How are you, my cousin Ally?
I feel bad that we sort of lost touch. Isn’t your lovely Sofia about to start school, just like my daughter? The last time we spoke, you were asking me about the education system in Canada. Well, a lot has changed since 1997 when I moved here. Can you believe it’s been 33 years? Remember, how I used to copy your homework in a chemistry class because I hated it too much to study, but still wanted to get a good grade to show my dad because he was a chemical engineer? Well, in Canada chemistry is no longer a mandatory class in high school! The most interesting development is that the grading system has been eliminated from about half of high schools. It started with a pilot project in 2013 (Nixon, 2017) and the concept was gradually accepted by many others. At the moment, a parent can choose between two different approaches. You are probably wondering how this divide came to be. Well, there are several reasons for it. One of the main anti-grading arguments is that grades do not motivate students to learn, because learning is about intrinsic motivation while grades act as extrinsic motivation (Blum, 2016, p.96). The domination of extrinsic motivation is claimed by Blum to have a negative effect because “extrinsically motivated workers act in accordance with a “minimax” strategy: They attempt to perform the bare minimum of work sufficient for the achievement of maximal rewards.”(Kruglanski et al., 1977, p.141). At the same time, grades do not provide a proper understanding of student’s knowledge and abilities because they assume the uniformity of input, process and output, although every student has a different life and academic experience (Blum, 2017). Moreover, 56 % of students reported being stressed about grades (American College Health Association, 2013).
While my personal experience confirms that the grading system is flawed as described, I wanted to explore the other side of the argument and find out why the grading system persists and is still acceptable among students and parents. Diseth et al., (2020) claim that “that there is no necessary contradiction between intrinsic and extrinsic motivation”(p. 972) and argue that the relationship between them and learning is more complex than once thought. Furthermore, Covington and Mueller (2001) claim that every student differs in what motivates them depending on their personality traits, some are driven by intrinsic motivation while others are more motivated by extrinsic motivation.
Perhaps, there is a place for each approach within a system. If one approach doesn’t work for my son, it’s good to know that I can transfer him to a school relying on a different approach. It’s also interesting how the increase in the use of Artificial Intelligence(AI) is affecting both systems. After Chui et al.(2020) were able to demonstrate that they could predict with a 90+% accuracy at-risk student via a machine learning algorithm, the traditional grading educational system (TGES) has started using AI to identify at-risk students so that they could be better supported. The eliminated grading educational system (EGES) could not take advantage of these learning algorithms because they heavily relied on grades. As a result, TGES has a lower drop-out rate and is considered a safer choice by parents who are worried about their kids’ performance. It has a reputation for producing well-rounded graduates. At the same time, EGES started using AI to identify gifted students. It all began after Hodges & Mohan (2019) claimed that “Where machine learning can be best utilized by researchers and educators is in classification tasks using nonlinear indicators. An example of a nonlinear indicator is a student academic product like a poem”(p.243). After eliminating grading, EGES naturally relied on paying more attention to non-linear indicators. Compared to TGES, EGES has a higher drop-out rate, but it is considered a better choice by parents who believe their kids have higher potential. It has a reputation for producing more exceptional graduates.
Academic learning is not everyone as you know, Ally. My oldest son was never into reading, as much as I tried to encourage him. I hope all those evenings of me reading to him at least gave him something, if not the love for learning and reading. Lucky him that all schools nowadays, regardless of the system, have AI assessments of non-academic talents. It was already possible a decade ago when Muazu Musa et al. (2019) demonstrated that a machine learning approach can predict an athletic high potential by analyzing physical fitness indicators. Who would’ve thought that my son has the potential to be among the top 5% in water polo? He really enjoys playing for his school team, they are very competitive in a junior national league!
Do you remember how back in the day teachers used to ignore someone who was having a rough day or simply told them to suck it up? Oh, how times have changed. Nowadays, AI is used in schools to identify and predict mental health problems. What’s great is that not only it pays attention to multiple variables, as Tate et al.(2020) demonstrated that “the model did not overly rely on any variable, thus the model would be relatively stable with the removal of any one variable, including those stable over time”. This means that if my son’s classmate calls him an asshole and hurts his feelings, but they make peace the next day, it won’t be reported to a school counsellor as a potential mental health problem. It might even help with eliminating bullying, although much of it happens online rather than in-person. The last time I spoke with a school principal, I was told that AI is being used to detect cyberbullying as well. Much progress has been made since Wu et al. (2020) created algorithms for bullying detection.
All these benefits come from increasingly invasive data collection, which concerns me as a parent, but I hope we can discuss it in our next conversation.
References
American College Health Association. (2013, September 11). Canadian Reference Group Executive Summary Spring 2013. https://www.acha.org/documents/ncha/ACHA-NCHA-II_CANADIAN_ReferenceGroup_ExecutiveSummary_Spring2013.pdf
Blum, S. D. (2016).“I love learning; I hate school”: An anthropology of college. Cornell University Press.
Blum, S. D. (2017, November 14). Ungrading. Inside Higher Ed. https://www.insidehighered.com/advice/2017/11/14/significant-learning-benefits-getting-rid-grades-essay
Chui, K. T., Fung, D. C., Lytras, M. D., & Lam, T. M. (2020). Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Computers in Human Behavior, 107, 105584. https://doi.org/10.1016/j.chb.2018.06.032
Covington, M. V., & Müeller, K. J. (2001). Intrinsic versus extrinsic motivation: An approach/avoidance reformulation. Educational psychology review, 13(2), 157-176.
David Nixon. (2017, February 5).B.C. leads the push to eliminate letter grades from school report cards. The Globe and Mail. https://www.theglobeandmail.com/news/british-columbia/bc-leads-the-push-to-eliminate-letter-grades-from-school-report-cards/article33907027/
Diseth, Å., Mathisen, F. K., & Samdal, O. (2020). A comparison of intrinsic and extrinsic motivation among lower and upper secondary school students. Educational Psychology, 40(8), 961-980. https://doi.org/10.1080/01443410.2020.1778640
Hodges, J., & Mohan, S. (2019). Machine learning in gifted education: A demonstration using neural networks. Gifted Child Quarterly, 63(4), 243-252. https://doi.org/10.1177/0016986219867483
Kruglanski, A. W., Stein, C., & Riter, A. (1977). Contingencies of exogenous reward and task performance: On the “Minimax” Strategy in instrumental Behavior1. Journal of Applied Social Psychology, 7(2), 141-148. https://doi.org/10.1111/j.1559-1816.1977.tb01335.x
Muazu Musa, R., P. P. Abdul Majeed, A., Taha, Z., Chang, S. W., Ab. Nasir, A. F., & Abdullah, M. R. (2019). A machine learning approach of predicting high potential archers by means of physical fitness indicators. PLOS ONE, 14(1), e0209638. https://doi.org/10.1371/journal.pone.0209638
Tate, A. E., McCabe, R. C., Larsson, H., Lundström, S., Lichtenstein, P., & Kuja-Halkola, R. (2020). Predicting mental health problems in adolescence using machine learning techniques. PLOS ONE, 15(4), e0230389. https://doi.org/10.1371/journal.pone.0230389
Wu, J., Wen, M., Lu, R., Li, B., & Li, J. (2020). Toward efficient and effective bullying detection in online social network. Peer-to-Peer Networking and Applications, 13(5), 1567-1576. https://doi.org/10.1007/s12083-019-00832-1