Learning analytics for lifelong career development: a framework to support sustainable formative assessment and...

Area of Science:

• Educational Psychology
• Career Development
• Metacognition

Background:

• The evolving job market presents challenges in developing student career self-efficacy.
• Traditional self-efficacy factors (direct/vicarious experience, social persuasion, physiological cues) are difficult to integrate into current educational programs due to unpredictable skill demands.
• Existing models struggle to prepare students for the unknown nature of future graduate competence.

Purpose of the Study:

• To propose a working metacognitive model for career self-efficacy.
• To equip students with adaptive skills for evaluating and developing their competencies.
• To address the limitations of traditional self-efficacy development in rapidly changing career contexts.

Main Methods:

• Development of a novel metacognitive framework for career self-efficacy.
• Conceptualization of the model as evolving complex sub-systems within an emergent milieu.
• Identification of specific cognitive and affective constructs for actionable learning analytics.

Main Results:

• The proposed metacognitive model offers a new approach to career self-efficacy.
• It emphasizes the importance of self-evaluation and adaptation of skills, attitudes, and values.
• The model identifies key targets for learning analytics in career development.

Conclusions:

• A metacognitive approach is crucial for fostering career self-efficacy in uncertain job markets.
• Students need to develop metacognitive skills to navigate evolving career demands.
• Actionable learning analytics can be derived from the model's cognitive and affective constructs.