conceptualization dichotomy is summarizedbelow in a table that generalizes for each; the cognitive processes involved,possible outcomes, and how these outcomes may be measured.

Table 1: Task intention summary

In summary, some of the design issues, relevant to theprogram end, that require consideration include:

Conclusions

There is a need for further research aimed specifically atthe effects of simulation interface design on concept learning. Typically,research studies have employed well-defined problem solving tasks such as the‘Tower of Hanoi’ or the ‘Eight-puzzle’ to test the effects of adjustinginterface features . Conclusions drawn suggest that success in the setactivities was characterized by the extent to which learners reflected on theiractions.

It seems plausible then to extend this notion to the learningof concepts in a simulated environment. However, there are fundamentaldifferences between the attributes of a puzzle tasks such as the Tower of Hanoiand those of concept-based simulations that could impact on this assumption.Some significant disparities are summarized in Table 2.

Table 2: Puzzle tasks vs. concept simulations

Performance can be measured by fewer moves or the reducedtime to complete certain tasks, and it has been shown that greater ‘planfulness’results from imposing costs on interactions, leading to greater efficiency .What is yet unclear is how concept development, rule induction, or ‘insightlearning’ (cognitive restructuring) is affected by similar conditions.

This article has deliberately not set out to review existingsimulations and their common applications, or to develop yet another taxonomybased on a new set of criteria. It has intended to identify key componentswithin the framework of a conveyor model, significant research relating to eachcomponent, and relevant design issues thus arising.

Blindly accepting the pedagogical applicability of the tenetsof direct manipulation has been shown to be inadequate when making designchoices aimed at maximizing learning outcomes. No simple rule of thumb can beapplied to give an optimal set of interaction parameters, if fact, a commonsuggestion throughout has been that any aspect of the Conveyor system cannot beaddressed adequately when viewed in isolation. This was highlighted whenconsidering synchronous vs. asynchronous feedback at the level of interfacemanipulation, as subtle variations to manipulation design automatically andinherently alter the nature of feedback timing.

Perhaps the most significant overarching concern binding alldesign facets together is that of educational context. If task intention, andusers’ situational and developmental stages are not clearly defined, thedesigner can be faced with creating a costly simulation that may ultimatelyprove to be of limited pedagogical value, and simply serve as yet anotherornamental offering to the demands of ‘technology in education’.

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