The article introduces the SOLO taxonomy, a five‑level framework for measuring how deeply you understand a book, contrasts it with Bloom's Taxonomy, and offers concrete self‑assessment tools and three practical steps to move from superficial recall to transferable insight.
On June 10, ACM announced the 2025 Doctoral Dissertation Award, granting the prize to Allen Liu of NYU for his thesis on learning‑theoretic foundations of quantum systems, while honoring Gal Arnon and MIT’s Rachit Nigam for advances in interactive oracle proofs and efficient hardware abstractions.
The article examines how AI-driven tools reshape learning conditions, argues that high task performance does not equal true understanding, and introduces the four pillars of Agentivism—a framework for reclaiming active, deep learning in the age of generative AI.
The article explains the true nature of the compound effect in learning, outlining its financial origins, three essential elements, why many potential compounds fail, and four mechanisms—continuous use, transfer, feedback, and connection—that enable knowledge to generate lasting, exponential growth.
The article argues that while AI can quickly generate seemingly correct answers for students, bypassing the slow, cumulative learning process undermines deep understanding and long‑term growth, much like the early stage of an exponential function.
This article presents 21 research‑backed conclusions about learning, covering cultural influences, learning types, knowledge and reasoning, motivation, school learning implications, learning technologies, and lifelong learning, with both Chinese summaries and English translations for each point.
This article explains why machine learning relies on big‑data statistical learning, illustrating human learning through induction and deduction, presenting case studies that highlight the limits of anecdotal reasoning, and introducing the law of large numbers and probabilistic trust as foundations for reliable AI models.
The author reflects on teaching practices, critiques overly fast and difficult lessons, proposes a set of assumptions about knowledge activation and teaching speed, and seeks a quantitative model to guide instructional design, pacing, and the impact of math‑modeling activities on student learning outcomes.
Exam success hinges not on rote review but on building layered knowledge structures and practiced problem‑solving pathways; this article explains how progressing from basic pre‑structure to abstract‑expansion structure, reinforced through repeated practice, transforms proficiency and reduces anxiety under exam constraints.
The article explains the Learning Pyramid theory, details each learning method’s retention rates, and illustrates its practical use through a U.S. classroom transcript, highlighting the benefits of active, multimodal teaching over passive lecture‑based approaches.
