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Integrated learning: explicit strategies and their role in problem-solving instruction for students with learning disabilities.

ABSTRACT: This study investigated the effectiveness of an explicit strategy as a means of linking facts, concepts, and problem solving in an unfamiliar domain of learning. Participants were 37 secondary students with learning disabilities. All students were taught health facts and concepts, which they then applied to problem-solving exercises presented through computer-simulation games. Students in the experimental group were taught an explicit strategy for solving the problems; the comparison group was given supportive feedback and encouraged to induce their own strategies. The explicit strategy group performed significantly better on two transfer measures, including videotaped problem-solving exercises.

* Elementary and secondary students spend much time completing routine, application-oriented activities. Some of these exercises challenge the students and demand a modest amount of problem solving, but most only require students to operate from rote memory and follow well-rehearsed procedures (Doyle, 1983, 1988; Goodlad, 1983; Resnick, 1987). Increasingly, educators are beginning to criticize the kind of process-product instruction that, while efficient and well-managed, fails to develop conceptual understanding (Schoenfeld, 198 8) or even a modest level of transfer to real-world problems (Salomon & Perkins, 1987).

Special education's emphasis on basic skills remediation leads to a similar, if not more accentuated style of instruction. Observational research has confirmed many instructional similarities between general and special education programs (Gersten & Woodward, 1990; Haynes & Jenkins, 1986; Ysseldyke, O'Sullivan, Thurlow, & Christenson, 1989), noting that even more time is devoted to seatwork activities in pullout settings (Allington & McGill-Franzen, 1989). This tendency holds true when students with learning disabilities use computers. Surveys and observational studies have consistently reported that low-achieving students and those with learning disabilities spend the vast majority of their time on computers working with drill-and-practice programs (Becker & Sterling, 1987; Rieth, Bahr, Okolo, Polsgrove, & Eckert, 1988; Semmel & Lieber, 1986).

Sophisticated computer programs such as simulations, microworlds, and LOGO purport to be stimulating alternatives to these routine activities (Clements, 1990; Feurzeig, Horwitz, & Nickerson, 1981; Roberts, 1984). Simulations, for example, provide complex environments for problem solving. They demand a kind of higher order thinking that is nonalgorithmic (i.e., paths or alternate means of working problems are not fully specified in advance). Multiple solutions are ofien acceptable. Students must carefully plan actions, evaluate outcomes, and monitor the success of their strategies. Simulations are one way for students to integrate knowledge. By allowing the application of declarative knowledge in an expanded number of contexts, simulations mitigate the tendency for facts and concepts to become stipulated or "inert" (Bransford, Sherwood, Vye, & Rieser, 1986). The benefits of this kind of higher order thinking for intellectual development are a consistent theme of the microcomputer revolution (Budoff, Thormann, & Gras, 1984; Ellis & Sabornie, 1986; Margalit, Weisel, & Shulman, 1987; Papert, 1980; Woodward & Carnine, 1988).

Research support for simulations and other sophisticated computer programs, however, is both limited and mixed (Bangert-Drowns, Kulik, & Kulik, 1985; Clements, 1990; Salomon & Perkins, 1987). The equivocal findings can be attributed to several factors: differences in instructional environments, the nature of the criterion measures used (many of which provide little information in the way of reliability or validity), and insufficient details on the interventions to enable successful replication. Researchers are prone to evaluate these programs with highly unusual measures, and many studies focus on social-emotional rather than cognitive variables (e.g., Nastasi, Clements, & Battista, 1990). In fact, these problems closely parallel those of noncomputer simulations. In addition, very few studies involving computer simulations have been conducted with students with learning disabilities (e.g., Margalit et al., 1987; Woodward, Carnine, & Gersten, 1988).

One reason for the mixed success of this research is that the interventions involve the nature of problem-solving environments. Simulations, for example, often require the activation of appropriate background or declarative knowledge, effective strategies (which may not be applied consciously when solving a problem), and metacognition. Success with simulations, then, depends on several intellectual processes working in concert. Researchers (Alexander & Judy, 1988; Pea & Kurland, 1984; Steinberg, 1985) suggest that metacognitive planning, evaluation, and monitoring are critical activities--all of which are distinct areas of weakness for students with learning disabilities (Swanson, 1989; Wong, 1985). Factoring out the effects of these different variables in domain-specific or "knowledgerich" problem-solving tasks (see Gick, 1986; VanLehn, 1989) is essential to understanding the potential benefits of complex interventions such as computer simulations.

The present study builds on prior research (Woodward et al., 1988), which demonstrated the positive impact of a computer simulation on the problem-solving abilities of students with learning disabilities. All students in the previous study were taught the same declarative knowledge (i.e., basic facts and concepts) in health and then randomly assigned to either a computer condition in which they completed short health simulations or a comparison group where they engaged in traditional, noncomputer enrichment exercises. This study showed a significant effect favoring the computer group on both the retention of health facts and problem solving with a near-transfer task.

One technique used with the computer simulation group was a cognitive strategy. Diverse interpretations of strategy instruction have appeared in the special education literature recently (deBettencourt, 1987; Derry, 1990; Harris & Pressley, 1991 ). The one used in this study is best described as an "explicit strategy." This strategy, presented visually as a "decision tree," reduced the complexity of information by focusing students' attention on the critical dimensions of each simulation game. It directly linked the declarative knowledge with problem solving, and it functioned as a flexible method for planning, executing, monitoring, and evaluating actions during a game. Although it provided a framework for successfully attacking and completing a problem (i.e., the particular simulation game), it did not specify a step-by-step algorithm that ensured success.

Post hoc analyses of the Woodward et al. (1988) study were unable to determine the effects of this explicit strategy on student performance because the strategy was taught to all students in the computer group. Thus, the current study was designed. Researchers were also interested in more subtle effects of transfer. Another measure, using videotaped vignettes of individuals discussing health problems, was designed to go beyond the paper-and-pencil transfer task used in the first study. It presented more novel situations for problem solving, situations that were closer to real-life issues and dilemmas.



Thiny-seven secondary students from a medium-sized city in the Pacific Northwest and a smaller community in Alberta, Canada, participated in this study. Five students were in the 7th and 8th grade, and the remainder of the students were 9th and 10th graders. All students were receiving special education services and were classified as having learning disabilities on their individualized education programs (IEPs).

Over 75% of the students had a reading disability; and by the eligibility criteria, which were the same in both locations where the study was conducted, these students were at least 2 years below grade level in their reading performance. The mean grade level on the reading comprehension subtest of the Metropolitan Achievement Test (MAT) for the 7th and 8th graders was 5.8 (range 4.0 to 7.1 ) and for the 9th and 10th graders, 6.7 (range 4.5 to 9.5).


Two different instructional materials were used in this study: a written curriculum and a microcomputer simulation. The written curriculum was used to teach basic health information, whereas the computer program (Health Ways, Carnine, Lang, & Wong, 1983) was designed to engage students in complex problem solving. This simulation was built around profiles of many different fictional characters, all of whom needed to make changes in their health habits in a specified period of time or face a shortened life span.

The Health Ways Supplement (Woodward & Gurney, 1985), which accompanied the computer program, presented typical secondary health information (e.g., diabetes, lung cancer, heart disease, cholesterol). This curriculum, written at 4th- to 5th-grade reading level to ensure readability, was the central medium for teaching the relevant declarative knowledge.

The Health Ways Simulation. The goal of any Health Ways game is to make the most appropriate health changes and to collect enough years so that the expected age (i.e., how long the characters will live if no successful changes are made in their health habits) eventually matches the winning age. The character's current age constantly advances as the game is played.

Figure 1 shows the main menu of the Health Ways computer simulation. It presents a basic description of a character's health habits and life-style. Each category (e.g., weight and diet, alcohol) leads to more specific submenus. Candice's profile in Figure 1 reveals that she is overweight, she consumes relatively little alcohol, and she gets a minimal amount of exercise each week. The history of heart disease in Candice's family is her most serious concern. The top of the screen also shows the character's current age, expected age, and winning age. Her willpower to make changes is initially high, and her stress level is low. This is typically an initial condition for each simulation game before changes are made to the character's diet, lifestyle, and so forth.

Figure 2 follows the WEIGHT selection from the main menu through three subsequent levels. Once the student identifies heart disease as an important hereditary factor and notices that the character is 30 pounds overweight, the choice to investigate diet becomes essential. Students make this link based on knowledge gained from the Health Ways Supplement curriculum.

The remainder of Figure 2 cascades through submenu information, which reveals that Candice excessively consumes sweets and other foods that are high in cholesterol. This menu also indicates that Candice doesn't eat breakfast and drinks a lot of caffeinated beverages. While there may be concern for these latter factors, they are not central to her current dietary problems. Instead, a successful, strategic learner would concentrate on the cholesterol and sweets because of its direct association with heart disease. Developing a plan to change these habits, one that prioritizes these factors, is critical to winning the game and understanding how to prioritize health changes.

Identifying critical health habits and linking them to heredity or a current disease is but one of three key variables that the learner must attend to while playing a game. Changes in health habits imply increased stress, a second factor that must be controlled as the character's current age advances toward his or her expected age. Stress is controlled by increasing the character's level of exercise (e.g., from one to three times a week), seeking counseling, or practicing meditation. If the learner does not control or reduce stress as it increases, the character may suddenly experience a fatal heart attack. Naturally, this ends the game immediately. This inverse relationship between willpower and stress is indicative of real-life conditions when many adults attempt to make health-related changes. The third factor requiring the learner's attention is maintaining changes once they have been made. The learner must choose the MAINTENANCE MENU option (from the main menu) on a regular basis, or else the successfully changed habit returns to its original state (e.g., the character becomes a heavy drinker again). Games with subtle profiles take 10 to 15 min to complete, and the learner must constantly apply relevant declarative knowledge to high-priority variables, evaluate the effect of each action on the overall progress toward the goal, monitor stress levels, and maintain changes. Successful play, then, depends on an activation of relevant declarative knowledge; a powerful, but flexible strategy for problem solving; and monitoring, planning, and evaluation.

Naive learners may not follow such an effective plan for improving Candice's health. The intentional complexity of the games and the high level of distracting or marginally useful information may lead to unsuccessful plans of attack. The learner may choose health changes of lower priority or ignore Candice's hereditary problem, which in turn could cause Candice to acquire heart disease. The learner also may ignore Candice's stress level while making stress-inducing changes or may simply be too slow in making appropriate changes. Any of these actions could lead Candice to a premature death.

The Explicit Strategy. The decision tree shown in Figure 3 conveys the critical variables needed to successfully play the simulation games. It was developed from an expert analysis of the simulations, access to the decision rules embedded in the program, and extensive pilot testing with junior high school students (Woodward et al., 1988). The diverse features of each simulation game prevented this strategy from being a guaranteed formula or algorithm for winning a game. Instead, as an explicit strategy, it guided the learner toward an optimal plan of attack, as well as provided a means for monitoring and evaluating the changing state of affairs as each game unfolded.


All students received a 3-day preliminary orientation to the Health Ways computer program. They were given an overview of essential vocabulary and basic health concepts used in the simulations. The teacher demonstrated how to play easy games, ways to acquire more information about a character, and how to access the HELP option that explained specific health terms and the goals of the simulation. This initial orientation assisted all students in acquiring basic rules for playing the simulation games and the screen locations of key features.

Following the orientation, students were matched on MAT reading subtest scores and randomly assigned to either the explicit strategy or comparison group. The subsequent intervention lasted 14 days.

The first 25 min of each class consisted of instruction on health facts and concepts. One teacher presented a 20-min lesson to the entire class (i.e., all students participating in the study). Students learned about the concepts and vocabulary that had been presented in the initial orientation, but in much greater depth. Information about blood pressure, respiration, diet, and so forth was presented, as well as their links to disease (e.g., excessive alcohol consumption can lead to liver disease).

During the last 5 min of this portion of the class, students were separated into two groups. Those in the explicit strategy group discussed the decision tree (see Figure 3). The instructor explained the various steps in the tree, when to use each step, and the advantages of the strategy while playing the game. The teacher also presented hypothetical situations as a means of discussing how optimal plans and actions could be derived from this explicit strategy. By the second week of the intervention, the decision tree was discussed only in a cursory manner. Students in the comparison group continued to review answers to Supplement questions during this 5-min period.

In the remaining 25 min of each class, students used the Health Ways simulation games. Students played two to four simulations during this time period. The teacher circulated among the students as they played games, asking and answering questions. Students in the explicit strategy group were reminded of the relevant steps in the decision tree (e.g., "Bill [the student's character] has a liver problem, but he also has a very high level of stress. What changes will you make first?").

In contrast, those in the comparison group were given encouragement, or their questions (e.g., "What do I do now?") were answered succinctly (e.g., "You might try getting Robert [the character] to lose weight. He's 15 pounds overweight."). They also were given supportive feedback or general suggestions if they continued to fail (e.g., "It seems as if you've figured out a way to win the games, that's great!" "If this one is too hard, why don't you try the 'Chet' profile? It's a little easier."). In other words, the teachers controlled their feedback so that no specific strategy was taught or reinforced as students worked the simulations. Thus, the students in the comparison group were allowed to induce problem-solving strategies on their own that enabled them to succeed at the simulations.

Assignment of teachers to treatment was counterbalanced. The two teachers changed groups halfway through the intervention.


Three different measures were administered to assess the effects of the intervention. The Nutrition and Disease Test--a 30-item, fill-in-the-blank measure, was used as both a pretest and as a posttest. The test was developed in the earlier study (Woodward et al., 1988) as a measure of students' retention of important health facts and concepts. Internal consistency reliability (coefficient alpha) for this measure was .84, based on a sample of 42 secondary students.

The second measure, the Health Diagnosis Test, also came from the earlier study. It was administered immediately following the intervention as a posttest. The individually administered test consisted of a set of three written health profiles. Each profile described a character's age, stress level, nutritional habits, and lifestyle habits. The test was designed to measure students' ability to (a) identify and prioritize health problems, (b) prescribe necessary changes to affect the health of the profile, and (c) control stress as it related to lifestyle changes. Methods for scoring this measure were reported in the previous study. Test-retest reliability for this measure was .81.

The third measure, the Video Diagnostic Test, was also administered as a posttest. This group-administered test measured students' ability to generalize their understanding of health problems to another medium. Three vignettes, each approximately 4 min long, were developed around conversations between friends or family members. Each vignette was a social occasion involving work or family relations, and at points the dialogue subtly turned to a discussion of health factors.

For example, in the course of a conversation between two family members, the target character describes a recent visit to the doctor regarding a specific malady (e.g., diabetes). At another point in the conversation, the character mentions a family history of these kinds of problems. Students needed to attend to subtle cues offered throughout the vignette to identify and prioritize nutritional and lifestyle changes needed by the target character. Emerging directions in direct testing of cognitive operations support this kind of measure (Frederiksen & Collins, 1989).

Students watched each vignette twice and then prioritized changes in health patterns that were important for the target character. Students were expected to identify and prioritize three of the key health changes by answering three short-answer questions (e.g., "Describe the most important health change for John." "Describe the second most important health change."). The student also had to justify the importance of each change and its ranking. Full credit was given for answers that were prioritized correctly and where the justification linked the change to the target character's current disease or heredity. Partial credit was awarded for correct answers that were not prioritized correctly and for explanations that contained the correct declarative knowledge, but failed to link this knowledge to the character's specific health profile. Interrater reliability for scoring the test was .90.


Nutrition and Disease Test

The Nutrition and Disease Test measured students' knowledge of the basic health facts and concepts taught during the 14-day intervention. A 2 x 2 (Treatment x Time of test) analysis of variance (ANOVA) with repeated measures on one factor (time) was performed on the total number of correct answers. The analysis indicated a significant main effect for time, F(1,34) = 98.5, p < .001. No significant difference was found between groups, nor was there a significant interaction.

The explicit strategy group had a mean score of 8.3 (SD = 5.9) on the pretest and 17.8(SD = 6.4) on the posttest. The comparison group had a mean score of 9.2(SD = 5.7) on the pretest and scored 15.6(SD = 5.6) on the posttest. This was a difference of 9.5 correct answers from pretest to posttest for the former, compared to a mean growth of 6.4 correct answers for the latter. These results suggest that learning did occur during the 14-day time period from pretest to posttest. As anticipated, there was no significant difference between the two groups on this measure. Students in both groups were taught the same declarative knowledge, which they applied to the simulation games, under the same instructional conditions.

Transfer Measures

Health Diagnosis Test. A planned comparison using the Nutrition and Disease Posttest as a covariate (ANCOVA) was performed on results of the Health Diagnosis Test. This measure represented a paper-and-pencil version of the Health Ways simulation. It was designed to assess problem-solving skills by requiring students to diagnose and suggest measures to improve the health of simulated profiles. Results of the ANCOVA showed a significant difference between groups, F(1,34) = 5.2, p < .03. Table 1 provides descriptive statistics for the two groups on this measure.

An analysis was also conducted to compare differences in performance between the groups on three major factors measured by this test. These were the ability to (a) identify health problems and the appropriate correlated changes, (b) prioritize changes necessary to yield improved health, and (c) effectively counter stress-inducing activities. Three t-tests were conducted to compare the two groups on each of these factors.

Results are shown in Table 2. Students in the explicit strategy group demonstrated significantly better skills at prioritizing needed health changes, t(1,36) = 2.1, p < .02; at taking action to reduce increasing stress levels, t(1,36) = 2.5, p < .01; and at making correlated changes for specific health problems, t(1,36) = 2.3, p < .01.

Video Diagnostic Test. The Video Diagnostic Test, like the previous measure, assessed the students' ability to transfer health problem solving abilities. However, this measure was designed to assess student ability in a novel environment. A planned comparison using the Nutrition and Disease Posttest as a covariate was also performed on this measure. Results of the ANCOVA show a significant difference between groups, F(1,34) = 4.97, p = .03. Table 3 provides descriptive statistics for the two groups on this measure. The results indicate that students who have been explicitly taught the cognitive strategy were more effective at solving health problems than were students in the comparison group.


Results of this study showed significant differences between the explicit strategy group and the comparison group on the two transfer measures. Students who were taught the explicit strategy demonstrated superior performance on the Health Diagnosis Test, a pencil-and-paper measure, and the Video Diagnostic Test, a measure of students' ability to transfer problem solving to a medium that reflected real-life situations. The superior effects for the explicit strategy group on these transfer measures are consistent with research indicating that naive students (e.g., remedial students, those with learning disabilities, and students new to a content area) benefit from explicit strategy instruction, particularly during the initial phases of learning a subject (O'Sullivan & Pressley, 1984; Prawat, 1989; Pressley, Ross, Levin, & Ghatala, 1984; Ross, 1988; Woodward, in press). In contrast, given the same declarative knowledge instruction and opportunity to learn from the simulation exercises, the comparison group was far less successful at inducing an effective strategy.

The groups performed comparably on the Basic Facts posttest, with the explicit strategy group showing a greater relative gain over the 14-day period. The difference between groups, however, was not significant. This finding was expected in light of the fact that students in both groups scored at roughly the same levels on the Basic Facts pretest and were taught the same curriculum under identical conditions. Furthermore, this measure was designed to be sensitive to growth in declarative knowledge only, not to how this knowledge could be applied in other contexts.

Significant differences between groups emerged when students were asked to apply their declarative knowledge on the Health Diagnosis Test. In this context, comparison group students were much less able to identify health problems, reduce stress levels in the context of other necessary changes, or suggest appropriate health-related changes. This finding underscores the kind of fragmented understanding that can evolve when students are presented with an extensive amount of information and no explicit strategy for integrating or reducing its complexity. Declarative knowledge remains "inert," and students tend to apply what they know in very limited contexts even though it is applicable in a wider set of contexts (Bransford et al., 1986).

Explicitly taught strategies for linking, organizing, or applying knowledge can help naive students overcome the tendency to blur essential and incidental information, as well as retain such knowledge at a shallow level (Bereiter & Scardamalia, 1986; Voss, 1987). The decision tree taught to the explicit strategy group served as an abstract and overarching framework for applying declarative knowledge to problem-solving activities. To this extent, it was in the tradition of schema theory, where the relationships among components are directly linked and expectations about these relationships are well formed (Anderson, 1984; Prawat, 1989).

Other research in science instruction also supports the integrative function of an explicit strategy like the decision tree. When presented concurrently with instruction, strategies (a.k.a. models, abstract frameworks) have been shown to improve conceptual understanding and successful transfer of knowledge to new and challenging problems in a domain (Bromage & Mayer, 1981; Mayer, 1985, 1989; Woodward & Noell, 1991). These studies indicate that once students have a related understanding of concepts, principles, and problem-solving strategies, transfer to novel problems in the domain is more likely. It can be argued, then, that by understanding the relationship between the declarative knowledge and the problem solving needed for the various simulations, explicit strategy students were in a better position to successfully work novel problems like the videotape vignettes.

One final issue involves the nature of the explicit strategy students' planning, monitoring, and evaluating--whether or not this reflected the use of domain-specific knowledge or general strategies. This is a particularly critical issue in light of the recurrent calls over the last few years to teach students with learning disabilities enduring, general strategies (Derry, 1990; Goldman, 1989; Harris & Pressley, 1991; Swanson, 1989). Presumably, general cognitive strategies help students develop planning and self-regulation of goal-directed behavior across a range of academic domains (Hams, 1990).

On the other hand, some researchers argue that the broad call for such instruction must be tempered by mounting and compelling evidence that general strategies are ineffective when taught in isolation (Perkins & Salomon, 1989; Resnick, 1987) and that these strategies rarely operate in a mutually exclusive manner (Alexander & Judy, 1988; Chi, 1985). An emerging view, though in its research infancy, is that general strategies are best taught in specific contexts.

In the current study, the explicit strategy enabled the experimental students to plan, monitor, and evaluate their actions in a "conditional" manner while they played the games. Students used conditional knowledge (Alexander & Judy, 1988) as a basis for knowing when and where to access facts or employ procedures within the domain (e.g., planning how to change a character's bad habits, evaluating the consequence of increasing exercise on stress levels, and monitoring the effects of multiple variables simultaneously).

Domain-specific, conditional knowledge (rather than general or metacognitive strategies) was more suitable to this intervention because of its short duration, as well as the fact that these students were just beginning to learn about the formal relationships of diet and health to diseases. By directly linking the array of new information to its conditional use, the explicit strategy enabled the experimental students to "mindfully engage" in learning and attain higher levels of intellectual performance than what is typically found in classrooms (Salomon & Perkins, 1987; Salomon, Perkins, & Globerson, 1991).

The domain-specific approach, then, was a function of experimental conditions and the knowledge state of the learners. This does not imply, however, that planning, monitoring, and evaluating always need to be presented in this manner. In fact, the same techniques could be presented as specific instances of general strategies under a wider, more integrated instructional program. This would enable students with learning disabilities to develop broad cognitive capacities and, at the same time, apply them in various content areas such as health. Naturally, this would occur over a much longer period of time than that of this study. As Brown and her colleagues (Brown & Kane, 1988; Brown & Palinscar, 1989) have noted, successful transfer of reasoning and self-monitoring are accomplished by both increasing student familiarity with problem domains and explicitly showing students how problems from different domains relate to each other. In this sense, what acts as conditional knowledge in a limited, experimental setting such as this study could be subsumed in a larger instructional agenda, one that promotes the use of general strategies.


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MAURICE HOLLINGSWORTH (CEC #683), secondary special education teacher, County of Lethbridge Public Schools, Lethbridge, Alberta. JOHN WOODWARD (CEC WA Federation), Associate Professor, School of Education, University of Puget Sound, Tacoma, Washington.

The research reported in this article was supported by Grant #G00870069 from the U.S. Department of Education, Office of Special Education Programs, to the University of Oregon. We wish to express our gratitude to Dr. Douglas Carnine for his extensive work on the Health Ways simulation, which served as a basis for this study.

Manuscript received July 1991; revision accepted January 1992.
 Means and Standard Deviations for the Health
Diagnosis Test
 Mean %
Group N M SD Correct
Explicit strategies 18 22.3 8.5 62
Comparison 19 14.3 9.8 40
 Means and Standard Deviations for the
Video Diagnostic Test
 Mean %
Group N M SD Correct
Explicit strategies 18 17.7 8.3 55
Comparison 19 11.7 7.4 37

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Author:Hollingsworth, Maurice; Woodward, John
Publication:Exceptional Children
Date:Mar 1, 1993
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