How humans learn, navigate and use technology

Effective deployment of mobile learning technology seems to require sufficient parental involvement and other sociometric support, according to the main finding of our recently submitted paper at the Stanford POMI in ED group.

Mobile technology, in particular, with its low cost and accessibility, has great potential to provide access to or supplement education in underdeveloped areas. Given that mobile learning devices could be effective in supplementing education particularly in a community with a poor educational infrastructure; this study selected 80 second-grade (7-8 years old) primary school students from an urban slum area and another 80 students in a rural village near the Mexico-USA border in the state of Baja California, Mexico. Both schools lack educational and technology resources and the general socio-economic status of the students is low. We examined whether mobile learning devices could have a differential effect in supporting students’ literacy learning skills in these two schools with their unique socio-economical strata.

Students in the experimental groups in both schools were equipped with a mobile learning device called TeacherMate with a headphone, preloaded with 18 mobile e-books (see Figure 1).

Figure 1. Students helping each other with the mobile learning device

In contrast, the control groups were only participating in classroom lectures without the supplement of our mobile devices. A standard Spanish language literacy pretest assessed all students’ achievement scores in September 2008 and the same test was administered 16 weeks later in a posttest. In addition to these quantitative data, we interviewed parents and educators as well for qualitative purposes.

The findings suggest that students in the rural village have benefited substantially more from mobile technologies than urban school students (see Figure 2). These devices have contributed to better literacy scores and added an extra enrichment activity tool in class. Considering the fact that the availability of educational resources at the rural school was even less than at the urban slum school (i.e., books, computer, Internet access, etc.), the extra educational resources and learning opportunities served these children more effectively.

Figure 2. Significant difference between the Experimental posttest groups at the two schools demonstrating that students with the TeacherMate mobile learning device in the Rural school (Rural Experimental) had higher literacy achievement scores than Urban school children also using the mobile learning technology (Urban Experimental).

The interviews revealed that the rural school parents pay more attention to the education of their children. The number of answers reflected that 9 in 10 of the rural school parents were aware of the mobile learning project taking place at school, whereas only slightly more than 1 in 10 of the urban school parents knew about the project. Additionally, the rural school parental involvement in education is much higher with 5 hours a week spent with the children compared to the urban school average weakly less than 2 hours. Finally, the average combined income of the rural school is slightly higher (i.e., average $720/month) than that of the urban school (i.e., average $410/ month).

In contrast, there was no evidence of interaction with parental education levels (the overall education experience of the rural parents was less than that of the urban slum school parents), the experience of teachers or school principals (teacher in the rural school also had 2 years less teaching experiences than the teacher of the urban school), or the teacher’s perception or preparation of the technology (teacher in the rural school did not embrace technology in the classroom).

In summary, the programmable open design of our mobile learning technology (i.e., Linux, Flash) enables the development of other mobile learning activities to increase phonemic awareness skills of children at multiple levels and offer opportunities to practice reading through interesting content and entertaining activities.

Programmable Open Mobile Internet (POMI2020) is an interdisciplinary research program at Stanford University. It is part of a larger initiative called CleanSlate and funded by the NSF. The key players in POMI2020 are Stanford professors largely from the computer sciences, such as Guru Parulkar and from other auxiliary departments, such as the School of Education.

In today’s POMI retreat meeting in Palo Alto, CA the presentations were promising more interconnected and highly decentralized technological frameworks by 2020. Interestingly, however, the issues that advisors such as Bob Iannucci (formerly Nokia), Rick Rashid (founder of Microsoft Research) and Bill Raduchel (formerly AOL) brought up were fundamentally human-related questions, not so techsavvy ones.

In the first set of presentations, Monica Lam (Stanford Computer Science Dept.) and her team of graduate students presented an open-access database platform (SocialLite) that is being developed in order to collect and formalize social and personal information such as friendships, emails, and photos. This is an effort to overcome the limitations in FaceBook-type social networks that only provide “shallow friendships on a centralized datapark and does all this for profit”. Monica’s suggestion is to build more ad-hoc, mobile, multi-player apps that sit on decentralized personal cloud butlers (as opposed to a single public cloud) and communicates with each other via formalized data queries (e.g., FriedOfAFriend-queries; FOAF). The advisors, however, raised the issue that this effort comes down to the ages-old question of how to standardize representations digitally? Or as Bob Ianucci asked which one wins, the algorithm or the human eye? Until the researchers focus merely on computer-to-computer and not as much on computer-to-human communications, there is little hope for a revolution.

Our group from the School of Education, led by Paul Kim (CTO at Stanford) led the second set of presentations. The starting point was an analysis of the mobile industy (based on the evolution model by Fine, 1998). A recent survey of industry leaders and developers suggest that the currently expensive and integral mobile technologies will move into a cheaper and more modular market phase, inviting more mobile users (well exceeding the currently already 4 billion!) and trigger even greater interest in content development communities. This cheap and modular market will make mobile technologies increasingly available, especially in underdeveloped countries of the World, like Africa or parts of India. A project paper about this model-based prediction is on its way to publication soon.

Current in-progress educational applications were presented accordingly by Aiditi Goyal and Theresa Johnson (two grad students in our PomiED group): WeClick – an open-source, platform independent clicker app to be used in classrooms; Environmental Sensing Network (ESN) is a sensor-based science project utilizing design-based teaching methods. Questions from the audience were focusing on the costs and wireless communication issues. I was glad -but slightly disappointed at the same time- that the usability and educational aspects (two that I had some impact) did not gain any criticism.

After the first coffee break, Gabriel Takacs’ (Stanford) from the lab of Bernd Girod (Stanford Electrical Engineering; on the picture) presentation was about an automated image tracking & recognition algorithm that performed locally on a mobile phone. His algorithm is quicker and less data heavy than existing solutions which usually send full images over the network. Graduate students of the team demonstrated mobile applications that recognized foreign DVDs or labeled parts of a building. After identification, the system also linked the information with how much others in the person’s social network liked it. Neat! Check out their cool YouTube demo here. My association about this project was that they are doing what the human eye does — preprocess the information before it arrives to the visual cortex and makes it quick and easy for higher level cognition to match features.

The final presentation block of the morning session was by John Mitchel (Stanford Computer Science). He talked about http://courseware.stanford.edu, a work-in-progress collaborative and decentralized CMS. The idea is to work between institutions (e.g., Stanford & USD) and have open-access materials. What I’m not sure was how different this approach is to other systems, say Moodle. Bill Raduchel raised another issue about copyrights, which could be a real issue for open-access. Eric Conner presented a Mobile Courseware application that is an iPhone equivalent of our team’s weClick approach. Three major features: vote, Q&A and course-notes. I’m really looking forward to see this in action and potentially collaborate with John & Eric!

I wish I could have stayed for the afternoon session, as I really enjoyed interacting with these amazing professors and grad students, but I had to get back to San Francisco before the Sun went down.