Guided Problem Solving Research Articles

Preliminary Results Exploring The Use OfAlternate Instructional Methods In Mathematics

Many classrooms across the world contain students with disabilities. This study examined four instructional methods that have been suggested to be appropriate for educators to utilize in classroom environments to improve student mathematical understanding. The study was conducted in a multi-level, inclusive classroom in the United States with a mixture of learning disabled and non-learning disabled students. This study examined four different mathematicsinstructional methods designed specifically for students with dyslexia. Over a 20-day period, 17 students were taught mathematical concepts using one of the four instructional methods. Students provided ratings for each lesson based on a specific set of criteria. The instructor also kept a reflective journal to assist in the comparison of the students’ perceptions to her own perception of how the students reacted to each lesson. The study found that lessons involving manipulation, guided problem solving and interaction with others were perceived as the better instructional methods. The study also discovered that the instructor’s perception correlated with the students’ perceptions of the methods.

Explanations from knowledge-based systems and cooperative problem solving: an empirical study

Explanations are expected to play an important role when knowledge-based systems are used for cooperative problem solving. In this context both human and machine contribute to the procedures, constraints and strategies used in the problem-solving process. An increased need for explanations in this context is congruent with a cognitive-effort perspective and the Production Paradox observed with on-line help systems. This article describes an investigation of the role of explanations in cooperative problem solving. An experimental field study was performed with 41 users and an operational system for personal financial planning. The experiment showed a requirement for cooperative problem solving was associated with greater use of explanations. This effect was more marked with those users who had more fully explored the use of explanations during a preliminary stage of guided problem solving. The frequency of use of explanations in total was positively related to problem-solving performance. There was some evidence that the positive relationship between explanations and improved performance was more noticeable when problems requiring cooperation were undertaken.

Instructional means to overcome transfer problems in the domain of economics: empirical studies

In a computer-based learning environment (the simulation of a company) multiple learning contexts were established. Additionally, the learners were provided with a problem-solving guidance. In a second learning environment based on worked-out examples, multiple examples as well as a guidance for example elaboration were employed. Multiple examples only proved to be effective in fostering transfer performance when they were combined with the respective supporting means. However, without additional support, learners were overtaxed by multiple learning conditions. As a result, transfer performance was comparatively low. In order to optimize the two learning methods, learning by problem solving should be combined with worked-out examples. Moreover, learners should be supported with additional supporting means, such as expert comments and “instructional elaborations.”

Instructional effects in complex learning: Do objective and subjective learning outcomes converge?

The influence of multiple learning contexts and problem solving guidance on the acquisition of applicable knowledge and subjective learning outcomes in complex learning was investigated. Using a 2 × 2 factorial design, 60 economics students from a vocational school were confronted with one of four conditions when learning with a computer-based simulation of a company (factor 1: learning context [uniform learning contexts versus multiple learning contexts]; factor 2: problem solving guidance [unguided problem solving versus guided problem solving]). No single learning condition was superior with regard to all learning outcomes. Multiple learning contexts in combination with problem solving guidance turned out to be the most suitable learning condition when objective learning outcomes were taken into consideration. The problem solving guidance procedure, however, led to a rather pessimistic self-evaluation of learning outcomes. The problem of self-evaluation of learning outcomes in complex learning is discussed, and remedies are proposed.

The subgoal learning model: Creating better examples so that students can solve novel problems.

Learners have great difficulty solving problems requiring changes to solutions demonstrated in examples. However, if the solution procedures learners form are organized by subgoals, then they are more successful. Subgoal learning is hypothesized to be aided by cues in example solutions that indicate that certain steps go together. These cues may induce a learner to attempt to self-explain the purpose of the steps, resulting in the formation of a subgoal. Across 4 experiments it was found that a label for a group of steps in examples helped participants form subgoals as assessed by measures such as problem-solving performance and talk aloud protocols. Abstract labels were more likely than superficial labels to lead participants to form subgoals with fewer ties to surface features. Subgoals guide problem solving by helping learners focus on the steps to modify in novel problems that involve the same subgoals but require new steps to achieve them.

Case based reasoning

In rule-based expert systems, knowledge is represented as rules. A rule-based system is one example of deductive reasoning. However, in tasks such as design, diagnosis, planning, management and assessment we often observe a different type of reasoning. An expert facing a new problem is usually reminded of similar situations, recalls their results and perhaps the reasoning. In other words, previous cases are applied to current problems by an analogical reasoning process. Case-based reasoning (CBR) is an area of machine learning research that endeavors to support this process. CBR is based on a memory-centered cognitive model. The basic idea is that past experiences can be remembered and adapted to guide problem solving. Computer systems that solve new problems by analogy with previous ones are called CBR systems, CBR systems base their intelligence and inference on known cases rather than on rules. There are two kinds of CBR systems: problem-solving systems and interpretive systems. A problem-solving system focuses on the construction of solutions suited to the new case by modifying previous case solutions. An interpretive system evaluates and justifies new cases based on the similarities or differences with the previous cases. However, most real-world problems require both types of CBR. AIDS-risky behavior screening, for example, requires both interpreting the behavior and then deriving a conclusion based on the precedents.

Storage and retrieval of solutions in the design of electromagnetic devices

The design of an electromagnetic device is a knowledge and experience intensive task. Human designers use previous designs extensively in solving new design problems. Case-base reasoning (CBR) is a method of using previous cases to guide problem solving when a now problem is presented. In this paper we describe the knowledge organization for a case database to represent the semantic content of electromagnetic devices designs. Using these representation methods, a heuristic search strategy can be used to guide the retrieval of similar design cases to tackle new design problems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cognitive Style, Marketing Problem Solving and Case Analysis

Psychological type and cognitive style have received considerable attention from educators with a wide range of interests. This article reviews the Jungian-based approach to cognitive style and describes a problem-solving approach using its component concepts. Though many opportunities exist for consideration of cognitive style in marketing, the authors focus on how the concepts can be used to guide problem solving in case analysis.

Explicit and Inexplicit Guidance in Problem Solving and Ability to State Principles

Problem solving of Ss who were given explicit verbal information and Ss who were given nonexplicit visual information before practice was studied. When Ss were given explicit information, those who verbalized a principle after practice solved significantly more problems than those who did not verbalize a principle, while when Ss were given nonexplicit information, no difference was found with respect to number of problems solved between Ss who verbalized a principle and those who did not.

The effect of varying amounts and kinds of information as guidance in problem solving.

The effect of varying amounts and kinds of information as guidance in problem solving.

Effective Guidance in Problem Solving

The amount of research in the field of problem solving, while not in proportion to the recognized importance of the subject, is still far from meagre. Unfortunately, for several reasons the results in some instances have been contradictory. Then, too, the published writings of prominent workers in the field have included assertions and recommendations that were not, at times, in accord with each other.