To what extent does a CV reflect someone’s appropriateness for a job? How much does it cost recruiters to read and select candidates from piles of CVs? How much effort and cost goes into developing the optimal resume?
This year in China, 33,000 graduates applied for 70 places on the French cosmetics company L’Oreal’s graduate recruitment scheme. Rather than submitting their CV, they were asked to answer three simple questions via their smartphones. Then, a Shanghai-based startup called Seedlink used predictive language analytics to match right candidates with L’Oreal’s recruitment criteria. Seedlink’s RCXUE product asked open ended questions such as “”If you had one month and a £4,000 budget to tackle any project your heart desired, what would you do?” The software then analysed the language used in the answers, and compared each candidate’s response to draw up a shortlist.
Prior to using this approach L’Oreal filtered candidates by selecting only from China’s top universities. However, L’Oreal is joining a growing band of top firms who are questioning the value of high academic achievement.
Google, for example, recognise that good grades are useful, but not a good indicator of future performance. According to Laszlo Bock, ‘head of hiring’ at Google, quoted in the NYT – “For every job the No. 1 thing we look for is general cognitive ability …. the ability to process on the fly and to pull together disparate bits of information… The second is leadership — i.e. when faced with a problem do you, at the appropriate time, step in and lead… Another is humility and ownership… Research shows that many graduates from hotshot business schools plateau. “Successful bright people rarely experience failure, and so they don’t learn how to learn from that failure.”
The implications of the use of language analytics in assessment are immense. For example, ATC21s, the 21st Century Skills assessment project at Melbourne used language analysis technology to analyse how well students were performing at collaborative tasks. As the technology and its implementation improves the idea of testing students in written examinations so they can pack their CVs with good grades is becoming rapidly dated.
This white paper – Assess. Analyse. Intervene. From E-Assessment to Personalised Learning – was written to help Ministries of Education, Local Education Authorities and prospective suppliers understand how to build on E-Assessment and E-Examination to create personalised learning experiences. Taking the three key building blocks of Assessment, Analytics and Intervention, the paper defines a Personalised Learning Platform and its interfaces within a broader schooling ecosystem – the Schooling Enterprise Architecture.
The central proposition to this paper is that using data generated by the growing use of E-Examination and E-Assessment process offers significant value for increasing the effectiveness of the schooling systems.
Schooling system needs to constantly innovate and evolve. This paper sets out a vision for how schooling leaders can make learning even more effective by personalising the learning experience for all school students – without introducing unmanageable complexities.
The implementation of the key recommendations of this paper should deliver the following benefits:
Effective learning – Intervention is about developing virtuous cycles of learning, tailored to individual needs
Deep insights – using deep analytics, new and unpredicted patterns can be found that can help inform decision makers about where to focus investments
Timely intervention – whilst E-Assessment takes essential “rear view mirror” snapshots of learning performance, predictive analytics can be used to constantly steer students in the right direction, maximizing the chances of doing well in assessment and examinations
Three interdependent processes combine to deliver a personalized learning experience:
Ongoing assessment from a range of sources is used to gather data about how individuals and groups of students are learning. This data is analyzed to help target students with tailored learning, and to make decisions that lead to increased effectiveness. Using data, interventions can be set up do deal with issues such as reducing drop-out rates; selecting the most effective ways of improving reading and mathematics; and dealing with risks before they become a problem. Ultimately interventions can be tailored for individuals and groups of students.
Each of these processes are interconnected in multiple ways –
The white paper explores these processes and how they integrate and can be implemented.