Holistic Assessment Approaches

Assessment sciences must move beyond the numbers. Here's how incorporating qualitative research methods can help us build better assessments: ▶️ 𝗘𝗻𝗵𝗮𝗻𝗰𝗶𝗻𝗴 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗩𝗮𝗹𝗶𝗱𝗶𝘁𝘆: Interviews with stakeholders can provide valuable insights into the knowledge, skills, and abilities most important to assess in a particular context. ▶️ 𝗜𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗜𝘁𝗲𝗺 𝗤𝘂𝗮𝗹𝗶𝘁𝘆: Discussions with target populations can reveal how individuals interpret questions, identify potential biases, and suggest improvements to item wording and clarity. ▶️ 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴 𝗔𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Focus groups with diverse examinees can provide valuable input on the usability and accessibility of assessment materials. ▶️ 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆𝗶𝗻𝗴 𝗕𝗶𝗮𝘀: Relying solely on numbers can hide biases that may be present in assessments. Qualitative methods can help identify and address potential cultural biases in assessment items and procedures. ▶️ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹𝗶𝘇𝗶𝗻𝗴 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Qualitative methods, like interviews and observations, help us understand the "why" behind performance, not just the "what." ▶️ 𝗕𝗲𝘁𝘁𝗲𝗿 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗻𝗴 𝗥𝗲𝘀𝘂𝗹𝘁𝘀: Discussions with score users on how best to report assessment performance can help to increase assessments' utility. Overall, for the assessment sciences to be truly effective, we must adopt a mixed-methods approach to training and research. Although resource-intensive, incorporating greater qualitative methods will help us create more valid, reliable, and equitable assessments. Check out Andrew Ho's latest paper for a great discussion on why assessment "must be qualitative, then quantitative, then qualitative again": https://lnkd.in/gxysNAjY ---- Disclaimer: The opinions and views expressed in this post are my own and do not necessarily represent the official position of my current employer.

This paper introduces a holistic AI literacy assessment matrix to integrate and evaluate generic, domain-specific, and ethical AI competencies across cognition, behavior, and attitude. 1️⃣ AI literacy is framed across three key types—generic literacy for all citizens, domain-specific literacy for professional applications, and AI ethics literacy to address ethical concerns in AI use. 2️⃣ The matrix uses a triad model: cognition (knowledge), behavior (skills), and attitude (values) to comprehensively evaluate AI literacy. 3️⃣ Examples from medicine, engineering, and education highlight unique AI-related competencies needed in each domain. 4️⃣ Ethics is emphasized as a distinct category to address broader societal impacts of AI use. 5️⃣ The paper critiques current AI literacy tools and proposes new methods like situational judgment tests to evaluate behavioral and ethical dimensions. 6️⃣ Calls for empirical validation of the matrix, exploration of relationships between AI literacy types, and development of tailored educational programs to bridge gaps in domain-specific AI literacy. ✍🏻 Nils Knoth, Marie Decker, Matthias Laupichler, Dr. Marc Pinski, Nils Buchholtz, Katharina Bata, Ben S. Developing a holistic AI literacy assessment matrix – Bridging generic, domain-specific, and ethical competencies. Computers and Education Open. 2024. DOI: 10.1016/j.caeo.2024.100177

One assessment method won’t cut it... Multi-methods unlock hidden potential. Relying on a single method misses the full picture: → It overlooks important skills and abilities. → It may lead to biased or incomplete evaluations. → It fails to identify specific areas for improvement. A multi-method approach paints a full picture: 1. Performance Reviews Deliver structured feedback to highlight growth areas. Focus on actionable steps to improve performance.     2. Surveys & Interviews Gain honest insights directly from key stakeholders. Uncover both strengths and hidden challenges.     3. Skills Gap Analysis Identify critical priorities for targeted development. Design plans to close gaps and build key skills.     4. Self-Assessments Encourage leaders to reflect on their unique strengths. Build self-awareness to fuel ongoing growth.     5. Team Discussions Foster collaboration to unlock team potential. Reveal hidden strengths within group dynamics. Mix at least three methods for real impact: ☑ Schedule regular feedback check-ins. ☑ Build impact skills like communication. ☑ Use tech for surveys and real-time data. Smart assessments drive future-ready leaders. Follow Jonathan Raynor. Reshare to help others.

"Full Stack QA: The Future of Holistic Software Testing" Full Stack QA is a holistic approach to quality assurance, covering end-to-end testing across UI, API, database, performance, security, and automation. Unlike traditional testing, it ensures seamless integration across all layers of an application, from frontend to backend, databases, cloud services, and CI/CD pipelines. A Full Stack QA Engineer is skilled in automation, exploratory testing, DevOps practices, and AI-driven testing methodologies to enhance product quality. This role bridges the gap between development and operations, ensuring faster releases, higher reliability, and better user experiences. Mastering Full Stack QA requires expertise in Shift Left Testing, API Contract Testing, Mutation Testing, Test Data Management, and AI in Testing—all of which play a crucial role in modern software development. #FullStackQA #SoftwareTesting #QAEngineering #AutomationTesting #DevOpsQA

In education, our perspective is powerful. When we see reality as isolated components, our instinct is to address each part separately. But what if we view reality as a network of interconnected elements? 🗺️ Focusing on the whole picture becomes crucial. As we face future challenges, systemic issues will arise. Whether financial crises or climate change, understanding intricate systems is vital. 🌐 It's about grasping the interplay of components in their entirety, not just fixing parts. In this complex age, our education system needs a paradigm shift. Regardless of the field, we must perceive the world as interconnected nodes, not disjointed elements. 🔑 Let's be architects of change, not just fixers. By honing skills to comprehend intricate networks, we can address root causes. I hope to see education evolve for holistic thinking, preparing us for grand challenges. 10 tips on how to start in your classroom today: 1. Define Clear Holistic Learning Outcomes Set overarching learning goals that emphasize interconnectedness and understanding relationships between concepts. 2. Identify Interdisciplinary Connections Use a mind-mapping tool to map out how different subject areas intersect and weave those intersections into your course structure. 3. Foster Systems Thinking Introduce the concept of systems thinking early on to help students understand the complex interplay of factors. 4. Integrate Real-World Challenges Incorporate projects that require students to address multifaceted, real-world problems. 5. Use Collaborative Projects Assign group projects that encourage students to combine insights from various disciplines. 6. Explore Ethical Considerations Discuss the ethical implications of various decisions within complex systems. 7. Holistic Assessment Methods Develop assessment methods that measure students' ability to connect concepts rather than rote memorization. 8. Embrace Ambiguity Teach students to navigate uncertainty and complexity, enhancing their adaptability. 9. Simulations and Role-Play Use simulations and role-play scenarios that involve dynamic interactions between different components. 10. Reflect on Personal Growth Prompt students to reflect on how their holistic understanding has evolved throughout the course.   #HolisticEducation #SystemsThinking #EducationForChange #NavigatingComplexity #LifelongLearning #HolisticCourseDesign #InterdisciplinaryLearning #SystemsThinking #EducationInnovation #LifelongLearning

RETHINKING AI SUCCESS: A HOLISTIC APPROACH BEYOND BENCHMARKS Why AI Measurement Must Evolve to Focus on Human Collaboration, Ethics, and Long-Term Reliability The evaluation of artificial intelligence (AI) and machine learning (ML) systems has traditionally centered on benchmarks, accuracy rates, and performance speeds—metrics that, while quantifiable, offer a limited perspective on AI's potential and responsibilities. This focus often overlooks critical aspects such as societal impact, ethical considerations, and long-term reliability. This imbalance prompts a vital question: How can we trust AI to serve humanity effectively if we fail to assess its real-world consequences comprehensively? Addressing this issue necessitates a paradigm shift in AI evaluation methodologies, integrating ethical and societal considerations alongside traditional performance metrics to ensure AI systems are aligned with human values and societal well-being. 💡 The Future of AI Measurement To ensure AI is ethical, reliable, and aligned with human values, we need new metrics that measure: ➤ Human-AI collaboration outcomes rather than standalone AI performance ➤ Bias and fairness in AI systems to ensure ethical decision-making ➤ AI’s ability to detect its own limitations and recommend human oversight ➤ The quality of human-AI partnerships in decision-making processes ➤ Alignment with long-term societal benefits, not just narrow optimization goals As AI continues to evolve, its true value won’t be measured by speed or accuracy alone—but by how well it enhances human potential and serves society. #ArtificialIntelligence #management #humanity #Innovation #performance