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Power Learning Project |
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The widespread availability of computer technology offers an untapped resource in
education. Researchers in this area have named such uses of computers as
computer-assisted instruction (CAI) and computer-based education (CBE). The most
popular example of computer-assisted instruction is Seymour Papert's LOGO, which
engages the computer as a teaching-learning tool. LOGO, based on a Piagetian model
of learning, proved to captivate and educate children by placing the child amidst
a mathematical programming world in which the student writes programs to control the
movement of a turtle on the computer screen. Problem solving skills, geometry skills,
and programming skills improved (Brookline LOGO Project, 1979; Papert, 1980).
Contrary to the teaching structure proposed by Skinner and Pressey, LOGO represented
an era in American educational psychology, where students were no longer told things,
but an environment was created in which the student could discover things for
themselves. Memorization was replaced with discovery. As an educational pedagogy,
LOGO is the dialectical opposite of Skinner's teaching machine. In LOGO, as with
most simulations, the computer provides an environment in which the student learns
by doing and exploring. The only structure is that imposed by the human teacher
through homework and class assignments. The success and educational merits of LOGO
must be underscored. As the old adage states, "we do not want to throw the baby out
with the bath water." The choice is not simulations versus teaching machines, but the
proper use and implementation of both. Our focus in this study is to properly define
and evaluate the role of computerized teaching machines.
Skinner makes an arguement that showing and telling are ways of priming behavior.
"We learn when what we do has reinforcing consequences. To teach is to arrange such
consequences," and by carefully "constructing certain contingencies of reinforcement,
it is possible to change behavior quickly and maintain it in strength for long periods
of time" (Skinner, 1987). The central process is called operant conditioning. A
growing body of research in computer-assisted instruction supports Skiner's view. A
structured teaching environment holds a lot of potential and promise for the computer
to provide meaningful instruction. (Siegel and Davis, 1986).
Unfortunately, the vast majority of educational software currently available is neither
a simulation nor a structured teaching environment. In fact, these programs offer
nothing more than drill over a small and limited domain. Common computer tutorials
simply display the contents of a textbook on the screen, with immediate search and
index capabilities and often some animation. Yet, the responsibility for teaching,
the integration of practice, and the assurance of mastery is not within the scope of
these programs.
The biggest problems hindering work on teaching machines are: (1) the vast amount of
development time required to produce properly structured programs; and, (2) the lack
of concrete programming guidelines to insure the student's mastery. The guidelines
for providing a meaningful teaching-learning environment on the computer must be
analogous to the human teaching experience.
In this study we designed and built a well-structured teaching machine covering a
small topic of course material. After establishing programming guidelines that
seamlessly incorporate instruction and practice and assure the student's mastery of
the material, we named such guidelines power learning. Some students used power
learning while others did not. Student attitudes towards the class, lecture, the
textbook, and the computerized teaching machine were measured for both groups.
Teaching machine. A teaching machine is a non-human device which is
responsible for the carefully structured presentation of new material and the
seamless incorporation of drill and practice into the learning process. A teaching
machine is an aid to teachers, but is not the same as traditional aids, such as a
chalk board or a book. In traditional teaching aids, the teacher remains responsible
for the presentation of material and the choice of when and how to incorporate
questions, drill and practice. In a teaching machine, these operations are performed
by the machine.
Computerized teaching machine. A computerized teaching machine is a teaching
machine based on the use of computer technology to produce the machinery used to
teach. This will involve some form of a stored, pre-programmed sequence of events.
It is important to emphasize that not all computer-assisted instruction can be
considered as teaching machines, since simulations such as LOGO, and programs
that only engage the student in drill and practice, are not responsible for the
structured presentation of material.
Power learning approach. Without a lengthy discussion of the guidelines
we follow, power learning can be defined as a tightly-structured, step-by-step
learning environment, that can guarantee each student's mastery of the material
presented in the environment. Such an environment must first adhere to these
fundamental requirements:
The subjects were 34 undergraduate and graduate students currently enrolled in a
first semester computer programming class at Harvard University. The computer
language being taught was the C programming language. No previous experience in
computer programming was required. The participants volunteered from a total class
of 90 students. Thirteen of the students used the power learning program, and 21
did not. Eleven students completed a pre-test survey. Of these 11 students, 8
students used the power learning program. All participants completed a post-test
survey.
The materials consisted of the power learning software program and computers,
a pre-test, a post-test, lecture, sections, additional course handouts, a textbook
and a homework assignment.
Power learning software program. The power learning program built for this
study covered the teaching and learning of the random number generator and Monte
Carlo simulations in the C programming language. The program was implemented on
3 computer platforms --IBM PC and compatibles, Macintosh computers and Unix computers.
Participants could use either of these versions, as many times as desired, at home or
at school, at anytime of day or night that best suited their schedule. The
development time ran 40 minutes of development for each minute of use. The total run
time was 45 minutes for most students. So, the total time to develop the program was
30 hours.
Pre-test and post-test. Some students were given a pre-test survey. All students
were given a post-test survey. The pre-test consisted of five, 8.5 x 11 in. sheets of
papers containing a total of 46 questions. The post-test was identical to the pre-test,
except it contained an additional cover page of 5 questions that identified the
student as using or not using power learning, and completing or not completing the
pre-test. A copy of the post-test is included as Appendix A. The questions were
grouped in 4 major groups: (1) feelings about the class and personal performance;
(2) attitudes about the usefulness and practicality of using power learning; (3) a
survey of the students knowledge of the material being taught; and, (4) a table of
attributes to describe lectures, sections, the textbook, and power learning.
Almost all questions had a continuum line, ranging from "low" to "high." Students
placed a mark on the continuum denoting their response. The table of attributes had
discrete ratings of low (1) to high (5). The attributes queried were: fun,
challenging, unfair, difficult, and frustrating. Weiner (1980) has shown that
"thoughts determine what we feel and feelings determine what we do." So asking
students about their feelings offers a good prediction of their action.
Students volunteered to participate in the study. After completing the pre-test
questionnaire, some students were not allowed to use the tutorial immediately.
We explained to these students that they constituted the control group. Of these
students 90% verbally protested and immediately insisted on one-on-one discussions
with me. These students illustrated extreme agitation, including crying. This made
it clear that the students volunteering for the study were very pressured and feared
not being able to complete the assignment without as much assistance as possible. The
next day all students were allowed access to the power learning program.
An aggressive homework assignment was given based in part on the material covered
in the tutorial, but demanded mastery of other new material as well. The assignment
required the creative use of the random number generator to produce a graphical
simulation of a drunk person walking down a street. The students had to track the
number of steps and be able to change the width of the street. The students had one
week to learn the material and complete the assignment. During that time, they had
one lecture, one section, course handouts and the textbook available. Each student
was expected to work alone, although tutors were available to offer assistance.
At end of the week, the homework was collected, and both the students using the
tutorial and those who did not, completed post-test questionnaires.
The results reported here are based on the median values of the composite scores.
No significant difference appeared between the medians and the averages on each
question.
Eleven of 13 students (84%) stated that power learning was 33% better than expected.
One student stated power learning was exactly as expected and one student stated it
was far worse (citing an expectation for power learning to cover all the material
needed for the assignment).
The students using power learning spent 17% more time with the package than they
expected.
Both students using power learning and those who did not, believed power learning
could help their performance in class. But using the program, increased the belief
in the usefulness of power learning by 10%. Further, when asked how much they could
learn from power learning in the pre-test, all students expected a 30% increase in
learning; but students who actually used the program reported a 60% expectation
afterwards.
The students participating in the study represented a cross-section of the students:
12% of the students participating in the study expected a failing grade in the class;
32% expected an average grade, and 56% expected an above-average grade. The range of
expected grades ranged from 8/100 to 100/100. So, it was not the students with the
lower grades that flocked to the study. Also, no significant difference was found in
student attitudes towards power learning based on their ranking in the class.
The students identified their ability and their self-confidence, as it related to the
course, at 75% of the maximum possible. Further, students characterized the class as
requiring high effort, 88% of the maximum possible; humility at 50% of maximum possible.
Other attributes such as anger and guilt rated lower at 25-33% of the maximum possible.
These findings are consistent with Jagacinski and Nichols (1987) showing that the class
environment requires high effort with low success, and some students, attribute their
inability to succeed on external factors. Further, because humility lies at 50%, the
class consists of a mix of ego-involved and task-involved students.
As for the specific material taught by power learning, students who used the power
learning software, reported learning 78% more of the material than the students who
did not use power learning. However, this is still 18% below what the students
expected to learn about the material beforehand.
Attributes assigned to power learning rated fun as expected, which was the same as
lecture and section, and twice as fun as the textbook. Power learning was half as
challenging as expected, which made power learning half as challenging as lecture,
section and the textbook.
Students who did not use the power learning software expected power learning to be
3 times more unfair than lecture, section and the textbook, but students who used
power learning found it to be just as fair. Likewise, power learning was expected
to be as difficult as lecture, section and the textbook, but was found to be 33%
less difficult than the others. Finally, along the same lines, power learning was
expected to be as frustrating as lecture, section and the textbook, but was found
to be half as frustrating, receiving the best rating possible. In fact, power
learning received the lowest rating possible in unfairness, difficulty and frustration.
The textbook rated worst, being assigned attributes as extremely unfair, difficult
and frustrating. Students characterized the textbook as challenging, difficult and
frustrating, while power learning was fun and far less demanding. Students described
lecture and section as fun and challenging, but difficult.
At a time when even smaller class sizes have soared to 25 or 30 students, it is
surprising that we do not employ common computer technology to personalize instruction.
No one can deny that computerized teaching machines offer personal attention to
students. Further, continuous monitoring of students is not typically available.
Lessons are not personalized to move at a pace determined by the needs of each
individual student. Yet, teaching machines provide exactly these features and can
be implemented with current technology.
Power learning shares Skinner's belief that good instructional programs "maximize
the effect of success as a conditioned reinforcer by asking students to take small
steps and making every effort to help them do so successfully" (Skinner, 1987).
Success has a powerful impact on the student, and power learning ensures success
by the careful selection of incremental steps and the constant interaction with
questions. Students get trapped in the power learning environment because paying
attention has reinforcing properties.
Skinner believed that with the help of teaching machines and programmed instruction,
"students could learn twice as much in the same time and with the same effort as in
the standard classroom" (Skinner, 1987).
The results of the study show that students overwhelming preferred power learning
to the textbook. Students who did not use the power learning software expected the
tutorial to be fun and very challenging , but unfair, difficult and frustrating.
The students who used power learning assigned the attributes: fun, not too challenging,
very fair, easy, and not frustrating at all. At the same time, these students reported
learning 60% of the material, while those students who did not use the program reported
learning only 30% of the material. So, the incremental steps which leads to constant
success allowed the students to use the program longer and yielded a friendly,
supportive learning environment.
This study has shown that we can build a competent power learning system that presents
the mechanics of the C language in small, manageable doses. But, this only accomplishes
part of what is required in a programming class. Each student must also demonstrate
mastery of the subject matter by constantly using newly acquired knowledge to solve
problems.
It is quite easy and common for students to understand program presentations and
concepts discussed in conversation, in a book, or even on a computer screen. But,
when left alone to solve a slightly different problem, the student is lost and confused.
Even mastery of how the C tools work, does not guarantee the student's success at
putting the pieces together to solve problems.
To address this concern, first, power learning incorporates special activities that
involve the student in applying newly learned material to specific problems. This way,
the student not only gains mastery of a function or feature, but also recognizes the
conditions under which to use this knowledge.
Second, power learning allows the student to learn from the mistakes of doing. The
student builds programs under the supervision of power learning. This allows power
learning to evaluate mistakes and make appropriate recommendations.
In using conceptual models to teach computer programming in the BASIC computer language,
Bayman and Mayer (1988) identify the skills and types of knowledge required in learning
how to program --syntactical, conceptual and strategic. Syntactic knowledge refers to
the knowledge of facts about how programs are put together. This is analogous in
writing English sentences, to knowing where to place commas, periods, and other
punctuation and the word order within sentences. By conceptual knowledge, we refer to
what is taking place inside the computer to make it behave as it does. You can't see
the act itself, but you can measure the consequences. To continue our writing analogy,
conceptual knowledge concerns the ability to abstract beyond the words to the bigger
picture of what is happening in a story or paragraph. Finally, the ability to
incorporate syntactical and conceptual knowledge to solve new problems, is termed
strategic knowledge. In writing, these are the ways we've learned to use outlines and
arrangements of information to put together essays, book reviews and the like. Given
the array of skills the student must master in learning to program, the success of
power learning in this domain predicts great success in other educational arenas as well.
Abstract
Power Learning: Using Computers as Teaching Machines
Method
Operational definitions
The resulting approach is always a sequence of very simple, incremental instructions.
Each step is so simple that a student can not help but understand what is presented.
The activity may even seem trivial to the student. Yet, when a student completes a
set of these steps, the student gains solid understanding of a complete subject matter.
A more graphical way of saying the same thing, is that by taking many very small steps,
the student ends up traveling a long distance.
Subjects
Materials
Design and Procedure
Results
Discussion