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    <title>Teaching Python - Episodes Tagged with “Diverse Learners”</title>
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    <description>Teaching Python is a podcast about Python programming, computer science education, AI literacy, software development, cloud computing, cybersecurity, data, and how people learn technical skills. Hosted by Kelly Schuster-Paredes, Sean Tibor, and Julian Sequeira, the show is for educators, developers, technology leaders, and lifelong learners who want to better understand how Python connects to the wider world of computing. Episodes explore not only how people learn to code, but also how they build technical judgment, understand systems, evaluate AI-generated code, work with data, think about security, and move from beginner programming into real-world software development. About the Hosts Kelly Schuster-Paredes is a teacher who codes whose work has expanded from classroom computer science into AI strategy, curriculum design, professional learning, educational technology, and responsible technology adoption. Her background in Python and computer science education shapes her focus on learning, AI literacy, computational thinking, and what people need to understand in an AI-shaped world. Sean Tibor is Vice President of Infrastructure and Cloud at Pfizer and a former computer science teacher. He brings expertise in cloud computing, infrastructure, engineering operations, and technical leadership, connecting what people learn about computing with how large-scale systems are actually built, operated, secured, and maintained. Julian Sequiera is a technologist, Fractional CTO, and Senior Program Manager with more than 20 years of experience in infrastructure, cloud, engineering operations, and large-scale technology programs. He is also the co-founder of PyBites, a Python learning platform and community that has helped thousands of developers improve their Python and software development skills. What We Cover Python Programming and Computer Science Education: Learning Python, teaching programming, computational thinking, debugging, code literacy, and helping beginners build strong mental models. AI and AI Literacy: AI-assisted programming, evaluating AI-generated code, responsible AI use, human judgment, and what learners still need to understand when AI can produce code. Cloud, Infrastructure, and Cybersecurity: Systems, networks, deployment, security, reliability, architecture, and the operational side of software. Data and Software Engineering: APIs, databases, testing, maintainability, version control, software design, and moving from simple scripts to real-world applications. Learning and Technical Growth: How people learn difficult technical concepts, get unstuck, build confidence, and develop the judgment needed to use technology well. Expert Interviews: Conversations with educators, developers, engineers, researchers, technology leaders, and others shaping the future of computing and technical education. Teaching Python remains grounded in Python, but the conversation extends beyond syntax. The podcast explores the knowledge, skills, systems, and judgment people need to learn, build, and make responsible decisions with technology.</description>
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    <itunes:subtitle>We're two computer science educators learning and teaching Python</itunes:subtitle>
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    <itunes:summary>Teaching Python is a podcast about Python programming, computer science education, AI literacy, software development, cloud computing, cybersecurity, data, and how people learn technical skills. Hosted by Kelly Schuster-Paredes, Sean Tibor, and Julian Sequeira, the show is for educators, developers, technology leaders, and lifelong learners who want to better understand how Python connects to the wider world of computing. Episodes explore not only how people learn to code, but also how they build technical judgment, understand systems, evaluate AI-generated code, work with data, think about security, and move from beginner programming into real-world software development. About the Hosts Kelly Schuster-Paredes is a teacher who codes whose work has expanded from classroom computer science into AI strategy, curriculum design, professional learning, educational technology, and responsible technology adoption. Her background in Python and computer science education shapes her focus on learning, AI literacy, computational thinking, and what people need to understand in an AI-shaped world. Sean Tibor is Vice President of Infrastructure and Cloud at Pfizer and a former computer science teacher. He brings expertise in cloud computing, infrastructure, engineering operations, and technical leadership, connecting what people learn about computing with how large-scale systems are actually built, operated, secured, and maintained. Julian Sequiera is a technologist, Fractional CTO, and Senior Program Manager with more than 20 years of experience in infrastructure, cloud, engineering operations, and large-scale technology programs. He is also the co-founder of PyBites, a Python learning platform and community that has helped thousands of developers improve their Python and software development skills. What We Cover Python Programming and Computer Science Education: Learning Python, teaching programming, computational thinking, debugging, code literacy, and helping beginners build strong mental models. AI and AI Literacy: AI-assisted programming, evaluating AI-generated code, responsible AI use, human judgment, and what learners still need to understand when AI can produce code. Cloud, Infrastructure, and Cybersecurity: Systems, networks, deployment, security, reliability, architecture, and the operational side of software. Data and Software Engineering: APIs, databases, testing, maintainability, version control, software design, and moving from simple scripts to real-world applications. Learning and Technical Growth: How people learn difficult technical concepts, get unstuck, build confidence, and develop the judgment needed to use technology well. Expert Interviews: Conversations with educators, developers, engineers, researchers, technology leaders, and others shaping the future of computing and technical education. Teaching Python remains grounded in Python, but the conversation extends beyond syntax. The podcast explores the knowledge, skills, systems, and judgment people need to learn, build, and make responsible decisions with technology.</itunes:summary>
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    <itunes:keywords>Python, Python programming, learn Python, teaching Python, computer science education, coding education, programming for beginners, Python for beginners, computational thinking, code literacy, debugging, software development, software engineering, data science, artificial intelligence, AI literacy, AI-assisted coding, generative AI, machine learning, cybersecurity, cloud computing, cloud infrastructure, APIs, databases, systems thinking, technical education, STEM education, educational technology, edtech, curriculum design, instructional design, professional learning, teacher professional development, coding for students, computer science curriculum, physical computing, robotics, responsible AI, digital literacy, data literacy, technology leadership, developer education, technical coaching, lifelong learning, learning to code, teaching programming, real-world programming, coding with AI, Python podcast, computer science podcast, technology education podcast, programming podcast</itunes:keywords>
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  <title>Episode 102: The Learner Variability Project</title>
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  <pubDate>Sat, 24 Dec 2022 14:00:00 -0500</pubDate>
  <author>Sean Tibor and Kelly Paredes</author>
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  <itunes:episode>102</itunes:episode>
  <itunes:title>The Learner Variability Project</itunes:title>
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  <itunes:author>Sean Tibor and Kelly Paredes</itunes:author>
  <itunes:subtitle>In Episode 102 of the Teaching Python podcast with Kelly and Sean, the topic is learner variability, based on an article by Jessica Jackson of the Digital Promise Project. This episode discusses how to effectively teach programming to a diverse group of learners, and how to adapt to the individual needs and learning styles of each student.</itunes:subtitle>
  <itunes:duration>49:39</itunes:duration>
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  <description>&lt;p&gt;In Episode 102 of the Teaching Python podcast with Kelly and Sean, the topic is learner variability, based on an article by Jessica Jackson of the Digital Promise Project. This episode discusses how to effectively teach programming to a diverse group of learners, and how to adapt to the individual needs and learning styles of each student.&lt;/p&gt;

&lt;p&gt;One key point discussed in the podcast is that students learn at different times and at different rates, and it is important for teachers to recognize and accommodate this variability. The old theory of learning styles, which suggests that individuals have a preferred style of learning and that teaching should be tailored to these preferences, has been debunked as a myth. Instead, research suggests that the most effective way to teach is to use a variety of methods and approaches, and to allow students to engage with the material in different ways.&lt;/p&gt;

&lt;p&gt;In addition to incorporating teaching strategies such as scaffolding, differentiation, and formative assessment, it is also important for teachers to consider the various learning factors that may impact their students. This includes designing for students who may be at the margins, such as those who are struggling or those who are advanced. It also involves designing with context in mind, taking into account the unique experiences and background of each student. This can include cultural factors, language barriers, and any prior knowledge or skills that may be relevant to the material being taught.&lt;/p&gt;

&lt;p&gt;Designing with the whole child in mind involves considering not just the cognitive aspects of learning, but also the social and emotional aspects. This may involve creating a positive and supportive learning environment, and being attuned to the needs and well-being of each student. Finally, designing for powerful experiences involves creating engaging and meaningful learning activities that are likely to have a lasting impact on students. By considering these various learning factors, teachers can create more effective and inclusive learning experiences for their students.&lt;/p&gt;</description>
  <itunes:keywords>Learner variability, Teaching programming, Diverse learners, Learning styles, Scaffolding, Differentiation, Formative assessment, Designing for learning, Marginalized learners, Contextual factors, Whole child learning, Powerful experiences</itunes:keywords>
  <content:encoded>
    <![CDATA[<p>In Episode 102 of the Teaching Python podcast with Kelly and Sean, the topic is learner variability, based on an article by Jessica Jackson of the Digital Promise Project. This episode discusses how to effectively teach programming to a diverse group of learners, and how to adapt to the individual needs and learning styles of each student.</p>

<p>One key point discussed in the podcast is that students learn at different times and at different rates, and it is important for teachers to recognize and accommodate this variability. The old theory of learning styles, which suggests that individuals have a preferred style of learning and that teaching should be tailored to these preferences, has been debunked as a myth. Instead, research suggests that the most effective way to teach is to use a variety of methods and approaches, and to allow students to engage with the material in different ways.</p>

<p>In addition to incorporating teaching strategies such as scaffolding, differentiation, and formative assessment, it is also important for teachers to consider the various learning factors that may impact their students. This includes designing for students who may be at the margins, such as those who are struggling or those who are advanced. It also involves designing with context in mind, taking into account the unique experiences and background of each student. This can include cultural factors, language barriers, and any prior knowledge or skills that may be relevant to the material being taught.</p>

<p>Designing with the whole child in mind involves considering not just the cognitive aspects of learning, but also the social and emotional aspects. This may involve creating a positive and supportive learning environment, and being attuned to the needs and well-being of each student. Finally, designing for powerful experiences involves creating engaging and meaningful learning activities that are likely to have a lasting impact on students. By considering these various learning factors, teachers can create more effective and inclusive learning experiences for their students.</p><p><a rel="payment" href="https://www.patreon.com/teachingpython">Support Teaching Python</a></p><p>Links:</p><ul><li><a title="Learner Variability Teacher Guide.pdf - Google Drive" rel="nofollow" href="https://drive.google.com/file/d/1pxoujo5cgrm77_QYdVMdslTFyp4gW0Kt/view">Learner Variability Teacher Guide.pdf - Google Drive
</a> &mdash; The Learner Variability Project In the Field:
A Guide to Understanding and Addressing
Learner Variability
</li><li><a title="Learner Variability Navigator | Learner Variability Project" rel="nofollow" href="https://lvp.digitalpromiseglobal.org/">Learner Variability Navigator | Learner Variability Project
</a> &mdash; Learners thrive when their experience is personal and meaningful. This free tool helps you find research-based strategies that support the whole learner so you can create better learning experiences today.
</li><li><a title="Homepage - Digital Promise" rel="nofollow" href="https://digitalpromise.org/">Homepage - Digital Promise
</a> &mdash; Digital Promise shapes the future of learning and advances equitable education systems by bringing together solutions across research, practice, and technology.
</li></ul>]]>
  </content:encoded>
  <itunes:summary>
    <![CDATA[<p>In Episode 102 of the Teaching Python podcast with Kelly and Sean, the topic is learner variability, based on an article by Jessica Jackson of the Digital Promise Project. This episode discusses how to effectively teach programming to a diverse group of learners, and how to adapt to the individual needs and learning styles of each student.</p>

<p>One key point discussed in the podcast is that students learn at different times and at different rates, and it is important for teachers to recognize and accommodate this variability. The old theory of learning styles, which suggests that individuals have a preferred style of learning and that teaching should be tailored to these preferences, has been debunked as a myth. Instead, research suggests that the most effective way to teach is to use a variety of methods and approaches, and to allow students to engage with the material in different ways.</p>

<p>In addition to incorporating teaching strategies such as scaffolding, differentiation, and formative assessment, it is also important for teachers to consider the various learning factors that may impact their students. This includes designing for students who may be at the margins, such as those who are struggling or those who are advanced. It also involves designing with context in mind, taking into account the unique experiences and background of each student. This can include cultural factors, language barriers, and any prior knowledge or skills that may be relevant to the material being taught.</p>

<p>Designing with the whole child in mind involves considering not just the cognitive aspects of learning, but also the social and emotional aspects. This may involve creating a positive and supportive learning environment, and being attuned to the needs and well-being of each student. Finally, designing for powerful experiences involves creating engaging and meaningful learning activities that are likely to have a lasting impact on students. By considering these various learning factors, teachers can create more effective and inclusive learning experiences for their students.</p><p><a rel="payment" href="https://www.patreon.com/teachingpython">Support Teaching Python</a></p><p>Links:</p><ul><li><a title="Learner Variability Teacher Guide.pdf - Google Drive" rel="nofollow" href="https://drive.google.com/file/d/1pxoujo5cgrm77_QYdVMdslTFyp4gW0Kt/view">Learner Variability Teacher Guide.pdf - Google Drive
</a> &mdash; The Learner Variability Project In the Field:
A Guide to Understanding and Addressing
Learner Variability
</li><li><a title="Learner Variability Navigator | Learner Variability Project" rel="nofollow" href="https://lvp.digitalpromiseglobal.org/">Learner Variability Navigator | Learner Variability Project
</a> &mdash; Learners thrive when their experience is personal and meaningful. This free tool helps you find research-based strategies that support the whole learner so you can create better learning experiences today.
</li><li><a title="Homepage - Digital Promise" rel="nofollow" href="https://digitalpromise.org/">Homepage - Digital Promise
</a> &mdash; Digital Promise shapes the future of learning and advances equitable education systems by bringing together solutions across research, practice, and technology.
</li></ul>]]>
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