Education provides the knowledge necessary to succeed in life, taking up decades to do so. Within these years a mountain of time data pertaining to academic experience and achievements is generated, but it is often underutilized, ignored or simply inaccessible.

School and college operational efficiency

Problem

  • From elementary school through to college, the efficient running of academic institutions is reliant on compliance with timelines. From admissions deadlines to essay submissions and exam dates, students and professionals alike are bound by a constant demand for punctuality. Given the high volume and potentially conflicting nature of deadlines, accidental tardiness is common. This has negative consequences for the students (missed opportunities, poor academic scores) and professionals (time spent on chasing students and other administration).

Solution

  • After completion of their annual examinations, colleges and exam boards could be incentivized via ANLOG rewards to submit all their records to the Timegraph. As this data would be verified as originating from the official source and subsequently recorded immutably on-chain, the Timegraph would henceforth serve as a reliable source of information regarding pupils’ achievements. This would minimize the time and expense taken verifying educational data and mitigating degree fraud. Similarly, if sufficient scale is achieved, it could conceivably reduce the need for paper degree certificates entirely. Awarded degrees could simply be stored on-chain and presented via the Analog API.

Academic certification and administration

Problem

  • With millions of students taking high school and college exams each year, the paperwork surrounding academic certification is significant. This is exacerbated by the large number of different exam boards and colleges all issuing their own certificates. Currently, most records of educational achievement are tracked via the issuance of paper certification and entries in centrally managed databases.
  • This presents a number of inefficiencies. For example, academic fraud (such as falsely claiming a degree or purchasing a counterfeit degree) is relatively common, meaning employers must spend heavily on imperfect verification methods. Similarly, students who wish to provide proof of their achievements may need to spend time and money liaising with numerous educational entities to replace lost or damaged certificates.

Solution

  • After completion of their annual examinations, colleges and exam boards could be incentivized via ANLOG rewards to submit all their records to the Timegraph. As this data would be verified as originating from the official source and subsequently recorded immutably on-chain, the Timegraph would henceforth serve as a reliable source of information regarding pupils’ achievements. This would minimize the time and expense taken verifying educational data and mitigating degree fraud. Similarly, if sufficient scale is achieved, it could conceivably reduce the need for paper degree certificates entirely. Awarded degrees could simply be stored on-chain and presented via the Analog API.

Talent databases

Problem

  • Currently, most colleges worldwide select their new intakes from the pool of candidates who apply for entry directly. While widely accepted as the norm, this methodology is imperfect. For example, colleges may have specialized or niche courses they wish to fill but are finding the talent pool applying for said course to not be of the standard they require.
  • Because there is no all-encompassing and searchable database of eligible students, it is therefore difficult for colleges to themselves identify and make offers to potential candidates who have not already directly applied. Similarly, high-quality candidates will be limited to offers from institutions they have directly applied to and will not be contacted regarding potentially interesting courses they were previously unaware of.

Solution

  • Similar to the point above, if or when a sufficient amount of data on individuals’ academic past is recorded on the Timegraph, the Analog API could facilitate customizable talent searches. For example, a college looking to fill a new master’s degree course on tiger conservation could filter for individuals with strong Environmental Science under-graduate degrees who have previously written essays on tigers or conservation. They could then approach the best of these candidates to propose joining their new master’s degree course. This has the benefit to them of increasing the likelihood of attracting the best candidates, while the individuals have the benefit of being made aware of academic options they may not previously have heard of.

Accountability of educational staff

Problem

  • While the average educational attainment of students at specified institutions is usually well documented, these public records are typically aggregated at a high level and thus obfuscate students’ records from specific classes or teachers/professors. As the academic outcomes of students learning under specified staff is often unknown, this reduces the ability of prospective students to apply to learn under the highest quality staff. It also reduces the incentive for staff to place teaching outcomes as a significant priority versus their other professional obligations.

Solution

  • Teachers or professors could be incentivized to submit their annual results to the Timegraph, which could subsequently be searchable by students looking for the best teachers.
  • While initially only strong performing teachers would wish to submit this data, should the trend achieve a certain scale it may force poorly performing teachers to either submit their data or answer questions as to why they have not. This trend would have the long term benefit of greater choice and transparency for students, professional success/recognition for the best teachers and more incentives to improve teaching quality.

Covid and educational achievement

Problem

  • In certain unusual circumstances, students may not be able to take their exams. For example, due to the recent Covid outbreak, many countries have endured school closures, obstructed teaching schedules and canceled exams. In many such cases, students due to take key exams have been awarded an ‘assumed’ grade based on an assessment of their likely academic outcome. The criteria for such an assessment can often be controversial, such as the UK’s decision to rely on subjective review by teachers and exam centers, as opposed to an objective statistical analysis of the pupils’ historic performance.
  • Due to the lack of publically available individualized data, colleges and other institutions lack the capacity to perform their own analyses on a student’s academic quality or potential. They must instead rely on imperfect and subjective assessments whose basis they either do not know or may disagree with.

Solution

  • If a large quantity of educational data becomes stored in the Timegraph, it allows for individualized analysis of an individual’s academic record. If, for example, an institution disagreed with Covid related exam results based on subjective measures, they could themselves obtain the academic history of their applicants. Instead of relying on the subjective exam scores, they could then perform their own statistical analysis of prior results and organize applicants by ability in accordance with whatever metrics they are interested in.

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