Contemporary education in relation to a technologically infused education future

In this post, I will be discussing the idea of contemporary learning, including what it means to me and how I believe it could be realized in the field of education today as digital technologies become more and more prevalent within schools. Despite the phrase seemingly being common knowledge amongst the articles that I have read, I would like to begin identifying a definition of contemporary learning as it is a phrase that conjures a different image in my head to the general consensus. Contemporary learning refers to modern learning, such that an article from the 1970s that refers to contemporary learning will be referring to the learning trends that were around in the 1970s. More recent contemporary learning theories and approaches are typically focused on “multiple intelligence theory, cooperative learning, problem-based learning, [and] constructivism” as well as others (Şad, Kiş, & Demir, 2017, p. 210), with these approaches incorporating students’ individual experiences to further their understanding of the content, including developing skills that students perceive as being useful in the outside world (Şad, Kiş, & Demir, 2017; Weber State University, 2006).

My view on contemporary learning is filtered through a mathematics lenses because of my background as a mathematic teacher. During my training, the mathematics department utilized problem-based learning as well as cooperative learning. Similarly, Pykett (2009) notes how the idea of personalized learning has become a popular term in education policy in England over the previous few years. Whilst I have not seen this form of learning in mainstream schools, I have seen personalized learning put to good use in a school for students with special educational needs (SEN) where they may require an altered curriculum in order to develop. Edwards (2016) puts forwards that students with SEN may require more explicit instruction on how to engage in certain social situations where their neurotypical counterparts would learn these skills implicitly through observation. An example Edwards gives is the idea of standing in lines may have to be taught to a child with autism as they may not instinctively understand the need to line up and wait their turn. In my experience, introducing personalized learning for a mainstream class or school would be difficult due to the class sizes and the amount of planning time that would be required would be uncontrollable. Şad, Kiş, and Demir (2017) found through their meta-analysis, that larger class sizes typically produced a negative effect on achievement in a mathematics classroom when compared to classes of a smaller size, and while their study does not involve personalized learning, I believe that the idea can be expanded to include it just by considering the logistics of planning a personalized curriculum for a mainstream school’s entire pupil base.

The increasing use of the internet in schools and the advancements in algorithm creation gives us a potential answer to combining the idea of personalized learning with a more traditional mainstream education (Thompson, 2017). In an earlier blog post, I reviewed the app DoodleMaths that used an adaptive responsive algorithm called Proxima to assign questions for a child’s homework and develop their understanding in regards to mathematics for students from the ages of 4 up to 15 (EZ Education, 2019). Traditional mathematics homework normally involves the class teacher giving the class a set of questions to complete in a single week with variation in these questions only occurring if the teacher has time to plan them. Research has shown that regardless of age, the use of these algorithms can greatly increase the attainment of students who are performing at average or below average levels (Geçer, & Dağ, 2012; Richards-Babb et al., 2018; Roschelle, Feng, Murphy, & Mason, 2016). Through the use of these algorithms in conjunction with teacher intervention, teaching staff could avoid the negative effect of increased class sizes found by Şad, Kiş, and Demir (2017) by having the algorithm guide those who are performing below or at average levels while leaving the teacher free to guide students who are performing at higher than average levels for whom the effects of adaptive response algorithms are far less profound when compared to traditional tasks (Richards-Babb et al., 2018; Roschelle, Feng, Murphy, & Mason, 2016). However, these studies have focused upon using algorithms to produce homework for students rather than using them as a full substitute for a teacher, and as such their effectiveness within the context of a classroom cannot be assumed.

However, moving to an algorithm-led, personalized style of teaching is not without its issues. One of the main issues that I find with using algorithms to teach students is the loss of spoken interaction within the classroom despite this being considered to be a vital aspect of a child’s cognitive development (Alexander, 2004). Likewise, the studies that support the use of adaptive response algorithms have focused mainly on using them to assign homework tasks to students, and as such, are typically completed away from the presence of a teacher and often come with additional support, such as DoodleMaths having a “help button” to go with each question. Because of this, it is almost a given that students will perform better in these environments than in more traditional homework tasks. However, in lessons, this “help button” is simply pressed by putting your hand up and asking the teacher for help, through which students will receive superior help as the teacher will be able to identify the specific point that has the student stuck while the computer will just provide a standardized support lesson.

Overall current contemporary learning theories seem to be in contradiction to one another, as some theories suggest that group work and problem solving are the correct way forwards to further develop student knowledge (Alexander, 2004; Şad, Kiş, & Demir, 2017) while others suggest a focus on personalized learning (Edwards, 2014) including through the use of adaptive response algorithms (Geçer, & Dağ, 2012; Richards-Babb et al., 2018; Roschelle, Feng, Murphy, & Mason, 2016; Thompson, 2017). While I believe Edwards ideas of personalized learning for students with Special Educational Needs can, and probably should be intertwined with the ideas of group work, problem solving and communicating, the use of adaptive response algorithms seems to block off the potential for group work and communicating with peers as the very nature of these algorithms would mean that most students would be working on separate topics at separate levels and as such will likely be unable to have a conversation over how they would go about solving a particular problem.

References

Alexander, R. (2004). Towards dialogic teaching: Rethinking classroom talk (Fourth ed.). Thirsk: Dialogos UK Ltd.

Cambridge University Press. (n.d.). Contemporary. Retrieved March 30, 2020, from Cambridge Dictionary: https://dictionary.cambridge.org/dictionary/english/contemporary

Edwards, S. (2016). The SENCO Survival Guide (Second ed.). Oxon: Routledge.

EZ Education. (2019, August). Technology that 'thinks' and sets children's work. Retrieved February 05, 2020, from DoodleMaths: www.doodlemaths.com/wp-content/uploads/2019/08/fact-sheet-proxima.pdf

Geçer, A., & Dağ, F. (2012). A Blended Learning Experience. Educational Sciences: Theory & Practice, 12(1), 438-442.

Lexico. (n.d.). Contemporary. Retrieved March 30, 2020, from Lexico.com: https://www.lexico.com/en/definition/contemporary

Pykett, J. (2009). Personalization and de-schooling: Uncommon trajectories in contemporary education policy. Critical social policy, 29(3), 374-397.

Richards-Babb, M., Curtis, R., Ratcliff, B., Roy, A., & Mikalik, T. (2018). General Chemistry Student Attitudes and Success with Use of Online Homework: Traditional-Responsive versus Adaptive-Responsive. Journal of Chemical Education, 95(5), 691-699.

Roschelle, J., Feng, M., Murphy, R. F., & Mason, C. A. (2016). Online Mathematics Homework Increases Student Achievement. AERA Open, 2(4), 1-12.

Şad, S. N., Kiş, A., & Demir, M. (2017). A Meta-Analysis of the Effect of Contemporary Learning Approaches on Students' Mathematics Achievement. Hacettepe University Journal Of Education, 32(1), 209-227.

Thompson, G. (2017). Computer adaptive testing, big data and algorithmic approaches to education, British Journal of Sociology of Education. British Journal of Sociology of Education, 38(6), 827-840.

Weber State University. (2006). Part I: Contemporary Learning Experiences. Ogden, Utah: Weber State University. Retrieved March 30, 2020, from http://faculty.weber.edu/psaunders/ed3780/3780bookpartI.09.06.pdf