Peer Reviews of Hattie's Visible Learning (VL).
"Our discipline needs to be saturated with critique of ideas; and it should be welcomed. Every paradigm or set of conjectures should be tested to destruction and its authors, adherents, and users of the ideas should face public accountability." (Hattie, 2017, p. 428).The peer reviews are saturated with detailed critiques of Hattie's work but most educators do not seem to be aware of them.
is to raise awareness of these critiques and investigate Hattie's claims in the spirit of Tom Bennett the founder of researchEd,
"There exists a good deal of poor, misleading or simply deceptive research in the ecosystem of school debate...
Where research contradicts the prevailing experiential wisdom of the practitioner, that needs to be accounted for, to the detriment of neither but for the ultimate benefit of the student or educator." (Bennett, 2016, p. 9).
Has documented significant issues with Hattie's work ranging from flawed methodology, calculation errors, misrepresentation, questionable interpretation to conflicts of interest, e.g.,
Terhardt (2011) - is suspicious of Hattie's economic interests.
Berk (2011) - "Statistical malpractice disguised as statistical razzle-dazzle."
Higgins & Simpson (2011) - "the process by which this number (effect size) has been derived has rendered it effectively meaningless." & Hattie has mixed-up the X/Y axis on his Funnel plot graph.
Schulmeister & Loviscach (2014) - "Hattie pulls the wool over his audience’s eyes." & "Hattie’s method to compute the standard error of the averaged effect size as the mean of the individual standard errors ‒ if these are known at all ‒ is statistical nonsense."
Wrigley (2015) - "Bullying by Numbers."
O'Neill, Duffy & Fernando (2016) - Detail the huge undisclosed 3rd party payments to Hattie.
Wecker et al. (2016) - "A large proportion of the findings are subject to reasonable doubt."
Bergeron & Rivard (2017) - "Pseudo-Science... House of Cards."
Nilholm (2017) - "Hattie's analyzes need to be redone from the ground up."
Nielsen & Klitmøller (2017) - "Neither consistent nor systematic."
Shannahan (2017) - "potentially misleading."
See (2017) - "Lives may be damaged and opportunities lost."
Biesta (2017) - "more akin to pig farming than science."
Eacott (2018) - "A cult... a tragedy for Australian School Leadership."
Slavin (2018) - "Hattie is wrong."
McKnight & Whitburn (2018) - "The Visible Learning cult is not about teachers and students, but the Visible Learning brand."
Ashman (2018b) - "If true randomised controlled trials can generate misleading effect sizes like this, then what monsters wait under the bed of the meta-meta-analysis conducted by Hattie and the EEF?"
Janson (2018) - "little value can be attached to his findings."
Larsen (2019) - "Blindness."
Wiliam (2019) - "Has absolutely no role in educational policy making."
Wiliam (2019b) - "Meta-meta-analyses, the kinds of things that Hattie & Marzano have done, I think have ZERO educational value!"
Simpson (2011, 2017, 2018, 2019) - "using these ranked meta-meta-analyses to drive educational policy is misguided."
Bakker et al. (2019) - "his lists of effect sizes ignore these points and are therefore misleading."
Zhao, Yong (2019) - "Hattie is the king of the misuse of effect sizes."
Slavin, Robert (2020) - "the value of a category of educational programs cannot be determined by its average effects on achievement. Rather, the value of the category should depend on the effectiveness of its best, replicated, and replicable examples."
Wolf et al. (2020) - Effect sizes conducted by a program's developers are 80% larger than those done by independent evaluators (0.31 vs 0.14) with ~66% of the difference attributable to publication bias.
Slavin, Robert (2020b) - "the overall mean impacts reported by meta-analyses in education depend on how stringent the inclusion standards were, not how effective the interventions truly were."
"...the paper makes a precise and subtle critique of Hattie‘s work, hence revealing several weaknesses in the methods and theoretical frameworks used by Hattie. Rømer and his critical contribution inform us that we should never take educational theories for granted; rather, educational theories should always be made subject to further research and debate."
"I’ve observed the same phenomenon with virtually every strategy and every innovation I’ve examined. I’ve come to the conclusion that you can expect anywhere from 20% to 40% of the studies in any given area to report negative results." (p. 34)
"The major message is that we need a barometer of what works best..." (VL, preface)
"One aim of this book is to develop an explanatory story about the key influences on student learning - it is certainly not to build another 'what works' recipe." (VL, p. 6).
"When teachers claim that they are having a positive effect on achievement or when a policy improves achievement, this is almost always a trivial claim: Virtually everything works. One only needs a pulse and we can improve achievement." (VL p. 16)
"Instead of asking 'What works?' we should be asking 'What works best?'" (VL, p. 18)
"It is not a book about classroom life, and does not speak to the nuances and details of what happens within classrooms."
"The model I will present... may well be speculative, but it aims to provide high levels of explanation for the many influences on student achievement as well as offer a platform to compare these influences in a meaningful way... I must emphasise that these ideas are clearly speculative" (VL, p. 4).Hattie & Hamilton (2020) now give an even more ambiguous picture,
"Most things that a teacher could do in a classroom 'sorta' work..." (p. 3)
"What I find fascinating is that since I first published this back in the 1990s, no one has come up with a better explanation for the data...
I am updating the meta-analysis all the time; I am up to 1400 now. I do that because I want to be the first to discover the error, the mistake." (Knudsen, 2017, p. 7).
"Bullying by numbers"
"Disappointingly, Hattie's response was in my opinion, inadequate" (p. 4).
"What's the story, not what's the numbers..."
"that’s why this will keep me in business to keep telling the story..." (Audio here).Hattie then admits his rankings are misleading and does not rank anymore! (Audio here).
"it worked then it got misleading so I stopped it"
Most people assume he does, but a brief look at Hattie's representation of the Class Size research should raise some questions! (more details on page links on the right menu).
In 2005, Hattie got the attention of educational administrators by labelling 'reducing class size' a disaster then later as going backwards (2005 ACER Lecture & VL, p. 250). He continued with Pearson (2015) naming 'reducing class size' as one of the major distractions! Then again, in the TV series Revolution School, claiming that, reducing class size does not make a difference to the quality of education!
The major class size study that Hattie used was by Glass & Smith (1979), they summarise their data in a graph and table:
"The curve for the well-controlled studies then, is probably the best representation of the class-size and achievement relationship...
A clear and strong relationship between class size and achievement has emerged... There is little doubt, that other things being equal, more is learned in smaller classes."Hattie never mentions this "Story" and he consistently reports just the ONE average, e.g., Hattie stated,
"Glass and Smith (1979) reported an average effect of 0.09 based on 77 studies..." Blatchford (2016, p. 106)I also contacted Prof Glass to ensure I interpreted his study correctly, he kindly replied,
"Averaging class size reduction effects over a range of reductions makes no sense to me.
It's the curve that counts.
Reductions from 40 to 30 bring about negligible achievement effects. From 20 to 10 is a different story.
But Teacher Workload and its relationship to class size is what counts in my book."Bergeron & Rivard (2017) reiterate,
"Hattie computes averages that do not make any sense."
"…right now meta‐analysis is simply not a suitable technique for summarizing the relative effectiveness of different approaches to improving student learning..."
"Teachers need to speak back to power, and one useful tool is to point to flaws in the use of data" (p. 3).
"Bullying by numbers has a restrictive effect on education, leads to superficial learning, and is seriously damaging teachers’ lives" (p. 6).This Blog:
is broken up into different pages (menu on the right) designed so you can easily go to what interests you most.
A critique of Hattie's methodology - Effect Size, Student Achievement, CLE and other errors and A Year's Progress???
Then an analysis of particular influences. I would recommend starting with what was his highest ranked influence Self Report Grades and then look at the controversial Class Size.
"To a medical researcher, it seems bonkers that Hattie combines all studies of the same intervention into a single effect size."
"What now stands proxy for a breadth of evidence is statistical averaging. This mathematical abstraction neglects the contribution of the practitioner’s accumulated experience, a sense of the students’ needs and wishes, and an understanding of social and cultural context...
When ‘evidence’ is reduced to a mean effect size, the individual person or event is shut out, complexity is lost and values are erased" (p. 360).
"I think you’ll find it’s a bit more complicated than that."
Where To From Here?
The conflicts of interest by the major players such as Hattie and Marzano, are now too big to ignore - see here.
Teachers need to understand the basics of these research methods and Teacher Unions, who have the resources to independently assess evidence, should provide critical awareness, summaries and training for teachers, with a focus on QUALITY evidence.
Wrigley (2018) quoting Gene Glass, suggests a start,
"Indeed, Gene Glass, who originated the idea of meta-analysis, issued this sharp warning about heterogeneity: 'Our biggest challenge is to tame the wild variation in our findings not by decreeing this or that set of standard protocols but by describing and accounting for the variability in our findings. The result of a meta-analysis should never be an average; it should be a graph.'(Robinson, 2004: 29)" (p. 367).