Case Study: Sociotechnical system in education

A discussion of sociotechnical systems in Brazilian education.

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Definition of key terms

  • Sociotechnical Systems: the interplay, impact, and mutual influence when technology is introduced into a social system, i.e. workplace, school, home, etc. (com, n.d.; Sociotechnical theory, n.d.) The social system comprises people at all levels of knowledge, skills, attitudes, values and needs (Sociotechnical theory, n.d.).
  • Formal Learning: scholastic learning in schools (Hayashi & Baranauskas, 2013)
  • Non-formal Learning: scholastic learning outside of schools (Hayashi & Baranauskas, 2013)
  • Informal Learning: other learning that occurs outside of schools (Hayashi & Baranauskas, 2013)

Case Study Description (Hayashi & Baranauskas, 2013)

This qualitative study introduced 520 donated laptops among the students (ages 6-14) and teachers in the public school, Padre Emilio Miotti School, in Campinas, Brazil.  With a goal of providing a detailed description of the results in order to inspire (transfer knowledge) over focusing on generalizing the results to other schools and scholastic-socio technological systems.  The sociotechnical system is defined by cultural conventions, where the participants in the study can be classified under in the formal, informal, and technical levels of a Semiotic Onion (Figure 1).

1.png

(Source: Adopted directly from Hayashi and Baranauska, 2013)

Therefore, the goal of this qualitative study was to understand how to insert the technological artifacts (the laptops), into the scholastic curriculum, that makes sense to the end users (scholastic community: teachers, students, etc.) into a meaningful integration across all aspects of the Semiotic Onion.  Data collection for this qualitative study was done through interviews and discussion in the Semio-participatory Workshops in 2009, as well as the authors being participant observers over a one year period in the scholastic activities.

There were four opportunities that should be considered (supporting forces for adoption):

  • Transforming homework assignments: Allowed for teachers to bring some homework into the classwork and allow the students to conduct their searches, normally done at home at school. Teachers could now observe the emotional flux of their students evolve while they complete the assignments.  This evolution of the emotional flux during homework use to be only observed by parents.
  • Integrating the school in Interdisciplinary Activities: In a collaborative fashion, teachers were able to create assignments using the laptop cameras to capture everyday objects or events of the students to help show them how to eat healthier, different animals and their behaviors, save on the electric bill, teach them about calories, watts, electricity, animals, etc. This creates a path of data to information to knowledge that helps motivate the students to learn more.
  • Laptops inside and outside the school walls: Students have more pride in using their own devices and were willing to showcase and educate the public about their technology and its effectiveness. This has far reaching results that were not explored in this study.
  • Student Volunteers: The use of older students to help troubleshoot younger student’s laptop problems, which taught some students patients and other skills across the Semiotic Onion. The students learned about responsibility, empathy, and other vital social skills.

There were issues across the Semiotic Onion that were also enumerated (challenging forces for adoption):

  • Technological: Internet connection was slow and intermitted even though there was broadband internet available and wireless routers
  • Technological: How to recharge 30 laptops at a time with only two wall sockets
  • Technological: How to transport laptops back and forth from storage rooms to classrooms
  • Technological: Laptop response times at certain periods of times were slow at best
  • Technological: Demand for technological support increases dramatically
  • Formal: The fear of laptops being stolen from the students on their way to or from school
  • Formal: Teachers worried that they could find or create technological assignments that fit their lesson plans
  • Informal: Teachers are not comfortable in teaching with technology they are not familiar with themselves
  • Informal: Most parents didn’t and couldn’t use the student’s laptop to assist them

This study concludes by saying that the introduction of technology into the education system in these scenarios for this case study had a positive response and that key lessons learned, assignments could be duplicated and studied in other scenarios.  Therefore, the authors emphasized on the transferability of the study rather than generalizability of the results.

Evaluation of this case study

This study was a case study of the socio-technological scholastic system when donated laptops were introduced into a Brazilian school.  This paper presented the socio-technological plan and its analysis.  The authors were thorough by listing all the opportunities (supporting forces for adoption) and issues (challenging forces for adoption) of technological inclusion into the scholastic system by evaluating it from the perspectives of the Semiotic Onions.  Therefore, this was a thorough study of this study’s positive introduction of technology to the scholastic, social system.  The only drawback in this study is that the researchers failed to interview how the laptops affected the world outside of the school walls and familiar homes.

This paper is a well-designed qualitative study that uses surveys, interviews, etc. to gain their primary results, but to improve the study’s credibility, the researchers become a participant observer for one-year videotaping and taking field notes to supplement their analysis.  They mention that case studies are done to foster transferability of ideas across similar situations rather than generalizing the results.  Therefore the authors stated the limitations of this study and how they mitigated issues that would arise about the study’s credibility.

References:

Big Data Analytics: POTUS Report

This has become a data-centric society, relying on real-time data and technology (i.e., cell phone, shopping online, social networking) more than ever. Although there are many advantages associated with the use of this data, there are concerns that the collection of massive amounts of data can lead to an invasion of privacy. In January, 2014, President Obama asked his staff to take the next 90 days to prepare a report for him on how big data is affecting people’s privacy. This post revolves around this report.

The aims of big data analytics are for data scientist to fuse data from various data sources, various data types, and in huge amounts so that the data scientist could find relationships, identify patterns, and find anomalies.  Big data analytics can help provide either a descriptive, prescriptive, or predictive result to a specific research question.  Big data analytics isn’t perfect, and sometimes the results are not significant, and we must realize that correlation is not causation.  Regardless, there are a ton of benefits from big data analytics, and this is a field where policy has yet to catch up to the field to protect the nation from potential downsides while still promoting and maximizing benefits.

Policies for maximizing benefits while minimizing risk in public and private sector

In the private sector, companies can create detailed personal profiles will enable personalized services from a company to a consumer.  Interpreting personal profile data would allow a company to retain and command more of the market share, but it can also leave room for discrimination in pricing, services quality/type, and opportunities through “filter bubbles” (Podesta, Pritzker, Moniz, Holdren, & Zients, 2014).  Policy recommendation should help to encourage de-identifying personally identifiable information to a point that it would not lead to re-identification of the data. Current policies for the private sector for promoting privacy are (Podesta, et al., 2014):

  • Fair Credit Reporting Act, helps to promote fairness and privacy of credit and insurance information
  • Health insurance Portability and Accountably Act enables people to understand and control how personal health data is used
  • Gramm-Leach-Bliley Act, helps consumers of financial services have privacy
  • Children’s Online Privacy Protection Act minimizes the collection/use of children data under the age of 13
  • Consumer Privacy bill of rights is a privacy blueprint that aids in allowing people to understand what their personal data is being collected and used for that are consistent with their expectation.

In the public sector, we run into issues, when the government has collected information about their citizens for one purpose, to eventually, use that same citizen data for a different purpose (Podesta, et al., 2014).  This has the potential of the government to exert power eventually over certain types of citizens and tamper civil rights progress in the future.  Current policies in the public sector are (Podesta, et al., 2014):

  • The Affordable Care Act allows for building a better health care system from a “fee-for-service” program to a “fee-for-better-outcomes.” This has allowed for the use of big data analytics to promote preventative care rather than emergency care while reducing the use of that data to eliminate health care coverage for “pre-existing health conditions.”
  • The Family Education Rights and Privacy Act, the Protection of Pupil Rights Amendment and the Children’s Online Privacy Act help seal children educational records to prevent misuse of that data.

Identifying opportunities for big data in the economy, health, education, safety, energy-efficiency

In the economy, the use of the internet of things to equip parts of product with sensors to help monitor and transmit live, thousands of data points for sending alerts.  These alerts can tell us when maintenance is needed, for which part and where it is located, making the entire process save time and improving overall safety(Podesta, et al., 2014).

In medicine, the use of predictive analytics could be used to identify instances of insurance fraud, waste, and abuse, in real time saving more than $115M per year (Podesta, et al., 2014).  Another instance of using big data is for studying neonatal intensive care, to help use current data to create prescriptive results to determine which newborns are likely to come into contact with which infection and what would that outcome be (Podesta, et al., 2014).  Monitoring newborn’s heart rate and temperature along with other health indicators can alert doctors of an onset of an infection, to prevent it from getting out of hand. Huge amounts of genetic data sets are helping locate genetic variant to certain types of genetic diseases that were once hidden in our genetic code (Podesta, et al., 2014).

With regards to national safety and foreign interests, data scientist and data visualizers have been using data gathered by the military, to help commanders solve real operational challenges in the battlefield (Podesta, et al., 2014).  Using big data analytics on satellite data, surveillance data, and traffic flow data through roads, are making it easier to detect, obtain, and properly dispose of improvised explosive devices (IEDs).  The Department of Homeland Security is aiming to use big data analytics to identify threats as they enter the country and people of higher than the normal probability to conduct acts of violence within the country (Podesta, et al., 2014). Another safety-related used of big data analytics is the identification of human trafficking networks through analyzing the “deep web” (Podesta, et al., 2014).

Finally for energy-efficiency, understanding weather patterns and climate change, can help us understand our contribution to climate change based on our use of energy and natural resources. Analyzing traffic data, we can help improve energy efficiency and public safety in our current lighting infrastructure by dimming lights at appropriate times (Podesta, et al., 2014).  Energy efficiencies can be maximized within companies using big data analytics to control their direct, and indirect energy uses (through maximizing supply chains and monitoring equipment).  Another way we are moving to a more energy efficient future is when the government is partnering with the electric utility companies to provide businesses and families access to their personal energy usage in an easy to digest manner to allow people and companies make changes in their current consumption levels (Podesta, et al., 2014).

Protecting your own privacy outside of policy recommendation

In this report it is suggested that we can control our own privacy through using the browse in private function in most current internet browsers, this would help prevent the collection of personal data (Podesta, et al., 2014). But, this private browsing varies from internet browser to internet browser.  For important information like being denied employment, credit or insurance, consumers should be empowered to know why they were denied and should ask for that information (Podesta, et al., 2014).  Find out the reason why can allow people to address those issues in order to persevere in the future.  We can encrypt our communications as well, in order to protect our privacy, with the highest bit protection available.  We need to educate ourselves on how we should protect our personal data, digital literacy, and know how big data could be used and abused (Podesta, et al., 2014).  While we wait for currently policies to catch up with the time, we actually have more power on our own data and privacy than we know.

 

Reference:

Podesta, J., Pritzker, P., Moniz, E. J., Holdren, J. & Zients,  J. (2014). Big Data: Seizing Opportunities, Preserving Values.  Executive Office of the President. Retrieved from https://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_may_1_2014.pdf