This is not really the kind of video I normally share on this blog, but I really like what the American Chemical Society and PBS Digital Studios do in their Reactions-video’s. A very nice example of science communication, imho.
A very relevant post!
I started this blog five years ago now. I can’t believe it’s been that long. It’s taken me from Chicago to San Francisco and back. But somewhere over this five years–over the course of this journey on which I’ve so appreciated you following me–I started to lose some of my inspiration.
It started somewhere towards the end of my fifth year teaching, which also was the end of my first year working for a personalized learning start-up and network of private schools in Silicon Valley.
I had gone into the school year with unrelenting energy, thrilled to be opening a brand new micro-school and to work on technology tools that were intended to personalize my students’ learning. The idea sounded exhilarating: I was set to work with real engineers on a technology platform for the classroom. It would allow me to send individualized “cards” to a child’s “playlist.” These cards…
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Maybe a bit of an acquired taste, but I like it: the history of teacher education and politics in England and Russia
When talking about research in education people tend to think about observations, surveys, maybe even anthropological research, but I’ve noticed that historical research is often overlooked. So maybe, this new comparative study is a bit of an acquired taste, but I like it as it looks how teacher training has developed during the past 4 decades in 2 very different regions: England and Russia. I do hope that indeed other countries will be added to the comparison.
From the press release:
The paper, titled “A tale of two countries – forty years on: politics and teacher education in Russia and England”, came out in European Journal of Teacher Education.
Professor Valeeva commented, “We chose England of the four countries of the United Kingdom because it has a peculiar system of teacher education. The last 40 years in the two compared nations have been somewhat alike in policy changes and decision-making in teacher education. So we made that decision on the scope.”
She mentioned some of those changes in Soviet Union and Russia, such as the closing of specialized teacher education universities and the program “Teacher of the Soviet Union” of 1988 which introduced concepts of inseparability of productive and reproductive functions of teaching staff and continuous education.
“In the late 80s there was research on a comprehensive model of teacher personality, a new emphasis on psychological testing of enrollees of pedagogical institutions. In particular, such research was conducted at Kazan Pedagogical Institute.”
“The 90s became truly transformative for the Russian educational system – processes of economic, political and social reform warranted the reshaping of educational approaches in ideological, methodological and conceptual aspects.”
Three stages of post-Soviet development of teacher education are mentioned in the article. Dr. Valeeva said this about the latest which started in 2013 – 2014, “Many pedagogical universities have been closed or merged with classical universities. The latter always provided fundamental knowledge, whereas the former were strong in practice-oriented studies. A new balance should have been found in these circumstances, which in the case of Kazan University proved to be feasible.”
“A unique structure of teacher education was formed at KFU, where advantages of specialized and classical universities were combined to create variable study trajectories for future teachers, namely, traditional, distributed, and integrated programs.”
The influence of globalization has created some similarities in the very different educational systems of Russia and the UK.
“The more visible traits of similarity are in the decisions to create specific standards of teacher education. Both Russia and England aspire to do just that.”, said our interviewee.
As an external observer, Professor Menter noted the comparatively bigger attention to psychological aspects in Russian teacher education. This can be seen at KFU as well. He opined that the integration of pedagogics and psychology is rather efficient.
Further plans include studies of the evolution of teacher education in the context of policy changes in different countries. A monograph is planned for publication at Oxford University Press.
Abstract of the study:
The relationships between politics and teacher education have become increasingly close over recent decades in many contexts around the world, often causing significant challenges as well as some opportunities. In this article, we draw on a project on the reform of teacher education in Russia and through a comparison with the development of teacher education policy in England – especially over the last forty years – we explore how the evolution of a new politics in both contexts has affected policy on teaching and teacher education. Looking, for example, at ‘post-communism’ and ‘neoliberalism’ and their respective impacts on political systems, a number of contradictions and paradoxes are identified, when comparisons are drawn between the two systems.
I’m quite sure that the title of this post may have upset some people already. The reason is that both heritability and intelligence can be regarded as problematic concepts by even researchers. Still, bear with me, because this new study is quite intriguing.
The following key points actually describe a steep evolution in our knowledge about both the genetic and heritability components of our intelligence (which aren’t synonyms to be clear):
- Until 2017, genome-wide polygenic scores derived from genome-wide association studies (GWAS) of intelligence were able to predict only 1% of the variance in intelligence in independent samples.
- Polygenic scores derived from GWAS of intelligence can now predict 4% of the variance in intelligence.
- More than 10% of the variance in intelligence can be predicted by multipolygenic scores derived from GWAS of both intelligence and years of education. This accounts for more than 20% of the 50% heritability of intelligence.
- Polygenic scores are unique predictors in two ways. First, they predict psychological and behavioural outcomes just as well from birth as later in life. Second, polygenic scores are causal predictors in the sense that nothing in our brains, behaviour or environment can change the differences in DNA sequence that we inherited from our parents.
- Polygenic scores for intelligence can bring the powerful construct of intelligence to any research in the life sciences without having to assess intelligence through the use of tests.
The key element in this paper is the concept of polygenic scores. What are they?
A polygenic score, also called a polygenic risk score, genetic risk score, or genome-wide score, is a number based on variation in multiple genetic loci and their associated weights (see regression analysis). It serves as the best prediction for the trait that can be made when taking into account variation in multiple genetic variants. (wikipedia)
My attempt to say it as easy as possible: intelligence isn’t located on one gene, but is due to the combination of a lot of genes.
Now the good thing and the thing that even frightened me. First the good thing: Plomin and von Stumm don’t say everything is down to our genes. That would be downright stupid, btw. That is why the last key point frightens me as it makes everything much too deterministic and limits intelligence to something purely native. Also, if I read the article correctly, there is still a long way to go.
Although the authors do go into the ethical side of their work, this element is not really discussed. Did I mention I think this paper is intriguing?
Abstract of the article in Nature:
Intelligence — the ability to learn, reason and solve problems — is at the forefront of behavioural genetic research. Intelligence is highly heritable and predicts important educational, occupational and health outcomes better than any other trait. Recent genome-wide association studies have successfully identified inherited genome sequence differences that account for 20% of the 50% heritability of intelligence. These findings open new avenues for research into the causes and consequences of intelligence using genome-wide polygenic scores that aggregate the effects of thousands of genetic variants.
One of my favorite newsletters has turned 5 recently and now has it’s own online Best Evidence in Brief archive. This is great news!
In the latest issue of this fine newsletter, this study caught my eye:
Peter Tymms and colleagues at Durham University’s Centre for Evaluation and Monitoring conducted a study of 40,000 children in England to examine what impact effective teaching in the first year of school has on achievement at the end of compulsory teaching at age 16.Children’s early reading and math development were measured at the start of school, at age four, using the Performance Indicators in Primary Schools (PIPS) assessments. They were assessed again at the end of their first school year and at ages 7, 11, and 16.By assessing children at the beginning and end of their first year, the researchers were able to identify effective classes – defined as a class where children made much larger than average gains from ages 4 to 5, controlling for pretests and poverty level.The study, published in School Effectiveness and School Improvement, found that children who were taught well in their first year of school went on to achieve better GCSE results (GCSEs are high-stakes exams in the UK) in English and math at age 16 (effect size = +0.2). Long-term benefits in achievement were also reported for those children who were in effective classes in Key Stages 1 and 2, however, these were not as large as those seen in the first year of school (Key Stage 1 is the equivalent of kindergarten to first grade in the U.S., and Key Stage 2 is the equivalent of second grade to fifth grade).The study concludes that the first year of school presents an important opportunity to have a positive impact on children’s long-term academic outcomes.
Direct instruction is nothing new. There is over 50 years of research. But lately there is a new fever surrounding the approach originally constructed by Engelmann and Becker. If you examine the latest PISA-results, you can see that they are not that far off from the results of the biggest experiment in education ever: Project Follow Through.
But between those two datasets there has happened a lot more research on Direct instruction. This research has now been brought together in a new meta-analysis that has gained a lot of attention in my Twitter-timeline.
And the results are pretty clear:
Our results support earlier reviews of the DI effectiveness literature. The estimated effects were consistently positive. Most estimates would be considered medium to large using the criteria generally used in the psychological literature and substantially larger than the criterion of .25 typically used in education research (Tallmadge, 1977). Using the criteria recently suggested by Lipsey et al. (2012), 6 of the 10 baseline estimates and 8 of the 10 adjusted estimates in the reduced models would be considered huge. All but one of the remaining six estimates would be considered large. Only 1 of the 20 estimates, although positive, might be seen as educationally insignificant.
What does this mean? Well, that Direct Instruction seems to be working quite well for reading, math, spelling, language,…
But there is more:
Earlier literature had led us to expect that effect sizes would be larger when students had greater exposure to the programs, and this hypothesis was supported for most of the analyses involving academic subjects. Significantly stronger results appeared for the total group, reading, math, and spelling for students who began the programs in kindergarten; for the total group and reading for students who had more years of intervention; and for math students with more daily exposure. Although we had expected that effects could be lower at maintenance than immediately postintervention, the decline was significant in only two of the analyses (math and language) and not substantial in either. Similarly, while literature across the field of education has suggested that reported effects would be stronger in published than in unpublished sources (Polanin et al., 2016), we found no indication of this pattern.
Contrary to expectations, training and coaching of teachers significantly increased effects in only one analysis (language). We suggest that readers interpret this finding cautiously for we suspect that it reflects the crude nature of our measure—a simple dummy variable noting if teachers were reported as receiving any training or coaching.
Are there no nuances to be made? Well, yes, of course as with all analyses. The researchers went to a great length to examine the quality of the studies, but didn’t include these insights in their analysis. And the researchers also the size and heterogeneity of the samples used in their research.
For instance, we did not attempt to compare the results of each of the DI programs with specific other approaches. Nor did we examine outcomes in subdimensions within the various subject areas, such as differentiating reading fluency and comprehension. In addition, many of our measures were less precise than could be considered optimal. The studies differed, often substantially, in the nature and amount of information given.
Abstract of the meta-analysis by Stockard et al:
Quantitative mixed models were used to examine literature published from 1966 through 2016 on the effectiveness of Direct Instruction. Analyses were based on 328 studies involving 413 study designs and almost 4,000 effects. Results are reported for the total set and subareas regarding reading, math, language, spelling, and multiple or other academic subjects; ability measures; affective outcomes; teacher and parent views; and single-subject designs. All of the estimated effects were positive and all were statistically significant except results from metaregressions involving affective outcomes. Characteristics of the publications, methodology, and sample were not systematically related to effect estimates. Effects showed little decline during maintenance, and effects for academic subjects were greater when students had more exposure to the programs. Estimated effects were educationally significant, moderate to large when using the traditional psychological benchmarks, and similar in magnitude to effect sizes that reflect performance gaps between more and less advantaged students.
Paul A. Kirschner
Disclaimer: Let’s sketch/frame the situation so there are no misunderstandings. Yes, I know that using a computer (e.g., laptop, tablet, smartphone) can be effective in certain situations so this blog isn’t plea for pure lecture or totally banning the use of laptops in schools. Yes, of course, there are situations where it’s necessary to use a laptop in the class, so add this to the previous. And finally, yes, some teachers can be boring but that’s not a reason to do something else in the class than learn.
Let’s start with an analogy. You’re a non-smoker and you go out to eat in a restaurant, catch a ride with someone in a car, or are…
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This morning big news in our Belgian media about a new study by Stijn Baert and his colleagues in which they checked the impact of smartphone usage on the academic performance of the students:
In this study, we contributed to recent literature concerning the association between smartphone use and educational performance by providing the first causal estimates of the effect of the former on the latter. To this end, we analysed unique data on 696 first-year university students in Belgium. We found that a one-standard-deviation increase in their overall smartphone use yields a decrease in their average exam score of about one point (out of 20). This negative relationship is robust to the use of alternative indicators of smartphone use and academic performance. As our results add to the literature evidence for heavy smartphone use not only being associated with lower exam marks but also causing lower marks, we believe that policy-makers should at least invest in information and awareness campaigns to highlight this trade-off.
I have to admit that I do think that while the researchers have taken a lot into account, there always still can be something else maybe causing this differences. The researchers have attempted to bypass this:
This study is the first to attempt to measure the causal impact of (overall) smartphone use on educational performance. To this end, we exploit data from 696 first-year students at two Belgian universities, who were surveyed in December 2016 using multiple scales on smartphone use as well as predictors of this smartphone use and a battery of questions concerning (potential) other drivers of success at university. This information is merged with the students’ scores on their first exams, taken in January 2017. We analyse the merged data by means of instrumental variable estimation techniques. More concretely, to be able to correctly identify the influence of smartphone use on academic achievement, in a first stage, the respondents’ smartphone use is predicted by diverging sets of variables that are highly significantly associated with smartphone use, but not directly associated with educational performance. In a second stage, the exam scores are regressed on this exogenous prediction of smartphone use and the largest set of control variables used in the literature to date.
In the interview this morning on the radio, the researchers didn’t plea for a total ban of smartphones, but still think it can be a very important element for students to take into consideration.
Abstract of the study:
After a decade of correlational research, this study is the first to measure the causal impact of (general) smartphone use on educational performance. To this end, we merge survey data on general smartphone use, exogenous predictors of this use, and other drivers of academic success with the exam scores of first-year students at two Belgian universities. The resulting data are analysed with instrumental variable estimation techniques. A one-standard-deviation increase in daily smartphone use yields a decrease in average exam scores of about one point (out of 20). When relying on ordinary least squares estimations, the magnitude of this effect is substantially underestimated.