Research pick: Students move on up with career-boosting model - "Particle swarm optimisation-based self-efficacy model for student learning and decision-making capabilities"

A new approach to career decision-making could boost students’ confidence in their choices by not only offering information and guidance, but by helping them believe in their ability to choose wisely. The research International Journal of Computational Systems Engineering draws on social cognitive theory. This theory emphasizes how individuals learn and make choices by observing others and reflecting on personal experiences. By incorporating this theory into the new model for offering careers advice, the researchers say their approach can strengthen student self-efficacy, their level of confidence in their own abilities.

The new model builds on what the researchers call the “implicit feature matrix”. These are the often unspoken, unconscious beliefs and assumptions that shape how students perceive different careers. These internal influences, which may arise from family expectations, past successes or failures, or cultural narratives, can nudge students towards particular choices and away from others. By identifying and incorporating these hidden factors into the new model, it is possible to produce more nuanced and accurate careers advice. This effectively overrides the assumptions and biases that might otherwise guide a student down a potentially unsuitable career path.

Conventional careers advice focuses on external characteristics, such as a student’s grades, their interests, and their stated preferences. It then attempts to match the student’s profile to suitable occupations. Unfortunately, such an approach can rely too heavily on stereotypes or statistical clustering and ignores the deeper motivational dynamics. The new model demonstrates has greater “structural validity” and captures how various psychological and social influences interact to shape choices.
One of the distinctive features of the model is that it uses particle swarm optimization, a computational technique inspired by the coordinated movement of social animals such as bees.

In this technique, individuals learn how to behave by observing and adapting to the behaviour of others in the group. In the educational context, students are the individuals, or agents, in the swarm, and can strengthen their self-efficacy by engaging with peers, learning from each other’s successes and strategies, and refining their own paths accordingly. This approach thus introduces a social-learning component to careers advice that goes beyond simple self-reflection and encourages communication skills, empathy, and collaborative problem-solving.

Zhou, Q. (2025) ‘Particle swarm optimisation-based self-efficacy model for student learning and decision-making capabilities’, Int. J. Computational Systems Engineering, Vol. 9, No. 10, pp.1–9.