Unlocking Potential with Digital Textbooks

Intelligent textbooks empower students to be at the centre of their learning and to create better communication flow between learners and teachers. Karin Bjerde shows how Box Hill School is benefitting from their implementation.

‘We weren’t aware that alternatives existed. We were happy enough using printed textbooks’, says Julian Baker, the IB Coordinator at Box Hill School, a GCSE and IB/A-level school in Surrey. They were among the first UK schools to fully implement Kognity, the digital publisher of GCSE and IB intelligent textbooks, and, after a couple of years, are now looking at the option of completely phasing out printed textbooks for their IB students.

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      string(5645) "In the age of algorithms and artificial intelligence (AI), the number of adaptive and personalised learning education products vying for educators’ attention and funds is immense. Using the latest advances in machine learning, big data and learning analytics, adaptive and personalised learning products can process a range of data, (educational, psychometric and social media), to map individuals quantitatively, compare them globally and generate recommendations for their learning potential and pathways. These techniques are being translated into impressive learning gains in the context of standardised curricula. For example, LEAP Innovations, a Chicago-based research and piloting company found a 13 per cent increase in literacy using an AI-based reading product versus the control group. Laudatory testimonials from schools featured on product websites means it is easy to slip into a ‘black box’ mentality, focusing on the application of these technologies, without further unpacking the mechanics of how they produce the outcomes. However, by understanding the mechanics behind the production of profound learning insights and results that these products offer, educators can develop personalised learning strategies that make the most of the strengths and limitations of AI. Opening the black-box: Artificial, adaptive and personalised learningKnowing which questions to ask relies on understanding how these products are designed. AI, adaptive and personalised learning are the latest education technology buzzwords, but what distinguishes them? While there are many types of AI, they all involve some form of learning and applying knowledge to achieve a purpose. Learning and problem-solving is something that humans do very well, but sophisticated AI systems, along with huge data sets and computing power, can process and compute complex problems at a scale unfeasible for humans. AI systems can learn autonomously, which means we do not have to invest time and resources into writing code ourselves. A classic example of AI in education technology products is adaptive learning, which refers to the data analytics strategies that algorithms use to continuously learn about how individual students learn, and thus optimise learning sequences.1 Adaptive LearningAs students use adaptive learning software, they generate countless data points, which are recorded and fed to algorithms. These algorithms have been designed to learn from this data to better perform against designed criteria. An advance in a type of machine learning, using neural networks, has accelerated the functionality and potential of machine learning. Artificial neural networks are statistical models that are based on the way that the brain computes and processes information in non-linear, continuous and adaptive ways. Deep learning occurs when these artificial neural networks and learning algorithms observe and analyse raw data, learn, identify patterns and make predictions to determine optimum outputs. Algorithm-based personalised learning In the case of the adaptive learning education technology products, such as ALEKS and Dreambox, these algorithms have been designed to identify the ideal learning pathways for an individual student based on the criteria derived from neuroscience and cognitive-based learning and memory theories. These theories emphasise the importance of reinforcement and retrieval in short and long-term memory, and how this can be improved through interleaved and spaced learning and assessment. Every student’s ideal pattern of interleaved and spaced learning, forgetting, retrieval and reinforcement will be different. The algorithm’s or ‘machine’s’ capability of operating autonomously and writing its own code means it can learn how the student learns, what they have mastered and which areas they require improvement in. These insights are used to diagnose gaps in knowledge and skills, make predictions of the ideal learning pathways that will fill these gaps, and translate these into personalised recommendations. With the current computing power available, such inferences can be made in nanoseconds. The algorithms learn from data, and continuously teach themselves to get better. Thus, the more a student uses such software, the more data there is generated, the more the machine learns and the better it gets in making predictions and determining the ideal sequence of interleaved learning and assessment, i.e. personalising learning. Put simply, machine learning provides personalised learning for mastery.More than personalised learning pathwaysIt is not just pace that can be personalised—more sophisticated products personalise according to learning preferences, complexity of tasks, students’ interests, and special needs. A few products enable educators to customise systems through gamification, using simulations and apps. Many products provide smart content services, using AI to process textbooks and resources into learning pathways and assessments for students, effectively designing digital courses for educators. For leaders and those desiring a macro perspective, some products offer aggregated analytics at the class, cohort and national level. Critical Considerations for Adaptive Learning There are many benefits these products provide that enable educators to justify the financial, time and human investment in adaptive and personalised learning technologies. 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Adaptive Technology

Adapting to AI in Education: Critical Questions for Educators

8 Aug 2019

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Leadership

Unlocking The Potential of Digital Learning

29 Jun 2017

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      string(1539) "Nano Simbox we develop for mobile and desktop first and adapt to VR second. Many edtech products are similarly being built to be hardware agnostic. While there are some interactive tasks that are easier to carry out in VR, we know that we can reach more learners if we develop virtual scientific worlds for mobile and desktop.  Ask industry to support your learning:Companies like ours want to spend time in the classroom and we are always looking for opportunities to expose students to our software. Schools can partner with a range of organisations to bring benefits of digital technology into the curriculum. Although sometimes patience is required in working with industry, particularly with start-ups, which have fewer resources but are often the companies working on the most innovative technology, these connections can also provide an opportunity for you, as a teacher, to get in early and get your ideas heard.Take it a bit at a time:Some edtech has per-learner pricing so it becomes possible to try various activities with one or two classes, making some of the more complex technology more affordable.Invest in infrastructure: Good basic infrastructure, such as good connectivity, will be worth the investment as cloud-based edtech tools become the norm. The better the connectivity the more likely your students will be able to collaborate, communicate and get creative with learners and professionals around the world, as we all develop our skills and overcome the global challenges we face together. "
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Digital Learning

Digital Doorways Unlocking Scientific Potential

22 May 2018

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Leadership Briefing 11.04(124)"
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This study, published by Frontiers in Psychology, shows thatinteractive media instruction, often via the use of iPads, can be equally as effective as one-on-one instruction when it comes to teaching young children.It also found that although interactive media was useful for teaching concrete knowledge (e.g.,science, addition, subtraction, counting, multiplication, and chemistry); skills such as self-regulationand empathy are best learned through interactions with peers and caregivers in naturalisticenvironments. Thus presenting the importance of both digital learning and traditional in personlearning in schools.Main Points: ■ EThe analyses revealed that children performed equally well in the Interactive Media conditionas they did in the Face-to-Face condition. That is, the 4- to 8-year-old children in the samplerecalled the facts regardless of whether they learned those facts from an adult researcher or viaan interactive iPad app in the presence of an adult researcher. ■ Unsurprisingly, children’s age in months was also positively correlated with their memoryperformance, which is consistent with a wealth of previous findings showing that children’slearning and memory improves with age. ■ Moreover, school-age children (ages 6 through 8) performed significantly better than preschoolage children (ages 4 and 5). This same developmental pattern of learning was observed in boththe Face-to-Face and Interactive Media Conditions. ■ These findings suggest that face-to-face instruction and interactive touch-screen applications canbe similarly effective learning methods for children ages 4 through 8. ■ Given the increasingly prominent role technology is playing in children’s lives, the findingsmake an important contribution to a small but growing body of literature on the comparativeeffectiveness of so-called ‘digital learning’ versus more naturalistic or traditional pedagogicalapproaches. ■ Many parents and educators hold negative attitudes toward interactive devices for learningpurposes compared to the perceived benefits of ‘real-world’ learning opportunities. TheseWeek Commencing 28th November 2016http://journal.frontiersin.org/article/10.3389/fpsyg.2016.01603/fullLeadership BriefingLeadership Briefing – www.teachingtimes.comReports Summaries – a weeklyservice of essential summaries,in-depth reports and criticalanalysis that’s new and relevantto education, linked to originalsource documents.perceptions might lead some individuals to expect superior learning in the Face-to-Facecondition. In contrast, our finding suggests that perhaps caregivers and educators do not needto be overly concerned about the use of technology for learning, given that interactive mediaappears equally effective as face-to-face instruction, at least for certain learning contexts. ■ The potential benefits of children’s use of interactive technology will ultimately depend on whatchildren are doing on the interactive device (e.g., whether the apps being used include aneducational component—intentionally or otherwise). ■ Although some research suggests that until around age 3 children have difficulty transferring ‘2Dlearning’ into the real 3D world; a so-called ‘video deficit’, contingent engagement helps childrensuccessfully transfer this knowledge. ■ The results offer much-needed empirical support for the validity of using interactive media forresearch purposes. We found that children ages 4 through 8 did not find it difficult to interact withthe iPad. Moreover, given that children’s performance was comparable to an analogous ‘live’experiment, this research suggests that interactive technology may be an appropriate methodto collect data to test research questions on a range of topics within developmental science,not just for research evaluating the efficacy of children’s learning from interactive media. In fact,there were some clear benefits of using the interactive device over a live interaction for researchpurposes. ■ The results demonstrate that children 4 to 8 years of age learned factual information equallywell from an interactive iPad application as they did during face-to-face instruction. Theseresults contribute much-needed data to the limited experimental evidence supporting the useof interactive media in children’s learning. The data may help alleviate the concerns of someparents and educators who believe that learning from interactive media is inferior to learning fromreal-life interactions.WEB LINKDigital And Traditional Learninghttp://journal.frontiersin.org/article/10.3389/fpsyg.2016.01603/full"
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      ["matchedWords"]=>
      array(3) {
        [0]=>
        string(9) "potential"
        [1]=>
        string(7) "digital"
        [2]=>
        string(9) "textbooks"
      }
    }
    ["categories"]=>
    array(1) {
      [0]=>
      array(3) {
        ["value"]=>
        string(10) "Leadership"
        ["matchLevel"]=>
        string(4) "none"
        ["matchedWords"]=>
        array(0) {
        }
      }
    }
  }
}

Leadership

Digital And Traditional Learning

25 Nov 2016