Abstract: Science and technology have had enormous impact on many areas of human endeavor but surprisingly little effect on education. Many large-scale field trials of science-based innovations in education have yielded scant evidence of improvement in student learning, although a few have reliable positive outcomes. Education involves many important issues, such as cultural questions of values, but we focus on instructional decision-making in the context of determined instructional goals and suggests ways to manage instructional complexity.

Abstract: Recently it has been argued that the worked-example effect, as postulated by Cognitive Load Theory, might only occur when compared to unsupported problem-solving, but not when compared to well-supported or tutored problem-solving as instantiated, for example, in Cognitive Tutors. In two experiments, we compared a standard Cognitive Tutor with a version that was enriched with faded worked examples. In Experiment 1, students in the example condition needed less learning time to acquire a comparable amount of procedural skills and conceptual understanding. In Experiment 2, the efficiency advantage was replicated. In addition, students in the example condition acquired a deeper conceptual understanding. The present findings demonstrate that the worked-example effect is indeed robust and can be found even when compared to well-supported learning by problem-solving.

Abstract: Recent studies have tested the addition of worked examples to tutored problem solving, a more effective instructional approach than the untutored problem solving used in prior worked example research. These studies involved Cognitive Tutors, software designed to support problem solving while minimizing extraneous cognitive load by providing prompts for problem sub-goals, step-based immediate feedback, and context-sensitive hints. Results across eight studies in three different domains indicate that adding examples to Cognitive Tutors is beneficial, particularly for decreasing the instructional time needed and perhaps also for achieving more robust learning outcomes. These studies bolster the practical importance of examples in learning, but are also of theoretical interest. By using a stronger control condition than previous studies, these studies provide a basis for refining Cognitive Load Theory explanations of the benefits of examples. Perhaps, in addition to other reasons, examples may help simply because they more quickly provide novices with information needed to induce generalized knowledge.

Abstract: 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.

Abstract: The present research investigated whether immediate metacognitive feedback on students’ help-seeking errors can help students acquire better help-seeking skills. The Help Tutor, an intelligent tutor agent for help seeking, was integrated into a commercial tutoring system for geometry, the Geometry Cognitive Tutor. Study 1, with 58 students, found that the real-time assessment of students’ help-seeking behavior correlated with other independent measures of help seeking, and that the Help Tutor improved students’ help-seeking behavior while learning Geometry with the Geometry Cognitive Tutor. Study 2, with 67 students, evaluated more elaborated support that included, in addition to the Help Tutor, also help-seeking instruction and support for self-assessment. The study replicated the effect found in Study 1. It was also found that the improved help-seeking skills transferred to learning new domain-level content during the month following the intervention, while the help-seeking support was no longer in effect. Implications for metacognitive tutoring are discussed.

Abstract: Authoring tools for intelligent tutoring systems are especially valuable if they not only provide a rich set of options for the efficient authoring of tutoring systems but also support controlled experiments in which the added educational value of new tutor features is evaluated. The cognitive tutor authoring tools (CTAT) provide both. Using CTAT, real-world "example-tracing tutors" can be created without programming. CTAT also provides various kinds of support for controlled experiments, such as administration of different experimental treatments, logging, and data analysis. We present two case studies in which example-tracing tutors created with CTAT were used in classroom experiments. The case studies illustrate a number of new features in CTAT: use of Macromedia Flash MX 2004 for creating tutor interfaces, extensions to the example-tracing engine that allow for more flexible tutors, a mass production facility for more efficient template-based authoring, and support for controlled experiments

Abstract: In our on-going endeavor to teach students better help-seeking skills we designed a three-pronged Help-Seeking Support Environment that includes (a) classroom instruction (b) a Self-Assessment Tutor, to help students evaluate their own need for help, and (c) an updated version of the Help Tutor, which provides feedback with respect to students’ help-seeking behavior, as they solve problems with the help of an ITS. In doing so, we attempt to offer a comprehensive helpseeking suite to support the knowledge, skills, and dispositions students need in order to become more effective help seekers. In a classroom evaluation, we found that the Help-Seeking Support Environment was successful in improving students’ declarative help-seeking knowledge, but did not improve students’ learning at the domain level or their help-seeking behavior in a paper-and-pencil environment. We raise a number of hypotheses in an attempt to explain these results. We question the current focus of metacognitive tutoring, and suggest ways to reexamine the role of help facilities and of metacognitive tutoring within ITSs.

Abstract: It is often assumed that engaging in a one-on-one dialogue with a tutor is more effective than listening to a lecture or reading a text. Although earlier experiments have not always supported this hypothesis, this may be due in part to allowing the tutors to cover different content than the noninteractive instruction. In 7 experiments, we tested the interaction hypothesis under the constraint that (a) all students covered the same content during instruction, (b) the task domain was qualitative physics, (c) the instruction was in natural language as opposed to mathematical or other formal languages, and (d) the instruction conformed with a widely observed pattern in human tutoring: Graesser, Person, and Magliano's 5-step frame. In the experiments, we compared 2 kinds of human tutoring (spoken and computer mediated) with 2 kinds of natural-language-based computer tutoring (Why2-Atlas and Why2-AutoTutor) and 3 control conditions that involved studying texts. The results depended on whether the students' preparation matched the content of the instruction. When novices (students who had not taken college physics) studied content that was written for intermediates (students who had taken college physics), then tutorial dialogue was reliably more beneficial than less interactive instruction, with large effect sizes. When novices studied material written for novices or intermediates studied material written for intermediates, then tutorial dialogue was not reliably more effective than the text-based control conditions.

DOWNLOAD PDF

Abstract: Idea generation is a cognitive process that plays a central role in inquiry learning tasks. This paper presents results from a controlled experiment in which we investigate the affect on productivity and learning from doing idea generation tasks individually versus in pairs, with versus without automatic support from a virtual brainstorming agent called VIBRANT. Our finding is that individuals brainstorming with VIBRANT produced more ideas than individuals who brainstormed with a human peer. However, an additional finding is that while brainstorming in pairs lead to short term process losses in terms of idea generation, with a corresponding reduction in learning in terms of pre to post test gains, it produced a productivity gain for a subsequent distinct individual inquiry task. Furthermore, automatically generated feedback from VIBRANT improved learning during idea generation but did not mitigate the process losses that were associated with reduced learning in the pairs conditions.

Abstract: Prior research has shown that tutored problem solving with intelligent software tutors is an effective instructional method, and that worked examples are an effective complement to this kind of tutored problem solving. The work on the expertise reversal effect suggests that it is desirable to tailor the fading of worked examples to individual students’ growing expertise levels. One lab and one classroom experiment were conducted to investigate whether adaptively fading worked examples in a tutored problem-solving environment can lead to higher learning gains. Both studies compared a standard Cognitive Tutor with two example-enhanced versions, in which the fading of worked examples occurred either in a fixed manner or in a manner adaptive to individual students’ understanding of the examples. Both experiments provide evidence of improved learning results from adaptive fading over fixed fading over problem solving. We discuss how to further optimize the fading procedure matching each individual student’s changing knowledge level.

Abstract: How to best sequence instruction in a collection of basic facts is a problem often faced by intelligent tutoring systems. To solve this problem, the following work details two tests of a system to provide drill practice (test trials with feedback) for foreign language vocabulary learning using a practice schedule determined to be optimal according to a cognitive model. In the first test, students chose between an optimized version and a version that merely cycled the vocabulary items. Examination of the time on task data revealed a preference for practice based on the decisions of the cognitive model. In the second test, the system was used to train the component parts of Chinese characters and measure the transfer of knowledge to subsequent learning of Chinese characters. Chinese character learning was improved for students with the relevant optimized training.