Week 9 has ended and we have continued exploring the world of algorithms and turned our attention to the specific applications of algorithms in education in the form of learning analytics.
It was fascinating to see what my colleagues came up with for last week’s exercise in exploring the algorithms used by internet companies to give their users individual suggestions and recommendations. Following up on my own explorations I delved into some issues raised by Jeremy, especially concerning privacy implications – in my mind the most pervasive and pressing issue surrounding the growing use of algorithms in our lives.
Later in the week I turned my attention to Twitter, reading and replying to my colleagues’ tweets as well as participating in this week’s scheduled EDC tweetstorm.
One particular question we were asked was what we give to algorithms and what they give us and I tweeted “We give them our history, they give us our future.” This statement was on purpose meant to be ambiguous. On a more surface level one could interpret it as us giving algorithms our search history or watched videos history and they recommend to us sites or videos we will watch in our future, thus shaping our future. In my opinion this goes even deeper however. Algorithms that predict the weather include not just historical weather data but also our own current understanding of maths, systems dynamics and meteorology – all developed in time. Especially in light of artificial intelligence it seems more and more likely to me that we are soon to be passing on the torch of knowledge creation to entities that will not be limited by their phyical and biological boundaries.
Considering the issues of learning analytics I see that in the future they will be able to considerably help people in their learning endeavours in a variety of ways. My prediction will be that models based on biofeedback, like heart rate, skin conduction, pupil dilation, blink frequency, brainwave measurements etc will be one day used to guide the student to maximise learning, perhaps by signalling perfect learning window times. As previously mentioned, such massive tracking carries its own set of problems, particularly with regards to privacy and data security.
To round things up this week I stumbled upon a new fun little game by Google that lets you play around with its autocomplete suggestion engine, scoring points for correctly guessing its guesses.
Mihael, I like the notion of ‘perfect learning window times.’ For me, certain cognitive tasks kick in at different times of the day, based on my routine habits. For example, when I take a daily lunchtime jog, words and thoughts flow and come together, helping with work tasks. I don’t need external stimuli, devices or algorithms for that to happen. My daily physical exercise has become routinized. I would, however, like to have some reminders and ‘programming’ of my leisure/study activities and interests: when to study foreign language, what EDC focus area (blog, tweet, research, reading) for the evening, or what book on the shelf suits my mood. Currently, that ‘mega-cognitive’ facility is inevitably mostly by happenstance, and not yet routinely habituated. Cheers, PJ