Monthly Archives: February 2014

Knowledge-based programming: Wolfram releases first demo of new language, 30 years in the making

This would be a proper way to start teaching coding in K-12 schools. Check the video, quite amazing. You can even get started with natural language, and the system proposes some code bits & visualizations to match it. And it will even be included in the upcoming Raspberry Pi computers, which are very affordable. Excited to see if this takes off!

Don’t Ban Devices Unless You Absolutely Have To

Tragicomic discussions in Finnish media these days: Should the members of parliament be forced to leave their iPads and smartphones behind when entering the parliament sessions?

MP Thomas Blomqvist watching Sochi olympics during a session. Source: Helsingin Sanomat

Banning devices addresses the effect, not the cause. You see, there is always a reason for sending emails, tweeting, or even watching olympics from the session. Probably several reasons. Why are the MPs doing so? This is the question we should tackle.

Could it be that sometimes the sessions are too slow, repetitive, or even straight up boring? The MPs are busy folks with a million things to do, also outside the chamber. Connected devices enable them to break free from the chamber and its ancient traditions. Maybe they can even do some fact-checking before speaking up. And indeed, nothing wrong with a bit of spectator sports IMHO.

This issue goes well beyond parliament, even to K-12 schools & BYOD policies. Let’s consider adults before children, however, because it’s somewhat easier. Consider a meeting: as long as a person doesn’t distract others for example with sounds emitting from the device, do not restrict him/her in any way. If they choose to fiddle their gadgets instead of participating, they might be in a wrong meeting or the meeting is poorly organized. Nothing more to this.

With children, it’s a bit more tricky. In a school setting, clear common rules should be laid down. When to use the device, what to do with it, and so on. It is naturally difficult for the teacher to monitor what the class is doing, especially in a BYOD scenario where the device base is heterogeneous. However, in the future when BYOD and online tools have become the norm, things are going to settle down.

The teacher just assigns students with certain tasks and then they perform them with their devices. If they finish early, why not let them play games or surf the web? As with adults, their activities should not distract others in the same classroom. And of course there are issues of cyber bullying and the like, but such are broader questions and for sure are not going to be solved by banning gadgets in the classroom.

Back to the parliament. These are the people who are supposed to come up with good laws for us, also regarding education. If they are denied their smartphones, what’s going to happen with students?


We Need Big Data Optimism in Education

Bringing up big data and learning analytics typically sparks up the discussion. We have people who are in favor of it, believing that it is an essential component in improving future learning results. And then we have the opposing camp, fearing it will make teachers obsolete, throw privacy out the window, and in general make the world a miserable place.

Big Data Can Generate Big Brainstorms

I am drawing caricatures here, but this is still roughly the situation. I belong in the optimism camp myself, with certain boundary conditions: For example, the minute data travels beyond the classroom boundaries, it should be anonymized. Also, teachers should be in control when utilizing learning analytics.

Yesterday I encountered yet another list of big data challenges, shared by the TeachThought blog. The list was borrowed from another web source, namely OpenColleges. The list is as follows, and the items should be addressed in order to successfully implement big data in education:

  1. Transparency
  2. Privacy
  3. Value to the learner
  4. Depth of measurement
  5. Expense
  6. Many factors influence learning
  7. Presenting data
  8. Readiness
  9. Infrastructure
  10. Openness

For me these are self-evident, however quite different from each other. Allow me to group them into three main categories in order for you to see what’s going on. First of all, there is the “Understanding” category. That underlies everything else. We should understand what this big data thing is and what are its benefits. The following items belong in this category:

  • Value to the learner, this is where it all starts
  • Readiness to work with massive amounts of data
  • Many factors influencing learning
  • Presenting the data so that it makes sense
  • Depth of measurement, going beyond traditional tests

When the above points are understood, it is possible to take the next step towards implementing the data-intensive learning system. Then the normal objections emerge. I group them into “Baseline” category:

  • Transparency, knowing where the data goes and who is using it
  • Openness on where and how the data should be utilized
  • Privacy and delicate processing on personal data

Finally, there are some other challenges to solve. These are not really bid data -specific but concern any new technological innovations. I collect these into the “External” category:

  • This is expensive. Yes it probably is, it should be considered as an investment
  • This needs new infrastructure. Again yes, but gladly nowadays cloud services are there to help us

Big data by itself is no silver bullet. If you collect data but don’t have idea what to do with it, it is useless. You should first explicate your goals and then harness the data collection, enrichment, analysis, and reporting tools to drive you towards those goals. Then you implement it in a way that doesn’t violate people’s opinions on privacy, transparency, etc.

Flickr image CC credits: Kevin Krejci