The Learning Scientists Podcast

This episode was funded by the Chartered College of Teaching, and listeners like you. For more details on how to help support our podcast and gain access to exclusive content, please see our Patreon page.Listening on the web? You can subscribe to our podcast to get new episodes each month! Go to our show on iTunes or wherever you get your podcasts.RSS feed: http://www.learningscientists.org/learning-scientists-podcast/?format=rssShow Notes:In our last episode, Yana interviewed Alexander Chamessian, an MD PhD student who has been consistently utilizing evidence-based learning strategies. In this bite-size research episode, Yana follows up with a study on retrieval practice with complex medical information.In this study by scientists at a department of Health and Kinesiology (1), students taking an exercise physiology re-read or practiced retrieval practice on background texts and journal articles, and then took critical analysis and factual texts. The debate between John Sweller (2) and Jeff Karpicke (3) on whether retrieval practice works with complex materials can be found in this special issue.The following table shows the phases in the experiment: Image from Dobson, Linderholm, & Perez (2018) The main result can be found in this figure: Image from Dobson, Linderholm, & Perez (2018) References:(1) Dobson, J., Linderholm, T., & Perez, J. (2018). Retrieval practice enhances the ability to evaluate complex physiology information. Medical Education, 52, 513-525.(2) Van Gog, T., & Sweller, J. (2015). Not new, but nearly forgotten: the testing effect decreases or even disappears as the complexity of learning materials increases. Educational Psychology Review, 27, 247-264.(3) Karpicke, J. D., & Aue, W. R. (2015). The testing effect is alive and well with complex materials. Educational Psychology Review, 27, 317-326.

This episode was funded by the Chartered College of Teaching, and listeners like you. For more details on how to help support our podcast and gain access to exclusive content, please see our Patreon page.Listening on the web? You can subscribe to our podcast to get new episodes each month! Go to our show on iTunes or wherever you get your podcasts.RSS feed: http://www.learningscientists.org/learning-scientists-podcast/?format=rssShow Notes:Over the last few months, we have been interviewing researchers who attended the the European Association for Research on Learning and Instruction conference (or, more simply, EARLI) for the special interest group on Neuroscience and Education (@EarliSIG22). We enjoyed recording these interviews so much that we decided to do another one! Alex Chamessian first wrote to us about a year ago - almost immediately after we released our first podcast episode. An MD-PhD candidate at Duke, Alex has been passionate about effective learning for years. He started using spaced repetition in 2010 in my first year of medical school, and when he noticed the benefits, he did a deep dive into more evidence-based practices, starting first with a blog, then a book. Alex asked if he could appear on our podcast, but at the time that he was writing, we hadn’t figured out whether - let alone how - we would conduct podcast interviews! A year later, Yana and Alex finally got together over Skype to record this interview. In our conversation, we discuss the following questions:Why/how did Alex get interested in learning strategies in medical school, and end up writing a blog and book on the subject? Do students need to understand the reasons why effective strategies work, or is it enough for them just to experience their effectiveness?Apart from medical school and classes and exams, how is Alex planning on applying effective learning strategies in his medical practice?And what about in his PhD - are there strategies also effective for being a successful scholar?

This episode was funded by The Wellcome Trust, Chartered College of Teaching, and listeners like you. For more details on how to help support our podcast, please see our Patreon page.Listening on the web? You can subscribe to our podcast to get new episodes each month! Go to our show on iTunes or wherever you get your podcasts.RSS feed: http://www.learningscientists.org/learning-scientists-podcast/?format=rssShow Notes:This is the ninth and final episode in a series recorded in London! In June 2018 we attended the European Association for Research on Learning and Instruction conference (or, more simply, EARLI) for the special interest group on Neuroscience and Education (@EarliSIG22). While there, we recorded live interviews with teachers and researchers. In this episode, we speak with Dr. Julien Mercier, professor at the University of Quebec at Montreal and director of NeuroLab, an educational neuroscience lab. Research in this lab focuses on cognition and affect during learning. This is done by collecting second-by-second (“on-line”) data from learners. The contexts in which these measures are collected include reading, science education, video games, and more applied workplace settings.Because of the complexity of this type of research, experts from a diverse set of fields are needed to make the project come together. First the physiological data are collected. Then, all the data are integrated into one huge complex system, segmented, and analyzed to look for patterns in reactions. For example, in a study on video gaming, the researchers were interested in what happens when a player is given an action prompt. The researchers collected measures that tap into cognitive load and engagement, and compare the relative activation before and after the prompt was presented.Through this research, Julien hopes to develop a methodology that will be able to handle much more complex learning situations than those currently considered in neuroscientific research.

This episode was funded by The Wellcome Trust, Chartered College of Teaching, and listeners like you. For more details on how to help support our podcast, please see our Patreon page.Listening on the web? You can subscribe to our podcast to get new episodes each month! Go to our show on iTunes or wherever you get your podcasts.RSS feed: http://www.learningscientists.org/learning-scientists-podcast/?format=rssShow Notes:This is the eighth episode in a series recorded in London! In June 2018 we attended the European Association for Research on Learning and Instruction conference (or, more simply, EARLI) for the special interest group on Neuroscience and Education (@EarliSIG22). While there, we recorded live interviews with teachers and researchers. In this episode, we speak with Dr. Miriam Reiner, head of The Virtual Reality and Neurocognition lab at Technion (Israel Institute of Technology). Miriam uses emerging technologies for high-validity research in neuroeducation: specifically, she examines how integration of virtual/augmented reality and EEG/eyetracking etc can provide a highly valid methodology for research in neuroeducation. In addition, Miriam studies technologies that activate brain mechanisms, such as neurofeedback, for enhanced memory consolidation, insight-problem solving, and spatial intelligence.Miriam’s research involves putting participants in a very rich virtual world where they can interact with their environment. Examples of situations might be a surgeon in the operating room who must burn specific areas in the abdomen, or someone trying to catch a falling pendulum - a task that can reveal common misconceptions about physics. While in this immersive experience, participants are connected to EEG, so that events in the world and in the brain can be correlated. This helps her to determine what brain activation relates to best performance (in terms of speed and accuracy) in problem-solving situations. Miriam believes that schools will evolve to mainly be places for social learning, whereas technology will replace some of the more traditional learning environments. In another line of research, Miriam investigates whether neurofeedback can be used to enable the typical learner to control their own brainwaves, and how this relates to the consolidation process that happens while we sleep (1) (see also this episode on sleep and learning). In the episode, Miriam discusses how these techniques might be used in education. References:(1) Reiner, M., Rozengurt, R., & Barnea, A. (2014). Better than sleep: Theta neurofeedback training accelerates memory consolidation. Biological Psychology, 95, 45-53.

This episode was funded by The Wellcome Trust, Chartered College of Teaching, and supporters like you. For more details on how to help support our podcast, please see our Patreon page.Listening on the web? You can subscribe to our podcast to get new episodes each month! Go to our show on iTunes or wherever you get your podcasts.RSS feed: http://www.learningscientists.org/learning-scientists-podcast/?format=rssShow Notes:This is the seventh episode in a series recorded in London! In June 2018 we attended the European Association for Research on Learning and Instruction conference (or, more simply, EARLI) for the special interest group on Neuroscience and Education (@EarliSIG22). While there, we recorded live interviews with teachers and researchers. In this episode, we speak with Jane Emerson who started off as a speech and language therapist, did her Masters in Human Communication, and after years of experience working with dyslexic students in schools founded a center, Emerson House, with a colleague. This center helps children with dyslexia, dyspraxia, and dyscalculia. Jane has taught children for many years, lectured widely, and written a number of books including The Dyscalculia Assessment and The Dyscalculia Solution. You can find her on Facebook at Jane Emerson - Freelance SEND Advisor. Image from CPD College on YouTube Dyslexic children have difficulties with phonics or phonological awareness, language, vocabulary, memory (both short- and long-term). Dyslexic students tend to have problems with spelling both because of difficulties with phonics, but also because of poorer visual recall. However, dyslexia is not only about spelling, but can have more pervasive effects on academic performance due to the impacts on memory.Dyscalculia might present itself as lack of common sense about numbers. That is, some students may have trouble transferring their understanding of a specific example (e.g., 1 + 2) to a similar but slightly different situation (e.g., 2 + 3), treating it as an entirely new problem and not applying a previously learned rule. For example, unlike others of their age, students with these difficulties may not be able to infer the answer to 13 x 2 after having memorized the 2 x tables up to 12. One way to help alleviate is to use physical manipulatives to demonstrate relationships between numbers.Dyspraxia is described as a developmental co-ordination delay or disorder. At the extreme, this disorder can result in the inability to learn to drive and great reluctance to use any machinery. At school, the first sign is often poor handwriting, and learning to touch-type as early as possible can help with this. Students with this disorder may receive accommodations such as having a scribe.Other accommodations such as extra time alone may not always be helpful, since often students may not know what to do with this extra time. The important thing is to learn skills to implement during that time. On the podcast, we discuss the subtle difference between learning styles and learning preferences, and how this plays out in students with learning difficulties.The Big TakeawaysFor reading, Jane says it’s very helpful to give children a lot of information about one character or set of characters so that they can really understand get into a particular genre before moving on to another one. For spelling, combining seeing and hearing words can be really important, and adding touch-typing on to that for multi-sensory learning is even better. And for math, concrete, multi-sensory materials to demonstrate the principles being learned.

This episode was funded by The Wellcome Trust, Chartered College of Teaching, and supporters like you. For more details on how to help support our podcast, please see our Patreon page. In today's episode, we feature one of our patrons, Abby Zavos.Listening on the web? You can subscribe to our podcast to get new episodes each month! Go to our show on iTunes or wherever you get your podcasts.RSS feed: http://www.learningscientists.org/learning-scientists-podcast/?format=rssShow Notes:This is the sixth episode in a series recorded in London! In June 2018 we attended the European Association for Research on Learning and Instruction conference (or, more simply, EARLI) for the special interest group on Neuroscience and Education (@EarliSIG22). While there, we recorded live interviews with teachers and researchers. In this episode, we speak with Jessica Massonnié, who is pursuing her PhD at Birkbeck University on the effects of noise in primary school classrooms. She Tweets at @jess_masso and you can find her website here. Primary school classrooms tend to be noisy (approximately as noisy as traffic or a vacuum cleaner). Jessica's work looks at how this noise affects learning outcomes. One of her first studies looked at the effect of recoded classroom noise on children's creativity in a lab environment. In the podcast, Jessica talks about some preliminary findings from this study.More recently, Jessica has moved on to classroom-based research. In a study that takes place in France, she is looking at individual differences in how annoying children find noise. This factor appears to be related to how difficult it is for children to switch between tasks, and how often they mind-wander. Jessica is also looking at the effects of a mindfulness intervention on noise levels, noise awareness, and French/math test performance. The big take-awayIt's important to be aware of noise levels; for example you can easily download an app to check noise levels in your environment. It would also be good to identify sources of noise in the learning environment - particular noises that are completely irrelevant to learning, such as the sounds of chairs scraping on the floor - and attempt to eliminate those noises.Relevant reading and links:Klatte, M., Bergström, K., & Lachmann, T. (2013). Does noise affect learning? A short review on noise effects on cognitive performance in children. Frontiers in Psychology, 4, 578.Mehta, R., Zhu, R., & Cheema, A. (2012). Is noise always bad? Exploring the effects of ambient noise on creative cognition. Journal of Consumer Research, 39, 784-799.Shield, B., & Dockrell, J. E. (2004). External and internal noise surveys of London primary schools. The Journal of the Acoustical Society of America, 115, 730-738.A website with yoga tutorials, created by the expert we have hired for my school study. A kit created by the association I am part of, to introduce children to the brain, to attention and distraction.Previous Episodes from this series:Episode 21 with Dr. Emma BlakeyEpisode 22 with Michael HobbissEpisode 23 with Rina LiaEpisode 24 with Ignatius GousEpisode 25 with Amanda Triccas and Clare Badger

This episode was funded by The Wellcome Trust, and supporters like you. For more details on how to help support our podcast, please see our Patreon page.Listening on the web? You can subscribe to our podcast to get new episodes each month! Go to our show on iTunes or wherever you get your podcasts.RSS feed: http://www.learningscientists.org/learning-scientists-podcast/?format=rssShow Notes:This is the fifth episode in a series recorded in London! In June 2018 we attended the European Association for Research on Learning and Instruction conference (or, more simply, EARLI) for the special interest group on Neuroscience and Education (@EarliSIG22). While there, we recorded live interviews with teachers and researchers. In this episode, we speak to Ms. Amanda Triccas and Dr. Claire Badger at The Godolphin and Latymer School. Amanda was Yana's teacher in the 1990s, and we've reconnected recently around the science of learning. Amanda has always worked in the private sector - usually in girls' schools - and a few years ago got into the science of learning. Claire's PhD is in Chemistry, and they both work at Godolphin and Latymer School for Girls. Amanda and Claire both got interested in the science of learning when Amanda found The Learning Scientists Twitter account and recognized Yana's name. For Claire, it was starting at the school with Amanda, and reading Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load by Clark, Nguyen, and Sweller.We discuss how cognitive psychology can help teachers and students. Amanda mentions efficiency, and Claire mentions having more time for teachers to do things in the classroom by optimizing learning. We also discuss students' resistance to change, and teachers' fear of embarrassing themselves in front of students.Claire and Amanda integrate strategies from cognitive psychology into their teaching, but they also explain their importance to their students, as well as to parents by providing them with the 6 strategies for effective learning posters.As a Senior Teacher in Teaching and Learning, Claire set up a teaching-learning community based on ideas by Dylan William (see this White Paper). This allowed teachers who were interested in the science of learning to come together and explore theory and practice. This community soon grew to encompass virtually all the teachers in the school. Similarly, Claire has created student learning communities, though these require more guidance to avoid misunderstandings.Amanda and Claire have some thoughts for how we can help. The illustrated work we've done with Oliver Caviglioli has been particularly useful, and they would like to see further resources produced for younger children. Claire also likes the Education Endowment Foundation (EEF), which provides concrete examples of how strategies can be used in the classroom. She would like to see more comprehensive reviews and summaries of the literature, with suggestions for teachers and students.Claire is currently pursuing her Masters in Learning and Teaching at the UCL Institute of Education.Previous Episodes from this series:Episode 21 with Dr. Emma BlakeyEpisode 22 with Michael HobbissEpisode 23 with Rina LiaEpisode 24 with Ignatius Gous

This episode was funded by The Wellcome Trust, and supporters like you. For more details on how to help support our podcast, please see our Patreon page.Listening on the web? You can subscribe to our podcast to get new episodes each month! Go to our show on iTunes or wherever you get your podcasts.RSS feed: http://www.learningscientists.org/learning-scientists-podcast/?format=rssShow Notes:This is the fourth episode in a series recorded in London! In June 2018 we attended the European Association for Research on Learning and Instruction conference (or, more simply, EARLI) for the special interest group on Neuroscience and Education (@EarliSIG22). While there, we recorded live interviews with teachers and researchers. This episode features Ignatius Gous.Ignatius begins the episode with an interesting explanation about the origins of his name. Ignatius is a professor at the University of South Africa, which is primarily a distance university, with students from all over the world taking online classes. He has developed a program for students to learn better and master content more effectively. This program is used by learners at the primary, secondary, and college levels, with advanced students of medicine and law, and even in the workplace. Ignatius has long wondered why neuromyths are so prevalent, and his theory is that people want to know about how the brain works, and these neuromyths fill the void. He thus set out to create a framework that would actually be useful and evidence-based, to help those interested in learning to do so more effectively - even those as young as primary school age.According to Ignatius, learning is not linear - it is more of a spiral - but it still needs structure. Ignatius built his model with the Fibonacci code as the basis. The spiral includes 6 aspects involved in the learning process, with metacognition as the 7th. You can see all the steps represented here in visual form: We talk in this episode about different mnemonic strategies that fit into this model, including the method of loci - you can read more about this method in this blog post. We also discuss the importance of learning basic facts before moving on to transferring learning to new, more complex situations. One idea Ignatius suggests is for students to memorize the headings of a chapter to use as a guideline for organizing and retrieving information. This ties in with this guest blog post by Yana’s former student, who used a similar method for retrieval practice after taking notes in class.Ignatius emphasizes that we need to always think about how students are going to use the material we are teaching them. He calls his model “the golden spiral for life-long learning”, because learning isn’t just something you do to cram for a test - learning happens until you die. Previous Episodes from this series:Episode 21 with Dr. Emma BlakeyEpisode 22 with Michael HobbissEpisode 23 with Rina Lia

This episode was funded by The Wellcome Trust, and supporters like you. For more details, please see our Patreon page. In today's episode, we feature one of our patrons, Josh Fisher, and his free math apps at Guzinta Math.Listening on the web? You can subscribe to our podcast to get new episodes each month! Go to our show on iTunes or wherever you get your podcasts.RSS feed: http://www.learningscientists.org/learning-scientists-podcast/?format=rssShow Notes:This is the third episode in a series recorded in London! In June 2018 we attended the European Association for Research on Learning and Instruction conference (or, more simply, EARLI) for the special interest group on Neuroscience and Education (@EarliSIG22). While there, we recorded live interviews with teachers and researchers. This episode features Rina Lai. (Check out Episode 21 for our first interview with Dr. Emma Blakey and Episode 22 with Michael Hobbiss!)Please excuse any issues with sound quality. We were quite literally recording on the fly! Rina P.Y Lai (黎栢凝) received her B.A in psychology at the University of British Columbia and MPhil in psychology & education at Cambridge University. She is now a PhD candidate and a member of a the INSTRUCT research group, a laboratory at Cambridge university that integrates cognitive developmental science to inform learning. She is particularly interested in the cognitive underpinnings of computational thinking. Currently, she is collaborating with computer scientists to develop a new computerized assessment of computational thinking that could be used by students, teachers and researchers. Rina is the founding member and internal vice president of the Cambridge China Education Forum. She has also co-founded the LT Academy, which provides consultancy services to STEM and robotics education to education institutions. You can find her on her website at rinalai.com and on Instagram @rina_py_lai. You can also check out PEDAL: Centre for Research on Play in Education, Development & Learning to see the work she is involved with.Rina's Masters project focused on the differences between executive function and metacognition. Both are higher cognitive domains, both linked to prefrontal cortex, and both relate to academic outcomes. Executive functioning is an umbrella term that includes a number of neuropsychological processes including working memory, cognitive inhibition, cognitive flexibility, and planning. Meta-cognition is a cognitive domain concerned with how we think about our own thinking, and how we regulate our thoughts to maximize learning. For example, if a child has a question in class, they have to use inhibition to ask the question at the right time, and working memory to remember the question. Rina's group looked at educational outcomes including arithmetic, vocabulary, verbal reasoning, and non-verbal reasoning (previous research focused on one or only a couple of outcomes). Interestingly, they found that executive function and metacognition have both shared and unique contributions to educational outcomes.After doing her Masters, Rina volunteered at a robotics camp for kids age 8-10, and this experience changed her trajectory. Now, Rina's research direction has shifted to studying computational thinking. Computational thinking is a set of cognitive processes that help us formulate a problem and devise a solution. These processes include decomposition, abstraction, algorithms, and debugging. Contrary to what it sounds like, this is not about using a computer! This is a skill that should be integrated into different subjects, rather than taught in isolation. Rina set out to look at computational thinking skills and how they relate to educational outcomes. In her literature review, she realized that there was no good assessment for computational thinking, since most are currently tailored to computer programming specifically. This has led Rina to focus on how to measure computational thinking to identify strengths and weaknesses of different processes within this domain.In the future, Rina hopes to use the assessment in her own research to look for the relationship between computational thinking educational outcomes, and she also hope that teachers might find it useful as a formative assessment.The big takeawayRina's biggest takeaway is a quote she shared with us:"We are currently preparing students for jobs that don’t yet exist . . . using technologies that haven’t yet been invented . . . in order to solve problems we don’t even know are problems yet."—Richard Riley, Secretary of Education under Clinton" —Richard Riley, Secretary of Education under ClintonPrevious Episodes from this series:Episode 21 with Dr. Emma BlakeyEpisode 22 with Michael Hobbiss

This episode was funded by The Wellcome Trust, and supporters like you. For more details, please see our Patreon page. In today's episode, we feature one of our patrons, Bob Reuter.Listening on the web? You can subscribe to our podcast to get new episodes each month! Go to our show on iTunes or wherever you get your podcasts.RSS feed: http://www.learningscientists.org/learning-scientists-podcast/?format=rssShow Notes:This is the second episode in a series recorded in London! In June 2018 we attended the European Association for Research on Learning and Instruction conference (or, more simply, EARLI) for the special interest group on Neuroscience and Education (@EarliSIG22). While there, we recorded live interviews with teachers and researchers. This episode features Michael Hobbiss. (Check out Episode 21 for our first interview with Dr. Emma Blakey!)Please excuse any issues with sound quality. We were quite literally recording on the fly! Michael Hobbiss started his career as a teacher for 8 years, teaching psychology and biology in the UK and abroad. He is now back in the UK, pursuing his PhD with Dr. Nilli Lavie at University College London. His focus is on attention, distraction, and cognitive control in adolescents. Mike tweets at @mikehobbiss and blogs at The Hobbolog.In the beginning of the episode, Mike describes the two main ways attention is captured:Bottom-up: the object or stimulus itselfTop-down: your prior knowledge, interest, motivationBoth of these processes are prone to distraction. But surprisingly, Mike says, we don't know all that much about how students get distracted during learning. We do know that attention is related to important educational outcomes: for example, teacher ratings of children's attention at age 5 correlate with the children's later academic success (although, teacher ratings are not always reliable and tend to vary between cultures). We also know that inattention can be related to being unhappy. For his PhD, Michael has set out to investigate attention processes in adolescents.The irrelevant distractor taskMike uses the "irrelevant distractor" task in his research. In this task, participants have to pick a particular object out of a visual display. For example, they might have to pick out the letter O from an array of Xs. This would be an easy task - one with low "perceptual load", because the other letters (Xs) do not look similar to the target letter (O). In a high perceptual load version of this task, participants would need to pick out the letter X from, say, letters like K or M, which are more similar. At the same time, during this task, random irrelevant distractors such as Sponge Bob will pop up on the screen. Image from a presentation by Dr. Sophie Forster Typically, when the task has higher perceptual load, people are less likely to notice and be distracted (in other words, slowed down) by the irrelevant distractor (1). However, Mike didn't find this pattern in his research with adolescents - in the episode he describes a very different pattern of results that involved adolescents' accuracy as well as speed. Interestingly, Mike found a relationship between students' self-reported level of distraction during class and their performance on this task.While these results are exciting, Mike warns against acting on these findings immediately in the classroom - we need to understand a lot more about how distraction varies within and between children before we build interventions to address it.The big takeawayWe tend to think of attention as a resource that we either have or don't have; this may not be a useful way to think about it. Many factors in the environment influence attention, so there is enormous potential to improve attention - for example, putting your phone away when you're trying to work, choosing what type of music you are listening to, and using pictures effectively.References:(1) Forster, S., & Lavie, N. (2009). Harnessing the wandering mind: The role of perceptual load. Cognition, 111, 345-355.Previous Episodes from this series:Episode 21 with Dr. Emma Blakey