Student goals play a crucial role in both practice and theory in higher education, particularly for understanding and enhancing student motivation. However, on a conceptual level, goals are studied through separate and isolated frameworks, which complicates the ability of practitioners and researchers to combine insights from both streams of scholarship effectively. This systematic review aimed to comprehensively examine and describe the frameworks (198 studies), and conceptual distinctions and characteristics (48 studies) used in research on goal setting and goal orientation in the context of higher education since 2018. The studies selected for analysis had to discuss students’ learning process, address goal setting and goal orientation, and be published in academic journals in English. Goal-setting theory was the most frequently used for the goal-setting concept, while achievement goal theory (2 × 2 model) dominated for goal orientation. Summative content analysis revealed that goal-setting frameworks primarily emphasised the concept of the goal itself, including its structure, goal commitment, implementation intentions, and the processes leading to goal achievement. In contrast, goal orientation frameworks delved deeper into the underlying motivations driving goal pursuit, exploring related attitudes, addressing specific educational objectives and strategies, emotional factors, and the detailed standards individuals set for their outcomes. Because these theories are complementary, we propose an integrated goal setting and orientation (IGSO) theory, which can help in studying and understanding how motivation for goal pursuit is connected with the setting, monitoring and evaluation of goals.
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Artificial Intelligence (AI) offers organizations unprecedented opportunities. However, one of the risks of using AI is that its outcomes and inner workings are not intelligible. In industries where trust is critical, such as healthcare and finance, explainable AI (XAI) is a necessity. However, the implementation of XAI is not straightforward, as it requires addressing both technical and social aspects. Previous studies on XAI primarily focused on either technical or social aspects and lacked a practical perspective. This study aims to empirically examine the XAI related aspects faced by developers, users, and managers of AI systems during the development process of the AI system. To this end, a multiple case study was conducted in two Dutch financial services companies using four use cases. Our findings reveal a wide range of aspects that must be considered during XAI implementation, which we grouped and integrated into a conceptual model. This model helps practitioners to make informed decisions when developing XAI. We argue that the diversity of aspects to consider necessitates an XAI “by design” approach, especially in high-risk use cases in industries where the stakes are high such as finance, public services, and healthcare. As such, the conceptual model offers a taxonomy for method engineering of XAI related methods, techniques, and tools.
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Knowledge of how professional youth work might prevent individual and social problems in socially vulnerable youngsters is poorly developed. This article presents a conceptual framework that clarifies the implicit methodical process used by professional youth workers and focuses on what stakeholders regard as the potential of professional youth work as a preventive service. A qualitative research synthesis approach was used to combine the findings of six practice-based studies conducted in six European countries. This synthesis revealed that professional youth workers employ a multi-methodic approach in their prevention efforts, strengthening the social skills and self-mastery of youngsters, reinforcing their social network, enhancing their civic participation and helping them find additional social or health services. Twelve methodic principles were identified as contributing to achieving these prevention efforts, shedding light on the process taking place between youngsters and youth workers. This conceptual framework provides essential information for future evaluation research.
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