Integrated Assessment Strategies

Each of these assessment methods brings its own lens to understanding student learning, and they shine especially when used together. Here’s a breakdown that dives a bit deeper into their purpose and power: 🧠 Pre-Assessments • What it is: Tools used before instruction to gauge prior knowledge, skills, or misconceptions. • Educator insight: Helps identify starting points for differentiation and set realistic goals for growth. • Example: A quick math quiz before a new unit reveals which students need foundational skill reinforcement. 👀 Observational Assessments • What it is: Informal monitoring of student behavior, engagement, and collaboration. • Educator insight: Uncovers social-emotional strengths, learning styles, and peer dynamics. • Example: Watching how students approach a group project can highlight leadership, empathy, or avoidance patterns. 🧩 Performance Tasks • What it is: Authentic, real-world challenges that require applying skills and concepts. • Educator insight: Shows depth of understanding, creativity, and the ability to transfer knowledge. • Example: Students design a sustainable garden using math, science, and writing demonstrating interdisciplinary growth. 🌟 Student Self-Assessments • What it is: Opportunities for students to reflect on their own learning, mindset, and effort. • Educator insight: Builds metacognition, ownership, and emotional insight into learning barriers or motivators. • Example: A weekly check-in journal where students rate their effort and note areas they’d like help with. 🔄 Formative Assessments • What it is: Ongoing “check-ins” embedded in instruction to gauge progress and adjust teaching. • Educator insight: Provides real-time data to pivot strategies before misconceptions solidify. • Example: Exit tickets or digital polls that reveal comprehension right after a lesson. These aren’t just data points they’re tools for connection, curiosity, and building bridges between where a student is and where they’re capable of going. #EmpoweredLearningJourney

"Back to exams" is not an effective strategy for resolving assessment validity in the GenAI era. 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝘁𝘄𝗶𝗻𝘀 might be one step forward... New preprint from Dr Jasper Roe SFHEA, Louie Giray and myself: 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗧𝘄𝗶𝗻𝘀: 𝗮 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹 𝗳𝗼𝗿 𝗔𝗜-𝘃𝘂𝗹𝗻𝗲𝗿𝗮𝗯𝗹𝗲 𝘀𝘂𝗺𝗺𝗮𝘁𝗶𝘃𝗲 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝗪𝗵𝗮𝘁’𝘀 𝗮𝗻 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝘁𝘄𝗶𝗻? Two deliberately linked assessment components that target the same learning outcomes, use different modes of evidence, and are scheduled close together so performance can be cross-checked to address a known vulnerability like GenAI completion or impersonation. So imagine a 2000 word essay. Regardless of the allowable use of GenAI, adding in a short in-class viva assessing the same learning outcomes in a similar time period. If the viva is weak, then consider capping the grades of the essay. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 🧪 Triangulates evidence to strengthen validity without defaulting to surveillance. 🧰 Keeps pedagogically useful formats (essays, projects) but pairs them with a complementary check (oral, in-class, demo). 📏 Works across cohort sizes with different implementation options and marking dependencies ⚖️ Surfaces equity and workload constraints we have to plan for, not ignore 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲 𝘁𝗵𝗲 𝗽𝗿𝗲𝗽𝗿𝗶𝗻𝘁 • A validity-first rationale using a unified framework • Practical design steps: identify vulnerabilities, align LOs, choose the complementary task, set interdependent marking rules • When to use twins, and when not to • Small, medium, large cohort strategies, plus limitations and research needs Thanks to Leon Furze and Thomas Corbin for early input in this. We get that this is not something that is going to be easily applicable in every situation (300+ cohorts) examples:, but we think that there are options for this (discussed in the paper), and that this approach could fit in nicely with a range of approaches that universities are taking in terms of broader GenAI assessment strategies.

Tens of millions of hiring assessments are used each year—potentially billions if you include any predictive hiring signal. But how many are set up to ensure AI based (and traditional) tools are being used properly and truly drive lasting, strategic impact? Through ongoing research and benchmarking, Alan Bourne and I are discovering what sets high-impact programs apart. Our work uncovers how organizations achieve lasting results—not just with technology, but with strategy and maturity across the hiring process. Assessments and predictive hiring tools offer a powerful promise: objective, fair data for better hiring decisions. But results are not automatic. Plugging in a tool without a clear plan rarely leads to improvement. Success is about maturity—not just adding tools, but building excellence across 9 critical areas: ---Technology Integration- How well your tools and platforms work together to support a seamless, scalable hiring process. ---Content Quality & Relevance- How accurate, fair, and job-relevant your assessments are—across all roles and formats. ---People Capability- The skills, knowledge, and confidence of those designing, selecting, and using assessment tools. ---Process Consistency-How reliably and efficiently assessments are delivered across teams, roles, and regions. ---Data & Insights-How effectively assessment data is captured, analyzed, and used to inform decisions. ---Candidate Experience- How fair, engaging, and transparent the assessment process feels from the candidate’s perspective. ---Impact & ROI- Whether assessments are improving outcomes like quality of hire, retention, and performance—and how well that impact is tracked. ---Operational Efficiency- The cost, speed, and effort required to run your assessment process from end to end. To raise the bar, organizations need clarity on where they stand vs these essential criteria. Benchmarking best practices brings direction and inspiration—and most critical, helps leaders avoid costly missteps in their predictive hiring programs, including making mistakes in the adoption of AI. This is far from easy- but No Pain- No Gain. A good diagnosis is the first step to being pain free! Curious about where you stand? We are actively collecting data and look fwd to sharing our findings - if you are using assessments and AI based predictive hiring tools would love your perspective.  (see link in comments)