Work on spoken English grading gets accepted at ACL, AM-R&D going to Beijing!

Good news! Our work on using crowdsourcing and machine learning to grade spontaneous English has been accepted at ACL 2015.

  • Ours is the first semi-automated approach to grade spontaneous speech.
  • We propose a new general technique which sits between completely automated grading techniques and peer grading: We use crowd for doing the tough human intelligence task, derive features from it and use ML to build high quality models.
  • We think, this is the first time anyone used crowdsourcing to get accurate features that are then fed into ML to build great models. Correct us if we are wrong!

Design of our Automated Spontaneous Speech grading system.

Figure 1: Design of our Automated Spontaneous Speech grading system.

The technique helps scale spoken English testing, which means super scale spoken English training!

Great job Vinay and Nishant.

PS: Also check out our KDD paper on programming assessment if you already haven’t.

- Varun

A re-beginning : Welcome to AM Research!

We finally have a place to feature the work which we began five years ago. Great effort, Tarun, to get this up and running.

We thought this was important since education technology and assessments are going through a revolution. We wish to add our two teaspoons of wisdom (did I actually say that!) to the ongoing battle against the conventional non-scalable and unscientific ways of training, assessing and skill matching. We look forward to making this as a means to collaborate with academics, the industry and anyone who feels positively about education technology.

Sector/Roles Employability(%)
Sales and Business Development 15.88
Operations/Customer Service 14.23
Clerical/Secretarial Roles 35.95
Analyst 3.03
Corporate Communication/Content Development 2.20
IT Services 12.97
ITes and BPO 21.37
IT Operations 15.66
Accounting 2.55
Teaching 15.23

Table 1: By using standardized assessments of job suitability, in a study of 60,000 Indian undergraduates, we find that a strikingly low proportion of them have skills required for the industry. All these students got detailed feedback from us to improve. The table shows the percentage of students that have the required skills for different jobs. (Refer: National Employability Report for Graduates, under Reports in Publications)

We think assessments will be the key to democratize learning and employment opportunity: it provides a benchmark for measuring success of training interventions, provides feedback to learners creating a ‘dialogue’ in the learning process and most importantly, helps link learning to tangible outcomes in terms of jobs and otherwise.

Let me state it simply: To scale learning and make employment markets meritocratic, we need to scale automated assessments. This is the space we dabble in!

If you are thirsty for data, refer to the table and figure in this post. It tells the story of the problem we are up against and trying to solve.

Figure 1: 2500 undergraduates were surveyed to find their employment outcomes one year after they got their undergraduate education. We categorized their colleges in three categories (tier 1-3) based on their overall performance in AMCAT, our employability test. We find that a candidate in a tier 3 college has 24% lower odds of getting a job and 26% lower salary when he/she has the same merit (AMCAT scores) as a tier 1 students. Similarly, a 1 point drop in college GPA (on a 10 pt scale) decreases job odds by 16% and salary by 9%. Neither of these two parameters are useful predictors of job success beyond AMCAT scores. This shows a clear bias in the employment ecosystem. (Refer ‘Who gets a job’ under Reports in Publications)

How do we solve it? Stay tuned to our subsequent job posts…