2015: Urban Myths about Learning and Education, 1st Edition

Together with Casper Hulshof I wrote a popular book in Dutch on educational myths in 2013.

In 2015 a whole new, updated and upgraded version will be published internationally by Elsevier/Academic Press, written by myself, prof. Paul A. Kirschner and Casper Hulshof.

The manuscript has been sent in and… check here.

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Smart teens rub off on teammates, Odds of going to college double with the right club or team

Choose wise the kids you play with, that’s the advice kids in school can learn from this new study. Joining an extra-curricular team or club with members that get good grades can double a high school student’s odds of going to college. Still, I do think it’s worth nothing what the authors write in their conclusion:

“Although the precise causal mechanisms operating within extracurricular associations are still uncertain, finding that “extracurricular associations” are strongly correlated with college enrollment suggests that EAs may be meaningful “local positions” that increase the risk of college enrollment for students from all GPA levels.”

Yep, correlation. Still, the researchers checked for many different elements (including education level of the parents).

Highlights of the study:

 

  • Study examines extracurricular participation with high achieving peers.
  • Study finds a link between the GPA of peers in an EA and college enrollment.
  • Study finds that only participation in academic activities has added impact on enrollment.
  • Study finds no association between the number of extracurricular activities and enrollment.

 

From the press release:

Brigham Young University sociologist and study co-author Lance Erickson knows how to sell the study to teens.

“Tell your parents, whatever they ground you from, it shouldn’t be from practice or a club activity,” said Erickson. “If they ground you from a school club, you are more likely to end up living at their house because you won’t be going to college.”

Erickson spent four years constructing a dataset and statistical model that could answer critics’ arguments. The sample includes 90,000 high school students and up to 10 of their friends. Since friends often join a team or club together, the model subtracts out the positive influence of friends who are also teammates. That isolates the impact of teammates who aren’t otherwise in a student’s social circle.

To the surprise of the researchers, the type of team or club didn’t really matter. It simply came down to being around high-achieving peers (as measured by GPA). So in one school that might be the swim team or the orchestra, while at another school it’s the computer science club or cross country.

“Typically you think the benefits of participating come from the type of club or the intensity of the skills you learned there,” said Ben Gibbs, the lead study author. “I think we’re the first to show that who you are hanging out with in those activities really matters.”

The study is forthcoming in Social Science Research, which posted an accepted version of the report. As noted in the study, simply participating in any extracurricular activity increased a student’s chances of college enrollment regardless of that team’s average GPA. In addition, the odds of college enrollment double for a student if they join a group with an average GPA that is 1 point higher – i.e. one with a 3.6 GPA rather than a team with a 2.6 GPA.

The role of teammates is an added piece of a puzzle that co-author Mikaela Dufur started in 2007. That’s when she published research showing that playing high school sports increased women’s chances of getting a college degree.

She notes that providing extra-curricular activities can be especially critical in schools that serve low-income students. And the earlier the start, the better.

“I would encourage middle schools and junior high schools to devote resources to those kinds of things so that as they transition to high school, they are prepared to join a team,” Dufur said.

Abstract of the study:

There is consistent evidence that student involvement in extracurricular activities (EAs) is associated with numerous academic benefits, yet understanding how peer associations within EAs might influence this link is not well understood. Using Add Health’s comprehensive data on EA participation across 80 schools in the United States, we develop a novel measure of peer associations within EA activities. We find that EA participation with high achieving peers has a nontrivial link to college enrollment, even after considering individual, peer, and school-level factors. This suggests that school policies aimed at encouraging student exposure to high achieving peers in EAs could have an important impact on a student’s later educational outcomes.

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Interesting read: Solutions for the Finnish school

Wait a minute? Are Finnish schools in trouble? Well, the latest PISA-results were less good than before (I won’t mention that Flanders tops Finland, in Europe only Liechtenstein beats us), but it’s not really about the rankings. Tommi Laitio is Director of Youth Affairs for the city of Helsinki, Finland and former president of the Finnish national student union.

And he sees the following problems based on discussions with experts.

Problem 1. Transition from class 6 to class 7.

Problem 2. Young people do not like school/learning.

Problem 3. Last 10 percent is falling behind in learning results.

Problem 4. Young people do not have confidence in their skills.

Problem 5. Young people consider school to be an unpleasant place.

Problem 6. Digital environment is lacking behind.

Problem 7. The school is not developed as a community.

Problem 8. Rather fixed gender roles.

Problem 9. Most of teachers´ further education is pedagogically outdated.

Problem 10. Teachers work alone.

Read the solutions here!

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The future of educational research? Computers ‘learn’ student behavior and ‘think’ as students would

Computer models are being used in many disciplines of science, and maybe educational research is next. Richard Lamb, a Washington State University professor who teaches science education at WSU’s College of Education, argues the process could revolutionize the way educational research is done. But I have to admit, I do have my doubts if the complexity can be taken into account. Still one could argue, present research has also it’s limitations.

From the press release:

Lamb’s research has just been published in Computers & Education journal. The article describes how computers examine student responses to science tasks – such as comparing liquid volumes – and thereafter mimic the way students think.

“Traditionally, we’d be confined to a classroom to study student learning for virtually every potential theory we have about science education and curriculum implementation,” Lamb said. “But now, instead of taking a shotgun approach, we can test the initial interventions on a computer and see which ones make the most sense to then study in the classroom.”

So in-person research becomes more finely targeted and requires fewer student subjects. It requires less time from researchers and costs less money.

“In the current model of research, we go into a classroom and spend months observing, giving tests and trying to see if changes to a specific model work and how to best implement them,” Lamb said. “It will still be necessary for researchers to go into the classroom; hopefully that never goes away. This just gives us more flexibility.”

Video games method

An artificial neural network is basically artificial intelligence that simulates the human brain. Lamb and his fellow researchers, including college colleagues Tariq Akmal and Kathy Baldwin, use an artificial neural network they named the Student Task and Cognition Model.

Students were given tasks to complete in an electronic game. The tasks were scientific in nature and required students to make a choice. The researchers used statistical techniques to track everything and assign each task as a success or failure.

“The computer is able to see what constitutes success, but it’s also able to see how students approach science,” Lamb said.

Because the computer is learning an approach to science, rather than just how to do a specific task, it will later try to solve a different problem the same way a student might.

“I’ve enjoyed this research in particular because it’s opening new understandings of learning and new avenues of teaching and assessment as a result,” said David Vallett, one of Lamb’s co-researchers from the University of Nevada Las Vegas. “It’s a novel yet practical blend of cognitive science and education.”

Testing multiple models at once

Lamb said most entertainment video games have the same characteristics as educational videos games. So long as it asks a singular task of the students, any game would suffice – Halo, Call of Duty, Mario Kart and more.

“The computer is learning to solve novel or new problems, which means we can test different educational interventions before ever getting to a classroom,” he said.

He said those initial tests will not only tell researchers if a specific educational model will work, but will give a specific percentage of success.

“Even with a large research team, it’s usually too difficult to test more than one intervention at a time,” he said. “Now we can run multiple interventions, choose the one that looks like it will work the best and then just test that one.”

Significant cost savings

And that will help the bottom line.

“For me to get 100,000 students, teachers to administer tests, professors doing research and all the rest, we could easily look at about $3.5 million,” Lamb said. “We can now get those 100,000 students for the cost of running software off a computer.”

It’s definitely a novel approach. And it is sure to get a few raised eyebrows. But Vallett said he wouldn’t expect any less from Lamb.

“Rich is an enthusiastically creative researcher and statistician,” Vallett said. “That creative spark is what sets him apart from most of the field; he’s not satisfied with merely adding a sliver of understanding to our existing knowledge of a topic.”

Abstract of the research:

There has been an increase in student achievement testing focusing on content and not underlying student cognition. This is of concern as student cognition provided for a more generalizable analysis of learning. Through a cognitive diagnostic approach, the authors model the propagation of cognitive attributes related to science learning using Serious Educational Games. One-way to increase the focus on the cognitive aspects of learning that are additional to content learning is through the use cognitive attribute task-based assessments (Cognitive Diagnostics) using an Artificial Neural Network. Results of this study provide a means to examine underlying cognition which, influences successful task completion within science themed SEGs. Results of this study also suggest it is possible to define, measure, and produce a hierarchical model of latent cognitive attributes using a Q-matrix relating virtual SEGs tasks, which are similar to real-life tasks aiding in the modeling of transference. © 2013 Elsevier Ltd. All rights reserved.

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Interesting read: ‘Close Reading': Effectiveness Questionable without Outside Knowledge

Daniel Willingham wrote a new piece and again it’s worth reading, a very close reading. I hope you have prior knowledge…

“As I’ve seen it described, close reading has three critical features. First, we assume we will spend a good deal of time with a text. We will not simply read, but reread, and likely reread again. The first reading may be devoted to straightforward comprehension, but further readings will uncover other layers of meaning, allusions, techniques of authorship, and so on.

Second, the extended time spent on a text will be devoted mostly to the author’s words. We will pay close attention to the particular words used, to the structure of the argument, and so on.

Third, we will view a text as being self-contained. We will only draw conclusions that are defensible via the author’s words. In fact, we will read the text as though we know nothing about the subject at hand; the author’s words will be not only necessary for our interpretation, we’ll consider them sufficient.

It’s that last bit that seems crazy to me.

Rereading? Sure. Paying close attention to the author’s words? Great idea. But pretending that one’s knowledge is not relevant to interpreting a text conflicts with how writers write and with how readers read.”
Check the whole piece here.

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Presentation: Are most positive findings in psychology false or exaggerated?

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How learning to talk is in the genes (research)

Actually, when you think of it, it seems quite logical that there is something in our genes that helps us to learn how to talk. But the findings in this research go much further. Researchers have found evidence that genetic factors may contribute to the development of language during infancy. Scientists discovered a significant link between genetic changes near the ROBO2 gene and… the number of words spoken by children in the early stages of language development.

From the press release:

Children produce words at about 10 to 15 months of age and our range of vocabulary expands as we grow — from around 50 words at 15 to 18 months, 200 words at 18 to 30 months, 14,000 words at six-years-old and then over 50,000 words by the time we leave secondary school.

The researchers found the genetic link during the ages of 15 to 18 months when toddlers typically communicate with single words only before their linguistic skills advance to two-word combinations and more complex grammatical structures.

The results, published in Nature Communications today [16 Sept], shed further light on a specific genetic region on chromosome 3, which has been previously implicated in dyslexia and speech-related disorders.

The ROBO2 gene contains the instructions for making the ROBO2 protein. This protein directs chemicals in brain cells and other neuronal cell formations that may help infants to develop language but also to produce sounds.

The ROBO2 protein also closely interacts with other ROBO proteins that have previously been linked to problems with reading and the storage of speech sounds.

Dr Beate St Pourcain, who jointly led the research with Professor Davey Smith at the MRC Integrative Epidemiology Unit, said: “This research helps us to better understand the genetic factors which may be involved in the early language development in healthy children, particularly at a time when children speak with single words only, and strengthens the link between ROBO proteins and a variety of linguistic skills in humans.”

Dr Claire Haworth, one of the lead authors, based at the University of Warwick, commented: “In this study we found that results using DNA confirm those we get from twin studies about the importance of genetic influences for language development. This is good news as it means that current DNA-based investigations can be used to detect most of the genetic factors that contribute to these early language skills.”

The study was carried out by an international team of scientists from the EArly Genetics and Lifecourse Epidemiology Consortium (EAGLE) and involved data from over 10,000 children.

Abstract of the study:

Twin studies suggest that expressive vocabulary at ~24 months is modestly heritable. However, the genes influencing this early linguistic phenotype are unknown. Here we conduct a genome-wide screen and follow-up study of expressive vocabulary in toddlers of European descent from up to four studies of the EArly Genetics and Lifecourse Epidemiology consortium, analysing an early (15–18 months, ‘one-word stage’, NTotal=8,889) and a later (24–30 months, ‘two-word stage’, NTotal=10,819) phase of language acquisition. For the early phase, one single-nucleotide polymorphism (rs7642482) at 3p12.3 near ROBO2, encoding a conserved axon-binding receptor, reaches the genome-wide significance level (P=1.3 × 10−8) in the combined sample. This association links language-related common genetic variation in the general population to a potential autism susceptibility locus and a linkage region for dyslexia, speech-sound disorder and reading. The contribution of common genetic influences is, although modest, supported by genome-wide complex trait analysis (meta-GCTA h215–18-months=0.13, meta-GCTA h224–30-months=0.14) and in concordance with additional twin analysis (5,733 pairs of European descent,h224-months=0.20).

 

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Great video on the weakness of eyewitnesses: a roomful of people condemn 5 innocent men

Also read this piece on the matter.

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Brain’s white matter highly predictive of reading acquisition (research)

Can we forecast early reading difficulties by looking at the brain? It’s what researchers at UC San Francisco have done. They used brain scans to predict how young children learn to read, giving clinicians a possible tool to spot children with dyslexia and other reading difficulties before they experience reading challenges.

From the press release:

In the United States, children usually learn to read for the first time in kindergarten and become proficient readers by third grade, according to the authors. In the study, researchers examined brain scans of 38 kindergarteners as they were learning to read formally at school and tracked their white matter development until third grade. The brain’s white matter is essential for perceiving, thinking and learning.

The researchers found that the developmental course of the children’s white matter volume predicted the kindergarteners’ abilities to read.

“We show that white matter development during a critical period in a child’s life, when they start school and learn to read for the very first time, predicts how well the child ends up reading,” said Fumiko Hoeft, MD, PhD, senior author and an associate professor of child and adolescent psychiatry at UCSF, and member of the UCSF Dyslexia Center.

The research is published online in Psychological Science.

Doctors commonly use behavioral measures of reading readiness for assessments of ability. Other measures such as cognitive (i.e. IQ) ability, early linguistic skills, measures of the environment such as socio-economic status, and whether there is a family member with reading problems or dyslexia are all common early factors used to assess risk of developing reading difficulties.

“What was intriguing in this study was that brain development in regions important to reading predicted above and beyond all of these measures,” said Hoeft.

The researchers removed the effects of these commonly used assessments when doing the statistical analyses in order to assess how the white matter directly predicted future reading ability. They found that left hemisphere white matter in the temporo-parietal region just behind and above the left ear — thought to be important for language, reading and speech — was highly predictive of reading acquisition beyond effects of genetic predisposition, cognitive abilities, and environment at the outset of kindergarten. Brain scans improved prediction accuracy by 60 percent better at predicting reading difficulties than the compared to traditional assessments alone.

“Early identification and interventions are extremely important in children with dyslexia as well as most neurodevelopmental disorders,” said Hoeft. “Accumulation of research evidence such as ours may one day help us identify kids who might be at risk for dyslexia, rather than waiting for children to become poor readers and experience failure.”

According to the National Institute of Child and Human Development, as many as 15 percent of Americans have major trouble reading.

“Examining developmental changes in the brain over a critical period of reading appears to be a unique sensitive measure of variation and may add insight to our understanding of reading development in ways that brain data from one time point, and behavioral and environmental measures, cannot,” said Chelsea Myers, BS, lead author and lab manager in UCSF’s Laboratory for Educational NeuroScience. “The hope is that understanding each child’s neurocognitive profiles will help educators provide targeted and personalized education and intervention, particularly in those with special needs.”

Abstract of the research:

This study examined whether variations in brain development between kindergarten and Grade 3 predicted individual differences in reading ability at Grade 3. Structural MRI measurements indicated that increases in the volume of two left temporo-parietal white matter clusters are unique predictors of reading outcomes above and beyond family history, socioeconomic status, and cognitive and preliteracy measures at baseline. Using diffusion MRI, we identified the left arcuate fasciculus and superior corona radiata as key fibers within the two clusters. Bias-free regression analyses using regions of interest from prior literature revealed that volume changes in temporo-parietal white matter, together with preliteracy measures, predicted 56% of the variance in reading outcomes. Our findings demonstrate the important contribution of developmental differences in areas of left dorsal white matter, often implicated in phonological processing, as a sensitive early biomarker for later reading abilities, and by extension, reading difficulties.

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The MOOC Revolution That Wasn’t

Pedro:

It’s almost a classic story of technology in education. You can call it a hype cycle, but it’s not that surprising. But don’t make the mistake of putting away MOOC’s as a failure. It’s just the high expectations they probably can’t fulfill.

Originally posted on TechCrunch:

Editor’s note: Dan Friedman is the co-founder of Thinkful.

Three years ago this week, Sebastian Thrun recorded his Stanford class on Artificial Intelligence, released it online to a staggering 180,000 students, and started a “revolution in higher education.” Soon after, Coursera, Udacity and others promised free access to valuable content, supposedly delivering a disruptive solution that would solve massive student debt and a struggling economy. Since then, over 8 million students have enrolled in their courses.

This year, that revolution fizzled. Only half of those who signed up watched even one lecture, and only 4 percent stayed long enough to complete a course. Further, the audience for MOOCs already had college degrees so the promise of disrupting higher education failed to materialize. The MOOC providers argue that completion of free courses is the wrong measure of success, but even a controlled experiment run by…

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The 2014 Gartner Hype Cycle for educational technology

No, I don’t have the actual drawing (yet), but I did find where the different technologies are placed on the hype cycle in 2014. Also missing is the time frame.

UPDATE: I found this interactive tool, but it seems the data is based on the 2013 hype cycle.

As a quick reminder, the hype cycle:

(via Wikimedia)

Some of the technologies in the list will sound unfamiliar to many of you (they did for me once), but the full 2013 report can help you out.

  • On the Rise
    • Exostructure Strategy
    • Mashware
    • Quantum Computing
    • SaaS SIS
    • Alumni CRM
    • Education Tablet
    • Affective Computing
    • SIS International Data Interoperability Standards
    • BPO
  • At the Peak
    • Citizen Developers
    • Open Microcredentials
    • Open-Source SIS
    • Student Retention CRM
    • Adaptive E-Textbooks
    • Big Data
  • Sliding Into the Trough
    • COBIT
    • Enterprise Mobile App Stores
    • Learning Stack
    • Wireless as a Service
    • 802.11ac Wave 1
    • Adaptive Learning
    • MOOC
    • Gamification
    • Digital Preservation of Research Data
    • EA Frameworks
    • Cloud Office Systems
    • ITIL
    • BYOD Strategy
    • Open-Source Middleware Suites
    • Cloud HPC/CaaS
    • Open-Source Financials
    • Mobile Learning Smartphones
    • Social Learning Platform for Education
  • Climbing the Slope
    • Emergency/Mass Notification Services
    • Hosted Virtual Desktops
    • Virtual Environments/Virtual Worlds
    • E-Textbook
    • Mobile-Learning Low-Range/Midrange Handsets
    • SaaS Administration Applications
    • Enterprise Architecture
    • IT Infrastructure Utility
    • Intellectual Property Rights and Royalties Management Software
    • Cloud Email for Staff and Faculty
    • Open-Source Learning Repositories
    • Unified Communications and Collaboration
    • Lecture Capture and Retrieval Tools
  • Entering the Plateau
    • 802.11n
    • Game Consoles as Media Hubs

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