Nice video by PEW explaining random sampling

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Funny on Sunday: one of the best signs of this week

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A Three Stage Plan to Prepare Our Youth for Jobs That Don’t Exist (Yet)

3-Star learning experiences

Mirjam Neelen & Paul A. Kirschner

robot w trumpet

Paul had the opportunity, through a fellowship of the Netherlands Institute for Advanced Study in the Humanities and Social Sciences (NIAS), to take a first, research-informed, step to solve the great societal and economic dilemma on how to educate and train the youth of today for a (employment) future where professions that they’re being trained/educated for a) probably won’t exist much longer and b) don’t even exist yet and we have no idea what they’ll look like. Let’s see what the exact problem is first.

The problem

The opening paragraph of The Future of Jobs: Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution (2016, p. 3) states:

Disruptive changes to business models will have a profound impact on the employment landscape over the coming years. Many of the major drivers of transformation currently affecting global industries are expected to have a significant impact…

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Interesting but seemingly rather depressing study on differentiated instruction based on data

I found this study by Faber et al. via this tweet by Paul Bruno who highlighted the one-phrase summary:

The study in itself is very interesting and relevant and more complicated than this one sentence. Although a personal frustration: every single time they used the abbreviation DI I had to correct myself that it’s not about Direct Instruction, but in this case stands for differentiated instruction.

What is this study about:

In this study, the relationship between differentiated instruction, as an element of data-based decision making, and student achievement was examined. Classroom observations (n = 144) were used to measure teachers’ differentiated instruction practices and to predict the mathematical achievement of 2nd- and 5th-grade students (n = 953). The analysis of classroom observation data was based on a combination of generalizability theory and item response theory, and student achievement effects were determined by means of multilevel analysis. No significant positive effects were found for differentiated instruction practices. Furthermore, findings showed that students in low-ability groups profited less from differentiated instruction than students in average or high-ability groups. Nevertheless, the findings, data collection, and data-analysis procedures of this study contribute to the study of classroom observation and the measurement of differentiated instruction.

This insight makes things even worse: low-ability groups profited less from differentiated instruction than students in average or high-ability groups.

Let’s dig a bit deeper. Some important definitions used in the study:

  • data-based decision making (DBDM): “Teachers, principals, and administrators systematically collecting and analyzing data to guide a range of decisions to help improve the success of students and schools” (Ikemoto and Marsh, 2007)
  • On differentiated instructions:

First, DI is planned, and instructional decisions should be based on the analysis of student data. Second, what makes DI observable in the classroom is the variation in learning goals, instruction content, instruction time, assignments, and learning materials aimed at addressing varying learning needs. In the present study, we tested whether these DI characteristics explain student achievement.

And now for the results:

…the findings of the generalizability study showed that, even though most variance was explained by differences between teachers, there was much variability between the lessons of the same teacher. These observation time effects were also found in a study by Praetorius, Pauli, Reusser, Rakoczy, and Klieme (2014), and such findings indicate that more research is needed on how valid and representative teacher observation scores can be obtained. Furthermore, our findings indicated that students from different ability groups do not profit from DI to the same extent. This finding is in line with previous research: Ability grouping can have a negative impact on the achievement of students in low-ability groups, ability grouping is effective for students in average-ability groups, and ability grouping has no impact on the achievement of students in high-ability groups (Lou et al., 1996; Saleh et al., 2005). In future research, it would be worth investigating whether lower teacher expectations, less stimulating learning materials, and a lack of self-regulation skills among low-performing students (Campbell, 2014; Hong et al., 2012; Nomi, 2009; Wiliam & Bartholomew, 2004) could explain the negative impact of DI on the achievement of students in low-ability groups. Furthermore, we expected that students taught by teachers who differentiate their instruction more, or by teachers who plan DI more, have higher student achievement levels. No such positive effects were found. A reverse causality between DI and student achievement (i.e., DI practices are executed more in classrooms with many low-performing students and a very diverse student population) might be an explanation for this finding (De Neve & Devos, 2016; Nomi, 2009). Another explanation might be the impact of DI on noncognitive outcomes such as students’ feelings of competence (Carver & Scheier, 1990). Especially for students in low-ability groups, there might have been an impact on noncognitive outcomes, and consequently on student achievement. Also, these findings may suggest that planning differentiation strategies in advance should always be combined with responsive ad hoc classroom differentiation practices. It may be that a balance between preplanned instruction and responsive teaching is most effective (Sawyer, 2004). In future studies, such effects should be studied to explain better how DBDM affects student achievement.

This makes it a bit less surprising. As noted in this paragraph, we know there are possible issues with ability grouping, at the same time this study does give a lot of food for thought when looking at differentiation.

Little note: as all studies, every study has it limitations. In this case two of those limitations were – for me – at first a bit surprising:

  1. the relationship between DBDM and DI was notexamined. Based on the DBDM literature, it was assumed that DBDM could result in more data-based DI practices in classrooms and that, if this is the case, student achieve- ment would consequently improve. If DBDM does not result in more data-based DI practices, then DI does not explain (potential) student achievement growth. So, our findings would have contributed more to our understanding of how DBDM influences achievement, if the relationship between DBDM and DI could also have been examined.
  2. In addition to this, the Focus intervention was based on the DBDM literature and not on the DI literature. As a result, some effective DI practices unfortunately were not included in the intervention.

So does this all mean that we shouldn’t differentiate in education. No, but it does suggest to be careful to get your hopes up too high when talking about data as basis for differentiation. It still depends on what you do with that data. Or: more data doesn’t necessarily make something work that didn’t work that well before (cfr ability grouping).


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Not really surprising but very interesting study: child’s home learning environment predicts 5th grade academic skills

This study isn’t surprising, but still pretty important: children whose parents provide them with learning materials like books and toys and engage them in learning activities and meaningful conversations in infancy and toddlerhood are likely to develop early cognitive skills that can cascade into later academic success (I also found an interesting presentation by one of the authors of the study) Oh, because some of you would ask: yes, they did check for IQ which makes this a very interesting study.

From the press release:

Children whose parents provide them with learning materials like books and toys and engage them in learning activities and meaningful conversations in infancy and toddlerhood are likely to develop early cognitive skills that can cascade into later academic success, finds a new study by NYU’s Steinhardt School of Culture, Education, and Human Development.

The study, published online in the journal Applied Developmental Science, followed a group of children from birth through 5th grade to track the influence of early home learning environments on later cognitive skills and understand the factors that might explain long-term influences.

“There is growing evidence for the power of early learning environments on later academic success,” said Catherine Tamis-LeMonda, the study’s lead author and a professor of applied psychology at NYU Steinhardt. “Our study confirms that strong home learning environments arm children with foundational skills that are springboards to long-term academic achievement.”

Research shows that the home learning environment powerfully shapes children’s language and cognitive development. Children’s participation in learning activities, the quality of parent-child interactions, and the availability of learning materials like books and toys are three key features of the home learning environment that support language and pre-academic skills in early childhood.

In this study, Tamis-LeMonda and her colleagues examined early home learning environments and whether they predict 5th grade academic skills for children of families from ethnically diverse, low-income backgrounds. The researchers studied 2,204 families enrolled in the Early Head Start Research Evaluation Project.

Children’s learning environments were measured through a series of home visits at 14 months, at 2 and 3 years, and at pre-kindergarten. The researchers looked at literacy activities (including book reading, storytelling, and teaching letters and numbers), learning materials in the home (including books, toys, or games that facilitate expression and learning), and the quality of mothers’ interactions with their children. Examples of high quality interactions included labeling objects in the environment and responding to children’s cues; these sensitive interactions are attentive to children’s needs and cognitively stimulating.

Learning environments were again assessed in 5th grade based on the number of books in the home and the quality of mothers’ engagement with children, both spontaneous interactions and during a discussion-based task.

At the pre-kindergarten and 5th grade visits, children were assessed on age-appropriate academic skills. The pre-K visit included measures of vocabulary, letter and word identification, and math problem-solving; the 5th grade visit measured vocabulary, reading, math, and general cognitive abilities.

The researchers found that early learning environments supported the emergence of pre-academic skills that persisted into early adolescence to predict children’s 5th grade academic skills. Pathways from early learning environments to later academic skill were similar for children from White, Black, Hispanic, English-speaking, and Hispanic Spanish-speaking backgrounds.

Notably, learning environments were highly stable over the 10-year study, suggesting that the experiences parents provide their infants as early as the first year of life may solidify into patterns of engagement that either continue to support or impede children’s emerging skills.

The study highlights the importance of early childhood experiences for children’s skill development and long-term academic success, and reinforces the notion that families have a major influence on children’s academic outcomes.

The researchers note that the findings have implications for policy and practice, including the design of interventions for young children and parents from disadvantaged backgrounds.

“Improvements to early learning environments, whether it be in the home or through early childhood programs like Early Head Start, can effectively support the development of children exposed to socioeconomic disadvantage,” said Tamis-LeMonda, who also co-directs the Center for Research on Culture, Development and Education at NYU Steinhardt.

Abstract of the study:

We examined whether the early learning environment predicts children’s 5th grade skills in 2,204 families from ethnically diverse, low-income backgrounds; tested the mediating roles of children’s pre-kindergarten school-related skills and later learning environment; and asked whether lagged associations generalize across White, Black, Hispanic English-speaking, and Hispanic Spanish-speaking samples. Children’s early learning environment comprised measures of literacy activities, the quality of mothers’ engagements with children, and learning materials assessed at 14 months, 2 and 3 years, and at pre-kindergarten; learning environments were again assessed in 5th grade. At pre-kindergarten and in 5th grade, children were assessed on pre-academic and academic skills respectively. Early learning environments predicted children’s 5th grade academic skills, and children’s pre-kindergarten skills and 5th grade learning environment mediated longitudinal associations. The early learning environment supports the emergence of pre-academic skills that are stable into early adolescence, and pathways generalize across ethnic/racial groups.

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Zombie Reforms and Personalized Learning (Part 2)

Larry Cuban on School Reform and Classroom Practice

In Part 1, I described an instructional innovation Professor Fred Keller designed in the mid-1960s aimed at transforming the traditional college undergraduate lecture course in psychology. Called Personalized System of Instruction, PSI was a course using behaviorist techniques that permitted students to move at their own pace in finishing assignments, taking tests, and completing the course. Similar courses in the social and natural sciences spread rapidly across university campuses throughout the 1970s and early 1980s.

Initially popular as they were in converting traditional courses into individually guided lessons, these university courses faded.  By the mid-1990s, few faculty used PSI for introductory courses.

Evidence of higher student scores for those completing the PSI course as compared to traditional lecture course, however, clearly supported the innovation. Dropping PSI, then, had little to do with its demonstrated success with students. Other factors played a part in the disappearance of PSI on college campuses…

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How we balance work and family life may be learned from our parents

There is a new qualitative study co-authored by Dr Ioana Lupu from Queen Mary University of London (QMUL) that looks at how parents influence the work-family life balance from their children later on in life. One insight? Women who had stay-at-home mothers ‘work like their fathers but want to parent like their mothers’.

From the press release:

Previous work-life balance research has focused more on the organisational context or on individual psychological traits to explain work and career decisions. However, this new study, published in Human Relations, highlights the important role of our personal history and what we subconsciously learn from our parents.

“We are not blank slates when we join the workforce — many of our attitudes are already deeply engrained from childhood,” according to co-author by Dr Ioana Lupu.

The study argues that our beliefs and expectations about the right balance between work and family are often formed and shaped in the earliest part of our lives. One of the most powerful and enduring influences on our thinking may come from watching our parents.

The research is based on 148 interviews with 78 male and female employees from legal and accounting firms. Interviewees were sorted into four categories by the researchers: (1) willingly reproducing parental model; (2) reproducing the parental model against one’s will; (3) willingly distancing from the parental model; (4) and distancing from the parental model against one’s will.

The study shows a number of differences between women and men who grew up in ‘traditional’ households where the father had the role of breadwinner while the mother managed the household. Male participants who grew up in this kind of household tended to be unaffected by the guilt often associated with balancing work and family.

Male participant in the study: “I’ve always had a very strong work ethic drilled into me anyway, again by my parents, my family. So, I never needed anyone looking over my shoulder or giving me a kick up the backside and telling me I needed to do something — I’d get on and I’d do it. So, I found the environment [of the accountancy firm] in general one that suited me quite well.” (David, Partner, accountancy firm, two children).

Women on the other hand were much more conflicted — they reported feeling torn in two different directions. Women who had stay-at-home mothers “work like their fathers but want to parent like their mothers,” says Dr Lupu.

Female participant in the study: “My mum raised us…she was always at home and to some extent I feel guilty for not giving my children the same because I feel she raised me well and she had control over the situation. I’m not there every day … and I feel like I’ve failed them in a way because I leave them with somebody else. I sometimes think maybe I should be at home with them until they are a bit older.” (Eva, director, accountancy firm, two children).

Women who had working mothers are not necessarily always in a better position because they were marked by the absence of their mothers. A female participant in the study remembers vividly, many years later how her mother was absent whereas other children’s mothers were waiting at the school gates.

Female participant in the study: “I remember being picked up by a child-minder, and if I was ill, I’d be outsourced to whoever happened to be available at the time . . . I hated it, I hated it, because I felt like I just wanted to be with my mum and dad. My mum never picked me up from school when I was at primary school, and then everybody else’s mums would be stood there at the gate . . . And it’s only now that I’ve started re-thinking about that and thinking, well isn’t that going to be the same for [my son] if I’m working the way I am, he’s going to have somebody picking him up from school and maybe he won’t like that and is that what I want for my child?” (Jane, Partner, law firm, one child and expecting another).

An exception was found in female participants whose stay-at-home mothers had instilled strong career aspirations into them from an early stage. In these cases, the participants’ mothers sometimes set themselves up consciously as ‘negative role models’, encouraging their daughters not to repeat their own mistake.

Female participant in the study: “I do remember my mother always regretting she didn’t have a job outside the home and that was something that influenced me and all my sisters. […] She’d encourage us to find a career where we could work. She was quite academic herself, more educated than my father, but because of the nature of families and young children, she’d had to become this stay-at-home parent.” (Monica, director, AUDIT, one child)

“We have found that the enduring influence of upbringing goes some way towards explaining why the careers of individuals, both male and female, are differentially affected following parenthood, even when those individuals possess broadly equivalent levels of cultural capital, such as levels of education, and have hitherto pursued very similar career paths,” says Dr Lupu.

She says the research raises awareness of the gap that often exists between unconscious expectations and conscious ambitions related to career and parenting.

“If individuals are to reach their full potential, they have to be aware of how the person that they are has been shaped through previous socialisation and how their own work?family decisions further reproduce the structures constraining these decisions,” says Dr Lupu.

Abstract of the study:

Prior research generally presents work–family decisions as an individual’s rational choice between alternatives, downplaying the crucial role that upbringing plays in shaping work and parenting decisions. This article emphasizes how habitus – historically constituted and embodied dispositions – structures perceptions about what is ‘right’ and ‘normal’ for working mothers and fathers. This relational approach explores how the entrenched dispositions of individuals interact dynamically with contextual imperatives to influence professionals’ work–family decisions. Drawing on 148 interviews with 78 male and female professionals, our study looks at much deeper rooted causes of work–family conflict in professional service firms than have hitherto been considered. We show how dispositions embodied during one’s upbringing can largely transcend time and space. These dispositions hold a powerful sway over individuals and may continue to structure action even when professionals exhibit a desire to act differently. In turn, this implies that the impediments to greater equality lie not only in organizational and societal structures, but within individuals themselves in the form of dispositions and categories of perception that contribute towards the maintenance and reproduction of those structures. Additionally, in a more limited number of cases, we show how dispositions adapt as a result of either reflexive distancing or an encounter with objective circumstances, leading to discontinuity in the habitus.

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Almost all cognitive abilities are positively related, also in adolescence

If you believe in talents, than you might think that missing one talent can be compensated by being better in another field. Sadly, often some have more than others as  almost all cognitive abilities are positively related. A new study confirms this as it shows that cognitive abilities – in this case vocabulary and matrix reasoning – seem to reinforce each other in adolescence and for young adults.

From the press release:

One of the most striking findings in psychology is that almost all cognitive abilities are positively related – on average, people who are better at a skill like reasoning are generally also better at a skill like vocabulary. This fact allows scientists and educational practitioners to summarize people’s skills on a wide range of domains as one factor – often called ‘g’, for ‘general intelligence’. Despite this, the mechanisms underlying ‘g’ and its development remain somewhat mysterious.

“What this so-called ‘g-factor’ means is still very much up for debate,” explains researcher Rogier Kievit of the Cognition and Brain Science Unit at the University of Cambridge. “Is it a causal factor, an artefact of the way we create cognitive tests, the result of our educational environment, a consequence of genetics, an emergent phenomenon of a dynamic system or perhaps all of these things to varying degrees?”

In a new study, scientists from Cambridge, London, and Berlin led by Kievit directly compared different proposed explanations for the phenomenon of ‘g’ and how it develops over time.Data was used from a Wellcome-funded longitudinal cohort (NSPN), where 785 late adolescents, ages 14 to 24, were tested on two occasions approximately 1.5 years apart. They focused two subtests reflecting key domains of ‘g’, namely fluid reasoning (solving abstract puzzles) and vocabulary (knowing the definitions of words). Their findings are published in Psychological Science, a journal of the Association for Psychological Science.

The team observed that the best explanation for the improvement in skills over time was the so-called ‘mutualism’ model. This model proposes that cognitive abilities help each other during development: In other words, better reasoning skills allow individuals to improve their vocabulary more quickly, and better vocabularies are associated with faster improvement in reasoning ability.

These findings are crucial to our understanding of cognitive abilities, as they suggest that small differences early on in childhood may lead to larger differences later on, and help partially explain how ‘g’ arises.

The work has implications for important outcomes in adolescence.

“Our findings may be relevant for early detection of developmental challenges,” says Kievit. “Often screening tests for difficulties focus only on individual outcomes (i.e., ‘Is a child achieving the desired level on some test?’), but studying the dynamics between cognitive domains is likely to paint a richer, more accurate picture of the expected trajectory of development.”

And the findings may also shed light on more long-term life outcomes.

“General cognitive ability is strikingly predictive of various important life outcomes ranging from academic and professional success, to mental and physical health and even longevity – to understand why this is so, we must better understand what this g-factor really is,” Kievit explains.

The researchers note that their observations regarding links between cognitive abilities are exciting, but they do not address whether the relationships are directly causal in nature.

“We hope to further tease apart the underlying mechanisms in future work,” Kievit concludes.

Abstract of the study:

One of the most replicable findings in psychology is the positive manifold: the observation that individual differences in cognitive abilities are universally positively correlated. Investigating the developmental origin of the positive manifold is crucial to understanding it. In a large longitudinal cohort of adolescents and young adults (N = 785; n = 566 across two waves, mean interval between waves = 1.48 years; age range = 14–25 years), we examined developmental changes in two core cognitive domains, fluid reasoning and vocabulary. We used bivariate latent change score models to compare three leading accounts of cognitive development: g-factor theory, investment theory, and mutualism. We showed that a mutualism model, which proposes that basic cognitive abilities directly and positively interact during development, provides the best account of developmental changes. We found that individuals with higher scores in vocabulary showed greater gains in matrix reasoning and vice versa. These dynamic coupling pathways are not predicted by other accounts and provide a novel mechanistic window into cognitive development.

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Good video: why ‘It’s hard’ is good for learning

Richard Wiseman one wrote on Twitter that there are only 3 important words when talking about learning:

This video is a more elaborated version of the importance of those 3 words:

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A personal note on Sunday: a diner in Memphis

No funny on Sunday today, just a personal note. 12 years ago, almost to date, my wife and I were traveling through the US for our honeymoon. The biggest part of our trip was route 66, but as we are both music lovers, we did a detour to Nashville and Memphis. It was in Memphis, just after visiting the Stax-studios and the National Civil Rights museum that it hit me, well it hit the both of us.

We were having a hamburger and a drink in a diner close to the hotel where Martin Luther King was shot. Nearby where we sat, there were some older black people also having lunch. They were in their late sixties, early seventies I guess. We realized that they had experienced the segregation. While for us it seemed like a strange and ancient history, it wasn’t.

Since that moment I’ve seen how this all is still a work in process. A process with sometimes big wins, such as an African-American in Office, sometimes with big setbacks. But a process that can’t be stopped.

As an European it’s flabbergasting to see symbols of our own awful past marching through the streets of Charlottesville, by doing so bringing together the worst of our combined histories. Symbols for ideas that are sadly enough also still popular with small groups in Europe, even in Germany.

But this old song adopted by the Civil rights movement says it all:


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