Baseet Now Available for ASU Engineering Majors

The Enstine Study/ Notes App is a very useful app for all the students especially engineering students. It provides AI powered notes generation useful for quick reference. It generates flash cards that helps in revision of key concepts and quizzes in both MCQ and short answer format, that helps in self evaluation. There are many other features such as mindmaps, Source specific AI chats, etc, that provide a total enabling online ambience for learning. Please try and forward the same in your network

We are excited to introduce Enstine Notes — a smarter study companion designed to help students organize their learning, revise faster, and study with greater clarity. With features like Smart Notes, Quick Revision, and AI Study Help, Enstine Notes is built to support students throughout their academic journey with a more focused and intelligent learning experience. This launch marks an important step in our mission to build meaningful education technology that empowers modern learners. Start studying smarter with Enstine Notes. https://app.angsystech.com Follow us at: https://lnkd.in/gdAaxU8k #Enstine #EnstineNotes #EdTech #EducationTechnology #AIinEducation #SmartLearning #StudentSuccess #DigitalLearning #StudyCompanion

One thing I’ve come to appreciate more deeply while working in the EdTech space is how often we confuse performance with understanding. A student getting the right answer does not always mean the concept is truly understood. In many cases, it may simply reflect familiarity with a pattern, repetition, or short-term recall. The challenge is that most traditional systems are designed to measure outcomes, not interpret how learning is actually developing underneath those outcomes. That gap usually stays hidden until the context changes. A slightly different problem or a new application is often enough to reveal whether the understanding was durable or fragile. A lot of our thinking at TutorCloud has been shaped by this distinction between familiarity and true mastery, and how learning systems should evolve to recognize it better. We explored that perspective in our latest blog: “Understanding Vs Familiarity: The Mastery Distinction No One Talks About” Read more here: https://lnkd.in/ey4N7bMs

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗩𝘀 𝗙𝗮𝗺𝗶𝗹𝗶𝗮𝗿𝗶𝘁𝘆: 𝗧𝗵𝗲 𝗠𝗮𝘀𝘁𝗲𝗿𝘆 𝗗𝗶𝘀𝘁𝗶𝗻𝗰𝘁𝗶𝗼𝗻 𝗡𝗼 𝗢𝗻𝗲 𝗧𝗮𝗹𝗸𝘀 𝗔𝗯𝗼𝘂𝘁 A student scores 90%. Another student also scores 90%. But only one of them truly understands the concept. The other? They're running on fragile mastery. Correct answers built on pattern recognition and guesswork, not real comprehension. This gap is invisible in traditional learning systems. And it's costing students more than we realize. In our latest blog, we explore what it actually means to understand something and why a correct answer is never the full story. Read more here: https://lnkd.in/ey4N7bMs #EdTech #AIInEducation #LearningAndDevelopment #PersonalizedLearning #TutorCloud 

[I’m surprised that very few students actually know or read about “Learning Science”] Why am I surprised that very few students actually know or read about “Learning Science”? Honestly, I knew and read about “Learning Science, specifically Self-Regulated Learning (SRL)” after graduation. When I first read about SRL, I had this thought: “Why did I not know about this kind of knowledge when I was a student?” Then I’ve talked with my friends and students, most of them have no idea either. Having conversations with students and asking them how to describe their learning, you know if they’ve read about “Learning Science” or not. For example, lots of students thought that “They are not smart, they are not just good at studying, they just don’t know how to study.” (they are not the language of Learning Science) So, I’m honestly and curiously asking, “What are the real reasons that very few students actually know or read about "Learning Science"? #LearningScience

🎯 The best students aren't the most consistent. They're the most adaptive. 📄 New open access paper in the British Journal of Educational Technology Joint work by Oleksandra Poquet, Jelena Jovanovic and me: Student profiles of change in formative assessment behaviour: Replication and evaluation for grade prediction: https://lnkd.in/dkVRbXxS Most learning analytics features count what students do. Submissions, logins, scores, sequences. But learning is path-dependent. A student adapting to a hard course looks very different from one stuck in a rut, even when their submission counts are identical. We asked: can we measure how student behaviour changes over time, and does that signal predict success? We applied Recurrence Quantification Analysis to formative assessment logs from 1,362 students in a large programming course at TUM. Three profiles of change emerged: 🔹 Stable high-entropy (66%): varied, adaptive patterns → best performance and timeliness 🔹 Stable low-entropy (16%): repetitive patterns → lowest performance 🔹 Increasing entropy (19%): students actively adapting in weeks 2 and 3 Two findings we'd pass on to anyone building learning analytics: 1️⃣ Dynamics-based features complement, rather than replace, conventional ones. The combined model outperformed every count-based model we tested. 2️⃣ The first three weeks matter disproportionately. That's where most of the entropy fluctuation happened, in the data and in the original study from 2023 by Poquet et al. we replicated. This is also, to our knowledge, one of the first methodological replications of a CDS-inspired learning analytics approach across a different country, LMS, and discipline. Replication work in our field is rare and undervalued; grateful to my co-authors and to BJET for supporting it. 👉 Read the paper (open access): https://lnkd.in/dkVRbXxS #LearningAnalytics #EdTech #HigherEducation #TUM #Research

Personalized learning is mistakenly seen as requiring digital tools; it is a pedagogical approach, not a tech-driven mandate.  Personalization does not mean creating a unique curriculum for every student, which is impossible. It focuses on adapting instructional methods to student needs.Personalized learning is focused on meeting learning goals and addressing skill gaps, which is not always based solely on student interests. Pacing is adjusted to the learner, but the content remains the same. Customized schedules and methods—such as project-based learning or one-on-one tutoring—based on progress is planned by teachers. Personalized does not mean someone else decides what your child learns and then customises the delivery. May be some schools have no knowledge of learning approaches and strategies. When a parent wants to ask who decides what their child is to learn, they will be told that government policies and educational statuatory bodies discuss over a long long years to define the core curriculum and standards. Parents could decide home-schooling for thier child and teach according to their pace of learning. Siddharth Rajgarhia

Schools say that your child will get "personalised" learning. But does your child get so? I was reading a book by the researcher Yong Zhao, who draws a line that most school brochures don't pay attention to. Personalized means someone else decides what your child learns and then customises the delivery. Personalizable means the system is built so the child decides what they want to learn and why. One is a tailor while the other is a mirror. When you walk into most schools today, which claim to be "personalized learning", you'll find adaptive software, differentiated worksheets and individual dashboards. All of it beautifully customises a journey someone else designed. But the destination was never the child's choice. When content is infinite and AI can teach any syllabus to any child at any pace, the only thing a school can uniquely build is a student who knows what they want to pursue and why it matters to them personally. A school that personalises is solving yesterday's problem. A school that is personalizable is building the one capability no algorithm or AI can substitute, which is self-directed human judgment. Ask the next school you visit this one question: Who decided what my child will learn? It is the most important question in education today. #education #indianchildren

🚀 A unified formula has long been a dream in the learning analytics field. How can we synthesize fragmented behavioral traces (clicks, posts, and study sessions) into a single, transparent, and actionable indicator without losing context? In my latest pice, "Online Learning Index for Actionable Feedback in Higher Education", I propose a framework to address this challenge: the Online Learning Index (OLI). OLI is based on first-order autoregressive AR(1) model. Unlike rigid, black-box predictive models, the OLI is designed to be: ✨ Context-sensitive (calibrated to the pedagogical structure of each course) ⏳ Decay-aware (reflecting the longitudinal and temporal nature of learning) 🧠 Theory-grounded (built on cognitive engagement, social participation, and self-regulated learning) Using the OULAD for validation, findings demonstrated how OLI successfully bridges the gap between raw data and theory-aligned, actionable student feedback. It’s not just about predicting who fails; it’s about providing transparent insights that empower students to self-regulate. Why does the Online Learning Index (OLI) change the game? It's not just a sum of clicks. It is a decay-aware, AR(1) recurrent model. It treats student activity like human memory: past actions decay naturally when absent, keeping the feedback window fresh, relevant, and mathematically decomposable into Cognitive, Social, and Self-Regulated dimensions! Online Learning Index for Actionable Feedback in Higher Education. Available on: DOI: https://lnkd.in/dMPsWycD #LearningAnalytics #HigherEducation #EdTech #EducationalTechnology #SelfRegulatedLearning #LearningDesign #AcademicResearch

Why Micro-Learning Works Better Than Long Study Hours A lesser-known finding in learning science: The brain doesn’t remember long study sessions it remembers repeated ones. A 2025 meta-analysis of 253 studies on mobile learning discovered a large learning effect (g = 0.90) proving that short, repeated digital sessions dramatically boost understanding and memory. Here’s the part most people don’t know: 🔹 The brain naturally forgets new information within hours (Ebbinghaus forgetting curve) 🔹 But micro-learning + spaced repetition interrupts that forgetting 🔹 Each short session strengthens memory pathways 🔹 Over time, this creates long-term mastery instead of temporary cramming In simple terms: 🔸️The more often the brain revisits information, even in tiny doses, the longer it stays. 🔸️ This is why modern learners using digital micro-learning outperform those relying on traditional long hours of study. MNEMOS , Ekins Kuuzie, MD, MS , Carl Anas Nelson, MD , Ronny Polle , Afia Afriyie Boakye #MicroLearning #LearningScience #SpacedRepetition #DigitalLearning #MedicalEducation #StudySmarter #EdTech #StudentSuccess #FutureOfLearning #MemoryRetention