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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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20. To test the effects of an inquiry-based nursing education program on CT development, Magnussen, Ishida, and Itano (2000) used pre- and post-testing on the WGCTA as a dependent variable. One hundred fifty nursing students at University of Hawaii were tested at admission and at graduation, before and after undergoing a fouryear program of case-based inquiry learning, where clinical groups of eight to ten students and a faculty member discussed what was known about the cases being treated, identified clients' needs, and decided how those needs could be met. The authors reported no significant change in the group's average CT scores overall; however, when the cohort was divided in three groups based on their pre-test scores (low, medium, or high), a spectacular example of regression to the mean was evident: the low-scoring group increased their average by close to half a standard deviation (d = +0.40), while the highscoring group scored lower than they had originally {d = -1.36), and the middle group also lost some ground (d = -0.80). The authors postulated that the test, administered at the end of the program, may have been unimportant to the students, and that they may not have made a wholehearted effort, but this does not explain why the initially low-scoring group managed to increase their scores.

Study Features Tables 1 and 2 present a number of demographic, methodological and pedagogical features of interest to anyone who interprets this research in terms of the educational processes and outcomes that were involved. The features selected for presentation here seem to be among the most salient ones (in terms of connecting processes and outcomes) that are generally reported in reports and dissertations (type of publication is shown in Table 2). The following subsections provide brief explanations of each feature, along with my interpretations of their potential relevance for relating processes and outcomes in the context of these twenty examples. According to Abrami et al, Studies 1 to 10 produced the ten highest effect sizes (calculated from descriptive data presented in the original reports, or estimated on the basis of statistical calculations originally presented); in contrast, Studies 11 to 20 produced the poorest results (considering that the purpose of all this research is to increase CT scores).

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Table 2. Studies reviewed, showing publication type (J - journal article, R professional report, D - doctoral dissertation), treatment duration and instructor training

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Author and Year of Publication No author is involved in more than one of the twenty studies under consideration, which clearly indicates the lack of a pattern with regard to this feature. Comparing the dates of publication of the top ten studies with the bottom ten reveals no clearly discernible difference in effectiveness of more recent, or less recent, interventions; the fact that the two oldest studies (both from 1969) produced negative effect sizes is counterbalanced by the relatively recent appearance of studies 13, 16, 18, and 20.

Education Level Here the distribution of elementary school and post-graduate interventions seems to be evenly distributed across both halves of the list (Table 1); however, the list is topheavy with secondary school studies (four in the first half, two in the second), and weighted towards the bottom with research on university students (two in the first ten, five in the rest). This result is consistent with those of Abrami et al, who reported that quantitative analyses of studies of elementary and secondary school students averaged significantly higher effect sizes (Hedges g, weighted for sample size, of+0.52 and +0.69 respectively) than university students (g = +0.25); average g for interventions at the postgraduate level was +0.62. These results may lead an inquirer to wonder why educational interventions to support CT development seem to have been least effective at the undergraduate level.

Type of Intervention Table 1 shows that, of the top ten studies, seven delivered explicit instruction in the development of deductive logic or other cognitive skills (analysis, evaluation, inference, assumption testing, etc.), and the other three interventions seem also to have been closely related to this particular topic. Study 7 concerned medical students analyzing cases; Study 9 coached students in the critical analysis of media advertising;

and Study 10 promoted the cognitive skills applied in the development of cognitive maps.

As far as the other ten studies are concerned, Hartman-Haas (1984, Study 15) reported partial success, as seventh grade students benefited from thinking skills instruction. Study 17 was unsuccessful in teaching conditional logic skills to early elementary school students through audiotaped lessons, while Study 19 demonstrated that university faculty members don't always benefit demonstrably from professional development programs which are intended to support them in developing their thinking skills. While the unsuccessful interventions were intended to promote CT, at least two provided no instruction whatsoever in the subject (the two articles on Independent Study).

We may infer that direct instruction in thinking skills benefits performance on tasks that are designed to test these skills. Of course, this accords with intuitive reason, especially with regard to short-term retention, and it also seems obvious that long-term practice of complex analysis, assumption testing, evaluation, inference, problem-solving, argument and explanation leads to long-term retention of these skills. Abrami et al.





reported that CT "immersion" (indirect teaching of cognitive skills without specific explanations of these skills) produced the lowest average effect size (g = +0.09) compared with direct instruction in generic CT (g = +0.38), infusion of direct instruction along with other subject matter (g = +0.54), and mixed instructional methods (g = +0.94).

Dependent Measures Just as a wide variety of effect sizes is evident in the reviewed studies, so is a wide variety of thinking skills tests, and results measured by standardized tests in the Abrami et al. study are not as high as those measured by teacher-created assessments;

average g for the latter was +1.43, compared to +0.24 for the former. Table 1 bears out this result; seven of the bottom ten studies used standardized CT tests as their dependent measure, and two used other thinking tests, while eight of the top ten used teacherdeveloped measures.

To interpret this result, there are several factors that can be taken into account.

Since the instructor is proximate to the instruction, she or he is well qualified to create assessment instruments that are closely related to the class material. Unless the instructor is specifically tailoring instruction to a standardized measure (which is certainly a possibility), the unfamiliarity of a standard test instrument, and its indirect relation to the subject matter of instruction, provides a less favourable performance environment.

Researchers ought to consider the likelihood that teachers have a vested interest in assessments that demonstrate successful educational achievement, and this bias may (and perhaps should) lead to the development of student-friendly assignments and examinations. Standard tests, of course, are not specifically designed to reflect students' (or their teachers') educational accomplishments.

Another possibility (as raised by Gibbs et al, 1988, Study 19, by Norton, 1985, Study 11, and also by Magnussen, 2000, Study 20) is that CT tests do not measure the complex skills used by expert thinkers; an inspection of these tests makes it obvious that they examine only the most basic of analytic and inferential skills, rather than more complex and dynamic cognitive functions (such as metacognitive self-regulation and explanation). Complex problem solving and explanatory skills are highly relevant to higher-order thinking in all disciplines, and these functions are not directly addressed by standard tests of generic thinking skills.

Treatment Duration Table 2 indicates that no pattern emerges from duration data; successful interventions range from nine months to very little instruction time (mere exposure of medical students to video cases and online discussions produced remarkable results in the first week, and two or three class periods of instruction resulted in leaps of thinking skill by secondary school students), while unsuccessful ones lasted eight months and four years. Abrami et al. declined to report any results on this measure, as we could arrive at no clear interpretation of the quantitative analysis. It seems intuitively obvious that the quality of an intervention is more salient to the outcomes than its duration, and (as mentioned above) high-quality long-term instruction seems more likely to produce longterm benefits than high-quality instruction of short duration.

Publication Type Six of the top studies were published as journal articles; seven of the last ten appeared as reports to professional organizations (Table 2). While this does not demonstrate that journal articles provide better data (or even that they describe better interventions), it is in line with the idea that journals are more likely to publish studies that accomplished their stated research goals {publication bias).

Instructor Training While we may presume that all of the instructors in these twenty sets of interventions were qualified to teach their classes, it is obvious that they varied in their training and their experience with regard to understanding and applying higher-order cognitive skills to their work. The last column in Table 2 presents what I could glean from the research report descriptions of the specific training received by the instructors in these interventions. Where "trained instructor" is listed, the reports mentioned only that the teachers received some training prior to the implementation of the teaching involved;

where a number of hours is listed, the report stated how long the training lasted. The rest of the articles did not mention any teacher training, so I have simply mentioned the positions of the instructors.

Abrami et al. reported that studies which mentioned instructor training had a higher average effect size (g = +1.00) than the others; sub-groups ranged from g = +0.13 (where CT was simply stated as a course objective) to +0.58 (where extensive observations of curricular activities were described). On this measure, five of the top ten studies mentioned that instructors received specific training for the intervention (and four of the others were led by experienced instructors or graduate students in education); three of the last ten mentioned special instructor training. While this result should be interpreted with caution (since not all instructor training is effective instructor training), it stands to reason that instructors who are well trained in the arts and science of higherorder thinking are in a better position to teach these skills to others.

Instrument Failure Since critical thinking is a highly complex construct, and since a wide variety of measures have been deployed to assess thinking skills, the issue of instrumentation is central to research in this area. The crucial question here is how researchers should deal with this tangle of sub-constructs and the plethora of operational measures. When measuring thinking skills, is it best to use standard measures, locally produced tests, or both? While the last choice may be best for research purposes (indeed, it sometimes seems that the more dependent measures we can cram into a research design, the more information we can gather, and the better off we are), practical concerns do not always allow for many options, and there is always the problem of selecting from standard tests, or of designing new ones. Psychometric issues such as this may entail great complexity.

Should meta-analysts compare results of educational interventions when the outcomes are measured on highly variant types of instruments? It might be better to analyze results of interventions measured by one type of assessment, rather than comparing quantitative results derived from unrelated instruments; however, we can draw conclusions only from whatever data is available. Thus, meta-analysts can only hope that enough (well designed, well controlled and well described) studies will soon be published, in order that sufficient numbers of comparisons will be available to inform us about which interventions, applied in which ways, with which students, produce consistently beneficial results for which well-defined set of thinking skills measured by a particular type (or means) of assessment.

As for the variety of instruments which are available, or might be created, we may infer (or hope) that most of them fulfil their specific purposes, namely to measure particular sets of cognitive skills. However, unless these skills are well and publicly defined, and the instruments themselves are published, consumers of research cannot evaluate the utility of the tests. For systematic reviewers to make sense of the empirical literature, the skills being tested, and the instruments used to measure the performance of these skills, must be well described by research reports. It also seems that a great deal of effort must be invested if we are ever to establish the specific value of applying any measure in one context or another.

Limits of Reporting Research reports are wonderful in their variety; it sometimes seems that the number of reporting styles published in educational journals is equal to the number of authors who produce the reports. From quantitative analyses rife with descriptive and inferential statistics (and hardly a nod to any theoretical idea involved in the project), to thick descriptions without a digit (or a Greek letter) in view, educational scholars produce many thousands of reports each year, and the job of selecting the best of these from the least utile ranges from difficult to impossible. Yet all share a shortcoming: They are static, coarse-grained representations of weeks (or months, or years) of participation by some number of individuals, and the processes (cognitive, affective, or educational) which are undergone by all of the people involved in an educational research study cannot be captured in a research report.



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