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The purpose of this paper is to present an alternate approach to entrepreneurship pedagogy development through an iterative journey of co-ownership between students, industry partners and academic course teams to enhance student satisfaction and learning outcomes.

Utilising design thinking, the pedagogy evolved over a three-year period (2013-2015) through iterative innovation in the delivery model and assessments, underpinned by notions of classroom community, constructivism, justice and equity, humour and role-play.

The findings strongly validate the integration of notions of justice and equity, constructivism, humour and role-play as learning principles and delivery elements in entrepreneurship pedagogy to enhance student satisfaction and learning outcomes. A critical outcome of this design and delivery process is the reduction of barriers between students and teachers and the impact this has on creating a shared learning journey; a journey that in this case has resulted in meaningful outcomes for all involved.

Further research with longitudinal data is needed to validate the link between design-led entrepreneurship pedagogy and enhanced student learning outcomes as well as implications relating to graduate employability. In global settings, further data collection could also validate whether the findings are culturally neutral or culturally sensitive.

Entrepreneurship educators will benefit from this pedagogical approach in seeking to meet the needs of business start-ups, intrapreneurial capacity-building and potentially, enhancement of graduate employability. The model also offers promise for other learning contexts.

Design thinking has received scant attention in entrepreneurship pedagogy. This case study demonstrates how design thinking can enhance student satisfaction and learning outcomes by integrating notions of constructivism, justice and equity, humour and role-play in entrepreneurship curricula.

The purpose of this paper is to investigate the possible benefits of a work‐based learning (WBL) model in “social entrepreneurship” for enhancing graduate employability and an appreciation for “responsible” entrepreneurship.

Pre‐ and post‐course experience surveys were conducted with the yearly cohorts, followed by focus group discussions with students in each semester between 2008 and 2011. In addition, ten not‐for‐profit (NFP) industry partners were interviewed for their feedback on the enhanced curriculum.

The findings strongly validate the adoption of WBL curriculum for social entrepreneurship in enhancing graduate employability and opportunities for responsible entrepreneurship education. The case study also provides insights into how to overcome the key challenges relating to designing and implementing WBL models through a curriculum innovation in social entrepreneurship.

Further research with longitudinal data is needed to validate the link between students undertaking work‐based learning and enhanced graduate employability. Future research should also investigate whether there is a major difference in the associated benefits and challenges of WBL initiatives between “social” and “for‐profit” enterprises.

Higher education providers could consider incorporating WBL as part of their response to the employability agenda, in a climate where employers are increasingly seeking graduates who possess entrepreneurial skills and an awareness of ethical and environmental concerns emerging from the new post‐global recession economic era.

Social entrepreneurship has received scant attention within the field of WBL. This case study demonstrates how this field can inform the WBL model to enhance graduate employability. It also provides a case for how WBL within the NFP sector can produce more socially responsible graduates who are capable of adding value to the CSR initiatives of organisations across sectors.

The widespread use of washing machines in textile dyeing and finishing boosts product quality while leading to significant wastewater production. This wastewater poses environmental risks due to the textile industry's high pollution levels and water consumption. Sustainability hinges on minimizing water usage and treating wastewater for reuse. This study employs Matlab R2020a and Python 2023 to model experimental designs for treating textile production wastewater using the Fenton oxidation method, aiming to address sustainability concerns in the industry.

The Fenton oxidation process's efficacy and optimal operating conditions were determined through experimental sets employing the Box–Behnken design. Assessing machine learning algorithms on the data, Matlab R2020a utilized an artificial neural network (ANN), while Python 2023 employed support vector regression (SVR), decision trees (DT), and random forest (RF) models. Evaluation of model performance relied on regression coefficient (R2) and mean square error (MSE) outcomes. This methodology aimed to refine the Fenton oxidation process and identify the most efficient parameters, leveraging a combination of experimental design and advanced computational techniques across different programming platforms.

The study identified optimal conditions: pH 3, Fe+2 concentration of 0.75 g/L, and H2O2 concentration of 5 mM, yielding 87% COD removal. The Box–Behnken design achieved a high R2 of 0.9372, indicating precise predictions. Artificial neural networks (ANN) and support vector regression (SVR) exhibited successful applications, notably achieving an R2 of 0.99936 and low MSE of 0.00416 in the ANN (LOGSIG) model. However, decision trees (DT) and random forests (RF) proved less effective with limited datasets. The findings underscore technology integration in treatment modeling and the environmental imperative of wastewater purification and reuse.

This study, in which water use and wastewater treatment are evaluated with technological integration such as machine learning and data management, reveals how to contribute to targets 6, 9, 12, and 14 within the scope of UNEP 2030 sustainable development goals.