3 Potential Futures for Education and Technology

In this blog post I will be exploring some potential futures of education in regards to the ever increasing influence of technology. As technology develops, the field of education often adopts new devices into its arsenal of teaching tools, ranging from mobile phone apps and websites for homework to the introduction of interactive whiteboards to enhance the presentation quality offered by teachers. In this regard, I will look at the potential futures afforded to education by augmented reality technology, adaptive response algorithms, and distance learning opportunities and the positive and negative aspects of their introduction.

Historically, research has looked to recreational technology like video games in order to identify the core features that help to maintain children’s motivation whilst playing in order to improve educational games (Hedberg, & Alexander, 1994). This has led to education following and adopting recreational technologies for their own use in order to maintain student engagement in educational games that had previously failed to maintain their attention (Dondlinger, 2007; Gee, 2008; Hedberg, & Alexander, 1994; McGonigal, 2010). An example of this blending of gaming and education can be seen around the area of augmented reality (AR) where computer generated images and text are overlaid on top of the real world (Dunleavy, Dede, & Mitchell, 2009; Pritami, & Muhimmah, 2018; Squire, & Jan, 2007; Tobar-Muñoz, Fabregat, & Baldiris, 2015; Wijers, & Jonker, 2010; Wijers, Jonker, & Drijvers, 2010). Tobar-Muñoz, Fabregat, and Baldiris found that students with attention deficit hyperactivity disorder (ADHD) particularly benefited from utilizing AR within this education, while Dunleavy, Dede, and Mitchell posit that AR is particularly effective for students who are labelled as a kinaesthetic learner through the VARK test, even if the validity of learning styles has now been called into question (Husmann, & O’Loughlin, 2019; Idrizi, Filiposka, Trajkovik, 2018; Papanagnou et al., 2016). These findings make sense when one considers that symptoms of ADHD include constant fidgeting or being unable to sit still in calm environments (NHS, 2018), and a kinaesthetic student’s aptitude for learning through hands on experience. These technologies can be fairly cheap to provide since the vast majority of students now have smartphones, and as such there is unlikely to be an issue of money for schools looking to invest in AR. However, while students have near universal access to the internet during school time, there is a large minority of students who do not have access to internet at home, with these usually being students from working class families (Livingstone, Bober, & Helsper, 2005). While the percentage of students who go online regularly during the week has increased, the number who own their own smartphone or tablet still sows some students still do not own these devices (Ofcom, 2019). As such, teachers should be mindful of utilizing AR outside of designated class time, such as for homework, as to prevent the exclusion of students who don’t have access to the technology.

A second potential future that I would like to explore briefly is one led by adaptive response algorithms (ARAs). Currently, these algorithms are used within educational apps on mobile phones and tablets, such as DoodleMaths, and are used to assign homework questions to students based on their own abilities (EZ Education, 2019). As the technology behind these apps develop over time, teachers and researchers are likely to look to them to support in student learning, especially as recent research shows that ARAs have a particularly strong effect on the learning outcomes of students who are performing below average or at average levels regardless of age (Geçer, & Dağ, 2012; Richards-Babb et al., 2018; Roschelle, Feng, Murphy, & Mason, 2016). Likewise, high attaining pupils also benefit from the use of ARAs, although the effect is not as strong as with their lower achieving counterparts (Richards-Babb et al., 2018; Roschelle, Feng, Murphy, & Mason, 2016). Since ARAs absorb data about individual students and presents questions and topics geared towards their current level, I would categorise them as being a form of personalised learning, and idea that has become a popular term within English education policy in recent years (Pykett, 2009). In particular, personalised learning is particularly important for students with special educational needs like autism where students may require additional teaching outside of traditional subjects like English and mathematics. Edwards (2016) gives the example of a child with autism requiring explicit instruction on how to behave in a social environment, such as how to line up in a queue for lunch. As ARAs develop alongside traditional subjects, I imagine that they will also begin to evolve to help support teachers in other more traditional subjects, such as by analysing the data on a particular student and suggesting the best method for teaching a student how to act in public or how to socialize based on the severity of their needs.

The third and final potential future that I am going to explore is influenced by the recent uptake of distance learning in the wake of the Coronavirus pandemic. Distance learning has been a topic of research amongst scholars for many years now (Thoms, & Eryilmaz, 2014), but has grown in public consciousness as many schools moved to employing distance learning in the wake of the Coronavirus pandemic as schools have been forced to close, with many of them moving learning to online classes to continue student learning (Wayman, 2020). Despite previous research, many teachers and schools felt underprepared to transition to distance learning, especially since they were given little notice that schools would be closed (Burke, & Dempsey, 2020). However, that isn’t to say that distance learning would work if teachers had been trained and given the necessary resources to transition to distance teaching. If we were to move to a distance learning model completely then we run the risk of losing the face-to-face interactions that have been found to be vitally important in supporting student cognitive development (Alexander, 2004). Group work is other area that greatly benefits students (Laal, & Ghodsi, 2011), and which would likely decrease in occurrence unless new streaming programmes are developed to support breaking a large group down into individual groups for short periods of time. During my time working in a special school, I had to travel to a home of a school refuser to teach them, as such I would argue that distance learning could help students suffering from anxiety to access the knowledge at their own pace and in the comfort of their own home (Thoms, & Eryilmaz, 2014).

Overall, all three futures share in their potential to help further student education, however, all three equally serve as potential vectors to increase the digital divide amongst students through any home learning activities implemented through their use of technology that not every house will have access to (Livingstone, Bober, & Helsper, 2005; Ofcom, 2019; Wayman, 2020). Ultimately, all three futures could prove useful to students under the right circumstances, and with the correct implantation, with AR offering a further way to vary learning in the classroom to engage students, ARAs potentially offering support to teaching staff in designing their lessons to best enable students to learn and distance learning can help students who are unable to attend school, be it because of anxiety or because of a long running illness.

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