Innovative Grading Methods

After 15 years teaching #entrepreneurship I discovered something game-changing: Our traditional grading system contradicts everything we teach about entrepreneurial thinking. We tell students to “fail fast and iterate” then penalize them with one-shot assignments. We preach “progress over perfection” then grade final products instead of journey. We champion “feedback loops” then give grades instead of actionable insights. Aome tips to change this nonsense: 1️⃣ The 60/40 Split → 60% traditional assessments (keeps academic rigor) → 40% effort-based completion (rewards entrepreneurial hustle) Result: Students take more risks without fear of failure 2️⃣ Unlimited Pitch Revisions Just like real startups iterate: • Week 1: Submit pitch deck • Week 2: Get detailed feedback • Week 3: Resubmit improved version • Repeat until mastery achieved 3️⃣ Practice Runs Count Break down that 30% “Final Pitch” grade: • 10% Practice pitch • 10% Final pitch • 10% Reflection + improvements Students learn that preparation IS the process. 4️⃣ Redefine “Participation” Instead of just showing up: ✓ Customer interview completions ✓ Market research worksheets ✓ Prototype iterations ✓ Peer feedback given Track effort, not just outcomes. The results? • More students complete multiple iterations • Students feel more entrepreneurial • Class engagement skyrockets • Final project quality improved Remember: If we want to create entrepreneurs, we need to grade like entrepreneurs think: ✓ Progress over perfection ✓ Iteration over isolation ✓ Effort over outcomes What grading experiments have transformed YOUR classroom? Drop a comment below. Let’s dlearn from each other 👇

🎯 Grading without Ceiling: What If Assessment Looked More Like a ReLU Than a Sigmoid? Grading plays a fundamental role in education. At its best, it helps assess what students have learned, guides their ongoing development, and, importantly, serves as a benchmark for competitive opportunities—be it entry into selective schools, scholarships, or jobs. But the way we grade today is deeply flawed. Nearly all grading systems follow a sigmoid-like response function: performance saturates at the top. Whether you're good or exceptional, your score plateaus at 100%. This design has some obvious (and not-so-obvious) issues. 1️⃣ It lacks resolution at the high end. Once someone consistently scores near the top, there's little room to distinguish truly exceptional understanding or creativity. 2️⃣ It conflates skills with process errors. Personal anecdote: in the International Chemistry Olympiad, a formatting mistakes in the titration section in experiment cut my score in half—even though my theory result was second in the world. The issue wasn’t chemistry—it was formatting. 3️⃣ It invites grade inflation. If everyone is clustered near the top, we lose meaningful differentiation. This dilutes feedback, creates systemic inequities, and can erode trust in the process. There are partial workarounds: AP and honors classes, optional bonus problems, or scores above 100% for extra credit. But these are patches, not principles. So here's a thought: What if grading was inherently open-ended—more like a ReLU function than a sigmoid? Imagine every exam or assignment structured with increasing levels of challenge. Students demonstrating proficiency get a solid score. But those who push further—solving harder problems or applying deeper insight—earn proportionally more. Not just "extra credit," but a new default: grading without a ceiling. Such a system would: - Better recognize high-end talent and outliers. - Reinforce curiosity and reward deep exploration. - Reduce the impact of minor errors in otherwise exceptional work. - Restore grading as a meaningful signal for both learners and selectors. I'm sure this idea has been explored before—but perhaps it's time to revisit it seriously. Would love to hear your thoughts. Should we design our assessments to measure not just what students know—but how far they're willing (and able) to go? #education #grading #assessment #highered #edtech #aiineducation #STEM

I have never seen such drastic changes in university education as what has happened during the past two years because of generative AI technologies. Especially student assessment is now a completely different activity than what it used to be. I am starting to think that this requires a complete paradigm change in student assessments. We should not merely measure individual student capabilities but start evaluating student-AI teams and the result of the collaboration between AIs and students. Traditional university assessments are designed to measure individual student knowledge, skills, and critical thinking. Exams, essays, and projects typically emphasize personal effort and originality, aiming to cultivate independent thinkers. While this model has worked well for centuries, it now feels increasingly disconnected from the realities of the digital age. AI tools like ChatGPT, DALL-E, and others can produce sophisticated outputs, ranging from code and essays to data analysis and creative designs. Denying students access to these tools in assessments not only misrepresents their future work environments but also hinders their ability to develop critical skills for the AI-integrated workplace. The workplace of tomorrow will not reward individuals who can outperform AI but those who can work with AI to achieve exceptional outcomes. Universities must therefore adapt assessments to evaluate how well students integrate AI tools into their workflow to address complex, real-world problems, how critically they evaluate AI outputs for accuracy and bias, and how creatively and effectively they use AI to enhance their projects and generate novel solutions. Furthermore, students’ understanding of ethical considerations, including data privacy, transparency, and responsible innovation, must also become a focal point of assessment. Transitioning to a model that evaluates collaboration between students and AI requires innovative approaches. Assignments could explicitly require AI assistance, such as asking marketing students to develop campaigns with the help of AI tools, assess their viability, and justify their strategic decisions. Grading systems might prioritize the process over the final product, evaluating how students choose and use AI tools, iterate based on feedback, and address errors in AI-generated outputs. Open-book exams could allow AI use, with students evaluated on their ability to interpret, critique, and expand upon AI-generated content. Simulated workplace scenarios, where students work as part of a team with AI, could also become a powerful tool to measure real-world readiness. However, this transition is not without its challenges. See the comment section for more. Have you already started to assess the results of student-AI collaboration or do you still consider the individual capabilities of students as the main thing to assess in university education? #AI #education #assessment #grading #capabilities

Bridging the Gap Between AI and Human Grading: How LLMs Learn from Expert Feedback ... What if AI could ask for help when it’s unsure how to grade student answers? Traditional automated grading systems often miss the mark on open-ended responses. Even with advanced LLMs, interpreting nuanced rubrics—like those for assessing critical thinking or pedagogical knowledge—remains a challenge. The solution? Letting AI collaborate with humans "during" the grading process. 👉 WHY THIS MATTERS Rubric-based grading requires understanding subtle distinctions in language and context. For example, terms like “effective reasoning” or “student-centered approach” vary in interpretation across disciplines. Fully automated systems lack the ability to clarify ambiguous criteria, leading to inconsistent or inaccurate evaluations. Human oversight is still essential—but manually reviewing every AI-generated grade defeats the purpose of automation. 👉 WHAT THEY BUILT The researchers developed GradeHITL, a framework where LLMs: 1. Grade responses using existing rubrics. 2. Identify uncertainties (e.g., vague rubric terms or conflicting examples). 3. Ask targeted questions to human experts for clarification. 4. Refine the rubric iteratively based on feedback. Unlike static AI models, GradeHITL treats rubrics as dynamic guidelines that improve with human input. 👉 HOW IT WORKS The system operates in three stages: 1. Grading: The LLM evaluates answers and flags low-confidence decisions. 2. Inquiring: The model generates questions about confusing rubric elements (e.g., “How do we distinguish between ‘partial’ and ‘full’ understanding in this context?”). 3. Optimizing: A reinforcement learning filter prioritizes high-impact questions, reducing noise. Human answers are integrated to update the rubric, creating a feedback loop. Over time, the rubric becomes more precise, and the LLM’s accuracy approaches human-level performance. Key Results - Outperformed existing automated methods (like GradeOpt and APO) across six pedagogical questions, with 8–12% higher accuracy. - Effectively reduced ambiguity in domain-specific rubrics (e.g., math teaching strategies). - Demonstrated that human-AI collaboration yields more reliable systems than fully automated approaches. 👉 Why This Changes the Game GradeHITL isn’t just about better grading—it’s a blueprint for human-AI teamwork. By allowing LLMs to “admit uncertainty” and seek guidance, we create systems that are both scalable *and* trustworthy. For educators, this means fewer hours spent on routine evaluations and more time for personalized instruction. Paper: LLM-based Automated Grading with Human-in-the-Loop Authors: Hang Li, Yucheng Chu, Kaiqi Yang, Yasemin Copur-Gencturk, Jiliang Tang Interested in AI’s role in education? Let’s discuss how hybrid systems could reshape learning outcomes.

This semester, I have switched from rubric-based grading to specifications-based grading. For context, I teach game design. Here are my take-aways. 1) Wider variety in grades. Many students earned As. Some earned Bs and Cs, and two failed by meeting fewer than half the specifications. This is much closer to reality, and because the specifications are clear, I felt no pressure to inflate grades. It is obvious when a specification such as "describe audiovisual elements in your review" is not met. 2) Most students met ALL the specifications, which improved the quality of the work overall, since the specs describe good work. This helped my students meet expectations. 3) Feedback to the work is now ACTIONABLE because the students are permitted to resubmit for a higher grade. This is more work for me, but it's OK because it takes the pressure off my students to be perfect in the first attempt, and provides very clear instruction on how to improve. Formerly, feedback felt more like general advice. 4) Safe failure is key for an open learning environment. The ability to resubmit assignments lets my students do their best without fear. Learning, like game design, should be iterative. 5) If a student really wants an A, there are no blockers. Formerly, if an "A student" receives a B, the only recourse would be to argue with the professor about the rubric. Now, they can easily take the feedback and resubmit. This fosters an environment where achievers can reach higher and the relationship with the professor is collegial instead of adversarial. What do you think? Would you try specifications-based grading?