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).
Review articles in the scientific literature can be classified as a general review article, a systematic review (SR), or meta-analysis (MA). The purpose of a review article is to provide readers with a summary of published research in a particular field. Reviews usually focus on areas of progress over the recent past, for example five years. A general review article attempts to summarize all the relevant, published literature and provide some analysis of the controversial areas of the field or topic. In addition, it may suggest some novel ways to advance the field further . Such review articles provide a concise analysis of a large body of literature and hence are important for readers from a variety of fields. Articles in PubMed, for example, can be searched based on whether they are classified as review articles.
Because nonhuman animal models (hereafter referred to as animal models or animals) have on multiple occasions been unsuccessful in predicting human response to drugs and disease (we will address this claim in depth), many have called for SRs in order to improve the models [-]. An example of this predicament would be the animal models used to determine which drugs to develop in an attempt to diminish neurological damage from ischemia events of the central nervous system (CNS) [, -]. By analyzing animal-based research with SRs, flaws in the methodology would also become apparent thus leading to eventual standardization of such studies. This would ostensibly also lead to better predictive values for humans (see table for calculating such values). Bracken supports this, stating:
It is seldom - perhaps never - possible to reach an absolute certitude when verifying a hypothesis. This is the case especially when the hypothesis is intended to hold true anywhere, i.e. also for the cases that are similar to those that have been examined. Therefore most modern researchers accept in practice the idea that when speaking of 'truth' of a hypothesis they actually mean verisimilitude or credibility. This distinction, nevertheless, has no decisive consequences in practice: you can use 'credible' findings exactly in the same way as 'true' findings.
One reason why animal experiments often do not translate into replications in human trials or into cancer chemoprevention is that many animal experiments are poorly designed, conducted and analyzed. Another possible contribution to failure to replicate the results of animal research in humans is that reviews and summaries of evidence from animal research are methodologically inadequate .
There methodological problems in current animal-based research. Pound et al.  highlighted some of the potential flaws when using animal models, including:
Systematic reviews are currently favored methods of evaluating research in order to reach conclusions regarding medical practice. The need for such reviews is necessitated by the fact that no research is perfect and experts are prone to bias. By combining many studies that fulfill specific criteria, one hopes that the strengths can be multiplied and thus reliable conclusions attained. Potential flaws in this process include the assumptions that underlie the research under examination. If the assumptions, or axioms, upon which the research studies are based, are untenable either scientifically or logically, then the results must be highly suspect regardless of the otherwise high quality of the studies or the systematic reviews. We outline recent criticisms of animal-based research, namely that animal models are failing to predict human responses. It is this failure that is purportedly being corrected systematic reviews. We then examine the assumption that animal models can predict human outcomes to perturbations such as disease or drugs, even under the best of circumstances. We examine the use of animal models in light of empirical evidence comparing human outcomes to those from animal models, complexity theory, and evolutionary biology. We conclude that even if legitimate criticisms of animal models were addressed, through standardization of protocols and systematic reviews, the animal model would still fail as a predictive modality for human response to drugs and disease. Therefore, systematic reviews and meta-analyses of animal-based research are poor tools for attempting to reach conclusions regarding human interventions.
the minimum information that all scientific publications reporting research using animals should include, such as the number and specific characteristics of animals used (including species, strain, sex, and genetic background); details of housing and husbandry; and the experimental, statistical, and analytical methods (including details of methods used to reduce bias such as randomization and blinding) .
The above claims are, however, in direct opposition to those advocating for SRs in order to improve the predictive ability of animal-based research. Before we survey the literature for empirical confirmation and present views of other scientists that strongly disagree with the above, we need to first define the term and refresh the reader's memory of how it is used in science.
Of course, researchers often use both methods, using a cross section to take the snapshot and isolate potential areas of interest, and then conducting a longitudinal study to find the reason behind the trend.
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).