Cognitive load theory has been designed to provide guidelines intended to assist in the presentation of information in a manner that encourages learner activities that optimize intellectual performance. The theory assumes a limited capacity working memory that includes partially independent subcomponents to deal with auditory/verbal material and visual/2- or 3-dimensional information as well as an effectively unlimited long-term memory, holding schemas that vary in their degree of automation. These structures and functions of human cognitive architecture have been used to design a variety of novel instructional procedures based on the assumption that working memory load should be reduced and schema construction encouraged. This paper reviews the theory and the instructional designs generated by it. KEY WORDS: cognition; instructional design; learning; problem solving.

Worked examples are instructional devices that provide an expert's problem solution for a learner to study. Worked-examples research is a cognitive-experimental program that has relevance to classroom in-struction and the broader educational research community. A frame-work for organizing the findings of this research is proposed, leading to instructional design principles. For instance, one instructional de-sign principle suggests that effective examples have highly integrated components. They employ multiple modalities in presentation and em-phasize conceptual structure by labeling or segmenting. At the lesson level, effective instruction employs multiple examples for each concep-tual problem type, varies example formats within problem type, and employs surface features to signal deep structure. Also, examples should be presented in close proximity to matched practice problems. More-over, learners can be encouraged through direct training or by the structure of the worked example to actively self:explain examples. Worked examples are associated with early stages of skill develop-ment, but the design principles are relevant to constructivist research and teaching. The Historical Context In recent years, learning from "worked examples " has received a consider-able amount of attention from researchers (e.g., Chi, Bassok, Lewis, Reimann, & Glaser, 1989; Ward & Sweller, 1990), particularly in such fields as mathematics, physics, and computer programming. Although there is no precise definition, worked examples share certain family resemblance (Wittgenstein, 1953). As instructional devices, they typically include a problem statement and a proce-dure for solving the problem; together, these are meant to show how other similar problems might be solved. In a sense, they provide an expert's problem-

Intelligent tutoring systems are highly interactive learning environments that have been shown to improve upon typical classroom instruction. Cognitive Tutors are a type of intelligent tutor based on cognitive psychology theory of problem solving and learning. Cognitive Tutors provide a rich problem-solving environment with tutorial guidance in the form of step-by-step feedback, specific messages in response to common errors, and on-demand instructional hints. They also select problems based on individual student performance. The learning benefits of these forms of interactivity are supported, to varying extents, by a growing number of results from experimental studies. As Cognitive Tutors have matured and are being applied in new subject-matter areas, they have been used as a research platform and, particularly, to explore interactive methods to support metacognition. We review experiments with Cognitive Tutors that have compared different forms of interactivity and we reinterpret their results as partial answers to the general question: How should learning environments balance information or assistance giving and withholding to achieve optimal student learning? How best to achieve this balance remains a fundamental open problem in instructional science. We call this problem the “assistance dilemma ” and emphasize the need for further science to yield specific conditions and parameters that indicate when and to what extent to use information giving versus information withholding forms of interaction.

Previous studies have demonstrated the learning benefit of personalized language and worked examples. However, previous investigators have primarily been interested in how these interventions support students as they problem solve with no other cognitive support. We hypothesized that personalized language added to a web-based intelligent tutor and worked examples provided as complements to the tutor would improve student (e- )learning. However, in a 2 x 2 factorial study, we found that personalization and worked examples had no significant effects on learning. On the other hand, there was a significant difference between the pretest and posttest across all conditions, suggesting that the online intelligent tutor present in all conditions did make a difference in learning. We conjecture why personalization and, especially, the worked examples did not have the hypothesized effect in this preliminary experiment, and discuss a new study we have begun to further investigate these effects.

Abstract. This study seeks to compare the relative utility for learning college-level physics of intelligent tutoring systems that have procedural based hints and worked-out examples. In order to test which produced better gains, a modified version of Andes was used in which participants either received hints or annotated, worked-out examples in response to their help requests. We found that providing annotated, worked-out ex-amples instead of hint sequences was more efficient in the number of problems it took to obtain basic mastery. 1

Research has shown that it is effective to combine example study and problem solving in the initial acquisition of cognitive skills. Present methods for combining these learning modes are, however, static and do not support a transition from example study in early stages of skill acquisition to later problem solving. Against this background, we propose a successive integration of problemsolving elements into example study until the learners solve problems on their own (i.e., complete example increasingly more incomplete examples problem to-besolved) . We tested the effectiveness of such a fading procedure against the traditional method of employing exampleproblem pairs. In a field experiment and in a more controlled lab experiment, we found that the fading procedure fosters learning, at least when near transfer performance is considered. Moreover, this effect is mediated by a lower number of errors under the fading condition as compared to the example-problem condition.

We explore the effects of interfaces to take notes on problem solving and learning in a scientific discovery domain. In 2 experiments (1 correlational, 1 experimental), participants solved a series of 5 scientific reasoning problems in a computer environment. We provided some participants with access to an online notepad and found 3 main results: (a) Using the notepad helped participants solve the problems more accurately; (b) the benefits of using the notepad persisted after participants had stopped using it; and (c) participants who used the notepad for problem solving and self-explanation learned more, regardless of the type of notepad interface that was provided. Implications for learning systems with online notepads are discussed.

The opinions and positions expressed in this practice guide are the authors ’ and do not necessarily represent the opinions and positions of the Institute of Education Sciences or the U.S. Department of Education. This practice guide should be reviewed and applied according to the specific needs of the educators and education agencies using it and with full realization that it represents only one approach that might be taken, based on the research that was available at the time of publication. This practice guide should be used as a tool to assist in decision-making rather than as a “cookbook.” Any references within the document to specific education products are illustrative and do not imply endorsement of these products to the exclusion of other products that are not referenced. U.S. Department of Education

Our work explores the assistance dilemma: when should instruction provide or withhold assistance? In three separate but very similar studies, we have investigated whether worked examples, a high-assistance approach, studied in conjunction with tutored problems to be solved, a mid-level assistance approach, can lead to better learning. Contrary to prior results with untutored problem solving, a low-assistance approach, we found that worked examples alternating with isomorphic tutored problems did not produce more learning gains than tutored problems alone. On the other hand, the examples group across the three studies learned more efficiently than the tutored-alone group; the students spent 21 % less time learning the same amount of material. Practically, if these results were to scale across a 20-week course, students could save 4 weeks of time – yet learn just as much. Scientifically, we provide an analysis of a key dimension of assistance: when and how often should problem solutions be given to students versus elicited from them? Our studies, in conjunction with past studies, suggest that on this exampleproblem dimension mid-level assistance may lead to better learning than either lower or higher level assistance. While representing a step toward resolving the assistance dilemma for this dimension, more studies are required to confirm that mid-level assistance is best and further analysis is needed to develop predictive theory for what combinations of assistance yield the most effective and efficient learning.

Research conducted in the laboratory and classroom has repeatedly found that self-explaining is a useful, self-directed learning strategy. Although the self-explanation effect has been replicated several times, the sources for its effectiveness are still under investigation. The present study attempts to address the question: Why does self-explaining work? Two alternative proposals are contrasted. The content account proposes that self-explaining is effective because of the additional information to which the learner is exposed. Alternatively, the generation account suggests that it is the activity of producing an explanation that is effective. The evidence, taken from learning curves collected in the classroom, predominantly supports the generation account of self-explanation, which highlights the benefit of actively processing the learning material, instead of simply attending to it. Keywords: Self-explanation; paraphrasing; physics education research; study strategies. To help smooth the transition from novice to expert-like performance, the cognitive and learning sciences have focused upon learning strategies that have proven to be effective across several different content domains. Among these domain-independent learning strategies is selfexplaining, which is defined as the sense-making process that an individual uses to gain a deeper understanding of some instructional material, including textbooks, workedout examples, diagrams, and other multimedia materials (Roy & Chi, 2005). Self-explaining has consistently been shown to be effective in producing robust learning gains in the