Pedagogy deals with the theory and practice of teaching. Pedagogy informs teaching strategies, teacher actions, and teacher judgements and decisions by taking into consideration theories of learning, understanding of students and their needs, and the backgrounds and interests of individual students. Pedagogy includes how the teacher interacts with students and the social and intellectual environment the teacher seeks to establish.
While teachers are generally free to use whatever approach they professionally feel best suits their students and themselves, sometimes groups of teachers, schools, and systems decide to focus on a particular approach to teaching. While the curriculum tries not to emphasise particular pedagogical approaches, Technologies Education does tend to support a Constructionist approach (as an application of Constructivism) taught through Project Based Learning, but is also supported by many online tutorials that adopt more traditional approaches such as Direct Instruction.
While there are many pedagogical approaches to teaching, in this course, we will focus on the two most common approaches, used in combination, for teaching the Technologies learning area. The Constructionist learning theory, supported by a Project Based Learning pedagogy, and the Instructivist learning theory, supported by a Direct Instruction pedagogy and Instructional Design models.
THE FUTURES OF LEARNING.pdfLearning theory describes how students receive, process, and retain knowledge during learning.
Ertmer_et_al-2013-Performance_Improvement_Quarterly.pdfBehaviorists look at learning as an aspect of conditioning and advocate a system of rewards and targets in education.
Constructivists believe that learning relies on what students already know and understand, and acquisition of knowledge should be an individually tailored process of construction.
Cognitivists study the learner rather than their environment - and in particular the complexities of human memory formation.
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Direct Instruction (DI) is a pedagogy that involves clear and concise instruction, teacher-led demonstrations, and immediate feedback to students. DI is a highly structured and systematic approach that aims to teach students specific skills or knowledge in a step-by-step manner.
In DI, the teacher presents information in a clear and concise manner, using a variety of visual aids, such as screencasts, slideshows, diagrams, and graphs, to help students understand the material. The teacher then models the skill or knowledge, providing students with an opportunity to see the skill in action. Students are then given a chance to practice the skill or knowledge, with the teacher providing immediate feedback on their performance.
DI is based on the idea that all students can learn when provided with explicit instruction and immediate feedback. It is most often used to teach lower-order thinking skills, such as remembering, understanding, and applying, but it can be used to teach more complex concepts.
Some key elements of DI include:
Clear and concise instruction: Teachers use simple, direct language to explain concepts and procedures.
Modelling: Teachers demonstrate how to perform the skill or knowledge.
Practice: Students practice the skill or knowledge with guidance and feedback from the teacher.
Immediate feedback: Teachers provide immediate feedback to students to correct errors and reinforce correct responses.
DI is a highly effective instructional method, especially for students who struggle with learning. It provides clear and structured guidance, which can help students develop skills and knowledge quickly and efficiently.
Behaviourism and motivation play a strong role in DI, but it can also incorporate aspects of Contructionism and Cognitivism.
Direct instruction is often associated with traditional, teacher-centred classrooms, but it can be used effectively in a variety of classroom settings, including combining it with project-based learning and student-centred classrooms. Direct instruction is particularly effective for students who are struggling with a particular skill or concept, as it provides them with structured support and clear feedback to help them succeed.
The Elemental Learning Design is a direct instruction pedagogy model used by the DTHub.
At the start of a learning sequence, we wish to hook the learners and pique their curiosity about the learning to come. This learning hook can come in many forms; it could be a short video, a question, an image, a puzzle or any other prompt that promotes thinking and curiosity.
Also known as an anticipatory set.
Once you have engaged the learners with a learning hook, which starts to help them understand what and why they are learning, you need to reinforce the what and why by looking at a learning map. This describes the learning intentions and helps to contextualise the learning in the bigger picture of a student's progress and also gives specific outcomes for the current learning sequence.
In terms of the specific learnings, you may wish to co-construct these with the learners themselves. Often we talk about learning outcomes in terms of the knowledge that the learners will acquire in a period of learning. While building knowledge is extremely important, we can validate and enhance other learning outcomes in the form of Thinking Skills used and developed, skillsets practised and toolsets used.
Once learners understand what and why they are learning, there may be some learning input that will provide new knowledge. This could be a teacher or student modelling to the whole class how to create an algorithm in Scratch before the learners attempt this themselves. It could equally be a video tutorial, peer learning, or exploration. The core commonality is that the learners acquire new knowledge, which they will be able to use to construct a deeper understanding.
Learning input should always be paired with learning construction. This is time when learners are actively constructing their understanding by doing. This is an opportunity to play with new knowledge – to experiment, push boundaries, fail and retry – and is essential in creating connections between new knowledge, which is the foundation of genuine understanding. An example of learning construction could be 'sandpit time' where learners are allowed a period of time to simply play and build using the software they are learning. This is the very premise of Minecraft, one of the most successful computer games available. Learners can then reflect on their play and share any new learning they have uncovered that may be of use to others.
Students are given a set period of time to put together an algorithm for making paper planes, in the form of a flowchart. They then give their algorithm to another group who try to build a paper plane using the instructions. There is an excellent example on code.org with some unplugged to try.
In order to make their learning explicit, we should provide all learners with the opportunity to participate in a learning demo where they demonstrate their understanding of the knowledge set we determined in the learning outcomes. This is important not only for the learner to have the opportunity to share what they know, but also for you as a teacher to gather data on progress and make decisions about the next steps in learning: do you need to cover something again, are the learners ready to move on to the next stage?
In teams, learners write an accurate algorithm for a simple task in the form of a flowchart and then demonstrate this algorithm to another team who are acting as critical friends. In their demonstration, they must use the term algorithm and represent their algorithm as a flowchart. The teacher can circulate and check on the levels of understanding demonstrated by each team, keeping a note of how successful the learner progress has been so as to adapt the next lesson.
The final element of learning design we will discuss is learning reflection. This is the opportunity for learners to reflect on themselves in terms of their mindset, skillset and toolset development. The ability to meta-reflect on one’s own development as a learner is an incredibly powerful capacity in a world where the cognitive demands upon us are such that we are constantly forced to reconceptualise and think elastically.
In the lesson idea Computer chatter, the learning reflection uses a PMI to assist students to think about how well their network operated and what changes made a difference. Plus, Minus and Interesting provides a useful scaffold to gauge what students have learned during the task.
The Elemental Learning Design Model, as illustrated above should not be seen as a simple algorithm. When designing learning, you may wish to have learning input, learning construction, learning demo and learning reflection happening numerous times within a sequence of learning. It is up to teacher pedagogical skill and knowledge of learners to adapt this model to best suit your situation, remembering that while each element is important, some elements may be very brief while some may take up most of the lesson. Just remember to include them all in some way so you have all of the ingredients of great learning, but in the right balance for your class.
The human brain can only process a small amount of new information at once, but it can process very large amounts of stored information.
Information is processed in the working memory, where small amounts of information are stored for a very short time. The average person can only hold about four ‘chunks’ of information in their working memory at one time.
Long-term memory is where large amounts of information are stored semi-permanently. Information is stored in the long-term memory in ‘schemas’, which provide a system for organising and storing knowledge.
If a student’s working memory is overloaded, there is a risk that they will not understand the content being taught and that their learning will be slow and/or ineffective.
With extensive practice, information can be automatically recalled from long-term memory with minimal conscious effort. This ‘automation’ reduces the burden on working memory because when information can be accessed automatically, the working memory is freed up to learn new information.
Cognitive load theory indicates that when teaching students new content and skills, teachers are more effective when they provide explicit guidance accompanied by practice and feedback, not when they require students to discover for themselves many aspects of what they must learn.
Worked example effect: which is the widely replicated finding that novice learners who are given worked examples to study perform better on subsequent tests than learners who are required to solve the equivalent problems themselves.
Expertise reversal effect: which shows that as students become more proficient at solving a particular type of problem, they should gradually be given more opportunities for independent problem-solving.
Strategy 1: Tailor lessons according to students’ existing knowledge and skill.
Strategy 2: Use worked examples to teach students new content or skills.
Strategy 3: Gradually increase independent problem-solving as students become more proficient.
Strategy 4: Cut out inessential information.
Strategy 5: Present all the essential information together.
Strategy 6: Simplify complex information by presenting it both orally and visually.
Strategy 7: Encourage students to visualise concepts and procedures that they have learnt.
QR1-Cognitive-load-theory.pdfblockmodel.pdf2017-cognitive-load-theory-practice-guide.pdfWorked examples are a powerful teaching tool in programming, where a teacher provides step-by-step solutions to programming problems. These examples are designed to help students understand the underlying concepts and processes behind solving problems in programming.
By using worked examples, teachers can break down complex programming problems into smaller, more manageable pieces, which can help students develop a deeper understanding of the problem-solving process. Worked examples can also provide a model for students to follow, which can help them build their problem-solving skills.
Worked examples improve learning by reducing cognitive load during skill acquisition. Worked examples provide instructions to reduce extraneous cognitive load and increase germane cognitive load for the learner initially when few schemas are available. Intrinsic cognitive load is a third type of cognitive load that provides a base load that is irreducible. Extraneous load is reduced by the scaffolding of worked examples at the beginning of skill acquisition. Finally, worked examples can also increase the germane load when prompts for self-explanations are used.
Worked examples are often used in combination with other teaching techniques, such as hands-on coding activities and group projects. This approach allows students to apply the concepts they have learned in a practical setting, using the worked examples as a guide.
Additionally, worked examples can help students identify and correct errors in their own code. By comparing their own solutions to the worked examples, students can identify mistakes or gaps in their understanding, which can help them to improve their coding skills.
QR2-Worked-examples.pdfworkedexamplesincs.pdfThe Use-Modify-Create (UMC) technique is a teaching methodology in programming that encourages students to learn by building on existing code. The technique involves three stages:
Use: In the first stage, students start by using existing code or programs to solve a specific problem. This can help students understand how the code works and identify its strengths and weaknesses.
Modify: In the second stage, students modify the existing code or program to meet a slightly different requirement or goal. This requires students to analyze the code, understand its structure and syntax, and apply their own logic to make changes.
Create: In the final stage, students create their own original code or program from scratch, based on what they have learned from the first two stages. This allows students to apply their newfound knowledge and skills to solve a new problem or create a new application.
The UMC technique is a powerful teaching tool because it encourages students to build on their existing knowledge and skills while gradually increasing the complexity of the tasks. This approach can help students gain confidence in their ability to code, develop their problem-solving skills, and deepen their understanding of programming concepts.
Speak Outloud, or Live programming, is a technique for teaching programming concepts and skills. The approach involves having the teacher or students speak out loud the code while they are typing or writing it. This allows the students to hear the code and the logic behind it, which can help them better understand how the code works and how to write it themselves.
By speaking the code out loud, students can also identify mistakes more easily, which can help them develop their debugging skills. Additionally, speaking out loud can help students improve their communication and problem-solving skills as they articulate their thoughts and work through programming challenges.
QR5-Live-coding.pdfRubber Ducking is a technique used in teaching programming, where a student explains their code line by line to an inanimate object, such as a rubber duck, or to someone who has no knowledge of programming. The idea behind this technique is that by explaining the code, the student can identify errors, inconsistencies, or gaps in their understanding.
The process works by encouraging students to break down their code into manageable pieces and to analyse each part in detail. By explaining each line of code, students can identify mistakes or logical gaps that they may not have noticed before.
Code tracing, also known as code walkthrough or debugging, is a method used in teaching programming to help students understand how a computer program executes line by line. It involves analyzing a piece of code by hand to identify the variables, their values, and how they change as the program runs.
Code tracing is a valuable teaching tool because it allows students to see the flow of a program and understand the logic behind it. It also helps them identify errors in their code and debug it effectively.
When teaching programming, code tracing can be used in various ways:
Debugging: Code tracing can be used to help students identify errors in their code by walking through the program and identifying where it deviates from the expected output.
Reinforcing Concepts: Code tracing can help students reinforce their understanding of programming concepts by allowing them to see how different parts of a program work together.
Examining Algorithms: Code tracing can be used to examine the logic behind algorithms and understand how they work to solve problems.
Evaluating Code Quality: Code tracing can be used to evaluate the quality of code by identifying inefficiencies, redundant code, and other areas for improvement.
QR14-Code-tracing.pdfPRIMM stands for Predict-Run-Investigate-Modify-Make. It builds on the Use-Modify-Create model.
Learners are presented with a program to read and try to predict what it will do. Learners should verbalise and discuss their predictions before writing things down. The Predict stage works well as a paired or small group activity. This helps to build the necessary vocabulary through dialogue. Learners should be focused on what will happen when the code is run.
The programming example that you provide for your learners should include familiar programming constructs and introduce only one or two new concepts.
This is where learners get to test their predictions from the previous stage. It is important to remember that the educator is an essential element in the success of the PRIMM approach. The educator can provide structure at this stage and use discussion to help the learners verbalise the function of the code and see whether it matched their predictions.
The Investigate stage uses questioning techniques called the Block Model. The focus of the model is code comprehension.
The model uses four levels of understanding:
Atomic: individual lines of code or even sections of those lines of code
Block: groups of adjacent lines of code that link very closely together
Relational: lines of code that are related but are not next to each other
Macro: the entire program or larger subprograms, depending on the size of the project
You should prepare activities or questions for your students to complete while investigating the code. The questions should be designed to cover all aspects of the Block Model.
Example activity types are:
Tracing, where you look at variables in a piece of code and trace their values throughout the running of the program. Tracing allows a learner to develop their logic skills. Learners should be able to confidently trace code through sequences, selection statements and loops.
Explaining, which can happen in pairs verbally to help support vocabulary development. Learners should be able to explain at each level of understanding in the Block Model, starting at the atomic level.
Annotating, where learners comment on each line or block of code by stating what it does.
Debugging, which ensures that the code works when the learner is investigating it. You might intentionally add a minor logic error or a piece of code that is not needed. Learners are then given activities to spot where those errors might be happening.
By using the Block Model to develop activities and questions, you can try to pinpoint where a student's misconceptions are. If a student has a gap in their understanding, they are most likely missing a level of understanding from the Block Model. You can then target questions and activities at specific areas to help support your students.
The Modify stage in PRIMM is very similar to the Modify stage in Use-Modify-Create. Learners complete a variety of modifications to the code that they have previously investigated, with an increasing level of challenge. The scaffolding is carefully removed as learners become more comfortable with the new concepts that they have learnt.
For the Make stage, learners are given a new problem to solve that uses the same structure and concepts that have been learnt. This gives them time to apply new learning in a familiar setting. The Make stage should involve algorithm design in which learners use their computational thinking skills to break the problem down into sub-goals and produce an algorithm for the solution.
QR11-PRIMM.pdfPedagogy+Quick+Read+12+-+Block+Model.pdfblockmodel.pdfParson's Problems are a type of teaching tool for programming that help students develop their coding skills by focusing on the structure and syntax of code. Parson's Problems are coding challenges where the code is presented in scrambled or random order, and students must drag and drop the code into the correct order to create a working program.
The idea behind Parson's Problems is to focus on the structure and syntax of code, rather than the logic and problem-solving aspects. This approach helps students develop their ability to recognize and apply the correct syntax, which can be especially helpful for beginners who may struggle with the structure of code.
Parson's Problems can be used to teach a wide range of programming languages, and they can be adapted to different skill levels and learning objectives. They can also be used in a variety of settings, such as individual practice, group work, or assessment.
The benefits of using Parson's Problems in teaching programming include:
Helping students develop their ability to recognize and apply the correct syntax
Providing a low-stress way for beginners to practice coding skills
Encouraging students to think about the structure and organization of code
Providing a way to assess students' understanding of programming concepts in a non-traditional way.
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Pair programming is a software development technique in which two programmers work together at one computer, sharing one keyboard and one mouse. In pair programming, one programmer takes the role of the "driver," typing code and controlling the computer, while the other programmer takes the role of the "navigator," guiding the driver and providing feedback.
The goal of pair programming is to improve the quality of code, reduce errors, and increase efficiency. By working together, student programmers can catch errors more quickly, brainstorm solutions more effectively, and produce better-quality code overall.
Pair programming can also help develop communication skills and foster a sense of collaboration between students. It can be particularly useful in an Agile development environment, where rapid iteration and collaboration are key.
Pair programming is often used in the context of Test-Driven Development (TDD), where one programmer writes the test cases and the other writes the code to pass those tests. This approach can lead to more comprehensive testing and more robust code.
Overall, pair programming is a powerful tool for improving the efficiency and quality of software development, and it can be a valuable technique for students to improve their programming skills and teamwork.
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Peer instruction is a teaching method that involves students engaging in discussions with their peers to improve their understanding of a particular topic. The process typically involves the following steps:
The teacher presents a question or problem to the class related to the topic being studied.
Students individually think about and formulate their answers to the question or problem.
Students share their answers with a peer or small group, and discuss and debate their reasoning and approaches to solving the problem.
The teacher then facilitates a class-wide discussion where students can share their group's answer and reasoning, and the teacher provides feedback and additional clarification if necessary.
The peer instruction model is based on the idea that students can learn from each other by sharing their different perspectives and approaches. By engaging in peer discussions, students can gain a deeper understanding of the topic by reflecting on their own thought processes and evaluating the perspectives of their peers. This approach encourages active learning and helps students develop critical thinking and problem-solving skills. Additionally, it can promote a more collaborative and inclusive classroom environment, where students are encouraged to learn from each other and feel more invested in their own learning.
QR4-Peer-instruction.pdfAlternate conceptions, also known as misconceptions, are pre-existing beliefs or ideas that students hold about a particular topic. In computing education, alternate conceptions about concepts such as algorithms, programming, and computer systems can be common and can hinder student learning if not addressed effectively.
Strategies that teachers can use to address alternate conceptions in computing education:
Diagnose student misconceptions: Before addressing alternate conceptions, it's important for teachers to first identify the specific misconceptions that students hold. This can be done through pre-assessments, formative assessments, or class discussions.
Provide accurate information: Once misconceptions have been identified, teachers can provide accurate information that directly addresses the alternate conception. This can be done through lectures, readings, videos, or interactive simulations.
Engage in active learning: Instead of relying solely on lectures, teachers can engage students in active learning activities such as group discussions, problem-solving exercises, and hands-on programming projects. These activities can help students to see the relevance of computing concepts and develop a deeper understanding.
Use analogies and metaphors: Teachers can use analogies and metaphors to help students connect new computing concepts with their prior knowledge and experiences. For example, a teacher might compare the structure of a program to the organization of a recipe.
Encourage reflection: Teachers can encourage students to reflect on their own thinking and learning processes. This can help students to identify and correct their own misconceptions over time.
QR19-Alternate-Conceptions.pdfSemantic waves are a teaching strategy based on Legitimation Code Theory (LCT) that help novices understand expert knowledge by shifting between abstract and concrete contexts, technical and simple meanings, and expert and novice understanding. It has been successfully applied by educators across many disciplines, including computing, and can help students master the technical terminology while developing a firm understanding of the concepts.
A good way to implement it is by using metaphors, analogies, and unplugged computing (learning without computers), but it is important to link the simpler meanings back to the abstract concepts and associated technical language. Teachers can plan lessons around a semantic wave structure, evaluate their lesson plans and explanations, and encourage learners to write their own explanations using semantic waves.
Semantic profiles are visual representations of changes in language and context within a learning activity, that allow teachers to critique and improve their teaching.
QR6-Semantic-waves.pdfConcept maps are graphical tools used to organise and represent knowledge by depicting concepts and their relationships. The components of concept maps include concepts arranged in a hierarchy, links connecting them to indicate relationships, labels on links specifying the relationships, focus questions providing context for the map, and examples of concepts to make them clearer.
They are used in education for planning, teaching, learning, and assessment. Educators use concept maps to capture the knowledge they aim to convey to learners, inform planning, and as a means of communication with other educators.
Concept maps can also be presented to learners as a way to provide or summarise information, support teaching and assessment, and as study guides or revision tools. They should be tightly integrated into the teaching and learning process and only used for assessment if they have been consistently used in teaching.
QR7-Concept-maps.pdfReframe from management to relationships. How you engage with your students will reflect how they engage with you.
develop high-quality student-teacher relationships;
provide structure, predictability, and opportunities for active student participation in the classroom; and
actively supervise students to keep them on task; and
respond to disengagement and disruptive behaviours, and support students to re-engage in learning.