During the 1980s educators believed that
teaching children simple
computer programming (e.g. LOGO) could enhance their problem-solving
capabilities. However, empirical studies fail to find significant
differences on
mathematics achievement or knowledge-dependent problem solving between
LOGO
learners and non-learners though the LOGO group significantly
outperformed the
control group in executive-level problem solving (Battista &
Clements, 1986).
How computer programming influences human cognitive capacities,
especially
problem-solving, still remains an unresolved problem.
The purpose of this study is to
examine the effects of including physical modeling and problem solving
as components of computer programming instruction for children. Problem
solving is defined as a mental modeling process in which relationships
of components are restructured in order to form a functioning whole.
Since physical modeling is a concrete representation to the abstract
relationships of a problem, it is believed that learning
objected-oriented programming along with physical modeling can help to
enhance problem-solving skills.
Literature review
The potential of using computer
programming languages to promote the development of higher order
thinking skills, such as problem solving, has interested educators for
some time (Palumbo, 1990). Many have argued that teaching computer
programming to children can result in improvement in problem solving
skills and attitudes toward using computers (Papert, 1990; Kafai &
Resnick, 1996). But, more research is needed to determine the efficacy
of computer programming activities on improving problem solving skills
and attitudes toward computing (Dalton & Goodrum, 1991; Liu, 1997).
Some studies report positive
effects of teaching programming on the development of problem solving
skills (Becker, 1992; Lehrer and DeBernard, 1987; Lehrer, Guckenber,
& Lee, 1988; Mayer, Dick, & Vilberg, 1986; McCoy & Dodl,
1989; Palumbo & Reed, 1987-1988; Reed & Palumbo, 1992; Swan
& Black, 1988). However, many empirical studies do not support the
use of computer programming as a means of developing logical thinking
skills (Blume & Schoen, 1988; Jansson, Williams & Collens,
1987), skills of planning and problem solving (Clements & Gullo,
1984), or as a means of accelerating the cognitive development of young
children (Ginther & Williamson, 1985).
Research suggests that
spontaneous transfer of problem solving skills gained through
programming to other domains is unlikely (Clement, Kurland, Mawby,
& Pea, 1986; Ginther & Williamson, 1985; Linn & Dalbey,
1985) and instruction on how to apply strategies to new domains may be
needed (Pintrich, Berger, & Stemmer, 1987;Salomon & Perkins,
1987). The literature on problem solving emphasizes two needs. The
first need is for providing students with guidance to develop a
self-inquiry approach in their learning (Avots, 1993; Newmann &
Wehlage, 1993). The second need is for providing instruction that
entails the active application of problem solving skills in the context
of specific problems (Cardelle-Elawar, 1993, 1992; Merideth &
Lotfipour, 1993, NCTM, 1989; Fay & Meyer, 1983). Therefore, this
study will investigate the effect of an explicit problem-solving
curriculum, which will be taught by providing instruction and practice
within the context of the programming the students are learning.
Objectives
Because of the inconsistent and
inconclusive results of previous research, this study attempts to
approach several unanswered questions. One objective of this study is
to examine the effect of including a problem solving curriculum when
teaching children, who are between the ages of 7 and 9, Object-Oriented
Programming (OOP) skills.
The second objective of this
study is to examine the effect of including physical modeling as a
component of teaching OOP skills. Students engage in physical modeling
by using LEGO programmable bricks and LEGO building blocks to construct
robots to execute the programs created in Robolab. The use of digital
manipulatives such as the LEGO programmable bricks and computer-based
modeling environments such as Stella, StarLogo, and Model-It have made
it easier for pre-college students to model and explore systems
phenomena such as feedback and emergence (Resnick, 1994; Roberts,
Anderson, Deal, Garet, & Shaffer, 1983; Jackson, Stratford,
Krajcik, & Soloway, 1996).
A third objective of this study
is to examine the impact of including a problem solving curriculum
and/or physical modeling in OOP language instruction. Specifically, the
objective is to measure the impact of including problem solving
curriculum and/or physical modeling on computer anxiety levels.
These objectives will be
addressed by evaluating students who participate in the Conexiones
Project (Collins, Wijesuriya, DiGangi, Jannasch-Pennell, & Cohn,
1999), a mentoring program for children of migrant farm workers.
Forty-eight children from grades 7 through 9 will learn Robolab's OOP
language (Johnson, 1997).
Problem solving, OOP, mental
modeling, physical modeling, as well as their relationships will be
explained in the following.
Problem solving
Problem solving has been defined
in a number of ways. It has been defined in terms of a academic
discipline, such as math or science, and it has been defined in terms
of the ability to process abstract concepts, such as spatial ability.
Problem solving in this study is limited to the context of the OOP
curriculum. Students who receive instruction in problem solving will
learn how to debug programs. They will be taught a specific approach,
developed and contextualized by the Conexiones staff. The
problem-solving curriculum that will be offered in this study is based
on the following research. Studies performed by Greeno (1980), Soloway,
Lockhead, & Clement (1982), Schoenfeld (1979), and Mayer (1983;
1986) suggest that the following procedures might be useful in
developing problem solving ability:
-
Translation training.
Providing sufficient practice for the problem solver in developing
internal representations of a problem.
-
Schema training. Providing
sufficient practice for the problem solver in putting the elements of a
problem into a coherent whole.
-
Strategy training. Providing
sufficient practice for the problem solver in the application of the
"process" of problem solving instead of the generation of "products."
-
Algorithm automicity.
Providing sufficient practice for the problem solver in the component
skills (e.g., computation) required to solve problems.
In the present study, problem
solving is approached from two perspectives. First, it is defined as
the ability to modularize a task and restructure the relationships
among objects or components to form a functioning model (Kohler, 1925;
1969). Second, it is viewed as processing previous relevant knowledge
learned in another situation and applying it to an unfamiliar situation
(Cronbach, 1988). From this perspective, if an evaluator presents to
the student a problem that exactly matches the learning objectives,
recalling, rather than problem solving, becomes the focused construct.
In real-world applications, most problems are vaguely defined and
unstructured, thereby requiring complex information processing skills
such as gathering scattered data and restructuring the relationships
between the components. In this view, the emphasis of problem-solving
is process-oriented rather than product-oriented (Keller, 1990). Thus,
by definition, problem solving involves transfer and transformation of
information. It is hypothesized that through instruction and experience
with OOP, learners can enhance their ability to form mental models of
problems and over time increase and refine their repertoire of problem
solving approaches.
Object-oriented
programming
In this study, every participant
will learn an object-oriented programming (OOP) language.
Object-oriented programming can be viewed as a specific application of
mental modeling. In OOP, a programmer can use and reuse ready-made
objects from a library of commands and operations. Each object belongs
to a specific class, has specific properties, and performs specific
functions. In addition, the objects can interact with each other by
exchanging messages and values or parameters.
The degree to which a programming
language constitutes a true OOP environment is debatable. For example,
Java and C++ (Stroustrup, 1997) are
considered OOP environments even though they require more code-based
manipulation than some programming applications that are more
consumer-oriented.
Participants will learn an OOP
language developed for children: ROBOLAB (LEGO, 1998). ROBOLAB is based
on LabVIEW, an industry-standard, graphical programming language
(LabVIEW, 1998; Johnson, 1997). In ROBOLAB, programmers write a program
by selecting, ordering, and structuring relationships among objects.
Mental and physical modeling
The physical modeling with LEGO
bricks is meant to complement the abstract mental modeling necessary
for programming by providing students with a digital manipulative that
can provide feedback as well as a physical representation of component
building. Mental modeling is a process of problem solving that entails
forming an abstract representation of a problem and visually
constructing a functional relationship between its components
(Barsalau, 1992; Muller, 1997). Object-oriented programming can be
viewed as a specific application of mental modeling in which
programmers construct functional relationships between components or
objects. When physical modeling is taught in conjunction with mental
modeling, students have a feedback mechanism and concrete manipulative
in addition to the abstract mental modeling necessary for programming.
Research Hypotheses
-
Null Hypothesis 1:
There will be no differences in computer programming gain scores among
students who learn OOP with an explicit problem solving curriculum and
students who learn OOP without an explicit problem solving curriculum.
-
Null Hypothesis 2:
There will be no differences in the problem solving skills among
students who learn OOP with an explicit problem solving curriculum and
students who learn OOP without an explicit problem solving curriculum.
-
Null Hypothesis 3:
There will be no differences in the level of computer anxiety among
students who learn OOP with an explicit problem solving curriculum and
students who learn OOP without an explicit problem solving curriculum.
Method
Population and sample
The target population of
this
study includes all migrant school children in Southwest America whereas
the accessible population consists of migrant school children in
Phoenix, Arizona. By convenience sampling, forty-eight students who
have been identified as migrant school
children will be chosen to participate in the study. The sample size
determination is based upon the power analysis program Sample Power
(SPSS, 1999). Given that the Cohen's effect size is .48, the alpha
level is .05, and the power is .80, the desired sample size would be 40
in total or 10 per cell. Eight extra students will be chosen to ensure
that
the sample size of 40 will be maintained even if there is attrition or
noncompliance.
Design
The design will be a 2X2 ANOVA.
Each participant will receive instruction in Robolab's OOP language.
The first factor is problem solving curriculum, which has two levels:
1) the presence of problem solving curriculum, and 2) the absence of
problem solving curriculum. The second factor is physical modeling,
which also has two levels: 1) the presence of physical modeling, and 2)
the absence of physical modeling. The design is orthogonal because all
cell sizes will be equal. Descriptions of the four groups follow:
1. Learning Robolab's OOP
language with problem solving curriculum
2. Learning Robolab's OOP language with physical modeling
3. Learning Robolab's OOP language with both problem solving curriculum
and physical modeling
4. Control: Learning Robolab's OOP language with neither problem
solving curriculum or physical modeling
Strength and integrity of
treatment will be ensured by the following means, which have been
identified by Sechrest et al. (1979). Primary instruction will be
provided by four computer experts with Masters degrees in Educational
Media and Computers and at least two years of teaching experience,
which includes teaching limited-English proficient students who have
participated in the Conexiones Program. These instructors will be
assisted by bilingual students in education and computer programming.
Curriculum will be developed based on the Robolab curriculum.
Curriculum development will be supported and supervised by a team of
university professors and research specialists who have PhDs in
education and at least five years of teaching experience, which
includes teaching limited-English proficient students who have
participated in the Conexiones Program.
Instructors and assistants will
participate in a forty hours of training that will prepare them for
teaching and curriculum implementation. During this training, they will
receive a detailed schedule of teaching activities that will be
monitored by the curriculum development supervisors via spot checking
of treatment sessions. Participant attendance will be monitored by
instructors and assistants who will record the dates, times, and names
of participants for each session. Video recording of a sample of
treatment sessions will be conducted so that sessions can be analyzed
for conformance to treatment requirements.
Instruments
There will be three dependent
measures. The first measure is a criterion-referenced test specific to
Robolab programming. This criterion-referenced test will require
participants to complete a programming task. Participants will be
videotaped as they engage in Think Aloud Protocol while they complete
the task. The videotape will be analyzed by four raters who are experts
in computer programming and who will evaluate the test on the basis of
efficiency, time necessary to complete the task, and accuracy. The
inter-rater reliability will be calculated. Because the participants
will have had no previous instruction in computer programming, a
pre-test will not be administered to assess their skills. However, a
test will be administered to all students after they have engaged in
one half of the curriculum for OOP.
The second measure is the change
score of an attitude survey, the Computer Anxiety Index (CAIN). The
CAIN is a standard instrument for measuring computer anxiety
(Simonson,, Maurer, Montag-Toradi, & Whitaker, 1987). A pretest to
assess computer anxiety will be administered before the treatment and a
posttest will be administered after the treatment.
The third measure is the gain
score of problem solving skills. Students will be asked to debug
Robolab programs. Students will be presented with this task after 10,
20, and 30 hours of programming instruction. The internal consistency
of the second and third instruments will be evaluated by Cronbach
coefficient Alpha.
Procedures
Students will be randomly
assigned to one of four treatment conditions. At the beginning all
subjects will be asked to complete the pretests in the CAIN and problem
solving skills. Students will also be surveyed to determine their level
of access to computers and whether or not they are receiving any
additional instruction in computer use or programming during the
treatment. If so, these factors will be used as covariates. To avoid
the threat against internal validity caused by a demoralizing effect
(Cook & Campbell, 1979), the four groups will be separated into
four rooms so that subjects will not be aware of what treatment their
counterparts will receive. This is especially important among children
who may or may not be using LEGO building bricks in their treatment.
Students will be tested with
different programs after intervals of 10, 20, and 30 hours of
instruction. To prevent test-retest effects, the problems will vary. To
avoid ceiling effects, the problems will increase in difficulty as the
students receive more instruction.
The entire study will last four weeks. Instruction will take place for
3 hours after school each weekday. Participant attrition and
non-compliance with treatment will be monitored by recording attendance
during all sessions. To account for attrition and non-compliance,
analyses of data will be conducted according to percentage of treatment
received.
Time and resource constraints for
both the program and the students involved preclude offering the
treatments for extended periods of time; however, the literature
suggests that significant periods of time are required for teaching
computer programming skills. Liu (1997) found that enhancement of
problem solving skills by learning HyperCard occurred after 150 hours
of instruction. In a study by Palumbo and Reed, significant benefits of
problem solving skill enhancement occurred after 91 hours of Basic
programming instruction. Nevertheless, Basic and HyperCard are not
Object-Oriented Programming languages. Second, they are not designed
for children. The time and resource constraints might cause a failure
to deliver a treatment with adequate intensity.
Proposed Analyses
Analysis will include examination
of both the main effects and the interaction effects of the factors.
Although the intended design is orthogonal, dropouts and outliers may
lead to uneven cell sizes and thus render the design non-orthogonal. To
compensate for this, Type III Sum of Squares analysis will be used.
Parametric assumptions such as
the homogeneity of variances and normality of sample data will be
examined. If a violation of the assumption is detected, data
transformation procedures will be employed.
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Paper presented at the annual meeting of the American Educational
Research Association, New Orleans, LA.
