Students career guidance beyond psychometric and aptitude tests—How domain-centric theme extraction uncovers the hidden career choices

Students careers

Theme extraction for intelligent career design patterns

Rather than finding what subjects the students should study and for what kind of career, we should reverse engineer the process by asking them their deep and hidden aspirations and dreams and then help them find what skills they should learn to realize those dreams. Conventional career counselling methods that are based on psychometric and aptitude tests cannot answer these most important questions in a student’s career. 

Many years ago we saw a career counselling agency doing analysis for a student’s career planning. They shared a report that explained the student’s strengths, analytical ability, and other personality and behavioral criteria and mapped these with possible career options—this is such a good feeling for the parents.

Recently when someone in our family used one such service, they did not find the report useful to their career decisions. Their argument was that the report’s findings do not really map with the student’s career path or aspirations. The family shared that more parents have similar concerns but they use such services for whatever feel-good they get for their kids because they do not  have a choice.

The problem is not with the analysis itself, it is with the collective goals as seen by the students, the parents, the counselling experts, and the way such products and the algorithm are designed—everyone is trying to find out what subjects the student should study in the college based on the students’ aptitude, personality, and analytical abilities. Subjects do not always make rewarding careers.

“Steering changes this game on its head—we help the students explore and share their deep, hidden, and often unsaid aspirations, life-dreams, and wishes as their career choice.”

We build their confidence in a conversation.

Intelligent theme extraction

We find meaning in what the students say, to find the gaps they see in their aspiration and college options, the confusion, the conflict, and the uncertainty. Our domain model is designed around scaleable taxonomy and is self-learning to respond to a new student’s response. This model informs us to design theme clustering, with metrics. 

This is the beginning of our sense-making process of what the students say, and give us a base for theme clustering.

We identify the gaps, conflicts, and the confusion in how they talk about their career, skills, and subjects they want to study in college. 

Steering's theme extraction process begins with finding meanings in the students' response, for their career guidance and counselling process to help them find the right college and career path.

Semantic theme clustering

There is a lot of human intelligence and human judgment involved in designing these models for theme clustering. For example a student says—”I do not want to work in an MNC.” 

Our model should understand that they might mean to say “corporate” or “big companies” or “international companies”. Here is a quick overview of how it works.

Layer 1: Semantic extraction

  • Parse text for entities, relationships, uncertainty markers
  • Create structured data mapping
  • Assign confidence scores for each extraction
 

Layer 2: Narrative analysis

  • Identify story patterns and emotional subtext
  • Assess the gaps and the conflict points, and the coherence
  • Map emotional journey through responses
 
Layer 3: Integrated analysis
 
  • Combine semantic and narrative data
  • Weight clusters based on both content and emotional markers
  • Create multidimensional student profiles

 Finally, the theme clustering with assigned scores

  • Primary clusters: Problem space alignment
  • Secondary clusters: Clarity level and confidence
  • Tertiary clusters: Family dynamics and constraints
  • Quaternary clusters: Learning style and preferences

Imagine if we had relied on psychometric tests to find the student’s resilience, empathy, focus, enthusiasm, concern for safety, and other behavioral skills, we could not have identified any directions for what they want to do in life. 

For example empathy in healthcare does not necessarily mean that the student will have empathy for the investors in stock market. Likewise, focus while doing carpentry does not necessarily mean the same focus while writing a policy document. 

See Steering’s intelligence playbook for more details (PDF, opens in a new tab).

Free trial

We have a free trial for you.

Our analysis structure in the free plan helps you in thinking deeply about your life-dreams, aspirations, wishes, and in connecting the dots for what you want to do, what skills you should learn, how to apply those skills in real life work, and what brings joy in your life when you grow up.

Steering is open for private access at present. You can take the free trial to get started.