Robert Slavin (2014), director of the Center for Research and Reform in Education, discussed five strategies to make cooperative learning powerful. He stated, "It is the "learning" in cooperative learning that is too often left out. But it needn't be. Using these five strategies, teachers can get the greatest benefit possible from cooperative learning and ensure that collaboration enhances learning" (para. 3):
Caution: Readers should also be aware that although determining learning styles might have great appeal, "The bottom line is that there is no consistent evidence that matching instruction to students' learning styles improves concentration, memory, self-confidence, grades, or reduces anxiety," according to Dembo and Howard (2007, p. 106). Rather, Dembo and Howard indicated, "The best practices approach to instruction can help students become more successful learners" (p. 107). Such instruction incorporates "Educational research [that] supports the teaching of learning strategies...; systematically designed instruction that contains scaffolding features...; and tailoring instruction for different levels of prior knowledge" (p. 107). Cognitive scientists Pashler, McDaniel, Rohrer, and Bjork (2009) supported this position and stated, "Although the literature on learning styles is enormous, very few studies have even used an experimental methodology capable of testing the validity of learning styles applied to education. Moreover, of those that did use an appropriate method, several found results that flatly contradict the popular meshing hypothesis" (p. 105). They concluded "at present, there is no adequate evidence base to justify incorporating learning-styles assessments into general educational practice" (p. 105) and "widespread use of learning-style measures in educational settings is unwise and a wasteful use of limited resources. ... If classification of students' learning styles has practical utility, it remains to be demonstrated" (p. 117). This position is further confirmed by Willingham, Hughes, and Dobolyi (2015) who concluded in their scientific investigation into the status of learning theories: "Learning styles theories have not panned out, and it is our
responsibility to ensure that students know that" (p. 269).
In general, deductive research is theory-testing and inductive research is theory-generating. Often people link deductive research with quantitative experiments or surveys, and inductive research with qualitative interviews or ethnographic work. These links are not hard and fast – for instance, experimental research, designed to test a particular theory through developing a hypothesis and creating an experimental design, may use quantitative or qualitative data or a combination. If your research starts with a theory and is driven by hypotheses that you are testing (e.g. that social class background and social deprivation or privilege are likely to affect educational attainment), it is, broadly speaking, deductive. However much research combines deductive and inductive elements.
Every teacher should have some knowledge on how students learn and be able to connect research to what they do in the classroom. In the , the Deans for Impact (2015) provide a valuable summary of cognitive science research on how learning takes place. In it you'll find cognitive principles and practical implications for the classroom related to six key questions on how students understand new ideas, learn and retain new information, and solve problems; how learning transfers to new situations; what motivates students to learn; and common misconceptions about how students think and learn (About section). Likewise, the Centre for Education Statistics and Evaluation (2017) in New South Wales, Australia elaborates on research that teachers really need to understand about cognitive load theory: what it is, how the human brain learns, the evidence base for the theory, and implications for teaching. For example, when teaching, you'll learn about the effect of using worked examples with novices and learners who gain expertise, the effect of redundancy (unnecessary information might actually lead to instructional failure), the negative effect of split-attention (processing multiple separate sources of information simultaneously in order to understand the material), and the benefit of using supporting visual and auditory modalities.
(CASEL) features information on this topic and programs based on social and emotional learning: What is SEL?, SEL in action, SEL in policy, SEL research.
As we discussed, SRs are only useful if there is scientific validity to the assumptions or axioms underlying the research. There is no reason to conduct SRs of homeopathy nor does complexity theory and evolutionary biology offer any reason to expect SRs of animal models to be productive. Regardless of how the problem is approached, animal and humans will always be differently complex. Personalized medicine puts this in perspective.
We note that the above scientists have not, to the best of our knowledge, agreed with us that animal models are incapable of being predictive modalities. We again attribute this to the fact that the discussion regarding evolved complex systems is relatively new. We also again note that SRs and standardization may contribute to the use of animals in categories 3-9 of table . We do not deny that animals can be successfully used for such endeavors in science and research and recognize the value of SRs in improving such uses. However, we have presented a case against expecting animal models to ever be predictive modalities for human response to drugs and disease regardless of improvement in methodology. Even if methodological issues were to prove the problem in some of the studies that reveal PPVs of ~0.5, the lack of studies revealing any animal model to be predictive modality (for example in teratogenicity, carcinogenicity, hepatotoxicity, efficacy for a class of drugs, mechanisms of a class of diseases) is consistent with our theory.
Sharp and Langer summarized the current situation: “The next challenge for biomedical research will be to solve problems of highly complex and integrated biological systems within the human body. Predictive models of these systems in either normal or disease states are beyond the capability of current knowledge and technology” .
Animal models have historically been unable to predict human response to drugs and disease and animal-based research has historically displayed methodological problems that make SRs difficult. One proposed solution that would address both problems is standardization of protocols thus permitting SRs of animal models, which would in turn improve the models thus possibly allowing accurate predictions, high PPV and NPVs, for human response to drugs and disease. We have argued that even if the methodology for animal models could be standardized and subject to SRs, animal models would still fail to be predictive modalities for human response to drugs and disease because of considerations from complexity theory and evolutionary biology. Put succinctly, humans and animals are complex systems with different evolutionary trajectories.
The reductionist method of dissecting biological systems into their constituent parts has been effective in explaining the chemical basis of numerous living processes. However, many biologists now realize that this approach has reached its limit. Biological systems are extremely complex and have emergent properties that cannot be explained, or even predicted, by studying their individual parts. The reductionist approach—although successful in the early days of molecular biology— underestimates this complexity and therefore has an increasingly detrimental influence on many areas of biomedical research, including drug discovery and vaccine development .
With all this in mind, Carole Frederick Steele (2009) would add that teachers need to be adept at improvising, interpreting events in progress, testing hypotheses, demonstrating respect, showing passion for teaching and learning, and helping students understand complexity. Fortunately, she reminded us that "No teacher is likely to excel at every aspect of teaching....What experts attend to and ignore is markedly different from what beginners notice. The growth continuum ranges from initial ignorance (unaware) to comprehension (aware) to competent application (capable) to great expertise (inspired)," paralleling Bloom's taxonomy. "Lack of awareness occurs before Bloom's categories. The awareness stage is a fair match for Bloom's stage of knowledge and understanding. Teachers at the capable stage use application and analysis well. Educators who reach the inspired stage have become skilled at synthesis and evaluation in regard to their thinking about teaching and learning" (Introduction section).