Category Archives: Education

How can schools optimize support for children with ADHD, a new review

With all the craze about meta-analyses, sometimes people forget that a good systematic review can be even more helpful. This new systematic review even including some meta-analysis led by the University of Exeter seems no exception as it want to give guidance on how schools can best support children with ADHD to improve symptoms and maximise their academic outcomes.

From the press release:

The study, led by the University of Exeter and involving researchers at the EPPI-Centre (University College London), undertook a systematic review which analysed all available research into non-medication measures to support children with ADHD in schools. Published in Review of Education, the paper found that interventions which include one-to-one support and a focus on self-regulation improved academic outcomes.

Around five per cent of children have ADHD, meaning most classrooms will include at least one child with the condition. They struggle to sit still, focus their attention and to control impulses much more than ordinary children of the same age. Schools can be a particularly challenging setting for these children, and their difficulty in waiting their turn or staying in their seat impacts peers and teachers. Research shows that medication is effective, but does not work for all children, and is not acceptable to some families.

The research was funded by the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care (CLAHRC) South West Peninsula – or PenCLAHRC. The team found 28 randomised control trials on non-drug measures to support children with ADHD in schools. In a meta-analysis, they analysed the different components of the measures being carried out to assess the evidence for what was most effective.

The studies varied in quality, which limits the confidence the team can have in their results. They found that important aspects of successful interventions for improving the academic outcomes of children are when they focus on self-regulation and are delivered in one-to-one sessions.

Self- regulation is hard for children who are very impulsive and struggle to focus attention. Children need to learn to spot how they are feeling inside, to notice triggers and avoid them if possible, and to stop and think before responding. This is much harder for children with ADHD than most other children, but these are skills that can be taught and learned.

The team also found some promising evidence for daily report cards. Children are set daily targets which are reviewed via a card that the child carries between home and school and between lessons in school. Rewards are given for meeting targets. The number of studies looking at this was lower, and their findings did not always agree. But using a daily report card is relatively cheap and easy to implement. It can encourage home-school collaboration and offers the flexibility to respond to a child’s individual needs

Tamsin Ford, Professor of Child Psychiatry at the University of Exeter Medical School, said: “Children with ADHD are of course all unique. It’s a complex issue and there is no one-size-fits-all approach. However, our research gives the strongest evidence to date that non-drug interventions in schools can support children to meet their potential in terms of academic and other outcomes. More and better quality research is needed but in the mean-time, schools should try daily report cards and to increase children’s ability to regulate their emotions. These approaches may work best for children with ADHD by one-to-one delivery”

Abstract of the full paper:

Non-pharmacological interventions for attention-deficit/hyperactivity disorder are useful treatments, but it is unclear how effective school-based interventions are for a range of outcomes and which features of interventions are most effective. This paper systematically reviews randomized controlled trial evidence of the effectiveness of interventions for children with ADHD in school settings. Three methods of synthesis were used to explore the effectiveness of interventions, whether certain types of interventions are more effective than others and which components of interventions lead to effective academic outcomes. Twenty-eight studies (n=1,807) were included in the review. Eight types of interventions were evaluated and a range of different ADHD symptoms, difficulties and school outcomes were assessed across studies. Meta-analysis demonstrated beneficial effects for interventions that combine multiple features (median effect size g=0.37, interquartile range 0.32, range 0.09 to 1.13) and suggest some promise for daily report card interventions (median g=0.0.62, IQR=0.25, range 0.13 to 1.62). Meta-regression analyses did not give a consistent message regarding which types of interventions were more effective than others. Finally, qualitative comparative analysis demonstrated that self-regulation and one-to-one intervention delivery were important components of interventions that were effective for academic outcomes. These two components were not sufficient though; when they appeared with personalisation for individual recipients and delivery in the classroom, or when interventions did not aim to improve child relationships, interventions were effective. This review provides updated information about the effectiveness of non-pharmacological interventions specific to school settings and gives tentative messages about important features of these interventions for academic outcomes.

 

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Beste Evidence in Brief: Effective programs in elementary math

There is a new Best Evidence in Brief and this time I pick this study:

Marta Pellegrini from the University of Florence and Cynthia Lake, Amanda Inns, and Robert E. Slavin from our own Johns Hopkins Center for Research and Reform in Education have released  a new report on effective programs in elementary math. The report reviews research on the mathematics achievement outcomes of all programs with at least one study meeting the inclusion criteria of the review. A total of 78 studies were identified that evaluated 61 programs in grades K-5.
The studies were very high in quality, with 65 (83%) randomized and 13 (17%) quasi-experimental evaluations. Key findings were as follows:
  • Particularly positive outcomes were found for tutoring programs.
  • One-to-one and one-to-small group models had equal impacts, as did teachers and paraprofessionals as tutors.
  • Technology programs showed modest positive impacts.
  • Professional development approaches focused on helping teachers gain in understanding of math content and pedagogy had no impact on student achievement, but more promising outcomes were seen in studies focused on instructional processes, such as cooperative learning.
  • Whole-school reform, social-emotional approaches, math curricula, and benchmark assessment programs found few positive effects, although there were one or more effective individual approaches in most categories.
The findings suggest that programs emphasizing personalization, engagement, and motivation are most impactful in elementary mathematics instruction, while strategies focused on textbooks, professional development for math knowledge or pedagogy, and other strategies that do not substantially impact students’ daily experiences have little impact.

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Something I do recognize: women much less likely to ask questions in academic seminars than men

I have been teaching today and I have to admit that reading this study afterwards and I do recognize it: while the majority of my students are female, I get more questions on average from male students than from female students. And it seems this remains the case later on, even when it’s not about college freshman like I was teaching today.

From the press release:

A new study reveals a stark disparity between male and female participation in a key area of academic life and offers recommendations to ensure all voices are heard.

Women are two and a half times less likely to ask a question in departmental seminars than men, an observational study of 250 events at 35 academic institutions in 10 countries has found.

This disparity exists despite the gender ratio at these seminars being, on average, equal. It also reflects significant differences in self-reported feelings towards speaking up.

The research, led by a then Junior Research Fellow at Churchill College, University of Cambridge, adds to a growing body of evidence showing that women are less visible than men in various scientific domains and helps to explain the “leaky pipeline” of female representation in academic careers.

Women account for 59% of undergraduate degrees but only 47% of PhD graduates and just 21% of senior faculty positions in Europe.

The bias, identified in a paper published today in PLOS ONE, is thought to be particularly significant because departmental seminars are so frequent and because junior academics are more likely to experience them before other kinds of scholarly events. They also feature at an early stage in the career pipeline when people are making major decisions about their futures.

“Our finding that women ask disproportionately fewer questions than men means that junior scholars are encountering fewer visible female role models in their field,” warns lead author, Alecia Carter.

Survey data

In addition to observational data, Carter and her co-authors drew on survey responses from over 600 academics ranging from postgraduates to faculty members (303 female and 206 male) from 28 different fields of study in 20 countries.

These individuals reported their attendance and question-asking activity in seminars, their perceptions of others’ question-asking behaviour, and their beliefs about why they and others do and do not ask questions.

The survey revealed a general awareness, especially among women, that men ask more questions than women. A high proportion of both male and female respondents reported sometimes not asking a question when they had one. But men and women differed in their ratings of the importance of different reasons for this.

Crucially, women rated ‘internal’ factors such as ‘not feeling clever enough’, ‘couldn’t work up the nerve’, ‘worried that I had misunderstood the content’ and ‘the speaker was too eminent/intimidating’, as being more important than men did.

“But our seminar observation data show that women are not inherently less likely to ask questions when the conditions are favourable”, says Dieter Lukas, who was a postdoctoral researcher at Cambridge during the data collection.

Question-asking behaviour

The researchers found that women were more likely to speak up, for instance, when more questions were asked. When 15 questions were asked in total, as opposed to the median of 6, there was a 7.6% increase in the proportion of questions asked by women.

But when the first question in a seminar was asked by a man, the proportion of subsequent questions asked by women fell 6%, compared to when the first question was asked by a woman. The researchers suggest that this may be an example of ‘gender stereotype activation’, in which a male-first question sets the tone for the rest of the session, which then dissuades women from participating.

“While calling on people in the order that they raise their hands may seem fair, it may inadvertently result in fewer women asking questions because they might need more time to formulate questions and work up the nerve”, said co-author Alyssa Croft, a psychologist at the University of Arizona.

The researchers were initially surprised to discover that women ask proportionally more questions of male speakers and that men ask proportionally more of female speakers.

“This may be because men are less intimidated by female speakers than women are. It could also be the case that women avoid challenging a female speaker, but may be less concerned for a male speaker”, said co-author Gillian Sandstrom, a psychologist at the University of Essex.

Linked to this, the study’s survey data revealed that twice as many men (33%) as women (16%) reported being motivated to ask a question because they felt that they had spotted a mistake.

Women were also more likely to ask questions when the speaker was from their own department, suggesting that familiarity with the speaker may make asking a question less intimidating. The study interprets this as a demonstration of the lower confidence reported by female audience members.

Welcoming the research, Professor Dame Athene Donald, Professor of Experimental Physics at the University of Cambridge and Master of Churchill College, Cambridge, said:

“asking questions at the end of talks is one of the activities that (still) makes me most nervous … Whatever anyone may think when they meet me about how assertive my behaviour is, it would seem that I too have internalised this gender stereotype’.

The most interesting part are the following recommendations:

  • Where possible, seminar organisers should avoid placing limits on the time available for questions. Alternatively, moderators should endeavour to keep each question and answer short to allow more questions to be asked.
  • Moderators should prioritise a female-first question, be trained to ‘see the whole room’ and maintain as much balance as possible with respect to gender and seniority of question-askers.
  • Seminar organisers are encouraged not to neglect inviting internal speakers.
  • Organisers should consider providing a small break between the talk and the question period to give attendees more time to formulate a question and try it out on a colleague.

Abstract of the study:

The attrition of women in academic careers is a major concern, particularly in Science, Technology, Engineering, and Mathematics subjects. One factor that can contribute to the attrition is the lack of visible role models for women in academia. At early career stages, the behaviour of the local community may play a formative role in identifying ingroup role models, shaping women’s impressions of whether or not they can be successful in academia. One common and formative setting to observe role models is the local departmental academic seminar, talk, or presentation. We thus quantified women’s visibility through the question-asking behaviour of academics at seminars using observations and an online survey. From the survey responses of over 600 academics in 20 countries, we found that women reported asking fewer questions after seminars compared to men. This impression was supported by observational data from almost 250 seminars in 10 countries: women audience members asked absolutely and proportionally fewer questions than male audience members. When asked why they did not ask questions when they wanted to, women, more than men, endorsed internal factors (e.g., not working up the nerve). However, our observations suggest that structural factors might also play a role; when a man was the first to ask a question, or there were fewer questions, women asked proportionally fewer questions. Attempts to counteract the latter effect by manipulating the time for questions (in an effort to provoke more questions) in two departments were unsuccessful. We propose alternative recommendations for creating an environment that makes everyone feel more comfortable to ask questions, thus promoting equal visibility for women and members of other less visible groups.

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How much English do non-english children learn outside the classroom?

This morning my colleague Vanessa De Wilde shared her first soon to be published scientific paper with me and I like to share the insights here too as they can be relevant to other people too. The study is soon to be published in Bilingualism: Language and Cognition and was co-authored by Marc Brysbaert and June Eyckmans.

I first want to share with you the abstract as it already summarizes the study clearly:

In this study we examined the level of English proficiency children can obtain through out-of- school exposure in informal contexts prior to English classroom instruction. The second aim was to determine the input types that fuel children’s informal language acquisition. Language learning was investigated in 780 Dutch-speaking children (aged 10-12), who were tested on their English receptive vocabulary knowledge, listening, speaking, reading and writing skills. Information about learner characteristics and out-of-school English exposure was gathered using questionnaires. The results show large language gains for a substantial number of children but also considerable individual differences. The most beneficial types of input were gaming, use of social media and speaking. These input types are interactive and multimodal and they involve language production. We also found that the various language tests largely measure the same proficiency component.

But I want to share some of the findings more in depth:

“The mean score for the receptive vocabulary test was 65% (53% when cognates were left out of the test), attesting to the degree of vocabulary that can be acquired when children areexposed repeatedly to a language through activities that do not focus on language learning but on the negotiation of meaning (e.g. while playing a game).”

“English is seen as a high-status language by the participants in our study (733 participants answered they think English is a fun language, only 27 claimed not to like English), which probably means that they enjoy engaging in (digital) interactions in English.”

“…our findings show the high divergence in the scores obtained, a finding that was also present in Lefever (2010). About a quarter of the students did not pick up much English (yet). ”

A considerable part of the differences in test results could be explained by the amount of exposure the children had received (exposure to the language explained 22% of the variability in the children’s overall proficiency scores). Other variables likely to be involved are individual differences in intelligence and language aptitude (Paradis, 2011; Sun, Steinkrauss, Tendeiro & De Bot 2016; Unsworth, Persson, Prins & De Bot, 2014), which unfortunately could not be addressed in the present study.

“…the two most regularly investigated in studies on contextual learning in a formal context did not turn out to be the most important. These are reading L2 books and watching subtitled television programs. Although both variables are positively correlated with L2 knowledge, the correlations are much lower than those of three other variables.”

“The three most important types of input for children’s language proficiency were: use of social media in English, gaming in English, and speaking English. These three types of exposure are the types which offer ample opportunities for social interaction and authentic communication in contrast with watching television, listening to music, and reading, which are far less interactive. Apparently, passive perception of a language is less effective than active use of the language,…”

“…listening to English music seems to have a negative influence on children’s contextual language learning, when the effects of the other variables are partialled out. This is in line with the finding that productive and multimodal types of input are more effective. The fact that the negative effect is significant is probably due to the nature of the input. Listening or even singing along to a song does not necessarily lead to understanding and learning the language. Furthermore, it takes away time from other activities that are more effective. At the same time, even though the variable is significant, it only explains some 1% of the variation.”

 

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Can rationality be enhanced through education?

Short answer: yes! This is an interesting study but with also an element of frustration. The study ticks many boxes (Randomized controlled trial, big sample,…) and it has clear results. So what’s to complain? Well, the why. How education enhances rationality? But I can live with this frustration as the researchers have found something very relevant – and because there are some theories about that why-question. Oh, and how would this affect boys as the study is only conducted with girls?

From  the press release:

There has been interest across behavioral and social sciences – including psychology, economics and education – in whether people are born to be rational decision-makers or if rationality can be enhanced through education.

Published in Science, a new study led by Hyuncheol Bryant Kim, assistant professor of policy analysis and management at Cornell University, found that education can be leveraged to help enhance an individual’s economic decision-making quality or economic rationality.

“Using a randomized controlled trial of education support and laboratory experiments that mimic real-life examples, we established causal evidence that an education intervention increases not only educational outcomes but also economic rationality in terms of measuring how consistently people make decisions to seek their economic goals,” Kim said.

Kim and his colleagues examined this hypothesis through a controlled trial of education support in Malawi, arranged by a nongovernmental organization, which provided financial support for education in a sample of nearly 3,000 female (2812 to be precise) ninth and 10th graders.

“We found that those who took part in the education intervention had higher scores of economic rationality, suggesting that education is a tool for enhancing an individual’s economic decision-making quality,” Kim said. “While we know that schooling has been shown in previous work to have positive effects on a wide range of outcomes, such as income and health, our work provides evidence of potentially additional benefits coming from improvements in people’s decision-making abilities.”

Traditional economic analysis assumes that humans make rational choices. However, mounting evidence shows that people tend to make systematic errors in judgment and decision-making and that there is a high level of diversity in how rational individuals are.

Kim points out that most other research on improving the quality of decision-making targets the reduction of decision biases. For example, behavioral economists have urged policymakers to intervene in markets and restructure choice environments, the way that a decision is presented, without restraining people’s freedom of choice.

“We take a different stand: proper policy tools can enhance general capabilities of decision making,” Kim said. “Education can better equip people for high-quality decision-making for their lives.”

“Governments must never neglect investments in human capital of their citizens,” he said, noting that Malawi is ranked one of the lowest in the world in human capital – the economic value of citizens. “In addition, this evidence provides an additional rationale for investment in education in resource constrained settings such as Malawi and other developing nations.”

Abstract of the study:

Schooling rewards people with labor market returns and nonpecuniary benefits in other realms of life. However, there is no experimental evidence showing that education interventions improve individual economic rationality. We examine this hypothesis by studying a randomized 1-year financial support program for education in Malawi that reduced absence and dropout rates and increased scores on a qualification exam of female secondary school students. We measure economic rationality 4 years after the intervention by using lab-in-the-field experiments to create scores of consistency with utility maximization that are derived from revealed preference theory. We find that students assigned to the intervention had higher scores of rationality. The results remain robust after controlling for changes in cognitive and noncognitive skills. Our results suggest that education enhances the quality of economic decision-making.

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Happy World Teacher Day and remember: every lesson shapes a life!

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Yes, retrieval and testing does work, but what limits the effect? Insights from a new meta-analysis

A new meta-analysis does confirm memory retrieval can be beneficial for learning, but also shows there are limits:

  • the frequency and difficulty of questions.
  • Simply asking a question is not enough; students must respond to see a positive effect on learning

Probably you now want to know how much is too much? Well, you’re in for a bit of a disappointment I’m afraid, as you can learn from the press release which explains it better than the original paper imho:

“Frequency is a critical factor. There appears to be a trade-off in how often you test students,” Chan said. “If I lecture nonstop throughout class, this lessens their ability to learn the material. However, too many questions, too often, can have a detrimental effect, but we don’t yet know exactly why that happens or how many questions is too many.”

The answer to that question may depend on the length of the lecture and the type or difficulty of the material, Chan said. Given the different dynamics of a class lecture, it may not be possible to develop a universal lecture-to-question ratio. Regardless, Chan says testing students throughout the lecture is a simple step instructors at any level and in any environment can apply to help students learn.

“This is a cheap, effective method and anyone can implement it in their class,” he said. “You don’t need to give every student an iPad or buy some fancy software – you just need to ask questions and have students answer them in class.”

Chan, Christian Meissner, a professor of psychology at Iowa State; and Sara Davis, a postdoctoral fellow at Skidmore College and former ISU graduate student, examined journal articles from the 1970s to 2016 detailing more than 150 different experiments for their analysis. The researchers looked at what factors influenced the magnitude of this effect, when it happens and when the effect is reversed.

Why testing helps

There are several explanations as to why testing students is beneficial for new learning. The researchers evaluated four main theories for the meta-analysis to examine the strengths and weakness of these explanations from the existing research. The data strongly supported what researchers called the integration theory.

“This theory claims that testing enhances future learning by facilitating the association between information on the test and new, especially related, information that is subsequently studied, leading to spontaneous recall of the previously tested information when they learn related information,” Meissner said. “When this testing occurs, people can better tie new information with what they have learned previously, leading them to integrate the old and the new.”

Learning new information requires an encoding process, which is different from the process needed to retrieve that information, the researchers explained. Students are forced to switch between the two when responding to a question. Changing the modes of operation appears to refocus attention and free the brain to do something different.

A majority of the studies in the analysis focused on college students, but some also included older adults, children and people with traumatic brain injuries. The researchers were encouraged to find that testing could effectively enhance learning across all these groups.

“Memory retrieval can optimize learning in situations that require people to maintain attention for an extended period of time. It can be used in class lectures as well as employee training sessions or online webinars,” Davis said. “Future research could examine factors that can maximize this potential.”

Abstract of the meta-analysis:

A growing body of research has shown that retrieval can enhance future learning of new materials. In the present report, we provide a comprehensive review of the literature on this finding, which we term test-potentiated new learning. Our primary objectives were to (a) produce an integrative review of the existing theoretical explanations, (b) summarize the extant empirical data with a meta-analysis, (c) evaluate the existing accounts with the meta-analytic results, and (d) highlight areas that deserve further investigations. Here, we identified four nonexclusive classes of theoretical accounts, including resource accounts, metacognitive accounts, context accounts, and integration accounts. Our quantitative review of the literature showed that testing reliably potentiates the future learning of new materials by increasing correct recall or by reducing erroneous intrusions, and several factors have a powerful impact on whether testing potentiates or impairs new learning. Results of a metaregression analysis provide considerable support for the integration account. Lastly, we discuss areas of under-investigation and possible directions for future research.

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What Makes a Top Teacher?

3-Star learning experiences

Paul A. Kirschner & Mirjam Neelen

What Makes a Top Teacher? This is a question with both a simple and a complex answer (and probably a whole spectrum in between). First, the simple answer. A top teacher is someone whose efforts inside and outside the classroom have a positive effect on a student’s learning progress, meaning an increase of knowledge and skills. The more progress, the better the teacher.

We can already hear some people mocking or expressing their anger and disgust. “Oh, dear!”, they’ll say (if they try to be polite). They’ll go on to grumble that this is such an old-fashioned thing to say and that a school in the 21st century shouldn’t teach kids ‘things’ but should rather help them to become curious, adaptive and engaged individuals with strong problem-solving and critical thinking skills, give them grit, make them flexible team workers, and so forth. They might…

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by | October 2, 2018 · 7:24 pm

Using AI to discover learning disabilities in children

Whenever you hear or read artificial intelligence one starts to dream. Ok, I admit: I do. Every time I use Siri I’m reminded what a long way we still have to go, except for when my children ask silly questions. But this study uses AI in a whole different way and way more serious: to check if the humans made a mistake when labelling a child with learning disabilities.

From the press release:

Scientists using machine learning – a type of artificial intelligence – with data from hundreds of children who struggle at school, identified clusters of learning difficulties which did not match the previous diagnosis the children had been given.

The researchers from the Medical Research Council (MRC) Cognition and Brain Sciences Unit at the University of Cambridge say this reinforces the need for children to receive detailed assessments of their cognitive skills to identify the best type of support.

The study, published in Developmental Science, recruited 550 children who were referred to a clinic – the Centre for Attention Learning and Memory – because they were struggling at school.

The scientists say that much of the previous research into learning difficulties has focussed on children who had already been given a particular diagnosis, such as attention deficit hyperactivity disorder (ADHD), an autism spectrum disorder, or dyslexia. By including children with all difficulties regardless of diagnosis, this study better captured the range of difficulties within, and overlap between, the diagnostic categories.

Dr Duncan Astle from the MRC Cognition and Brain Sciences Unit at the University of Cambridge, who led the study said: “Receiving a diagnosis is an important landmark for parents and children with learning difficulties, which recognises the child’s difficulties and helps them to access support. But parents and professionals working with these children every day see that neat labels don’t capture their individual difficulties – for example one child’s ADHD is often not like another child’s ADHD.

“Our study is the first of its kind to apply machine learning to a broad spectrum of hundreds of struggling learners.”

The team did this by supplying the computer algorithm with lots of cognitive testing data from each child, including measures of listening skills, spatial reasoning, problem solving, vocabulary, and memory. Based on these data, the algorithm suggested that the children best fit into four clusters of difficulties.

These clusters aligned closely with other data on the children, such as the parents’ reports of their communication difficulties, and educational data on reading and maths. But there was no correspondence with their previous diagnoses. To check if these groupings corresponded to biological differences, the groups were checked against MRI brain scans from 184 of the children. The groupings mirrored patterns in connectivity within parts of the children’s brains, suggesting that that the machine learning was identifying differences that partly reflect underlying biology.

Two of the four groupings identified were: difficulties with working memory skills, and difficulties with processing sounds in words.

Difficulties with working memory – the short-term retention and manipulation of information – have been linked with struggling with maths and with tasks such as following lists. Difficulties in processing the sounds in words, called phonological skills, has been linked with struggling with reading.

Dr Astle said: “Past research that’s selected children with poor reading skills has shown a tight link between struggling with reading and problems with processing sounds in words. But by looking at children with a broad range of difficulties we found unexpectedly that many children with difficulties with processing sounds in words don’t just have problems with reading – they also have problems with maths.

“As researchers studying learning difficulties, we need to move beyond the diagnostic label and we hope this study will assist with developing better interventions that more specifically target children’s individual cognitive difficulties.”

Dr Joni Holmes, from the MRC Cognition and Brain Sciences Unit at the University of Cambridge, who was senior author on the study said: “Our work suggests that children who are finding the same subjects difficult could be struggling for very different reasons, which has important implications for selecting appropriate interventions.”

The other two clusters identified were: children with broad cognitive difficulties in many areas, and children with typical cognitive test results for their age. The researchers noted that the children in the grouping that had cognitive test results that were typical for their age may still have had other difficulties that were affecting their schooling, such as behavioural difficulties, which had not been included in the machine learning.

Dr Joanna Latimer, Head of Neurosciences and Mental Health at the MRC, said: “These are interesting, early-stage findings which begin to investigate how we can apply new technologies, such as machine learning, to better understand brain function. The MRC funds research into the role of complex networks in the brain to help develop better ways to support children with learning difficulties.”

Abstract of the paper:

Our understanding of learning difficulties largely comes from children with specific diagnoses or individuals selected from community/clinical samples according to strict inclusion criteria. Applying strict exclusionary criteria overemphasizes within group homogeneity and between group differences, and fails to capture comorbidity. Here, we identify cognitive profiles in a large heterogeneous sample of struggling learners, using unsupervised machine learning in the form of an artificial neural network. Children were referred to the Centre for Attention Learning and Memory (CALM) by health and education professionals, irrespective of diagnosis or comorbidity, for problems in attention, memory, language, or poor school progress (n = 530). Children completed a battery of cognitive and learning assessments, underwent a structural MRI scan, and their parents completed behavior questionnaires. Within the network we could identify four groups of children: (a) children with broad cognitive difficulties, and severe reading, spelling and maths problems; (b) children with age‐typical cognitive abilities and learning profiles; (c) children with working memory problems; and (d) children with phonological difficulties. Despite their contrasting cognitive profiles, the learning profiles for the latter two groups did not differ: both were around 1 SD below age‐expected levels on all learning measures. Importantly a child’s cognitive profile was not predicted by diagnosis or referral reason. We also constructed whole‐brain structural connectomes for children from these four groupings (n = 184), alongside an additional group of typically developing children (n = 36), and identified distinct patterns of brain organization for each group. This study represents a novel move toward identifying data‐driven neurocognitive dimensions underlying learning‐related difficulties in a representative sample of poor learners.

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Nothing new: personalized education (but does the article add something new?)

Nihil sub sole novum, we may think the idea of personalized education is new, although defenders of the idea such as Zuckerberg and Gates often refer to a study by Benjamin Bloom from decades ago. But in a new paper published in Nature David Dockterman argues that the idea is even much older than that. But if that’s the case, why didn’t it catch on and even more important: why would it now?

The article pleas for a new kind of pedagogy – and of course that got me triggered – but than seems to fall in many mistakes other people thinking about reform in education have done before by not being critical enough towards both the need for personalization and possible consequences. Biesta describes three tasks of education: the personal development, qualification and socialization. The author does mention something similar by stating

It isn’t enough to scale an instructional system around a single aspect of learner need, like content competence or social acceptance. A robust personalized learning model must respond to whatever needs matter for each individual learner.

But the starting point is the individual. This hides a world view. Nothing wrong with that, but when discussing this one needs to know and acknowledge this. It might also explain in part why some reforms have been failing over and over again…

Abstract of the paper:

Current initiatives to personalize learning in schools, while seen as a contemporary reform, actually continue a 200+ year struggle to provide scalable, mass, public education that also addresses the variable needs of individual learners. Indeed, some of the rhetoric and approaches reformers are touting today sound very familiar in this historical context. What, if anything, is different this time? In this paper I provide a brief overview of historical efforts to create a scaled system of education for all children that also acknowledged individual learner variability. Through this overview I seek patterns and insights to inform and guide contemporary efforts in personalized learning.

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