Welcome to Digital Education Futures
Welcome to Digital Education Futures, a critical journey through emerging technologies reshaping the education landscape. In this module, part of the MA Digital Technologies, Communication and Education program, we will unravel the complexities of generative AI, augmented/virtual reality, data visualisation and more, through detailed case studies and hands-on experiences.
Reading for this week: the preface of Big Data in Education
Reading for this week: the preface of Big Data in Education
What do you expect to happen over the next year? What do you expect to happen over the next twelve weeks as students on Digital Education Futures?
What does it mean to talk about the future? The literal meaning of the word is “a period of time following the moment of speaking or writing” or “time regarded as still to come”. In this sense to talk about the future is simply to talk about what hasn’t happened yet but what we expect will or might happen. To talk about the future of education means to talk about what we expect will or might happen in education in the coming years.
Consider your own experience as new students on the MA DTCE.What do you expect to happen over the next year? What do you expect to happen over the next twelve weeks as students on Digital Education Futures? There are some expectations which are likely to be accurate such as studying for two semesters, undertaking a graduation and exploring Manchester in your free time. There are other expectations which might be less accurate because there will inevitably be some things in the next year which surprise you, hopefully in positive ways.
We all have expectations about the future but it is far more difficult to make predictions which are reliable. There is a long history of trying to use magical means (such as a crystal ball) to predict the future which reflects the fundamental difficulty of knowing what is going to happen. But the fact we can’t know what is going to happen does not mean we cannot say anything meaningful. By focusing on technology it becomes easier to trace out potential developments.
In this unit we explore the technological developments currently taking place within education in order to ask questions about where these might lead in the future. It is called futures (plural) to recognise that there are many possible outcomes and that we have the capacity to influence which future comes to be realised. There are many people, networks and organisations trying to influence what the future will look like in order that it will serve their interests and reflect their ideas. In this sense we will bring together the social and the technical to understand the changes underway within education and where these might lead in the future.
Consider your own experience as new students on the MA DTCE.What do you expect to happen over the next year? What do you expect to happen over the next twelve weeks as students on Digital Education Futures? There are some expectations which are likely to be accurate such as studying for two semesters, undertaking a graduation and exploring Manchester in your free time. There are other expectations which might be less accurate because there will inevitably be some things in the next year which surprise you, hopefully in positive ways.
We all have expectations about the future but it is far more difficult to make predictions which are reliable. There is a long history of trying to use magical means (such as a crystal ball) to predict the future which reflects the fundamental difficulty of knowing what is going to happen. But the fact we can’t know what is going to happen does not mean we cannot say anything meaningful. By focusing on technology it becomes easier to trace out potential developments.
In this unit we explore the technological developments currently taking place within education in order to ask questions about where these might lead in the future. It is called futures (plural) to recognise that there are many possible outcomes and that we have the capacity to influence which future comes to be realised. There are many people, networks and organisations trying to influence what the future will look like in order that it will serve their interests and reflect their ideas. In this sense we will bring together the social and the technical to understand the changes underway within education and where these might lead in the future.
The case studies we will be exploring
There are three main case studies in this unit. We will explore what these technologies can offer as well as the risks associated with them.
- Augmented/Virtual RealityAugmented and virtual reality involve the use of technology to create immersive 3D environments; with AR integrating digital information with the physical environment in real-time, and VR creating a fully simulated environment. In education, these technologies can facilitate experiential learning, offering students immersive, interactive 3D environments to explore complex subjects and concepts in a hands-on way.
- Generative Artificial IntelligenceGenerative AI refers to systems that can generate new content, whether that's text, images, or even videos, based on patterns and information it has learned during its training. In the realm of education, it can assist in creating diverse learning materials, including automated content creation, where it can help in generating questions, summarising information, or even providing feedback on student work.
- Data Visualisation in EducationData visualisation is the graphical representation of data, translating complex datasets into visual graphs, charts, and graphics to aid in the understanding and interpretation of the data. In education, it aids in presenting complex educational data in a more digestible format, facilitating clearer and more effective communication of information between educators and students.
- Big data
Big data refers to extremely large datasets that can be analysed computationally to reveal patterns, trends, and associations, mainly relating to human behaviour and interactions. In the educational landscape, it is leveraged to analyse complex student data to gain insights into learning behaviours, streamline educational processes, and to personalise learning experiences - Gamification
Gamification involves the application of game design elements and principles in non-gaming contexts to enhance user engagement and motivation. In education, it translates to incorporating game-like features in learning environments to increase student engagement and enhance learning experiences by making them more interactive and enjoyable. - MOOCs
MOOCs, or Massive Open Online Courses, are online platforms offering course materials accessible to unlimited numbers of learners, usually for free or at a lower cost than traditional education avenues. In education, they have provided access to high-quality educational resources from well known institutions globally, fostering lifelong learning and skill development. - Machine learning
Personalised learning refers to education models that tailor the teaching approach, curriculum content, and learning environment to meet individual students' needs and preferences. In educational settings, it often utilises technology to analyse students' learning styles and paces, facilitating more tailored, student-centric learning experiences.
How this unit works
- Weekly online resourceEach week there will be an online resource which presents core material for the unit. This will be a mix of text, graphics and videos. It includes a compulsory reading for that week which provides necessary context. This includes a podcast for distance learning students.
- Regular meetingsFor onsite students there will be a compulsory weekly meeting from 3pm to 5pm on Fridays. For distance learning students there will be two compulsory seminars (at the start and end of the unit) as well as up to four one-to-one tutorials where we will explore these ideas in relation to your own work.
- Individual and group explorationIndividual reflection and group work are central to this unit. Your first assessment will be individual but your second assessment will be a group project. Please engage with these exercises consistently because they will be crucial for your later work.
What will the future of education look like?
This image by Jean-Marc Côté was produced in 1910 and imagined what school might look like in the year 2000. The teacher places books into a machine in order to transmit their knowledge into the brains of students, who are sitting at their desks plugged into their networks. I am writing a text on my computer at home which is being transmitted to you via the internet for you to read on your own computer elsewhere.
Technology has always been used in education
In this unit we'll be exploring digital technologies and what they mean for the future of education. Technology has always been used in education if we think about this category in broad terms, such as the abacus, chalkboard, and pen and paper. These tools often fade into the background and we rarely think of them as technology. What we often mean by 'technology' is emerging technologies which have been recently implemented, recently developed, or imagined to be on the verge of development. There is a long history of 'teaching machines' which are imagined to transform education in the near future. These machines can seem strange and confusing when we look at them in the 2020s but it is likely our current technologies will appear the same way to future educationalists. By examining both the history and future of educational technologies, we can develop a nuanced understanding of their possibilities and limitations in the context of teaching and learning.
Teaching machines and programmed learning
In this video one of the most influential psychologists of the 20th century B.F. Skinner describes how his teaching machine will revolutionise learning. He claims this device will support students in learning more effectively than is possible with traditional classroom techniques. He suggests the person who programs the machine is like a textbook author who can interact directly with the students through the machine.
Video from Giovanni Bonaiuti on YouTube. The captions are set to Italian by default. You can change them by clicking on the settings icon under the video.
The questions which Skinner asks about his teaching machine are still relevant now:
- What are teaching machines?
- How are they used?
- What can they teach?
- Who prepare the material they teach?
- How does this material differ from textbooks, lectures and educational television?
- What impact will teaching machines have on school organisation?
Choose one of the following examples of machines used in teaching and try to answer Skinner's questions:
- Learning Management System e.g. Blackboard
- Online resources tied to a course e.g. this website
- Video conferencing software e.g. Zoom, Tencent Meeting
Emerging educational technologies are increasingly driven by the use of 'big data.' As we generate vast amounts of digital data about students through their online activities, this data can provide new insights about learning that were not possible before. Large datasets on student behaviors, performance, and engagement collected through digital learning platforms, assessments, and administrative systems allow educators to analyze trends and patterns that were previously invisible. For example, analyzing millions of student clickstream interactions in a course website can surface information about how different types of learners engage with materials.
The American journalist Kenneth Cukier played a leading role in promotion the concept of 'big data'. In this talk he introduces the idea and explains why it is so significant.
What is data? It is the 'raw material' from which information and knowledge is constructed, generated through counting, categorizing, and measuring. Data is stored through digital (binary digits) or analogue form. Analysis and interpretation tries to generate meaning and knowledge from the data, often by comparing it with other data (e.g. counting registered students vs attendance). We now have many examples of 'big data' - large datasets which challenge existing techniques for collection, storage and analysis. These are produced through the use of digital systems that generate transactional data, like the 2.16 billion debit and credit card transactions in the UK in May 2022, 1 billion videos viewed daily on TikTok, or 7.6 billion Uber trips globally in 2022. Writers refer to the 'data deluge,' 'data avalanche' or 'data flood.' Big data is characterized by volume, velocity, and variety, often generated in real time as an unstructured side effect of online activities. It represents the raw reality of what we do in digital environments. As education moves online, it too produces large datasets with the power to reveal new insights, if we can develop methods to interpret it skillfully.
The trail of data we create
"When we wake up in the morning, we check our e-mail, make a quick phone call, walk outside (our movements captured by a high definition video camera), get on the bus (swiping our RFID mass transit cards) or drive (using a transponder to zip through the tolls). We arrive at the airport, making sure to purchase a sandwich with a credit card before boarding the plane, and check our BlackBerries shortly before takeoff. Or we visit the doctor or the car mechanic, generating digital records of what our medical or automative problems are. We post Blog entries confiding to the world our thoughts and feelings, or maintain personal social network profiles revealing our friends and tastes."
Lazer, D., Brewer, D., Christakis, N., Fowler, J., & King, G. (2009). Life in the network: the coming age of computational social. Science, 323(5915), 721-723.
Lazer, D., Brewer, D., Christakis, N., Fowler, J., & King, G. (2009). Life in the network: the coming age of computational social. Science, 323(5915), 721-723.
What data footprint have you left this week?
Consider the different devices, platforms, services and systems which you use in your everyday life. This can be everything from using a credit card, searching the internet, posting on social media or ordering a taxi. What data footprint have you left this weeK? What does this reveal about you?
What does it mean to say big data is both technical and social?
What does it mean to say that big data is technical? What does it mean to say that it is social? What is the significance of it being both technical and social?
How could big data improve education?
What are some examples of how big data can be used in education? How would these improve education? Can you think of any potential downsides to their use? The video below might give you some ideas.
