Discussion with Prof Daniel Schwartz

Today I met with Prof Daniel Schwartz of Stanford University’s School of Education to talk about ways to improve school maths and science skills using technology. The meeting was in preparation for my new role as Communications & Analytical Skills Development Fellow at the Shuttleworth Foundation (SF). Our discussion covered much ground across a number of topics. The key points were as follows:

When asked What tools exist that help to improve maths and science skills, which teach analytical skills that learners can apply to the whole of their lives?, Dan answered, “I don’t know.” That doesn’t mean there aren’t good approaches and software that fit this bill, but that on the whole, we’re not there yet. As he explained, the task is big and complicated and no one solution stands out as a clear winner. Much research and exploration still need to be done.

Teachers
Teachers are stressed, overworked and underpaid. This isn’t just a South African phenomenon, but a global issue. Any tool to improve maths and science must make teachers’ jobs easier, not harder. Dan said that many worthy curricula, projects and teaching approaches are great at teaching a subject, but they rely on either really good teachers or very excited/animated/energetic teachers. Approaches such as exploratory inquiry are valuable for learners, but require a lot of work on the part of teachers. Is it realistic to expect that from all teachers across the board?

Role of a foundation
An important question for the SF is whether its goal is to raise the median score of all school learners in maths and science, or to facilitate the surfacing of bright kids who’ll become mathematicians and scientists? Each goal requires different approaches. For large-scale change, any solution must be aligned with the national or provincial curriculum. It also mustn’t rely on champion teachers. It must work for your average teacher in an average classroom setting.

Creating buzz
An important aspect of improving maths and science skills is to simply create interest in these subjects among learners. The SF already does this through its Hip2b² initiative. Some educational projects don’t “move the needle” for widespread change. They might only work in a particular context, such as one school, and with the help of a lot of outside support, but they raise awareness, create interest, show what’s possible. They become a beacon, attracting interest and generating energy for similar projects. Eventually enough momentum is generated.

Video
Dan suggested that the use of video in education has much potential, and has hitherto not been fully explored. Educational videos are good for getting across the facts, but actually getting learners to create video – using, e.g. iMovie, Windows MovieMaker or KiNO – not only mitigates against the risk of passive consumption of information, but actively engages youth. I have seen this in digital storytelling workshops, how learners who would normally not be interested in school work are suddenly engaged by the process of digital media creation. This has been formally proven by the WestEd study of Streetside Stories. While video doesn’t teach reasoning, it can teach scientific inquiry. For this reason it is probably better suited to developing science than maths skills. “The key,” says Dan, “is to have a driving question for the creation of videos, e.g. Why does the moon rotate around the earth?” This anchors the learners, focussing their efforts. Unchecked, these efforts might only develop creativity (nothing wrong with that, but we need to keep coming back to maths and science skills). Lastly, developing video falls squarely within the realm of communications skills, which the SF wants to develop. Based on this and the work done in the last year on the Digital Hero Book Project, digital media production will be strongly considered in the Communications & Analytical Skills Development focus area.

Teachable agents
One of Dan’s research areas is software-based teachable agents (TA), based on the premise that one learns by teaching. Learners teach their TA and then assess its knowledge by asking it questions or by getting it to solve problems. “The TA uses artificial intelligence techniques to generate answers based on what it was taught. Depending on the TA’s answer, students can revise their agents’ knowledge (and their own). TAs do not replace real students. But, they do provide unique opportunities to optimize learning-by-teaching interactions.” (from the to-be-published Pedagogical Agents for Learning by Teaching: Teachable Agents.) More on this here soon.

Already, interesting software, approaches and off-the-shelf curricula, available as proprietary or open content, exist. But there is room to continue work in this space. It’s important to first define a target audience, its demographic and the intended educational goal – moving the needle or pushing the boundaries through focussed research – as part of developing a strategy for communications and analytical skills development in South Africa.