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Incremental reading

Incremental reading is a learning technique developed by Polish computer scientist Piotr Woźniak, the creator of the SuperMemo software, which enables users to process vast amounts of textual material efficiently while converting key insights into long-term knowledge through spaced repetition.^[1] This method allows for the simultaneous handling of numerous articles by breaking down reading into incremental steps, prioritizing important excerpts, and deferring detailed comprehension to optimize time and retention.^[2] The process begins with rapid skimming of texts, such as web articles, where users identify and extract salient passages without immediate deep analysis, then transform these into flashcards—often using cloze deletion prompts—for review via SuperMemo's algorithm.^[1] These elements are scheduled for repetition based on the user's performance, ensuring gradual mastery and minimizing forgetting, with the software automating the deferral and prioritization to prevent overload.^[2] Introduced in the early 2000s as an evolution of spaced repetition systems, incremental reading builds on Woźniak's prior innovations in learning algorithms dating back to the 1980s.^[1] Key advantages include dramatically increased reading speeds—potentially processing an entire article in seconds—combined with 95-98% long-term retention rates, far surpassing traditional speed-reading techniques that often sacrifice comprehension.^[2] Empirical tests, such as a 2006 speed-reading contest analyzed by Woźniak, demonstrated that incremental reading reduced processing time for complex material while achieving 87.9% recall after 17 years, highlighting its efficacy for lifelong learning and creative idea generation.^[1] By fostering iterative refinement of knowledge, the technique supports applications in language acquisition, professional development, and broad interdisciplinary study.^[2]

History

Origins in Spaced Repetition

The foundations of incremental reading trace back to the early scientific study of memory, particularly Hermann Ebbinghaus's seminal work on the forgetting curve in 1885. In his book Memory: A Contribution to Experimental Psychology, Ebbinghaus conducted self-experiments using nonsense syllables to measure the rate of forgetting, demonstrating that memory retention declines rapidly over time without reinforcement, often following an exponential pattern.^[3] This curve illustrated how information is forgotten at a decreasing rate as time passes, providing empirical evidence that spaced reviews could counteract decay and optimize long-term retention, though Ebbinghaus himself did not explore spaced repetition intervals systematically.^[4] His findings laid the groundwork for modern spaced repetition algorithms by quantifying forgetting dynamics and emphasizing the need for timed interventions to enhance recall.^[5] Building on this psychological basis, Polish researcher Piotr Woźniak advanced spaced repetition in the 1980s through his development of the SuperMemo system while studying molecular biology at Adam Mickiewicz University in Poznań. Frustrated with inefficient memorization for exams, Woźniak began self-experiments in 1982, using paper-based methods to test recall intervals for English vocabulary.^[5] By 1985, he formulated the first computational spaced repetition algorithm, SM-0, which optimized review timings based on personal forgetting data, such as intervals of 1, 7, 16, and 35 days for stable retention.^[6] This evolved into SuperMemo 1.0, released in December 1987 for MS-DOS, incorporating Algorithm SM-2 with an "easiness factor" (E-Factor) ranging from 1.3 to 2.5 and a 0-5 grading scale to adjust intervals dynamically for individual items.^[7] Woźniak's innovations focused on memory optimization, enabling users to learn large volumes of material—such as 10,000 English words in a year—with minimal daily effort, around 40 minutes.^[5] During the 1990s, spaced repetition systems like SuperMemo transitioned from simple flashcard-based tools to frameworks capable of managing extended reading materials, driven by advances in software and data collection. Early versions handled discrete question-answer pairs effectively, but growing user data from thousands of repetitions highlighted the limitations for processing continuous texts, prompting enhancements in algorithm adaptability and multimedia integration.^[8] By mid-decade, Woźniak and collaborators refined models like the two-component theory of memory (stability and retrievability), informed by exponential forgetting curves derived from empirical data.^[9] This period saw SuperMemo shift to Windows platforms, with versions incorporating hypermedia structures to link items hierarchically, facilitating the handling of longer, interconnected content.^[10] A pivotal step occurred with SuperMemo 8, released in 1998, which introduced preliminary tools for processing extended texts through hypermedia knowledge trees and Algorithm SM-8's power regression for optimum intervals.^[11] These features allowed users to import and structure longer articles, marking an early evolution toward incremental processing by enabling incremental extraction and review of key elements from voluminous sources, thus setting the stage for formal incremental reading.^[12]

Development and Key Milestones

Incremental reading emerged as an extension of spaced repetition techniques within the SuperMemo software ecosystem, with foundational features introduced in SuperMemo 99 in 1999. This version added reading lists, extracts, and basic cloze deletions to facilitate efficient processing of articles, allowing users to prioritize and break down longer texts into manageable portions.^[13] These elements laid the groundwork for handling electronic articles without overwhelming the learner, marking the initial shift toward incremental processing of reading material.^[14] The term "incremental reading" was formally coined on March 27, 2000, with the release of SuperMemo 10 in November 2000 introducing its full implementation, including prioritized reading queues, webpage import tools from Internet Explorer, and A-Factor-based topic repetition for optimized review scheduling.^[14] Subsequent enhancements in SuperMemo 11 (2002) incorporated HTML-based processing and automatic web imports, while SuperMemo 12 (2004) added rich statistics and A-Factor optimization to refine article prioritization.^[13] Key expansions continued with SuperMemo 13 (2006), which implemented a priority queue for massive reading workflows and simplified imports from sources like Wikipedia.^[14] By SuperMemo 14 (2008), support extended to images, audio, and incremental video via YouTube integration.^[13] In 2011, an add-on for the Anki spaced repetition software was released, providing initial support for incremental reading features such as text iteration and flashcard creation from long documents.^[15] Further adaptations followed, including compatibility updates for Anki 2.0 and 2.1 through alternative add-ons, with ongoing maintenance of unofficial clones as of 2025.^[16]^[17]^[18] Recent developments in SuperMemo focused on refining article processing, with SuperMemo 18 (released April 2020) optimizing overall incremental reading efficiency and SuperMemo 19 (2023) integrating with browsers like Microsoft Edge and Chrome for seamless web knowledge transfer.^[14] Despite these advancements, incremental reading has maintained a niche status, with adoption limited to less than 5% of SuperMemo users due to its steep learning curve and lack of immediate accessibility.^[19]

Principles

Core Concepts of Incremental Processing

Incremental reading employs the concept of the knowledge funnel to transform expansive sources of information into refined, atomic units of lasting knowledge. This process initiates with the importation of broad articles, which are then selectively narrowed through user-driven curation to a personalized subset of material. Subsequent steps involve distilling these into key extracts or highlights that capture essential insights, culminating in the conversion of those extracts into minimalistic, question-based elements—such as cloze deletions—for efficient long-term assimilation.^[20] The funnel structure ensures that overwhelming volumes of input are progressively simplified, maximizing the density of retained knowledge while minimizing redundancy.^[21] Central to incremental processing is the division of reading into brief, digestible sessions, generally spanning 5 to 15 minutes, which enables learners to tackle material without succumbing to cognitive overload. During these sessions, users focus on comprehensible segments, such as one or two paragraphs, while deferring sections that are non-essential, overly complex, or not immediately aligned with their interests to later review cycles. Prioritization occurs dynamically based on subjective relevance and engagement, allowing high-interest content to advance ahead of less compelling material and fostering a non-linear, interest-guided progression that sustains motivation.^[21] This method contrasts with conventional reading by emphasizing partial comprehension over complete coverage in a single pass, thereby accelerating overall knowledge acquisition.^[20] The technique adapts to individual attention spans by aligning processing intensity with momentary cognitive capacity, scheduling increments to prevent the exhaustion typical of extended linear reading. Sessions are calibrated to end upon signs of waning focus—such as boredom or diminished understanding—thus preserving peak concentration and reducing the mental fatigue that hampers retention in prolonged study bouts. By interspersing varied topics and deferring demanding elements, incremental reading maintains a balanced cognitive load, optimizing the value extracted per unit of time invested.^[22] Priority queues form the organizational backbone of this approach, systematically ranking articles, extracts, and knowledge units by their assessed relevance and potential impact on the learner's goals. High-priority items, often rated closest to zero on a sliding scale, are queued for immediate processing to ensure that the most valuable information receives precedence, while lower-priority content is automatically postponed—potentially for weeks or months—to manage workload. This queuing mechanism enables efficient navigation through expansive reading lists, countering information overload by directing limited attention toward content that yields the greatest learning returns.^[21] Incremental reading leverages spaced repetition for reviewing these atomic units at expanding intervals to secure durable memory traces.^[20]

Integration with Spaced Repetition Systems

Incremental reading leverages spaced repetition systems (SRS) to schedule reviews of extracted elements, ensuring long-term retention by presenting material at optimal intervals determined by the user's recall performance. When users extract key phrases or clauses from articles during the reading process, these become reviewable items that are initially treated as temporary elements. Spaced repetition then intervenes to minimize forgetting by timing subsequent reviews based on the difficulty of recall; easier recalls lead to longer intervals, while harder ones prompt shorter, more frequent reviews to reinforce memory traces. This integration transforms passive reading into an active, adaptive learning system, where the SRS algorithm calculates intervals to achieve a target retention rate, typically around 95%.^[23] The conversion of reading excerpts into reviewable items begins with temporary extracts, which are snippets pulled from the source material and scheduled for initial processing. These extracts evolve into permanent flashcards—often in the form of cloze deletions or question-answer pairs—once the user deems them suitable for long-term review. At this stage, the SRS takes over, assigning an initial interval and stability measure to the flashcard based on its perceived difficulty and the user's first interaction. This process ensures that only high-value, refined knowledge enters the permanent review queue, with the SRS managing progression from temporary to enduring memory consolidation.^[23] The algorithmic foundation for this integration in SuperMemo relies on advanced spaced repetition algorithms like SM-17, which compute review intervals using a two-component model of memory incorporating stability (S) and retrievability (R). In SM-17, the next interval is given by the formula

\text{Int} = S[n-1] \times \text{SInc}[D,S,R] \times \frac{\ln(1 - \text{rFI}/100)}{\ln(0.9)}

where S[n-1] is the previous stability, \text{SInc}[D,S,R] is the stability increase factor adjusted for difficulty (D), stability (S), and retrievability (R), and \text{rFI} is the requested forgetting index. This mechanism, evolved from earlier algorithms like SM-2 (which used a simpler easiness factor multiplier), enables SM-17 to handle diverse review timings—from seconds to years—while integrating seamlessly with incremental reading's extracted elements.^[24]^[25]

Method

Step-by-Step Reading and Extraction Process

The incremental reading process begins with importing articles from various electronic sources, such as web pages or PDF files, into a centralized reading list. This step involves capturing content through methods like copy-pasting or automated imports, after which the system assigns priorities based on factors like recency, user-specified importance, or algorithmic estimation to organize the queue efficiently.^[23]^[26] In the initial skim phase, users review the title, abstract, or opening sections of an article to make quick decisions. Relevant options include marking the article for deferral if it requires more background knowledge or lower priority, extracting key portions for deeper analysis, or dismissing irrelevant content entirely to streamline the workflow. This decision-making ensures focus on high-value material without committing to full reads upfront.^[23]^[27] Subsequent incremental reading sessions involve processing articles in small, manageable chunks rather than in one sitting. Users read a portion, create temporary extracts—shortened versions of undecided or promising sections—and set them aside for later review, allowing the brain to consolidate information subconsciously between sessions. These extracts serve as placeholders, enabling parallel handling of multiple topics without cognitive overload.^[23]^[28] Gradual refinement occurs across multiple sessions, where users revisit extracts to elaborate, simplify, or fully process them into usable knowledge, ultimately discarding what proves unvaluable. This iterative approach builds comprehension incrementally, adapting to daily time availability by prioritizing a subset of items each day. As a result, learners can manage over 100 articles simultaneously, spacing exposure to prevent burnout while maximizing retention through spaced repetition.^[23]^[26] Extracts may later inform flashcard creation techniques for long-term memorization.^[27]

Flashcard Creation Techniques

In incremental reading, flashcard creation involves transforming selected excerpts from articles into concise, reviewable items that facilitate long-term retention through active recall. This process emphasizes brevity, context, and hierarchy to minimize cognitive load during repetitions.^[23] Cloze deletion is a primary technique where key words or phrases are removed from sentences to create fill-in-the-blank prompts, prompting the user to recall the missing information. For instance, from the excerpt "Incremental reading uses cloze deletion to break down texts," a flashcard might read: "Incremental reading uses [cloze] deletion to break down texts," with the answer being "cloze." This method leverages the sentence's natural context to enhance understanding and is central to processing dense material efficiently.^[29]^[23] The question-and-answer format builds on extracts by formulating explicit queries from key points, often incorporating images or additional contexts for clarity when textual descriptions alone are insufficient. An example is deriving the question "What is the capital of Sierra Leone?" with the answer "Freetown" from a relevant article sentence, allowing for targeted recall of facts. This approach is particularly effective for isolated concepts, ensuring flashcards remain focused and testable.^[23]^[30] Hierarchical extracts involve creating layered structures from multi-session processing, where broader concepts are extracted first and subdivided into supporting details over time, forming incremental outlines or concept maps. For example, an initial extract on "the greenhouse effect" might spawn child extracts like "Without the greenhouse effect, Earth's temperature would be -18°C," building a tree of related knowledge. This technique supports coherent learning by prioritizing foundational ideas before delving into specifics.^[31]^[30] The refinement process entails iterative editing of flashcards to achieve conciseness, eliminate redundancies, and resolve overlaps between related items, often by adding contextual cues or rephrasing for precision. During reviews, users might simplify "PageMaker failed to improve and was outdistanced by competitors" to "PageMaker lost ground to [Quark]," reducing interference and improving recall accuracy. Linking related cards through references further integrates the knowledge base.^[29]^[23] Incremental annotation evolves user notes into flashcards progressively, where initial highlights or comments on excerpts are revisited and formalized across sessions. For instance, a note on "greenhouse effect implications" added during reading might later become a cloze deletion like "The greenhouse effect raises Earth's temperature by [33°C]," incorporating the annotation's insights. This method allows knowledge to mature organically without overwhelming the initial reading phase.^[30]^[31]

Tools and Software

SuperMemo Implementation

SuperMemo, developed by Piotr Woźniak since 1987, serves as the original and primary software platform for implementing incremental reading, with the technique emerging as a flagship feature starting in 2000.^[32]^[23] This Windows-based application integrates incremental reading with spaced repetition algorithms to facilitate the processing of extensive reading materials into manageable learning elements.^[33] At its core, SuperMemo employs an article registry to store and organize imported articles from sources such as web pages or local files, enabling users to maintain a comprehensive library for ongoing processing.^[23] A priority queue further structures this workflow by assigning priorities (ranging from 0% for highest to 100% for lowest) to articles and extracts, automatically sorting them daily to ensure high-priority items are reviewed first based on user-assessed importance.^[34] In versions 10 and later, the extract function (activated via Alt+X) allows users to select and isolate key text fragments into independent mini-articles for focused review, while the cloze function (Alt+Z) transforms sentences into fill-in-the-blank questions, such as converting "The capital of Sierra Leone is Freetown" to "The capital of Sierra Leone is [...]."^[23] These tools support the incremental breakdown of articles into atomic pieces suitable for long-term retention. Advanced features enhance the system's efficiency and adaptability. Neural network-based predictions, particularly through the A-Factor metric introduced in earlier algorithms and refined in later versions like SM-18, estimate item difficulty (from 0 for easy to 1 for difficult) to dynamically adjust review intervals and optimize spacing.^[35]^[36] Import capabilities from RSS feeds and web browsers (via Shift+F8) streamline the influx of new material, supporting mass ingestion directly into the registry.^[23] Additionally, integration with sleep cycles optimizes review scheduling by aligning repetitions with circadian rhythms and memory consolidation periods during rest, reducing cognitive load.^[23] SuperMemo 19, released in 2023, introduced further enhancements such as web import from Microsoft Edge and Google Chrome browsers, optional text parsing, and support for unlimited collections, improving compatibility with modern web content for incremental reading.^[37]^[38] The user interface features dedicated toolbars, such as the Read toolbar for navigation and the Learnbar for editing, within an element window that displays articles and flashcards in a structured, resizable format.^[23] SuperMemo remains Windows-exclusive, with the latest version SuperMemo 19 available for purchase as of 2025, while SuperMemo 16 (released in 2013) was made available as freeware in 2019 and continues to be downloadable for users seeking a no-cost entry into incremental reading.^[39]^[33] The software efficiently handles thousands of articles simultaneously, scaling to support intensive knowledge acquisition without performance degradation.^[23]

Alternatives and Adaptations

While SuperMemo remains the primary platform for full incremental reading, community-developed adaptations have emerged in other tools to approximate its workflows. The Anki flashcard application features an "Incremental Reading" add-on, originally released in 2011 by developer Frank Raiser, which enables users to import articles, extract key passages, and create cloze deletion flashcards during iterative reviews.^[15] This add-on supports Anki version 2.1 and later, including compatibility with the 2025 updates to Anki's scheduling engine, allowing basic text processing and card generation from long-form content.^[40] An unofficial clone (ID 999215520), maintained by community developer vhong, was updated in April 2025 to address compatibility issues with newer Anki versions, preserving core extraction and cloze functionalities while relying on Anki's built-in spaced repetition for scheduling.^[17] Other platforms offer partial implementations through plugins or scripts that facilitate incremental workflows, though they emphasize note-taking integration over comprehensive article processing. In RemNote, a bidirectional note-taking app, users can approximate incremental reading by tagging text blocks for scheduled review or using cloze deletions within its spaced repetition system, as suggested in official forum discussions since 2020.^[41] Obsidian, a markdown-based knowledge base tool, includes the "Incremental Writing" plugin, released in 2021, which allows users to queue notes or blocks by priority for gradual refinement and review, adapting incremental principles to writing and linking tasks rather than pure reading.^[42] For Emacs users, org-mode supports custom scripts like the "org-mode-incremental-reading" package from 2021, which processes text blocks and exports them to Anki for further spaced repetition, enabling Emacs-native text handling inspired by SuperMemo's extraction methods.^[43] These alternatives, however, exhibit key limitations compared to dedicated systems, particularly in handling complex prioritization and large-scale content. The Anki add-on employs a basic priority-based scheduling algorithm that does not fully replicate advanced queuing or dependency tracking, leading to manual interventions for extensive libraries.^[44] Community-driven updates, such as the 2023 revisions to the Anki clone for better Anki 2.1.50+ compatibility, highlight reliance on volunteer maintenance rather than institutional support.^[45] While these tools cover essential extraction and review mechanics, they often struggle with managing thousands of articles due to collection size constraints and lack of automated fragmentation for very long texts.^[46]

Benefits and Applications

Learning Efficiency Gains

Incremental reading enhances learning efficiency by enabling users to process substantially more material than traditional linear reading methods. By deferring detailed comprehension and breaking texts into manageable chunks for later review, it allows for rapid initial skimming, potentially up to 10 times faster than conventional speed-reading techniques in certain scenarios, such as covering a text in 2.5 minutes compared to over 7 minutes for focused reading.^[2] This deferral mechanism eliminates the need for immediate deep processing, permitting the handling of vast volumes of information without proportional increases in time investment. When integrated with spaced repetition systems, incremental reading significantly boosts long-term retention, achieving 95-98% lifetime recall rates through optimized review scheduling that minimizes forgetting.^[2] For instance, in a documented case, 58 knowledge items derived from incremental reading maintained 87.9% retrievability after 17 years, requiring only about 65 minutes total review time across that period, or roughly 5 minutes annually.^[2] This approach aligns with the Ebbinghaus forgetting curve by strategically timing repetitions to stabilize memories at high efficiency, reducing the overall effort needed for sustained knowledge preservation.^[47] The technique also reduces cognitive load by fragmenting complex information into digestible segments, preventing overload and allowing focused attention on prioritized elements without the stress of exhaustive immediate analysis.^[2] This chunking process fosters deeper comprehension over multiple passes, with evidence showing recall and understanding improving up to tenfold after iterative exposures, while keeping mental effort low.^[2] Overall, these gains enable 2-5 times faster knowledge acquisition compared to non-incremental methods, as demonstrated in long-term usage metrics from the system's implementation.^[2]

Practical Use Cases

Incremental reading finds application in academic research, where scholars process extensive journal articles incrementally to prepare for thesis work or in-depth studies. Researchers import scientific papers, such as those on environmental science, extracting key sentences—like details on the greenhouse effect—and converting them into cloze deletion flashcards for spaced review, allowing gradual mastery of complex topics without overwhelming cognitive load.^[23] This approach enables handling symbol-rich content, such as particle physics explanations, by delaying intricate sections like those on the Higgs boson until foundational knowledge is solidified through prior extractions.^[48] In professional development, incremental reading supports continuous learning from dynamic sources like news feeds and technical documentation in fields such as programming and medicine. Professionals import web articles or PDFs on emerging technologies, prioritizing high-value segments for incremental processing, which builds durable skills through interleaved reviews.^[31] For instance, a developer might break down documentation on distributed systems into small chunks, creating flashcards from essential concepts to ensure long-term applicability in software projects.^[49] Similarly, medical practitioners can manage clinical updates by extracting facts from research papers, fostering expertise without linear reading constraints.^[23] For personal knowledge building, incremental reading facilitates lifelong learning by managing book summaries and exploratory dives into resources like Wikipedia. Users import entire books or encyclopedia entries, splitting them into digestible portions—such as chapters or paragraphs—for progressive review, promoting retention of diverse topics over time.^[31] A practical example involves a learner processing thousands of articles annually on subjects like artificial intelligence, extracting hundreds of flashcards from key insights to maintain a broad, interconnected knowledge base.^[23] In contemporary adaptations as of 2025, incremental reading integrates with AI tools for efficient initial skimming, where users query language models like ChatGPT or Bing Chat for summaries of complex topics—such as "essential concepts in distributed systems"—before importing the generated overviews and linked articles into the system for further extraction and review.^[49] This hybrid method accelerates onboarding to vast materials while preserving the core incremental process for deep retention.^[48]

Criticisms and Limitations

Common Challenges and Critiques

One of the primary challenges in adopting incremental reading is its steep learning curve, which requires users to master a complex workflow involving importing articles, prioritizing content, extracting key points, and generating flashcards, often taking several months of consistent practice to achieve proficiency. This initial phase demands significant time investment to understand the software's nuances and develop effective strategies, with beginners frequently experiencing frustration due to the non-linear and interrupted nature of the process.^[23] Critics have noted that the technique can appear time-inefficient during the early stages, particularly for shorter or simpler materials, where the overhead of processing, such as breaking down text into incremental steps and scheduling reviews, may exceed the benefits compared to traditional linear reading. For instance, traditional methods might outperform incremental reading in the short term—up to 1-2 months—for new users, as the setup and formulation of elements introduce substantial initial costs that slow overall progress.^[23] The perceived overcomplication of incremental reading stems from its intricate integration of spaced repetition with dynamic reading, leading some users to argue that it unnecessarily fragments attention on minor details rather than allowing fluid comprehension of the whole. This complexity is acknowledged even by proponents, who emphasize that while the method minimizes cognitive load over time, the chaotic ordering of reading sessions and the need for ongoing prioritization can feel overwhelming without extended familiarity.^[23] A specific limitation highlighted in discussions of the technique is its lower efficacy for non-textual knowledge, such as visual diagrams, videos, or audio content, where retention relies heavily on textual extraction and may not capture holistic or contextual elements as effectively without additional adaptations. Incremental reading is optimized for processing articles and prose, making it less intuitive for multimedia that demands integrated sensory processing beyond text-based incrementalization.^[23] Furthermore, the strong dependency on specialized software like SuperMemo restricts portability to some extent, as the full workflow is optimized for the Windows desktop version and web application, with cross-platform access available via browsers on macOS and Linux. While mobile apps exist for basic review on Android and iOS, they do not fully support incremental processing, potentially hindering seamless integration into varied learning environments on non-desktop devices.^[50]

Barriers to Adoption

Despite its potential for enhancing long-term knowledge retention, incremental reading has seen limited mainstream adoption due to low awareness among the general public. The technique remains in relative obscurity, with most individuals never encountering it amid competing priorities such as social media and busy lifestyles that limit exploration of advanced learning methods.^[19] Language barriers further exacerbate this, as primary resources are predominantly in English, slowing dissemination to non-English-speaking regions.^[19] This lack of exposure aligns with its historical pattern of low uptake, confined largely to dedicated learning enthusiasts.^[19] A significant barrier stems from its strong dependency on specialized software, particularly SuperMemo, which is a Windows application requiring at least 2 GB of RAM for optimal performance but offers a web version for access on macOS, Linux, and mobile devices via browsers and apps.^[51]^[50] This improved cross-platform support as of 2025 reduces exclusivity, though users of non-Windows systems may encounter workarounds like Wine emulation for legacy versions, which can introduce compatibility issues such as incomplete web support.^[52] Consequently, the technique's accessibility is somewhat hindered for a broad audience accustomed to fully cross-platform tools, contributing to its niche status even among spaced repetition system (SRS) users.^[19] Cultural and behavioral preferences in contemporary digital environments also impede adoption, as modern users favor rapid consumption of content over the deliberate, incremental processing required by the method.^[19] In an era dominated by short-form media and instant gratification, the sustained effort and delayed rewards of incremental reading clash with expectations for immediate results, often leading to dismissal as overly time-intensive.^[19] Stressful lifestyles further discourage experimentation with complex learning paradigms.^[19] Adoption has remained stagnant, with estimates indicating that fewer than 5% of SuperMemo users—itself a fraction of the broader SRS community, which includes over 100 million users—fully engage with incremental reading.^[19] This low penetration persists despite the growth of SRS tools like Anki, which boasts millions of active users but offers limited incremental reading support.^[53]

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Incremental Reading v4.13.0(unofficial clone) - AnkiWeb
Apr 23, 2025 · This is a clone of Incremental Reading addon (935264945) luoliyan (Joseph Lorimer), the maintainer of Incremental Reading, has been offline for more than a ...
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Explicit incremental reading implementation - RemNote
It's possible to hack together incremental reading by either slapping a ::incremental reading at the end of the rem or cloze deleting a part of it.<|control11|><|separator|>
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An incremental writing plugin for Obsidian where you add ... - GitHub
An incremental writing plugin for Obsidian where you add notes and blocks to prioritized queues and review them incrementally over time.
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org-mode + Anki - incremental reading inspired by SuperMemo
Sep 11, 2021 · I created a package org-mode-incremental-reading that sends this block to Anki (through anki-connect ) so I can learn/remember all my notes.A SuperMemo-like package for incremental learning in Emacs - RedditIncremental Reading V4 : r/Anki - RedditMore results from www.reddit.comMissing: 2007 | Show results with:2007
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https://forum.remnote.io/t/explicit-incremental-reading-implementation/195
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Incremental Reading Add-on (unofficial clone) - Page 2 - Anki Forums
Jul 9, 2022 · That being said, this addon has the basic functionalities required for incremental reading as per this youtube video by Experimental Learning: ...Missing: development | Show results with:development<|separator|>
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Incremental reading in long term future? - Suggestions - Anki Forums
Jun 29, 2020 · I personally believe that incremental reading is one of the reasons why starting intervals/exponential growth in Supermemo actually can be so huge.
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Spaced repetition - SuperMemo Guru
Sep 2, 2025 · Incremental reading with SuperMemo 17 ... In this is case, the formula yields 90% recall after 4 days.<|separator|>
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Advantages of incremental reading - SuperMemo Guru
Mar 30, 2025 · Incremental reading is the fastest way of reading. Despite the speed of reading, incremental reading minimizes cognitive load and nearly eliminates the problem ...
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Using AI for learning with SuperMemo - SuperMemopedia
Apr 19, 2023 · This text explains a simple algorithm for employing ChatBots in conjunction with textbooks using incremental reading. Highlights. Incremental ...Missing: integration 2024 2025
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On what devices and systems can I use SuperMemo?
On what devices and systems can I use SuperMemo? You can use SuperMemo on a desktop computer, laptop, tablet, and smartphone with Android or iOS systems.
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Minimum requirements - SuperMemo 19 Help
Jul 22, 2025 · Windows 10 (recommended). · 2 GB memory (1 GB might also suffice, but SuperMemo might be slower) · 20 MB free disk space (more recommended for ...Missing: only adoption
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SuperMemo for Windows under Wine - SuperMemopedia
Feb 14, 2023 · SuperMemo can run on Wine with limitations, including incomplete support for Internet Explorer and web standards, and some cosmetic differences.
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Exponential adoption of spaced repetition - SuperMemo Guru
The proportion of active users of spaced repetition kept dropping with wider adoption. In 2007, we estimated the reach of SuperMemo to be 5 million, of which ...Missing: statistics | Show results with:statistics