FROM CHALK AND TALK TO WALKING THE WALKEducation and Training 2012, 54(2/3): 152-166.
The purpose of this paper is to examine the notion of designing and developing applied, industry-engaged learning environments that embrace ambiguity and uncertainty in overcoming pedagogical inertia in educating young entrepreneurs and innovators. The research reported on proposes a solution to the dual expectations of producing entrepreneurship graduates who can either hit the ground running in driving innovation for employers, or create, launch and sustain their own ventures. Design/methodology/approach: The research is longitudinal in nature, employing a mixed methods approach using both quantitative and qualitative instruments in measuring outcomes, along with development and validation of the proposed Create-Substantiate-Activate (CSA) scale. Findings: Significant triangulated evidence is provided that validates the proposed dynamic, industry-engaged learning model. The skill and capability development of the entrepreneurship students, as well as the positive impacts upon self-confidence and self-efficacy, support the approach adopted in moving beyond the business planning paradigm into rapid innovation prototyping. Research limitations/implications: The paper reports on one program within an undergraduate Entrepreneurship degree at one Australian university. Therefore, the suitability for adoption of the proposed model will be subject to factors germane to particular contexts. Practical implications: The research provides direction for designing and managing collaborative industry-engaged learning programs for students of Entrepreneurship and Innovation. Critical elements of the learning process are identified that foster contexts for producing Entrepreneurship graduates that are both highly valued by employers and capable of launching and sustaining innovative new businesses. Originality/value: The Innovation Fastrack Program reported on in this paper is ground-breaking in the way it engages industry, promotes rapid and deep learning contextualised by creativity, curiousity, uncertainty and volatility and in the way it fosters social interaction in dealing with real-world problems and opportunities.
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