Tag Archives: friesen

Block 1 – Reflection

This block gave me a view of data capture and the overwhelming feeling I took away from it was:

The data that is collected is not what should be collected

The learning management systems (LMS) that are being utilised today gather information on the interactions and time users (the systems are tracking everyone) spend with the system. The value of this information improving peoples learning is questionable since it is a limited set of variables to do with interaction with the system. As discussed by Bulger (2016) most if not all current education systems are responsive, meaning that the system needs a ‘cause’ to then ‘react’. A teacher in most cases would be far more adaptive and be able to adapt to students prior to an issue arising.

Over the course of the three visualisations, I have tracked personal details such as how much interaction I had with course materials, how much sleep and exercise I got and finally how often I would have food or drink or snacks. These items show the level of interaction, how effective that interaction might be due to cognitive capacity and the distraction of having a cup of tea instead of focusing on course material.

The above items in my view are more important to understand, as the social life of the person shows more about what that person might be able to achieve. For example, if I do not sleep well that has a greater effect on my learning than what a system can tell from me rewinding a video several times. One of the ways to allow the LMS to see such a situation would be through wearables, those wearables as discussed by Knox et al (2020) could also provide feedback to students that would optimise when to learn and rest.

Personalisation within education has the goal of a single teacher to a single student (Friesen 2020). One of the main questions coming out of personalisation is if that teacher were a machine that could pass the Turing test is that not equal to a human teacher? In Benjamin Bloom’s paper “The 2 sigma problem” (1984) it shows that having the one to one, master – student relationship is highly beneficial. The problem though at this moment is that the master in machine learning does not exist, the technology has that goal of reaching it one day but currently it is not the case. We currently have a system as discussed above that has a limited set of variables to work from and no context around those variables e.g. sleep. A human working in a one-on-one relationship with a student can determine many different variables and through dialogue can understand the mood of the student and on a day to day basis manage their studies based on this.

With personalisation there is always a risk that a system might create a feedback loop for students. If the system recommends a certain subject or the administrator wants more focus on certain subjects they could ensure that the system nudges people in a certain direction (Knox et al 2020). Once a student is on that path perhaps the system continually pushes that new direction and the student unbeknownst to them is being nudged down a path they would not have wanted if they had freedom to explore.