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«Facilitating Higher-Order Thinking: Synthesizing Pedagogical Frameworks for the Development of Complex and Coherent Conceptual Systems Michael A. ...»

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The bane of the meta-analysts with whom I have discussed this issue is the retrieval of a well-designed study, with clearly defined interventions, experimental and control groups, good theoretical grounding, and appropriate operational measures, but lacking a crucial bit of statistical data (e. g., standard deviations) which disqualifies it from inclusion in a meta-analytical review. While such difficulties may be irritating, even a study that contains adequate statistical data for effect size calculations may not provide enough information about the process so that reviewers can possibly extract enough descriptive features from the report to account for the conditions of the treatment. Since meta-analysts are concerned not only with the magnitude and the direction of any experimental effect, but also with methodological, pedagogical, demographic and contextual features of the experimental setting (so that they can account for variables which promote, or mitigate the effects), the best studies (for the purpose of producing clear meta-analytical results) are those that provide both complete statistics and many rich descriptions of the setting. Unfortunately, it seems that only a small proportion of research reports are both statistically complete and thick with description. Here researchers are faced with the hope for a large and general improvement in the quality of educational research.

Unmeasured Variables While many educational research projects ask learners to rate the interventions in which they participated, such self-reports (most often delivered at the end of the project) may not closely reflect the attitudes that determined the qualities of each participant's interactions with the instructor, the content material, the pedagogical methods, and his or her classmates. Moreover, each individual's motivational networks (including, but not limited to, self-efficacy, self-regulation, task value, and competing demands outside of the project) may not be well represented in their summative evaluations of a course, an instructor, or a method. While inter-individual differences (including learning styles and prior knowledge) are generally acknowledged to influence learning outcomes, and while quantitative theorists hope that large-sample random control trial experiments can be designed to obviate such factors, it seems that a) individual and group psychodynamics, which affect operational learning outcomes such as achievement and satisfaction measures, are unlikely to be measured (in the foreseeable future) with sufficient levels of precision for their variability to be taken into account through statistical measures, and b) it is extremely difficult to test educational interventions through the use of large-sample random control trials.

It is possible that the most salient variables in determining the outcomes of educational processes are psychological, dynamic, and emergent (rather than pedagogical, static, or reducible to one-dimensional measures). The feelings, attitudes and commitments brought to the classroom by each participant, the collective organization of these personalities, and the changes in these basic human motivational factors during the course of a lesson, a day, or a school year, can only be described in the most qualitative of terms. We cannot measure abstract constructs such as "motivation," "self-regulation," "reflection", or "collaboration," and our operational measures that relate to such terms may currently be considered inadequate to fulfil our research purposes. If this is the case, then researchers should recognize that we need better tools than have been available to date, and that educational research programs should be organized in ways that will enable the systematic analysis of reports of assessments of particular interventions.

Using Educational Technology to Teach Thinking Computer-Assisted Instruction As Lajoie (2000) pointed out, "[LJearning theories can guide the design of computer-based learning environments that provide cognitive tools for learners" (p. xvii).

Lajoie and Azvedo (2000) concluded, Computer-based environments... provide more authentic contexts for studying scientific reasoning... [Intelligent systems can observe patterns in tool use, and researchers can draw appropriate inferences regarding learner understanding from such patterns... These rich instructional and assessment platforms, when joined with traditional cognitive methodologies of verbal protocol analyses of students dialogues, can add to our understanding of learning, reasoning, and problem-solving

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Kester, Kirschner, and van Merrienboer (2005) studied the effects of screen design on the process of learning the functions of electric circuits. In the context of cognitive load theory, these researchers presented circuit diagrams and their associated textual descriptions in two distinct formats to high school students, who were randomly assigned to two groups. In the control condition, a circuit diagram was presented on the left side of a split screen, while a textual description was on the right; experimental subjects saw a single diagram, with text boxes integrated into the circuit diagram.

Cognitive load theory predicts that a split attention effect should result in lower scores for students who were presented with the split screen condition, since cognitive resources for members of this group would be drained by a greater effort required to integrate the textual information with the diagram, while the integrated text format would carry less (extraneous) cognitive load. After nine practice problems and ten test problems, it was found that experimental subjects scored significantly higher on transfer test scores, which supports the hypothesis that this group found the task easier to complete due to a lesser degree of extraneous cognitive load. Research such as this contributes to our understanding of how instructors and instructional designers can support their students in learning to deal with complex data.

Hulshof, Eysink, Loyens and de Jong (2005) studied the use of interactive computer-based learning modules (called ZAPs) to facilitate the learning of psychology by university students, and by students who were enrolled in higher vocational training.

The ZAPs were designed to present textual information with regard to the principles, evidence and applications of psychological phenomena and processes (e. g., stimuli, responses and associations characteristic of classical conditioning). In addition, they provide for a discovery activity which allows the user to manipulate the elements involved in the lesson; in the example of classical conditioning, a user can schedule the virtual presentation of stimuli (a light, a bell, and a food reward), and is then presented with the resultant measure of the dependent variable (a picture of a dog salivating and a graph of the measurements of salivation over the time). Using random assignment and a two-group experimental design, removal of the discovery component from one group's ZAPs was not associated with lower post-test performance; however, the authors nevertheless concluded (on the evidence of superior long-term retention by the group that had access to the discovery activity) that the discovery activity supports understanding of the lesson's psychological principles. In addition, Hulshof et al. noted that students and teachers evoked a rare enthusiasm for this instructional tool, because it provided for a rich, hands-on learning experience.

Graesser, McNamara, and VanLehn (2005) reviewed three computer-based learning environments (CBLEs), which they developed in order to facilitate deep comprehension, metacognition, inquiry, and explanatory skills. These tools {Point&Query, AutoTutor, and iStart) were designed as tutoring agents, on the basis of two learning theories: Vygotskian social learning (which emphasizes the importance of cognitive scaffolding and feedback), and the Piagetian notion of cognitive disequilibrium, which postulates that learning environments should be designed to create dilemmas. In particular, these authors emphasize the cultivation of question-asking skills, lamenting the infrequency of classroom inquiries and the shallow character of most student questions, so they have designed their electronic tutors both to model and to encourage the production of deep questions.

Point&Query is described as a "hypertext-hypermedia system with the augmentation of a question-answering and asking facility" (p. 227). Designed for high school and university students, this application presents a list of questions on a topic, and the learner points and clicks on these to be presented with the answers. Results of research showed that students preferred asking shallow questions, but a controlled experiment demonstrated that students could be prepared beforehand to inquire deeply by pre-assigning a difficult task (one which required deep thinking about cause and effect) prior to the tutoring session.

AutoTutor engages learners in a dialog, and elicits complete explanations of observed phenomena (e. g., in physics). An animated head (complete with facial expressions) prompts students for information, provides hints and assertions, and (when all else fails) supplies answers to facilitate the process.

A third tool described by Graesser et ah, iStart, supports adolescents and young university students in monitoring and evaluating their comprehension as they read, providing instruction on reading strategies (comprehension monitoring, paraphrasing, bridging inferences, prediction and elaboration), posing questions, and providing information. Specific training in strategy use is provided (modeled on screen by animated characters), and the system provides feedback to the learners as they integrate prior knowledge and prior text with the current content.

Graesser et al. 's review of the evidence, which they have gathered to date on the utility of their tools, is favourable; this is to be expected, given the careful attention that they have devoted to producing and testing them. At the least, it would seem that such efforts are laudable, and it is possible that these (and similar) applications will provide great benefits to generations of future students.

White and Frederiksen (2005) have used CBLEs to support young learners in developing metacognitive expertise in the context of learning communities. They note that group collaboration and reflective learning, combined with appropriate scaffolding from technology tools and human tutors, support not only the development of selfregulatory skills, but also developmental expertise, "expertise about how you improve your capabilities through inquiry and reflection" (p. 211). The software environment Inquiry Island was developed to support learning through a cycle of inquiry which includes developing a research question, generating hypotheses, designing an investigation, recording and analyzing data, creating a model, and evaluating the utility and the limitations of the model. Software agents {advisors, such as Quentin Questioner, Ivy Investigator, Sydney Synthesizer, Pablo Planner, Molly Monitor, Keiko Collaborator and Manny Mediator) support each step in the cycle, prompting the students to think about each part of the process, and to evaluate their progress as they proceed. As an adjunct to this process, students also participated in group discussions to analyze novels they had read, and took turns acting the roles of the twelve cognitive, social and metacognitive advisors (including theory manager, evidence manager, synthesis manager;

collaboration manager, planning manager, etc.). They were then asked to reflect on their experiences of playing these roles, and to write about the purposes of cognitive, social and metacognitive regulation. Quantitative measures (in the absence of a comparison group) showed significant gains by students in researcher assessments of metacognitive skills and inquiry skills, and the researchers claim that enjoyment of the role-playing exercise had "important motivational ramifications" (p. 221). While pre-experimental or quasi-experimental research does not provide evidence for causal claims of the effectiveness of interventions, it stands to reason that metacognitive scaffolding, such as White and Fredericksen have described, can be of great benefit in the development, internalization and proceduralization of self-regulatory skills, and that the use of tools such as Inquiry Island can support the development of self-regulation by providing explicit instruction in metacognitive performance.

Quintana, Zhang, and Krajcik (2005) bring a disciplined approach to metacognition and self-regulated learning (SRL) to bear on their analysis of CBLEs, which have been designed to facilitate online searches (Artemis, Digital IdeaKeeper and Symphony). Their theoretical framework, derived from contemporary literature on the theory of SRL, includes three sets of metacognitive functions (foresight factors related to task understanding and planning; monitoring and regulation; and reflection) applied to four types of cognitive activities (asking questions, searching, evaluating and synthesizing). The authors addressed the features of the various software packages in terms of how they support each of the metacognitive functions: understanding the inquiry task and planning the process, monitoring and regulating the inquiry process, and reflecting on different aspects of the work. They conclude "software can help make the implicit nature of metacognition more explicit to learners" (p. 242), and they propose that computer-based scaffolding can be organized around metacognitive issues.

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