A Context for Computers in Education

by

Michael Huggett

B.Sc. University of Toronto, 1999

A MASTER’S ESSAY SUBMITTED IN PARTIAL FULFILLMENT OF

THE REQUIREMENTS FOR THE DEGREE OF

MASTER OF SCIENCE

in

THE FACULTY OF GRADUATE STUDIES

DEPARTMENT OF COMPUTER SCIENCE

___________________________________________

___________________________________________

THE UNIVERSITY OF BRITISH COLUMBIA

April 2001

© Michael Huggett, 2001

Contents

1 Introduction 1

2 Technology in Education 3

3 A Non-Technological Alternative 4

4 Appropriate Application of Technology 6

5 The Intelligent Tutoring System (ITS) 7

5.1 Student Modeling 9

5.2 Making Uncertainty More Certain 14

5.3 Novel Approaches 18

5.4 ITS Effectiveness 21

6 Out of the Lab 22

7 Politics, Economics, and Tradition 25

8 The Future of Schools 28

9 Conclusion 29

References 30

“I believe that the computer presence will enable us to so modify the learning environment outside the classroom that

much if not all the knowledge the schools presently try to teach with such pain and expense and such limited success will

be learned, as the child learns to talk, painlessly, successfully, and without organized instruction.” Seymour Papert

[Papert]

1 Introduction

While the Information Age continues to advance in leaps and bounds, the institution which would best

prepare us for its challenges seems bound to the past. Instead of tackling difficult structural problems of

pedagogy and bureaucracy, education is allowing itself to be distracted by the flash and dazzle of

technology. Some advocates claim that computers are the answer to education’s woes, but their ‘quick-

fix’ point of view amounts to little more than throwing money at the problem. Unfortunately, without a

more fundamental re-examination of education’s priorities, past errors seem likely to be repeated. While

some research has suggested that the computers already in classrooms have had no significant impact on

learning, still more computers are being pushed into classrooms with apparently reckless haste, with no

consideration of potential adverse effects; across North America, arts programs are being slashed to

make way, even though evidence suggests that the arts best develop crucial thinking skills in children

[Oppenheimer]. As a result, students’ increasingly mass-media-defined perspectives become ever more

narrowed.

Steve Jobs, founder and CEO of Apple Computer, is certainly in a position to comment,

I used to think that technology could help education. I've probably spearheaded giving away more computer

equipment to schools than anybody else on the planet. But I've had to come to the inevitable conclusion that the

problem is not one that technology can hope to solve. What's wrong with education cannot be fixed with technology.

No amount of technology will make a dent… We can put a Web site in every school - none of this is bad. It's bad only

if it lulls us into thinking we're doing something to solve the problem with education. [Wolf]

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Computers are being pushed into an institution that is fundamentally outdated, a product and

reflection of a bygone era. Education today is founded on assumptions that have been passed down for

centuries – indeed, one might claim that our first great educator lived some two millennia ago. But while

Socrates based his Method on a genuine intuition for an individual student’s learning style, such

pedagogical luxury faded with the advent of the Machine Age. A sudden demand for a skilled, educated

work force drove mass education into the mainstream, and emphasis inevitably shifted from teaching to

training. Due perhaps to economies of scale, but also partly due to industrial imperatives of discipline

and respect for authority, educators came to rely on the lecture as the primary, unquestioned method of

mass instruction.

And yet, there is evidence that the group lecture is actually the worst way to learn a subject,

while one-on-one tutoring is the best [Bloom 84, Cohen, Graesser, peer]. Placing industry ahead of

enrichment has not served students well. “In the first half of this century, schools were designed on the

‘factory model,’ in which thousands of students traveled through enormous, anonymous high schools

like products on an assembly line” [Mosle]. As the manufacturing base expanded further, disciplined,

reliable workers were essential to drive the economy; schools became a means of preparing citizens for

their limited role as economic drones. Education today is a direct descendant of this tradition, and retains

a functional interest in weeding out ‘defects’–not of really teaching students, but of running them

through a homogenizing and filtering process which, by real-life standards, could only be considered

artificial.

Criticism of such traditions comes from all sides. While one would expect Nobel prize winners,

the foremost products of formal education, to praise it as their guiding light, some have surprisingly

negative comments. Bertrand Russell, winner of the 1950 Nobel Prize in literature, said that education is

“one of the chief obstacles to intelligence and freedom of thought.” [Schilpp] Albert Einstein, the most

hallowed of laureates, went so far as to say –

It is, in fact, nothing short of a miracle that the modern methods of instruction have not yet entirely strangled the holy

curiosity of inquiry; for this delicate little plant, aside from stimulation, stands mainly in need of freedom; without this

it goes to wreck and ruin without fail. It is a very grave mistake to think that the enjoyment of seeing and searching

can be promoted by means of coercion and a sense of duty. [Schilpp]

Author and educator Grace Llewellyn was so disgusted with formal education that she helped to found

the ‘unschooling’ movement, which seeks to educate students virtually anywhere but in an institutional

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setting: “Learning is not a product of teaching - that was the most radical concept for me to get.” People,

she says, “are born learning. They learn how to walk, how to talk. They're basically little scientists. If we

don't stop that process, it will continue.” [Llewellyn]

Fred Keller, distinguished psychologist and educator, sees shortcomings in more pedagogical

terms. Psychologists recognize that learning is much more an individual than a group phenomenon. “The

traditional group method assumes that all the students are much the same, but everyone knows this isn’t

true. Some students will move quickly through the material; others more slowly.” [Chance] This is

perhaps the most detrimental aspect of traditional education, “if the material is cumulative, as it is in

mathematics, science, and languages, then the slower student gets further and further behind.” [Chance]

Many slow learners lose motivation and ultimately curtail their studies, yet speed of learning is not

related to depth—it is worth noting that Einstein himself was a slow learner, leaving one to wonder how

many similarly ‘slow’ students have been lost along the way.

2 Technology in Education

The simple introduction of computers into the existing educational system, then, cannot hope to improve

the situation. Applying a new tool according to an old paradigm can arrest its potential; although there

are always latent possibilities (both good and bad) in any technology, they are only tacitly implied, and

may not be powerful enough to overwhelm established standards. Despite whatever power of social

reform technology is claimed to possess, in schools it has a general history of failure. Fifty years ago,

radio was touted as the great reformer that would bring the world into classrooms. Soon filmstrips, and

finally television, promised great benefits. [Troxel] Computer critic Clifford Stoll writes that televised

education –

... has been around for twenty years. Indeed, its idea of making learning relevant to all was as widely promoted in the

seventies as the Internet is today. So where's that demographic wave of creative and brilliant students now entering

college? Did kids really need to learn how to watch television? Did we inflate their expectations that learning would

always be colorful and fun? [Stoll]

Technology critic Neil Postman is similarly emphatic in his indictment of technological panaceas, “I

thought that television would be the last great technology that people would go into with their eyes

closed. Now you have the computer.” [Postman]

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Unfortunately, the clamour for computers is growing. Seduced by technology, American teachers

in a 1997 poll ranked computers higher in importance than reading classical and modern literature, than

studying history and the sciences, and than discussing drugs or family problems. [Oppenheimer] Research

claiming a net benefit from using computers in education has been consistently disappointing. “The

circumstances are artificial and not easily repeated, results aren’t statistically reliable, or, most

frequently, the studies did not control for other influences, such as differences between teaching

methods,” says Edward Miller, former editor of the Harvard Education Letter, “most knowledgeable

people agree that most of the research isn’t valid. It’s so flawed it shouldn’t even be called research.

Essentially, it’s just worthless.” [Oppenheimer]

If one follows this line of reasoning, it becomes apparent that, if computers are to be used in a

truly effective manner, they should be integrated into a system of learning based more on human needs

than industrial imperatives, in short, one that consciously acknowledges the cognitive mechanisms by

which people actually learn. The question is, are computers strictly necessary to manage this system?

3 A Non-Technological Alternative

Mastery Learning is not a new idea. Indeed, it was first suggested by Benjamin Bloom in 1963 [Bloom 76,

Bloom 84], and its popularity has been growing gradually ever since. Its premise is simple: a program of

instruction is laid out as a sequence of ‘units’ that chart a course through a subject, and which typically

increase in complexity. The purpose and goal of each unit is explicitly specified. In order to proceed

through the program, each unit should be mastered, that is, learned to an ‘A’ level, before proceeding to

the next. Unlike current classrooms, fast and slow learners are not bound together in lock-step, avoiding

the boredom and frustration common to group instruction. Instead, each student may take the time they

need to master a unit before continuing.

Tests are merely diagnostic. Indeed, if the purpose of a test is to reflect a student’s knowledge,

why should students be forever bound to a grade that they received while they were still in the process

of learning? If you were to receive an average grade in a subject, but then through further study refine

your knowledge, why should it not be possible to ‘upgrade’ the mark to reflect your improved level of

understanding? That is precisely the point of professional certification – why can’t the same principle be

applied in academia? In Mastery Learning students are allowed to retake a test in a particular unit as

often as desired, until they have demonstrated mastery of the material and thus the readiness to continue

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to the next unit. Fred Keller, developer of a mastery system called the Personal System of Instruction

(PSI), defends the purpose of such stringency, “What’s the point in letting the student squeak by? If he

doesn’t really understand the material, then we’re just kidding ourselves in thinking he’s getting an

education.” On the other hand, “there is no penalty for flunking unit tests. What matters is mastery of the

material, not whether the student has achieved mastery quickly or slowly.” [Chance]

Bloom summarizes Mastery Learning in four rules, which stand in stark contrast to traditional

education’s essentially pessimistic view of human ability. First, “a normal person can learn anything that

teachers can teach.” In other words, according to Mastery advocates, given enough time, 90% of

students can learn their lessons to a mastery level, that is, earn an ‘A’. This directly challenges the

established notion that most students have ‘average’ (or worse) capability, and are incapable of

understanding above a ‘C’ grade level. Second, “individual learning needs vary greatly.” Alternate

explanations should be available, so that a problem may be approached from a perspective which seems

sensible to the learner. Third, “under favourable learning conditions, the effects of individual differences

approach the vanishing point, while under unfavourable learning conditions, the effects of individual

differences are greatly exaggerated.” Certainly students differ in their distribution of skills and aptitudes,

but mastery educators argue that variations in student achievement have more to do with man-made

factors; given some flexibility these differences have proven no hindrance to understanding. Fourth,

“uncorrected learning errors are responsible for most learning difficulties.” If student errors are

corrected in the context in which they arise, confusion will not accumulate. This point alone can lead to

significant increases in performance. [Bloom 76, Bloom 84, Chance]

Perhaps the most surprising (and to many, outrageous) aspect of the mastery system is its de-

emphasis of grades. Quite simply, if you complete a series of units, you get an ‘A’; your transcripts need

only list the subjects which you have mastered. Says Keller, “If [a student] has mastered quadratic

equations or Pavlovian conditioning, who needs grades?” [Chance]

One university-level program in the sciences claims great success with such techniques –

There are many advantages to this approach. Students create their own set of notes as they create their own

understanding of a topic. They are active learners and demonstrate long term retention. In class they are more skillful

at using concepts from previous courses and previous lessons. They develop stronger skills for deciphering complex

written material, and they display levels of self confidence that are not seen with ordinary didactic instruction.

Students demonstrate this by the ease with which they approach new problems in the text and the freedom they show

when volunteering an explanation of a topic or problem during class. The students also become more aware of their

own skills and more proficient at communicating complex concepts to teachers and classmates. [Zielinski]

5

Thus mastery methods give students the skill and confidence to teach themselves and others. Ideally,

shouldn’t this skill be the ‘hidden agenda’ of all teaching? Surely schools should foremost teach

intellectual independence. Students will not forever be in school; they should be adequately prepared to

deal with the unforeseen challenges that they will face throughout their lives.

Mastery learning, however, is not without its costs. First and most obvious is that, since students

are learning at different rates, one of two problems will arise: either fast students will have to wait for

slower students to catch up, or teachers will have to spread themselves more thinly across the syllabus,

simultaneously instructing students at various stages of progression. This has prompted some critics to

lament that teachers will need to work harder –

The main drawback of mastery learning is the increased need for instruction, since for all students to achieve a 95%

mastery of a subject, the instructional component should be increased by 10% to 20%. This cannot be considered a

reasonable demand on teachers since most teachers do not have that kind of time to spare. [Horton]

– or that the need for inspired teachers itself poses a restriction –

The problem with model programs … isn’t that they don’t work (they usually do) but that their successes almost

always depend on factors that cannot be endlessly reproduced: limited numbers of particularly talented and dedicated

educators who are drawn to new and innovative programs. [Mosle]

4 Appropriate Application of Technology

Fortunately, if correctly introduced, computers are ideally suited to precisely such problems, which

many educators since Papert have pointed out

In many ways, computers are the ideal teacher. Unlike their human colleagues, computers are never too harried to

answer a question, never too distracted to notice that a student is puzzled. They always proceed at each child's own

pace, presenting information in a variety of ways until students show that they understand the material. The best

computerized tutors can capture and hold a child's attention for hours. [Cetron]

This provides individualized instruction and appropriate learning time for students, and learning becomes a rewarding

experience for them. Aside from the initial preparation of materials, this also frees the teacher to help with remediation

of students with greater learning problems. [Caissy]

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The results of such computer-assisted mastery methods regularly generate surprising testimonials in the

media, and in some unexpected environments –

As a freshman at McDonough 35 High School in inner-city New Orleans, Corey Flagg completed the entire Algebra I

curriculum—all 15 chapters of the textbook—by the second week of February. He used a computerized education

system called I CAN Learn. It was no small feat: Students in blackboard-taught classes at McDonough are lucky to be

on chapter 10 by the end of the school year in June. [Button]

Nonetheless, as computerized teaching gains ground, it gives new life to worries that “my kids are going

to turn out to be robots. What concerns me is that … their entertainment and education may be so

thoroughly reliant upon computers that their opportunities for creative expression and exploration will

be gone” [Halpern], and that they will ultimately fail to appreciate more pedestrian printed sources.

Indeed, there seems little need to build a system that claims to do it all, replacing notebook, text, and

other learning aids in one stroke. Putting such extreme faith in technology makes little sense –

Why should we discard excellent textbooks and other familiar teaching materials? Suppose instead that we begin by

asking what the machine can do well, see if it is possible to assign some tasks to the machine, other tasks to books and

printed materials and some tasks to verbal interaction between the teacher and student. In other words, let's explore a

systems view of the teaching process. [Tyree]

And this, in fact, is what some research programs have been doing, in a Mastery-congruent manner, for

the last 25 years. If human resources are strained by Mastery Learning, then perhaps here the computer

can help. Let us consider for a moment the state of the art in research on computerized instruction.

5 The Intelligent Tutoring System (ITS)

Largely unheralded outside of the academic circles which study them, ITSs nonetheless represent the

future of computer-based learning. Born at the crossroads of artificial intelligence, cognitive science,

and education, ITSs stand in contrast to the more traditional drill-and-practice approach in which

computers are often used as sophisticated workbooks. The emphasis is on adding intelligence to

computerized education: “[artificial intelligence] programming techniques empower the computer to

manifest intelligence by going beyond what’s explicitly programmed, understanding students’ inputs,

and generating rational responses based on reasoning from inputs and the system’s own database.”

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[Shute] ITSs seek to extend and add subtlety to interactions along several dimensions: by offering

appropriate advice and explanations at the appropriate time, by gauging a student’s readiness to advance

to new material, by adaptively planning the presentation of a lesson, by giving feedback on progress,

and by choosing the timing and style of remediation.

Historically, the ITS follows from Computer Assisted Instruction (CAI) systems. While there is

some debate over the finer points of what ‘ITS’ actually stands for, the ITS always represents a big step

forward from CAI’s rigidity –

An ITS differs from CAI in that: (a) instructional interactions are individually tuned at run-time to be as efficient as

possible, (b) instruction is based on cognitive principles, and (c) at least some of the feedback is generated at run-time,

rather than being all canned. [Wes Regian, in Shute]

The intent of the “I” in ITS was to explicitly recognize that a tutoring system needs to be exceedingly flexible in order

to respond to the immense variety of learner responses. CAI, as the forerunner of ITS, didn’t have the range of

interactivity needed for learning. In fact, the movement...to ITS was to further distance the new type of learning

environments from the rigidity of CAI. [Elliot Soloway, in Shute]

Breaking away from the stereotype of impersonal, machine-like rote learning, part of the promise

of many research-based approaches derives from their ongoing basis in cognitive theory. Getting the

computer to behave more intelligently partly involves “examining issues related to the representation

and organization of knowledge types in human memory ... classic CAI used pages of text to represent

knowledge, but that had little psychological validity.” [Shute] Indeed, much of the seminal ITS work has

explored the implications of fundamental psychological research into learning, memory, reasoning,

collaboration, and retention. Cognitive scientist John Anderson is one of the most widely-cited pioneers

of the ITS field –

Each of [our] tutors has had a production system model of the skills they were supposed to teach. This involved

developing a set of rules which in combination would perform the target skill. A typical tutor would involve as many

as 500 of these rules. These rules reflected a cognitive model which we thought could underlie successful problem

solving in that domain. [Anderson 91]

This perspective sometimes takes form as a revelation; when a tutoring module was added to MYCIN,

the first practical expert system, the developers came to the realization that their role as educators was

not to shovel facts into empty heads so much as to assist the students in building mental models, in

8

“making concrete the reasoning process,” developing natural representations of the way the domain

knowledge was organized –

This is an amazing change. Ten years ago I thought I was trying to teach parameters and rules, and now I’m saying

that I want to teach the student to be an efficient model builder. What can we tell the student that will help him

critique the model that he’s constructing? [Clancey]

The irony is that researchers, of necessity stripped of conventional classroom assumptions, purposely or

otherwise employ at least some of the principles of Mastery Learning as defined by Keller and Bloom

[Bloom 76, Bloom 84, Chance]. While not all systems hold the student back until ‘mastery’ of a unit has been

demonstrated (especially see discovery environments, below), without the need to keep an entire class on

the same page, self-paced learning is so suited to computerized instruction that it is evident in all ITS

systems. Furthermore, since the computer must be able to deal effectively with a student’s confusion,

efforts are typically made to provide it with a sufficiently large database of alternative hints and

explanations. The material is also frequently subdivided into independent discrete tasks or steps – in

fact, in order to make the difficult task of student evaluation manageable, ITSs require that students’ use

of the interface be discretized into events or states that the computer can recognize, and that the

student’s level of understanding be modeled in terms of relevant cognitive parameters. Just as classical

Mastery was developed to be more responsive to the needs of the learner, so too do ITSs seek to adapt

themselves intelligently to their users. This is the foundation of perhaps the single most important facet

of an ITS: the way in which it forms a picture of an individual user, termed the student model, in order

to adapt to particular needs and aptitudes –

The main promise of computer tutors ... lies in their potential for moment-by-moment adaptation of instructional

content and form to the changing cognitive needs of the individual learner, and our task... is to find principles which

can guide the construction of tutors which fulfil that promise. [Ohlsson]

5.1 Student Modeling

Unfortunately, while considered crucial by most researchers, student modeling is also by far the most

difficult aspect of constructing “intelligent” teaching systems.

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The task is a big one, so fraught with difficulties that some would deem student modelling an “intractable problem”.

The intense difficulty of constructing and dynamically updating models of student understanding is one reason why,

more than 2 decades after computers have been introduced to learning in schools, the promise of computers to provide

individualized instruction has not yet been fulfilled. [Katz 92]

The reasons for this difficulty are obvious: human students lack the predictability of algorithms or

machines, their behaviours are more complex and ambiguous, and are not particularly consistent over

time. This introduces a significant amount of uncertainty to the problem, which requires methods which

are best suited to its resolution.

Student modeling – the task of building dynamic models of student ability – is fraught with uncertainty, caused by

such factors as multiple sources of student errors, careless errors and lucky guesses, learning and forgetting. [Katz 92]

Advocates of student models would wish to go beyond the analyses of student performance in terms of surface

mistakes. They would like to isolate the underlying misconceptions which are the “cause” of the mistakes, because

remedying such misconceptions might eradicate a whole set of mistakes. But defining, representing, and recognizing

such misconceptions is even more difficult than identifying a procedural mistake. [Self]

Such uncertainty has led to a thriving specialization in ITS research: how to interpret a vast and

ambiguous range of human behaviours in a consistent, usefully accurate way. When reasoning from a

position of certainty, one uses deduction, the use of fundamental axioms to build more complex facts –if

X implies Y and X is true, then Y must be true. When reasoning from a position of uncertainty,

however, induction is the tool –if X implies Y and Y is true, then X is more plausible. The most popular

approach to handling uncertainty is numerical and probabilistic. Bayesian Networks (BNs, also called

Belief Networks) are a simple and consistent set of rules for induction, model selection, and comparison.

Formally, BNs are a way to calculate a posterior probability distribution for a set of queries, given exact

values for a set of evidence. [Baldi]

An agent gets values for evidence variables from its percepts (or from other source of reasoning), and asks about the

possible values of other variables so that it can decide what action to take. [Russell]

The Bayesian approach provides a principled, mathematical approach which shows how to

change existing beliefs in the light of new evidence; thus it allows scientists to combine new data with

their existing knowledge or expertise. In a Bayesian model, if one element can be considered a

precondition to another, then the second element should be considered more or less probable based on

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the probability of the first. A BN is a graph possessing certain properties. A set of random variables

represents the nodes of the network, and a set of directed links connects pairs of nodes in such a way

that if a link points from a precursor node X to a node Y, then X has direct influence on Y. Each node

also has a conditional probability table (CPT) which defines the influence that its precursor (“parent”)

nodes have upon it; Y may have an arbitrarily large number of precursor nodes which point to it. To

avoid infinite looping, a network built on these properties should have no cycles (and is therefore a

DAG, or Directed Acyclic Graph). [Russell] As a set of familiar concepts are combined to provide

evidence of an outcome, Bayesian networks facilitate a common-sense interpretation of statistical

conclusions. [Gelman]

A popular introduction to Bayesian belief networks (judging by this writer’s exposure to

undergraduate and graduate courses) is Russell and Norving’s Burglary / Earthquake example (fig. 1)

[Russell]. A new alarm system is installed which reliably responds to burglaries, but is also occasionally

triggered by minor earthquakes. Two persons are tasked to respond to the alarm. John always calls when

he hears the alarm, but sometimes confuses it with other alarm sounds, and calls then too. Mary likes

loud music and occasionally misses the alarm completely.

Figure 1: A simple Bayesian belief network

The topology of the network in Figure 1 represents the general structure of causal relationships

between elements of the domain; thus the probability of the alarm going off is directly affected by

burglaries and earthquakes, but whether John or Mary respond only depends upon whether the alarm

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Burglary Earthquake

JohnCalls MaryCalls

Alarm

goes off; they do not witness burglaries nor feel minor earthquakes. Factors such as Mary listening to

loud music or John confusing various alarm types are accounted for in the uncertainty inherent in the

links from Alarm to JohnCalls and MaryCalls. It would be very difficult in a practical sense to

determine and weigh all the various reasons why a person may not respond as expected: John and Mary

may simply not be home, may be asleep, may have water in their ears, and so forth. For the alarm as

well, there are potentially an infinite number of reasons why it might go off (e.g. construction, small

animals, sun flares, etc.) or might not (e.g. faulty wiring, poorly tuned sensors, etc.). The probabilities

used summarize these potentials, and therefore allow a BN to function simply but approximately

correctly in a complex world, while still allowing the accuracy of the approximation to be improved

further by adding more relevant information.

Once the topology is fixed, the conditional probability table (CPT) in each node must be set,

such that the probability of the node being true is stated for every truth combination of its parent nodes.

Thus Alarm would have a stated probability of being true (i.e. probability of ringing) for each of the

following: neither burglary nor earthquake occurring (lowest probability), just an earthquake occurring

(slightly higher), just a burglary occurring (highest), and both a burglary and earthquake occurring(also

highest). Thus more generally, there are 2n probabilities which must be specified in a node with n

parents. A node with no parent nodes, such as Burglary or Earthquake above, is initialized with

just the prior probability of each of its possible values–in this case the probabilities of both true and

false–that the event might or might not occur.

In the context of education uncertainty is common, since student reactions are inherently “noisy”

and difficult to interpret. Here BNs can be used to determine the probability that a hypothesis about a

student’s ability is correct, given the (sometimes conflicting) evidence of behaviour. Certain activities

which the student has performed are taken as evidence that the student has understood a concept, and the

student can thereby be represented as a collection of cognitive parameters that indicate learning. A

specification of the relationship between the activities and the concepts makes it possible to predict

learning based on the evidence of behaviour, or conversely, to infer that prerequisite cognitive skills

have been achieved based on a high solution score.

A major advantage of the Bayesian approach in general, and for educators in particular, is its

relative ease of use, as “it is usually easy for a domain expert to decide what direct conditional

dependence relationships hold in the domain.” [Russell] This is especially important when the “domain

expert” is a teacher or administrator working in a relatively non-technical field such as education: a

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BN’s causes and effects are represented in a hierarchical tree-like form that makes intuitive sense to

non-scientists, and it can be constructed and debugged using user-friendly graphical tools. Domain

experts are sometimes able to work with them effectively after just a few hours of use [Horvitz].

Despite their widespread appeal for dealing with uncertainty, Bayesian networks nonetheless

present some problems. First, in order to calculate any probabilities at all, the network must be

initialized with some prior beliefs, which can be problematic:

... if a tutoring system is being deployed for the first time in a new school with a different type of pupil than before,

there may be no way of obtaining a meaningful prior distribution ... such cases are often handled through the

assignment of equal prior probabilities to all hypotheses; but as advocates of [other approaches] point out, this method

does not distinguish between a state of ignorance about a variable and a genuine belief that all of its values are equally

probable. It also means that valuable observational evidence may end up being combined with largely arbitrary prior

assumptions. [Jameson]

Thus BNs can be criticized for their “high knowledge engineering demands”; that is, although domain

experts can be taught to work with the networks, these “knowledge engineers” then face a difficult

challenge in specifying variables’ prior probabilities and the conditional probabilities of the links. [Katz

92] Sometimes this can be reduced to an educated guess:

In most of the systems...most or all of the required numbers were apparently entered by the designers on the basis of

intuitive judgment. Even in cases where systematically collected empirical data were used, the designers themselves

warn against optimistic assumptions about the accuracy of the numbers... [Jameson]

Second, defining the structure of the network is also problematic. “We require not only that each

variable is directly influenced by only a few others, but also that the network topology actually reflects

those direct influences with the appropriate set of parents. Because of the way that the [BN’s]

construction procedure works, the ‘direct influencers’ will have to be added to the network first if they

are to become parents of the nodes they influence.” [Russell] Adding nodes in the wrong order can result

in links that represent tenuous relationships and require difficult and unnatural probability judgments,

such as assessing the probability of Earthquake given Burglary and Alarm. Bad design can lead

to poor representation of relationships, and the specification of a lot of unnecessary numbers.

Third, the conditional probability table at each node requires many numbers, even when a node

has a relatively small number of parents. While the relationships between a node and its parents are not

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arbitrary and can often be reduced to a type classification, in the worst case, filling in a CPT can take a

lot of time and experience with all the possible conditioning cases. [Russell]

Fourth, and more generally, Bayesian networks can be demanding of computation; in fact, it has

been proven that the exact application of inference techniques (as used in BNs) is generally NP-hard,

and even some of the approximate applications are similarly complex. Thus, while it may be possible to

calculate a result, it may not always be practical; although seldom mentioned by researchers in the user

modeling field, this is often a problem. [Jameson]

The question then is why should Bayesian inference, with these shortcomings and among all

possible methods, be so compelling? The perhaps unexpected answer is that it can be shown in a strict

mathematical sense that the Bayesian approach is the only consistent way of reasoning in the face of

uncertainty, [Baldi] and as such has made previously difficult problems solvable. While on occasion

researchers may make tenuous assumptions about the objectivity of their methodology, the Bayesian

approach requires that all assumptions be made explicit –

Bayes theorem and, in particular, its emphasis on prior probabilities has caused considerable controversy. The great

statistician Ronald Fisher was very critical of the “subjectivist” aspects of priors. By contrast, a leading proponent I.J.

Good argued persuasively that “the subjectivist (i.e. Bayesian) states his judgements, whereas the objectivist sweeps

them under the carpet by calling assumptions knowledge, and he basks in the glorious objectivity of science”

[Jameson]

This direct quantification of uncertainty means that there is no impediment in principle to fitting models

with many parameters and multiple layers of relations, and this flexibility and generality allow the

Bayesian approach to cope with very complex problems. The result is a conceptually simple numerical

method for coping with multiple parameters. [Gelman]

5.2 Making Uncertainty More Certain

Perhaps the trickiest aspect of formalizing any method, numerical or otherwise, lies in its reliance on

researchers’ initial assumptions of what human activities should be observed and how they should be

interpreted. Attempts to refine these assumptions by consulting with domain experts, or by running

empirical studies on human cognitive characteristics, are compromised by the difficulty of preserving

nuances of meaning as the observations are formally represented in the system. In terms of the Bayesian 14

inference methods mentioned above, the problem is as much how to reify subtly interrelated concepts as

how to set the network’s initial states –

A vague concept has to have a membership function, and the various pieces of input data for a complex rule have to

be combined according to some operators. These internal representations can in principle be just as unrealistic as

arbitrarily chosen input probabilities... So the problem remains of how to choose the right ones. [Jameson]

How well do the data structures and procedure calls in the network correspond to the structures and skills that we

expect people to learn? From the network designer’s point of view, the psychological validity can be improved or

denigrated by choosing one structural decomposition instead of another ... Measuring the “correctness” of a particular

network is a problematic issue as there are no clear tests of validity. [Brown]

The problem in observing human behaviour is the same regardless of whether recording is done by

human or machine –

One major issue ... is what Smith & Geoffrey (1968) have termed the “two-realities” problem -- the fact that the notes

as recorded cannot possibly include literally everything that has actually transpired. Hence, a source of potential bias

is the possibility of selective recording of certain types of events. [Schofield]

The problem is not just what, but also how much is recorded. Student activities are the inputs to a

diagnosis which then infers what the student understands. The fewer activities recorded, or the coarser

the granularity of observation, the more difficult diagnosis becomes. Bandwidth becomes the measure of

the amount and quality of input [VanLehn]; ideally (for education perhaps, but not for privacy) machines

would be able to read minds, but this is currently impossible. By either actively asking enough

questions, or by observing at a sufficiently fine level (e.g. ‘the symbol-by-symbol’ basis of Anderson’s

LISP tutor [Anderson 95]), an ITS can obtain indirect information that approximates the students’ mental

states. In more complex problem solving situations, such as chess games or algebra problems, mental

states may be beyond reach, but the machine can still observe and react to the trail of intermediate states

which the student produces, from the posed problem to its eventual solution (e.g. Andes [Conati],

Sherlock II [Katz 92], Self’s logic tutor [Self], and various reviewed in [Collins]). The problem is akin to

inferring the traits of an invisible person–you can see objects move as they are being manipulated, but

cannot directly see the cause. The difficulty of inferring complex deep cognitive structures from a

limited set of interactions is clear.

One researcher has suggested ways to bypass problems inherent in student modeling by

proposing pragmatic heuristics, which aim at reducing the complexity of the task. For example, don’t

15

guess: ask the student to tell you what you want to know; don’t waste resources diagnosing problems

that you can’t fix; don’t be pedantic – students may have valid reasons for their mistaken beliefs; also it

is misleading for a machine to feign omniscience – perhaps it is more realistic to adopt instead a “fallible

collaborator” role. [Self]

Other researchers have teased out methods to automatically ‘debug’ students’ faulty reasoning. A

single misconception about, say, subtraction could cause a student to err on every question on a test,

even though in each case the student is consistently applying just a single incorrect operation.

Debugging one problem based on apparent gibberish in answers can be impossible for a human teacher,

but perhaps not for machines:

By being able to synthesize such deep-structure diagnostic models automatically, we can provide both a teacher and

an instructional system with not only an identification of what mistakes a student is making, but also an explanation of

why those mistakes are being made. Such a system has profound implications for testing, since a student need no

longer be evaluated solely on the number of errors appearing on his test, but rather the [fewer] fundamental

misconceptions which he harbours. [Brown]

Thus the advantage of this approach is that bug-aware systems may be able to provide very specific

feedback to the student about the nature of the misconception. [Shute] In terms of organization, the idea

is that –

[Such bug-aware] systems represent both misconceptions and missing conceptions. The most common type of student

model in this class employs a library of predefined misconceptions and missing conceptions. The members of this

library are called bugs. A student model consists of an expert module plus a list of bugs. This bug library technique ...

diagnoses a student by finding bugs from the library that, when added to the expert model, yield a student model that

fits the student’s performance. [VanLehn]

While finding the appropriate bug in a large library might be expensive, there is evidence that the

judicious choice of a specific numerical technique (such as Dempster-Schaffer theory over Bayesian

networks) can be “very efficient in tracking down and identifying the bug a student has, when applied to

an intelligent buggy system.” [Tokuda]

However, the buggy approach has some drawbacks. First, the student’s errors must be matched

with a bug in the system’s “bug library” in order to be recognized; bugs unanticipated by the system’s

designers may pass unnoticed and un-remediated. “The correct model must contain all of the knowledge

that can possibly be misunderstood by the student or else some student misconceptions will be beyond 16

the modeling capabilities of the system.” [Brown] Should the system fail, “it may totally misdiagnose the

student’s misconceptions.” [VanLehn] The system may also try to match the error to some combination of

known bugs, however it then must consider the “nonobvious” interactions of multiple bugs: “in general,

the interactions between bugs can be arbitrarily complex.” [Brown]

Second, several distinct bugs may generate the same answer. In the worst case, a student may

have several misconceptions which actually combine to produce a correct answer; how then should a

machine diagnose such an ‘error’?

Third, for student errors which do not match any bugs in the library, it would be desirable to

expand the library to include new sources of error (indeed, this would be consistent with the definition

above of what makes an ITS ‘intelligent’). While “bugs have to be hand-coded into the network now...

one can envision generatively producing bugs by a set of syntactic transformations” or for non-syntactic

bugs, by inferring from some semantic theory. [Brown] Along these lines, bugs may be constructed ad

hoc during diagnosis from “bug parts” using production rules (as seen in Langley & Ohlsson’s ACM

system), rather than being predefined (and combined, as seen in Brown & Burton’s BUGGY and

DEBUGGY systems). One advantage of this is that libraries based on rules rather than distinct cases

may be made smaller while specifying as many bugs; the implication is that this may actually make the

problem of filling bug libraries easier, although ultimately the online analysis demands seem to be

higher. [Langley & Ohlsson, in VanLehn].

Last, the system may not be able to discern between a genuine student error and a careless slip.

While the WEST system [Burton R] “has compiled into it diagnostic routines for many typical errors that

a student is apt to make (such as precedence errors in arithmetic),” the authors can only hint at the

problem of handling careless errors: “if a student makes a potentially careless error, be forgiving. But

provide explicit commentary in case it was not just careless.” [Burton R] However WEST does employ

some subtle alternative strategies in its attempt to detect “mind bugs” in the student’s understanding of

the structure of the game, or to detect an “alteration in the spirit of the game” wherein the student “no

longer cares about winning ... but instead in psyching out the actual teaching strategies embedded in the

system.” Nonetheless, the system’s approach to interventions is very conservative – in the face of an

uncertain situation, “if the student is doing something completely ‘off the wall’” the system is unlikely

to intervene.

17

5.3 Novel Approaches

Other explorations have led to some compelling and even counter-intuitive findings with respect to

learning and instruction. For instance, Latent Semantic Analysis (LSA) is a statistical technique that

compresses a large corpus of texts into a space of 100 to 500 dimensions [Graesser]; experiments using

the AutoTutor system with LSA are based on the premise that a knowledgeable tutor is not a critical

requirement. In a study of human tutoring sessions, the authors –

– found that the human tutors and learners have a remarkably incomplete understanding of each other’s knowledge

base and that many of each other’s contributions are not deeply understood. It is not fine-tuned student modeling that

is important, but rather a tutor that serves as a conversational partner when common ground is minimal. [Graesser]

It is this role that AutoTutor is designed to fill. Students type natural-language answers to questions

which AutoTutor poses (using a talking head with synthesized speech); the response set of words typed

by the student is compared with sets of words related to good answers; with LSA as its evaluation

module, AutoTutor “exhibits the performance of an intermediate expert,” which compares well with

“the vast majority of human tutors”. AutoTutor scores the student on how well they answer a series of

questions, using “simply the mean of the...scores for all the previous learner turns in the tutorial

session,” while each individual answer score is “based on its resonance with the ideal good answer of

the current topic.” AutoTutor does not otherwise formulate a student model, and yet the authors’

experiments show that it performs very well, apparently due to its ‘conversational’ nature.

AutoTutor simulates the normal tutor’s attempt to collaboratively construct answers to questions, explanations, and

solutions to problems. It does this by formulating dialog moves that asses the learner in an active construction of

knowledge, as the learner attempts to answer the questions and solve the problems posed by the tutor. Thus AutoTutor

serves as a discourse prosthesis, drawing out what the learner knows and scaffolding the learner to an enhanced level

of mastery. [Graesser]

From an educational perspective, ITS research into collaboration is of particular interest. In an

attempt to reach the goal of better instruction through individualized tutoring [Bloom 84, Cohen], ITSs

have typically been designed for use by individual learners. Some researchers have followed a different

path, in designing collaborative systems in which students work together at one computer, according to

the notion that students are likely to learn better in the presence of their peers [peer] since they can

combine forces to help each other overcome errors in reasoning or deficient knowledge. Indeed, “many

18

researchers have shown impressive student gains in knowledge and skill acquisition from collaborative

learning environments,” [Shute] and there are a dizzying variety of social and psychological paradigms

which seek to explain and measure such human interactions. [Dillenbourg] A critical issue with respect to

student modeling is that the stakes have been raised: the machine now has to form a model based on a

jointly shared rather than individual problem-solving space. There is also the issue of unintended or

unforeseen collaborative effects resulting from the introduction of an ITS to a classroom. Anderson et

al. found that even in cases where students were working at individual machines, “there is a constant

banter of conversation...in which different students compare their progress and help one another...we

have come to realize that our tutors would be less successful if such peer learning were not available.”

[Anderson 95] In one intensive study involving a “state-of-the-art artificially intelligent geometry proofs

tutor”, researchers noted that the role of the human teachers had also been affected: “specifically, the

teachers’ behavior changed from that of a rather distant expert to that of a collaborator...rather than

addressing the entire class in a relatively formal manner the teacher worked on an individual basis with

students,” and noted that as teachers kept busy responding to student requests, students took a more

active role in initiating the interaction. [Schofield]

A different view takes the computer itself as the collaborator, as suggested above by Self (as an

alternative heuristic to student modeling). In such case, “an interesting issue concerns the necessity to

have a plausible co-learner.” [Dillenbourg] The problem is that human learners are not necessarily very

tolerant of a computerized collaborator’s silly mistakes, and tend to avoid further interaction with a

simulated peer if it is repeatedly wrong. Still, “the advantage of human-computer collaborative systems

for the study of collaboration is that the experimenter can tune several parameters” in the computerized

collaborator [Dillenbourg], or alternatively can use the computer, as in the case of the Clarissa system, as a

“test-bench for examining collaborative activity,” generating “some implications for a new range of

software agents capable of plausible collaborative behaviour.” [Burton M] In any case, additional research

is required to test the efficacy of single versus collaborative learning, with or without computers. [Shute]

Another interesting approach involves discovery learning environments (or microworlds),

typically comprised of a simulation environment with a simple interface and some tools. In an attempt to

make computerized learning more flexible, such environments allow students to more freely explore and

interact with them. [Burton R, Collins, Shute] The student is placed in the potentially motivating situation of

being able to form a hypothesis about the environment, and is given the tools needed to investigate.

Contrasting heavily with “shoveling facts into empty heads,” microworlds encourage an active

19

relationship between the learner and the knowledge and skills to be acquired, a relationship claimed vital

by the legendary developmental psychologist Piaget [Shute] in that things discovered by one’s self tend

to be better remembered and more highly valued. The designers of the Smithtown system, for example –

– believe that discovery learning can contribute to a rich understanding of domain information by enabling students to

access and organize information themselves...Thus, applying interrogative skills is the ‘active process’ that leads to

learning in discovery situations...since Smithtown was designed to be a guided discovery environment, there is no

fixed curriculum. [Shute 90]

Instead, students generate their own hypotheses and test them by executing a series of actions in the

environment; the series of actions comprising a student’s ‘experiment’ represents their answer, and is

evaluated against known good and bad behaviours. In exploring scientific principles in the domain of

microeconomics, students are provided with various tools: a notebook, spreadsheets, plots, a calculator,

and a point-and-click interface in which they construct hypotheses in English and submit them to the

system. Other notable discovery systems provide interactive instruction in the domains of geometry,

algebra, spatial reasoning, logical reasoning about errors, troubleshooting skills, steam plants, and LISP

programming (as reviewed in [Burton R]), and physics [Conati], each imposing different degrees of

structure on what the student is allowed to do.

While adaptable to a wide range of users, the problem with microworlds is that not all students

are sufficiently motivated or skilled enough in exploratory behaviour to explore the environment

effectively; [Shute] in short, they may not have the necessary experience to form and test hypotheses,

although this behavioural shortcoming can itself become a target for effective remediation. [Bunt] As

computer power increases and supporting technologies are refined, virtual reality (VR) will undoubtedly

be used to enhance immersive learning environments. Research to date indicates its generally positive

value for learning: VR can “make information more accessible through the evolutionarily-prepared

channels of visual and perceptual experience,” [Shute] making the discovery experience even more

impressive.

5.4 ITS Effectiveness

This is incidentally also a stated goal of some Mastery approaches, which themselves become a type of ‘discovery environment.’ [Chance, Tyree, Zielinski]

20

Poised at the apex of computerized instruction, given the enormous time, effort, and diversity of

approaches involved in developing ITSs in their various forms, how well then do they perform? While

one might expect that better individualization of instruction would lead to more efficient skill

acquisition, results of evaluations are split: “for some learning situations and some curricula, using fancy

programming techniques may be like using a shotgun to kill a fly.” [Shute] The issue is clouded by the

fact that researchers often do not test their systems in controlled studies, and there is little agreement

over standardized reporting of results [Shute]. Shute & Psotka’s meta-evaluation of six such studies noted

that while all appeared very positive regarding the effects of the systems, “we are familiar with other

(unpublished) tutor-evaluation studies that were conducted but were ‘failures.’” Nonetheless, the

generally positive trend was viewed as encouraging, and of the six reviewed, “the findings indicate these

systems do accelerate learning with no degradation in final outcome.” [Shute] One of these six was the

LISP tutor [Anderson 95]; in one evaluation students completed exercises 30% faster than controls, and

performed 43% better on a post-test when using the tutor; a second study showed also showed

significant gains of 64% and 30% respectively. Another of the six was the Smithtown discovery

environment [Shute 90] mentioned above; control and experimental groups showed similar improvement

in their post-test scores, although the Smithtown group received half as much instruction, in line with

results for the LISP tutor. In short, students using either system learned faster with no loss of

performance as compared to traditional learning methods.

Other systems show even more positive outcomes. The Practical Algebra Tutor (PAT) [Koedinger]

was introduced to three urban high schools in Pittsburgh, and was made an integral part of the 9 th grade

algebra course. Compared to traditional methods, students using PAT scored 15% higher than their

classmates on standardized tests, and a staggering 100% higher on tests targeting curriculum objectives

of the Pittsburgh Urban Mathematics Project (PUMP), for which PAT was designed (PUMP itself is

consistent with the curriculum recommendations of the National Council of Teachers of Mathematics).

The authors also provide anecdotal evidence that PAT is popular with teachers as well, who “like the

way that the tutor accommodates a large proportion of student questions and frees teachers to give more

individualized help to students with particular needs.” This support was pivotal in convincing the

Pittsburgh school board to expand the program to other schools. As the authors state, “this study

provides further evidence that laboratory tutoring systems can be scaled up to work, both technically and

pedagogically, in real and unforgiving settings like urban high schools.” [Koedinger]

6 Out of the Lab

21

Unfortunately, successes on the scale of the PAT tutor are rare, and even the designers of the Grace

Tutor [McKendree] make concessions in relation to the “qualified” success of their own system (which

was very closely based on the LISP tutor):

There is still the major hurdle of having the tutor taken from the developers hands and used every day in real

classrooms. We feel that this is possibly the major challenge facing educational technologies such as ITSs today.

There are countless examples from conferences and labs of clever, effective, well-designed systems which are not

being used to any great extent. [McKendree]

The key problem, as they see it, is that of introducing new technologies into old environments;

instruction based on the traditional “accumulation model” sees students as “a storehouse for facts and as

long as the facts are right, effective learning will occur.” This leads to “a serious mismatch between the

cognitive theories and learning-by-doing approaches that underlie ITSs and the traditional classroom

instruction.” [McKendree]

Conversely, sometimes developers of ITSs themselves seem to avoid contentious issues. For

example, PAT employs a “client-centered design” to fit into a client-proscribed curriculum, rather than

suggesting possible alternatives. Elsewhere, it is also interesting to note, for example, that while many

debates continue over cognitive and technical details, it seems that none of the researchers discuss the

issue of grading. While this could be seen as congruent with Mastery Learning principles, it could also

be viewed as a symptomatic avoidance of education’s thornier issues:

I would like to believe that a decade of research in this area has given [the researchers] a solid perspective on what to

teach, how to teach it, and how to assess the effect of that instruction. Instead of providing guidance to educators in

this area, [they] seem willing to abrogate this responsibility and to settle into the role of technologists, teaching what

the current curriculum dictates regardless of the appropriateness. [Anderson 95]

In fact, since ITSs are often narrowly constructed as test-beds for cognitive theories or

algorithmic strategies, relatively few of these systems have taken a comprehensive approach which

would adapt them into full featured, commercially available products. Robust, reliable systems are

difficult to develop; even in better-funded industry labs, promising developments are sometimes stripped

from major products as they head to market. [Horvitz 98] For researchers, this represents a vicious circle:

big challenges require big money, and the discrete bits of research that do manage to get completed are

often too small to have an effect on the outside world.

22

While a handful of systems have been successfully implemented and retained by the institutions

in which they were tested [Gertner, Koedinger], and some ITS-based course materials are now available for

institutional purchase [Carnegie], other well-conceived and useable systems, through no fault of their

own, have fallen into disuse due to purely logistical factors, such as lack of user feedback channels or a

maintenance plan [Katz 98]. Of the school environment, one professor of education laments –

A looming crisis in most schools is the lack of technical personnel to keep technology working and running ... I have

seen schools where teachers have given up on computers because they cannot get the school district to fix them in a

timely manner ... for now the greatest problem for computer acceptance in "curriculum integration" is not its

applicability to school-related tasks but rather the inability to get service and keep it running. Long-term funding for

technology is essential for maintenance and repair, scheduled replacement, training, and support. [Marsh]

Unfortunately, the ongoing technical challenges of implementation still distract researchers from

more general pedagogic issues of curriculum, such as how computer use should be integrated into the

existing syllabus (though PAT’s success is instructive), and how educational software can be made more

easily adaptable and modifiable to stay relevant to a curriculum which continues to evolve and change.

As such, researchers do not always have a clear perspective of the realities of daily use, while those who

best understand the curriculum –the teachers themselves– may be substantially overwhelmed by the task

and the technology involved in ITS development. Tellingly, “the construction of a ‘pseudo-Socratic’

machine is costly. Best estimates of costs range between 100 and 400 hours…to build a one hour

tutorial. Once built the programs are generally not easy to modify, making them unsuitable for many

areas of…teaching” [Tyree]. Teachers are not expected to write their own textbooks anymore; neither

should they be expected to develop, their own computer tutorials from scratch and at onerous cost.

In order that better approaches, algorithms, and machines become standardized, there are two

parallel developments which offer progress in the short term. The first solution lies in changes taking

place in academia. For better or worse, widespread cutbacks in academic funding and escalating

academic costs (not to mention the large discrepancy between academic and industrial salaries) have

encouraged research institutions to take a more entrepreneurial tack, to patent and develop their own

commercial products [Reichman]. As a result, there has been a mushrooming of academic start-ups which

promote the fruits of research for hard cash; in the field of educational systems there are a growing

number of examples [Carnegie, WBT, WebCT]. Taking this trend one step further, a university might well

fund the development of sophisticated computerized materials for its own courses, thereafter generating

some income by adapting and selling them to outside interests. It would indeed be costly for individual 23

teachers to develop their own materials; it would be vastly preferable to establish a consistent, organized

system of production whose foundation is built more on empirically-derived Mastery-consistent

principles than on the profit motive. It seems unlikely that academic research will ever become primarily

concerned with the bottom line; its concern is instead with theory and empiricism, which if properly

pursued should discover and illuminate hidden causes and processes. While itself imperfect, the stated

goal of science is an abstracted ‘truth’.

Conversely, the second solution may lie with industrial educational-software publishers; many

already offer both completed tutorials and tutorial-editing tools. While many industrial products are

well-designed and useful, a general caveat might be that their primary function is to generate profit and

assure corporate survival. As such they are clearly more likely to be influenced by market surveys,

which are often used to discern what people intuitively prefer [Jackson], or to cannibalize popular

existing products, or even advertize trademark and teach brand loyalty (as seen in educational products

from Walt Disney Corporation and Warner Bothers [Disney, Warner]). In a market economy, all other

concerns are by definition secondary to profitability. The hope is then that industry can be convinced of

the value of existing research, although this may be difficult; as yet there is scant empirical evidence that

the cost and risk of implementing a more sophisticated system would result in enough of an advantage.

Regardless of the means of production, once the materials are prepared, the potential impact of

research-based systems on a Mastery-type school environment is not lost. More sophisticated models

with smaller grain size and more robust inference algorithms can result in more reliable mastery

learning, better helping in the difficult and sometimes tedious tasks of record keeping, low-level

curriculum organization, and tracking student understanding and progress. Furthermore, the initial

investment in development time would be amortized across many sessions, as “teachers do not have to

devote time to repeatedly individualize rates of instruction or to prepare additional lesson plans. The

schedule of the educational institution can remain unchanged” as the system is assimilated into the

school [Koohang]. This assumes, of course, that educational software be specifically designed to integrate

easily into existing curricula; as mentioned above, this is a vital issue which ITSs must ultimately

address.

Another potential hurdle is that a flexible mastery-type curriculum needs to allow students to redo

assignments and retake unit tests at will. Schools may argue that they do not have the resources to

administer them–this would be true in the current system of education. Nonetheless, textbook publishers

typically offer hundreds of test questions with every text that they publish; ITS publishers should do the

24

same. A unique randomized sample of these questions could then be assembled ad hoc whenever a test

was requested. The ease of this system, once in place, would increase exponentially [Tyree].

Furthermore, fears abound that human teachers will disappear. “I've had people say to me: ‘Oh,

you're trying to replace the teacher,’” says Dorelia Harrison, a teacher at McDonough. “No. It has given

me more time to do individualized instruction, and a lot of it” [Button]. Mastery proponents are quick and

unanimous in their insistence on teachers as the strong link in the chain. Says Keller, “The teacher has a

great deal to do. It is the teacher who designs the course, prepares the study guides and questions, selects

and trains the proctors [teaching assistants], handles disputes on test scoring, and generally supervises

the entire process.” [Chance]

Thus human interaction should always be valued over programmed computer responses; computers

are in no way replacements for teacher or textbook. “A software program and a teacher are vastly

different; the teacher humanistically responds in ways the software program cannot” [Caissy]. ITSs are

best used within a larger instructional context – as research has shown, computers are best used as tools

to develop specific skills; they certainly cannot as easily convey an infectious enthusiasm for ideas.

7 Politics, Economics, and Tradition

Unfortunately the exhaustive deliberations and finely-tuned cognitive models of the ITSs have had little

influence to date on the growing public fervour for educational technology. Citing much of the flawed

research which so concerned Edward Miller [Oppenheimer], at the transition to the 20th century, the

Clinton Administration adopted a proposal to put 40 billion dollars of high-tech teaching tools into

schools [USGov], while much equipment already in place goes unused. The initiative’s advisory panel is

populated two-to-one by representatives of the computer industry. Given the nature of North American

capitalism, it is not too cynical to suggest vested interests; clearly industry would like to see large sums

spent on its products, regardless of the results.

One of the fundamental criticisms leveled against computer-assisted instruction is that an infatuation with hardware

has minimized the concern about the educational merits of the courses. Critic after critic has bemoaned the poor

quality of the material. As the market has grown, the rush to produce software has resulted in a lowering of quality.

[Rosenberg]

25

When panel member Esther Dyson, the oft-quoted and influential “queen of cyberspace”, was asked

what discussion the group had had about the potential downside of computerized education, she

answered that there hadn't been any [Oppenheimer]. The new Bush administration shows every indication

of continuing in Clinton’s footsteps, albeit with less fanfare. [USGov] This apparent obsession with

technology seems like a wasteful distraction to some.

The limitations of technology in providing the answer to educational problems are revealed in a simple insight

underlying what has come to be called the Comer process, after Dr. James Comer, a Yale University psychiatrist: “...a

child’s home life affects his performance at school, and ... if schools pay attention to all the influences on a child, most

problems can be solved before they get out of control. The Comer process ... encourages a flexible, almost custom-

tailored approach to each child.” These observations seem so obvious and so unglamorous compared to high

technology that it is not surprising that it has taken so long for adequate attention to be paid to them. [Rosenberg]

Philosophers such as Foucault and critics of first-world industrial practice would find the

government’s initiative recklessly irresponsible [Wenk]. Members of Clinton's panel may have blindly

sung the praises of their products for education, but as Foucault so passionately argued, tools – and the

technology that they embody – are not neutral.

They are born into, developed for, and applied on behalf of the interests of power…the major purpose of television is

the accumulation of wealth, a purpose that some futurists also claim for software and for education generally. Many

observers believe that the development of technology and education along these lines has reached a dangerous point –

where ‘technological solutions’ threaten to overturn democracy altogether. [Howley]

In one lengthy education supplement, the New York Times exposed numerous examples of how donors

of education technology were seeking to control the schools’ curriculum [Oppenheimer].

On the other hand, the reaction of the educational establishment to methods which propose

change, even if there are clear advantages, is equally disturbing. Kenneth Koedinger, co-developer of

PAT and a professor of computer science at Carnegie-Mellon University, notes that although the I CAN

Learn system proved successful in an inner-city school setting, it makes conservative educators uneasy

in its challenge to the existing order: “having a system whose major virtue is student achievement—I

mean, it's sad but true—is not necessarily a winner.” [Button] Fred Keller similarly does not offer much

hope of progress. After ten years of research which proved the significance of Mastery Learning, “the

evidence was so clear. I thought, ‘Well, we have got a better mousetrap and the world will beat a path to

our door.’ It didn’t happen. I had no idea how immovable the educational establishment was” [Chance].

26

Albert Shanker, for 22 years the president of the American Federation of Teachers, has long struggled

with educational administration:

Our persistent educational crisis shows that we've reached the limits of our traditional model of education. Given our

present and foreseeable demographic, economic, social, and educational circumstances… the capacity for responding

to new challenges must itself be institutionalized. Unfortunately, the bureaucratic nature of our system of public

education makes it impossible for our schools to work in these ways. [Shanker]

Schools have many reasons to perpetuate bias, however artificial. One is the cachet of intellectual

elitism; if everyone earns an A, those who benefit most from the existing system (and their children)

lose their power advantage. Another reason to keep the current grading system involves the companies

that recruit graduates; they need some simple way of distinguishing between applicants. If everyone

earns an A, hirings become less clear-cut. In the face of such attitudes, educators like Keller see the key

obstacle to student achievement as “the force of tradition. We’ve built a structure around group

instruction to serve and protect it.” [Chance]

It is reasonable to assume, therefore, that while schools will be willing recipients of vast

technological donations, they will only employ them within their accustomed, traditional paradigm.

Unfortunately, the effective introduction of ITSs may generally require

...substantial changes in both teachers’ and students’ behaviour. For example, effective usage of intelligent tutors is

likely to require much greater role change on the teachers’ part than usage of more traditional drill and practice

applications in which computers are often used as sophisticated electronic workbooks and thus fit much more readily

into established classroom roles and routines. [Schofield]

Unfortunately, says educator L.J. Perelman, “The common practice of trying to simply add-on

technology to education while actively prohibiting transformation of the rest of the system’s social

infrastructure is just what has made so much of the technological experiment in education fruitless.”

[Perelman]

27

8 The Future of Schools

The advent of security guards at an increasing number of high schools, as well as metal detectors, and

even police patrols on one hand, and bullying, stabbings, and shootings by students on the other, only

reinforce the notion of school as a “minimum-security” institution. If sophisticated, adaptive educational

software such as ITSs could be used at home, schools – with their increasing negative connotations –

could become more of a part-time option than a full-time requirement.

A growing number of wage earners work and shop at home by computer; this trend will continue

to expand to other areas of life. Indeed, there are already ITSs that function effectively over an internet

connection [Brusilovsky]. In the post-secondary arena, “wired” education is taking firm root as more

universities and colleges offer on-line courses, while a few pioneers even eschew campuses altogether to

adopt an exclusively on-line presence [CEW]. Political considerations aside, there is no obvious reason

why this paradigm should not be extended to child education; the possibility is already being explored at

Canada’s own Virtual High School. Promisingly, their self-paced on-line courses –

– have animations, visuals and auditory devices to reinforce the curriculum. They are also scripted to provide

feedback for formative problem solving. These online courses also have formative evaluation vehicles built into them

to give the student feedback about the quality of their learning. [VHS]

Each course is moderated by a human teacher who monitors student progress and answers any questions

which may come up. In answer to the question of whether it would be better to take courses in a typical

high school classroom, their response is:

Yes, a typical high school class offers a host of benefits and advantages that an Internet delivered course can never

hope to achieve. However, these courses have benefit in that they allow the student to develop their own self-

motivation skills, event management skills and other attitudes necessary for survival in today's academic and work

milieu. [VHS]

By contrast, the “benefits” of a regular classroom are those that can never be fully replaced by

technology. Schools are socialization centers for those who want or need socialization, and can offer

precisely what is not available in the home: labs, workshops, recreational facilities, and expert

remediation. But clearly, the virtual approach as stated has its own benefits: an introduction to personal

responsibility, where success (made more likely through self-pacing and individualized feedback) has a

positive impact on one’s future, and failure costs little more than time, since one can always try again

28

without the stigma of being publicly ‘held back’ a year. Students who would enroll in the virtual school

are listed as –

Those students who cannot go to a regular high school for a whole host of reasons; confined to home due to a

disability; home-schooled; parents; students who cannot get a particular course in their high school; students who

cannot get a particular course at a particular time; summer-school students; students who would like to fast track or

upgrade, etc. [VHS]

The implications for homeschooling are compelling. In this context, educational software in

general, and ITSs in particular, would greatly assist work-at-home teacher-parents. While some people

may find it disturbing that computers would ‘separate’ family members to be each quietly absorbed in

their own tasks (and thus not ‘communicating’), this way of life was common until family shops gave

way to mass-production at the beginning of the 20th century. Instead, by keeping the family in closer

proximity, computer-assisted education may actually improve family coherence; it is arguable that the

often-repeated complaint that children today receive inadequate parental supervision may be partly due

to the fact that, with the Machine-Aged insistence on mandatory attendance at school and work, family

members see precious little of each other as it is. If parents’ work requires that they leave home every

day, supervision of homeschooling is indeed an issue. In such case, schools could assist by more

actively tracking progress and providing supplemental social structure.

Thus a ‘hybrid’ model of education emerges, wherein students mostly study at home by virtue of

ITS, and attend local schools on a drop-in basis, to discuss problems, run experiments, participate in

sports and clubs, and attend assemblies and dances.

9 Conclusion

While other influential sectors of society race forward as fast as the pace of discovery can take them,

education lags behind. In particular, it proves remarkably slow to accept the growing wealth of

knowledge regarding human cognition, clinging instead to familiar procedures and practices which were

developed in another time, for a purpose which no longer applies. The forecasts of our societal future,

and the future of the planet we are so busily corrupting, grow worse daily. If we are to redress our evils,

and live more by our ideals, we must lay the foundation for coming generations by developing the best

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possible system of education, one that creates a citizenry which can deal confidently and intelligently

with the challenges to come.

There are admittedly many dangers implicit in information technology: too much enthusiasm for

computers sends the message that the digital world is somehow more compelling than the real one; the

manipulation of software can be mistaken for the manipulation of concepts; there are some who see in

computers the power to encourage children to sit still and be quiet; and certainly, placing a child in front

of a computer will not necessarily solve her problems if she was never read to by her parents. But

consider for a moment how different, how much better, society could be if its basis education were

founded on principles of enlightenment instead of clockwork, where most of its members were raised to

be knowledgeable and independent, and thus empowered. Naturally, those who profit by ignorance

might oppose such circumstances, but suppose that, if all were to enjoy learning and to learn well, we

might be taking a first small step toward a better world.

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