Location-based Learning

Personalizing Education

Personalization in education has been evolving from teacher-based classroom interventions to personalized tablets and data-driven individualized learning plans. The learning experience is getting more tailored to individual progress, interests, schedules, and goals while continuously adapting to changes in these conditions.

Stimulating students with a sense of ownership of the content they access to and learn from, promises to target both under and over-performing students more effectively, whereas the focus of traditional teaching is tailored to the average student’s performance. Traditionally, student achievement and performance have been tied to a teacher’s capacity to personally carry the content to them through active instruction. By personalizing the content according to the student's needs, it encourages education as a whole to level the playing field, effectively creating equal access to learning opportunities by democratizing information and instruction.

For many personalized learning systems, student data such as age, grades, and proficiency assessments are collected and analyzed. Using data-driven content recommendations, based on the student’s unique background, specific behaviors, and answers, the system responds by changing the learning experience to better suit the unique needs of that particular student. These insights are full of recommendations and help recalibrate individual student learning curves, customizing the instruction, or when necessary, suggesting remedial education with teacher intervention.

Geofencing: Location-based Learning

Geofencing is a business model widely used for marketing campaigns. It targets individuals based on their current location, providing on-the-spot information for users. When applied for education, this model helps students and trainees to explore their learning environment while simultaneously targeting a specific audience during recruitment fairs, community college transfer days, at local businesses, on-campus events, and more. It can also notify users about detailed historical information from sites around the city or warn students of spreading health issues on and off-campus.

With the spread of mobile devices, location-based learning offers other educational purposes outside of campus or company contexts. With data aggregated about distinguished zones, geofencing apps could send notifications to users with content regarding the area they enter, serving as a virtual tour guide. It also serves as a cultural heritage platform, educating tourists as well as the local community about a particular region. In a regional education program for farmers, educational content about various crops is segmented in different dialects, offering the possibility to switch languages depending on the area.

Also, with the help of blockchain technology, education tracking could make verification and recording transitions free of fraud. The method of Blockchain Asset Tracking could potentially reduce false claims of unearned educational credits by issuing blockchain-based certificates unable to be falsified. Students' e-portfolios could be verified, while the distributed ledger checks, secure, and shares educational records. Institutions would have access to a student's curriculum, and digital academic transcripts would be managed with more accuracy. This protocol would establish more transparent systems and create more accountability of academic achievements between students and schools as blockchain would allow both professors and students to track the entire learning process.

Another exciting aspect is the use of Smart Contracts while issuing and validating certificates in the blockchain. They would have the potential to reduce paperwork significantly, and they could be applied to verify students' attendance or assignment completion. School or university libraries might as well use this protocol to expand their services by creating an enhanced metadata archive of their collections.

In addition, in the face of the refugee crisis, education is a critical aspect of the international refugee response. Simple actions such as enrollment become difficult as host institutions cannot have access to people's educational records. In order to deliver a more inclusive and quality education for all, private and governmental institutions could have some of this process made more manageable, as they use a blockchain educational tracking system in the admission processes.

Future Perspectives

As schooling moves toward the “more data is better” model of tech startups and personalization, concerns continue to mount regarding why certain data is collected, with whom it will be shared, and how it will be used. Data collection is not limited to proficiency assessments but is increasingly overlapping with the personal lives of students via school-issued home-based devices, and the monitoring of student social media accounts. The tipping point when student data collection shifts from instructional benefit to oversight and surveillance is debatable. Depending on how a system is programmed, there is a danger of false positives that can use data points to indicate disinterest or lack of engagement when the opposite is true.

The adoption of location-based learning methodologies at scale requires significant changes in how teachers operate, such as changing the way they facilitate the education of students and empower them to chart their own paths. Even modern digital assessments still mostly involve glorified multiple-choice questions and digital systems cannot comprehend or rate creative approaches to problem-solving or even complete tasks as simple as grading essays. Although rapid advances in Natural Language Processing have resulted in models which can comprehend language at a basic level, they are far from being able to read, understand, and score even junior-level essays.

Although significant questions remain, the transition to location-based learning will be gradual and is already finding its way into the classrooms of today. Using Voice Recognition, Natural Language Processing, and Machine Reasoning these proactive learning guides could adapt the learning material to student needs in real-time while naturally interacting with them. If done properly, advancements in Neural Machine Translation and collaborative databases could help make educational resources available worldwide, irrespective of social class or demographic background, provided the students have access to the internet.

A criticism of personalizing education to such a level is that they neglect the inherent social factor present in traditional learning contexts. If each student has an entirely personalized learning experience, exchanges with their classmates may become secondary, making critical and teamwork skills suffer. An unintended side-effect of constant digital assessment could lead students to pursue performance goals to achieve a score to please teachers or parents rather than actually learning the value of the process and hard work. Furthermore, personalized learning technology may highlight more broadly evident issues in education: it is difficult to measure or optimize success when success is not clearly defined. Should students be scored more on their ability to become productive members of the workforce or their academic abilities? Should hard-to-measure factors such as creative or artistic pursuits or improving future happiness play a role?