Category Archives: Research

The only video you’ll ever need to watch about Gluten

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.

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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.

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Where is the heritability of intelligence hidden in our genes?

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.

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What people are saying about my new book… The Ingredients for Great Teaching.

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What is the long-term impact of effective teaching? (Best Evidence In Brief)

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.

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This new meta-analysis is making the news: direct instruction

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.

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New study on smartphone use and academic performance (spoiler: it’s bad)

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.

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Interesting interview with John Hattie: “I’m a statistician, I’m not a theoretician”

This Hattie-interview by Hanne Knudsen is very interesting although I think some statisticians will frown if they read the quote used as title “I’m a statistician, I’m not a theoretician”.

It advice to read the full article, but this excerpt explains the quote more in dept:

…if I had known that it would go to an audience larger than just researchers, I probably would have had a whole lot more of theory in it. I had never dreamed it would catch on like this, so you are right, it is quite devoid of theory in terms of how it is written. I am a measurement researcher, I am a statistician, I am not a theoretician, so I haven’t written a lot of theory. But of course I have a very strong model of teaching. I have worked for many years with some of the more well-known people on the theoretical side of teaching around the world. But you are right, it did not come through, and sometimes I think I should write a book about teaching as a profession – which indeed I am doing right now. I get accused more of not taking into account sociology, and the background of kids. Of course I think that is very critical, but the book was never written for that kind of general notion. You are right, it is a criticism, and maybe I should write something more theoretical, but it is not really my strength. I do have some very strong theories on how theory works and the concept of teaching. It just didn’t come through in the book

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Does VR help to learn? Well, this app doesn’t seem to.

I have to admit: despite the fact that I get sick every time I’m using virtual reality goggles, I really think VR and augmented reality (AR) is just impressive. But… does it help learning. It is too early to tell, but this study by Makransky et al that I found via a tweet by Paul Kirschner is pretty clear: in this study VR doesn’t improve learning . The study is extra interesting as it looks at some important principles for learning such as the redundancy principle (Mayer was involved in the study), and while the students did get more motivated, the learning was not better (even worse). Do note that the the amount of participants was pretty low: 52 (22 males and 30 females) students from a large European university.

Also interesting is to know what application the researchers were using:

The virtual simulation used in this experiment was on the topic of mammalian transient protein expression and was developed by the simulation development company, Labster. It was designed to facilitate learning within the field of biology at a university level by allowing the user to virtually work through the procedures in a lab by using and interacting with the relevant lab equipment and by teaching the essential content through an inquiry-based learning approach.

In short:

 

  • The consequences of adding immersive virtual reality to a simulation was examined.
  • The impact of the level of immersion on the redundancy principle was investigated.
  • EEG was used to obtain a direct measure of cognitive processing during learning.
  • Students reported higher presence but learned less in the immersive VR condition.
  • Students also had higher cognitive load based on EEG in the immersive VR condition.

Abstract of the study:

Virtual reality (VR) is predicted to create a paradigm shift in education and training, but there is little empirical evidence of its educational value. The main objectives of this study were to determine the consequences of adding immersive VR to virtual learning simulations, and to investigate whether the principles of multimedia learning generalize to immersive VR. Furthermore, electroencephalogram (EEG) was used to obtain a direct measure of cognitive processing during learning. A sample of 52 university students participated in a 2 × 2 experimental cross-panel design wherein students learned from a science simulation via a desktop display (PC) or a head-mounted display (VR); and the simulations contained on-screen text or on-screen text with narration. Across both text versions, students reported being more present in the VR condition (d = 1.30); but they learned less (d = 0.80), and had significantly higher cognitive load based on the EEG measure (d = 0.59). In spite of its motivating properties (as reflected in presence ratings), learning science in VR may overload and distract the learner (as reflected in EEG measures of cognitive load), resulting in less opportunity to build learning outcomes (as reflected in poorer learning outcome test performance).

 

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The power of families eating together

It’s very hard to examine if the correlation between families eating together and a better life for children and adolescents later on is a causal relation. It could well be that other elements play a role that cause both the better health and the chance of eating together. This new Canadian study doesn’t deliver the smoking gun, but does add some weight to the idea that eating together as a family can be very powerful.

From the press release:

“There is a handful of research suggesting positive links between eating family meals together frequently and child and adolescent health,” Pagani said. “In the past, researchers were unclear on whether families that ate together were simply healthier to begin with. And measuring how often families eat together and how children are doing at that very moment may not capture the complexity of the environmental experience.”

The study looked at chilldren who had been followed by researchers since they were 5 months old as part of the Quebec Longitudinal Study of Child Development. At age 6, their parents started reporting on whether or not they had family meals together. At age 10, parents, teachers and the children themselves provided information on the children’s lifestyle habits and their psycho-social well-being.

“We decided to look at the long-term influence of sharing meals as an early childhood family environment experience in a sample of children born the same year,” Pagani said, “and we followed-up regularly as they grew up. Using a birth cohort, this study examines the prospective associations between the environmental quality of the family meal experience at age 6 and child well-being at age 10.”

When the family meal environment quality was better at age 6, higher levels of general fitness and lower levels of soft-drink consumption were observed at age 10. These children also seemed to have more social skills, as they were less likely to self-report being physical aggressive, oppositional or delinquent at age 10.

“Because we had a lot of information about the children before age 6 — such as their temperament and cognitive abilities, their mother’s education and psychological characteristics, and prior family configuration and functioning — we were able to eliminate any pre-existing conditions of the children or families that could throw a different light on our results,” said Harbec. “It was really ideal as a situation.”

Added Pagani: “The presence of parents during mealtimes likely provides young children with firsthand social interaction, discussions of social issues and day-to-day concerns, and vicarious learning of prosocial interactions in a familiar and emotionally secure setting. Experiencing positive forms of communication may likely help the child engage in better communication skills with people outside of the family unit. Our findings suggest that family meals are not solely markers of home environment quality, but are also easy targets for parent education about improving children’s well-being.”

“From a population-health perspective, our findings suggest that family meals have long-term influences on children’s physical and mental well-being,” said Harbec.

At a time when fewer families in Western countries are having meals together, it would be especially opportune now for psycho-social workers to encourage the practice at home — indeed, even make it a priority, the researchers believe. And family meals could be touted as advantageous in public-information campaigns that aim to optimize child development.

Abstract of the study:

Objective:

Past research suggests a positive link between family meals and child and adolescent health. Although researchers have often relied on how often families eat together, this may not capture the complexity of the experience. Using a birth cohort, this study examines the prospective associations between the environmental quality of the family meal experience at age 6 years and child well-being at age 10.

Methods:

Participants are 1492 children of the Quebec Longitudinal Study of Child Development. When children were age 6, parents reported on their typical family meal environment quality. At age 10, parents, teachers, and children themselves provided information on lifestyle habits, academic achievement, and social adjustment, respectively. The relationship between early family meal environment quality and later child outcomes was analyzed using a series of multivariate linear regression.

Results:

Family meal environment quality at age 6 predicted higher levels of general fitness and lower levels of soft drink consumption, physical aggression, oppositional behavior, nonaggressive delinquency, and reactive aggression at age 10. These relationships were adjusted for child characteristics (sex, temperament problems and cognitive abilities, and baseline body mass index [BMI]) and family characteristics (family configuration and functioning, maternal education, depression, and BMI).

Conclusion:

From a population-health perspective, our findings suggest that family meals have long-term influences on children’s biopsychosocial well-being. At a time when family meal frequency is on a natural decline in the population, this environmental characteristic can become a target of home-based interventions and could be featured in information campaigns that aim to optimize child development.

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