It is nothing short of a miracle that modern methods of instruction have not yet strangled the holy curiosity of enquiry.
Week 5 Pedagogy
This week we are examining the importance of developing thinking skills (Design, Computational and Systems) through Digital Technologies. Exploring planning processes that support student-directed learning and the integration of activities through project-based learning. There are two broad pedagogical approaches to the teaching of Digital Technologies, the first is through the use of Direct Instruction involving teacher led presentations and activities or scripted computer/online videos and activities. The second is through the use of Project Based Learning (PBL) involving teacher supported inquiry or student directed investigations. The two can be complimentary, with direct instruction supporting fundamental concept and skill development subsequently used by students in PBL that develops higher order thinking skills.
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, understandings 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.
The Ertmer paper provides an overview of traditional pedagogies, while the literature review introduces you to more recent developments (focus on sections 2 to 6).
Innovations in Pedagogy
Computer education has been involved in reforming the pedagogy used in schools, largely because it has also been something new, but more importantly, because the nature of teaching computer education has encouraged different approaches to teaching.
Challenge Based Learning
Challenge-based learning (CBL) is a framework for learning while solving real-world Challenges. The framework is collaborative and hands-on, asking participants (students, teachers, families, and community members) to identify Big Ideas, ask questions, discover and solve Challenges, gain in-depth subject area knowledge, develop 21st-century skills, and share their thoughts globally.
Framed by the Australian Curriculum as a problem-solving method that involves various techniques and strategies that can be implemented by digital systems. Techniques and strategies may include organising data logically, breaking down problems into parts, defining abstract concepts and designing and using algorithms, patterns and models.
The curriculum is designed so that students will develop and use increasingly sophisticated computational thinking skills, and processes, techniques and digital systems to create solutions to address specific problems, opportunities or needs. Computational thinking is a process of recognising aspects of computation in the world and being able to think logically, algorithmically, recursively and abstractly. Students will also apply procedural techniques and processing skills when creating, communicating and sharing ideas and information, and managing projects.
A number of key concepts underpin the Digital Technologies curriculum. These establish a way of thinking about problems, opportunities and information systems and provide a framework for knowledge and practice. The key concepts are:
abstraction, which underpins all content, particularly the content descriptions relating to the concepts of data representation, and specification, algorithms and implementation
data collection (properties, sources and collection of data), data representation (symbolism and separation) and data interpretation (patterns and contexts)
specification (descriptions and techniques), algorithms (following and describing) and implementation (translating and programming)
digital systems (hardware, software, and networks and the internet)
Interactions (people and digital systems, data and processes) and impacts (sustainability and empowerment).
The concepts of abstraction, data collection, representation and interpretation, specification, algorithms and implementation correspond to the key elements of computational thinking. Collectively, these concepts span the key ideas about the organisation, representation and automation of digital solutions and information. They can be explored in non-digital or digital contexts and are likely to underpin future digital systems. They provide a language and perspective that students and teachers can use when discussing digital technologies.
Abstraction is a process of reducing complexity to formulate generalised fundamental ideas or concepts removed from specific details or situation. For example, the idea that a cricket ball is a sphere in the same way that a soccer ball is, or the concept that data can be organised in records made up of fields irrespective of whether the data are numbers, text, images or something else.
Abstraction involves hiding details of an idea, problem or solution that are not relevant, to focus on a manageable number of aspects. Abstraction is a natural part of communication: people rarely communicate every detail, because many details are not relevant in a given context. The idea of abstraction can be acquired from an early age. For example, when students are asked how to make toast for breakfast, they do not mention all steps explicitly, assuming that the listener is an intelligent implementer of the abstract instructions.
Central to managing the complexity of information systems is the ability to ‘temporarily ignore’ the internal details of the sub components of larger specifications, algorithms, systems or interactions. In digital systems, everything must be broken down into simple instructions.
In Digital Technologies, discrete representation of information using number codes. Data may include characters (for example, alphabetic letters, numbers and symbols), images, sounds and/or instructions that, when represented by number codes, can be manipulated, stored and communicated by digital systems. For example, characters may be represented using ASCII code or images may be represented by a bitmap of numbers representing each ‘dot’ or pixel.
The concepts that are about data focus on the properties of data, how they are collected and represented, and how they are interpreted in context to produce information. These concepts in Digital Technologies build on a corresponding statistics and probability strand in the Mathematics curriculum. The Digital Technologies curriculum provides a deeper understanding of the nature of data and their representation, and computational skills for interpreting data. The data concepts provide rich opportunities for authentic data exploration in other learning areas while developing data processing and visualisation skills.
Data collection describes the numerical, categorical and textual facts measured, collected or calculated as the basis for creating information and its binary representation in digital systems. Data representation describes how data are represented and structured symbolically for storage and communication, by people and in digital systems, and is addressed in the knowledge and understanding strand. Data interpretation describes the processes of extracting meaning from data.
Step-by-step procedures required to solve a problem. For example, to find the largest number in a list of positive numbers:
Note the first number as the largest.
Look through the remaining numbers, in turn, and if a number is larger than the number found in 1, note it as the largest.
Repeat this process until complete. The last noted number is the largest in the list.
An algorithm may be described in many ways. Flowcharts are often useful in visualising an algorithm.
The concepts specification, algorithms and implementation focus on the precise definition and communication of problems and their solutions. This begins with the description of tasks and concludes in the accurate definition of computational problems and their algorithmic solutions. This concept draws from logic, algebra and the language of mathematics, and can be related to the scientific method of recording experiments in science.
Specification describes the process of defining and communicating a problem precisely and clearly. For example, explaining the need to direct a robot to move in a particular way. An algorithm is a precise description of the steps and decisions needed to solve a problem. Algorithms will need to be tested before the final solution can be implemented. Anyone who has followed or given instructions, or navigated using directions, has used an algorithm. These generic skills can be developed without programming. For example, students can follow the steps within a recipe or describe directions to locate items. Implementation describes the automation of an algorithm, typically by using appropriate software or writing a computer program.
Digital hardware and software components (internal and external) used to transform data into a digital solution. When digital systems are connected, they form a network. For example:
a smartphone is a digital system that has software (apps, an operating system), input components (for example, touch screen, keyboard, camera and microphone), output components (for example, screen and speakers), memory components (for example, silicon chips, solid state drives), communication components (for example, SIM card, wi-fi, bluetooth or mobile network antennas), and a processor made up of one or more silicon chips.
a desktop computer with specific software and hardware components for dairy farming. The computer is connected via cables to milking equipment and via wi-fi to sensors that read tags on the cows. Through these hardware components the software records how much milk each cow provides. Such systems can also algorithmically control attaching milking equipment to each cow, providing feed and opening gates.
The digital systems concept focuses on the components of digital systems: hardware and software (computer architecture and the operating system), and networks and the internet (wireless, mobile and wired networks and protocols). This concept is addressed in both strands. The broader definition of an information system that includes data, people, processes and digital systems falls under the interactions and impacts concept below.
Interactions and Impacts
The interactions and impacts concepts focus on all aspects of human interaction with and through information systems, and the enormous potential for positive and negative economic, environmental and social impacts enabled by these systems.
Interactions refers to all human interactions with information systems, especially user interfaces and experiences, and human–human interactions including communication and collaboration facilitated by digital systems. This concept also addresses methods for protecting stored and communicated data and information.
Impacts describes analysing and predicting the extent to which personal, economic, environmental and social needs are met through existing and emerging digital technologies; and appreciating the transformative potential of digital technologies in people’s lives. It also involves consideration of the relationship between information systems and society and in particular the ethical and legal obligations of individuals and organisations regarding ownership and privacy of data and information.