# 04 — Canon Research: AI Adaptive Learning

## Source Manifest

| source_id | url | bucket | last_checked | author/host | one-line note |
|-----------|-----|--------|--------------|-------------|---------------|
| T04-S001 | https://link.springer.com/book/10.1007/978-3-642-14363-2 | surrogate_primary | 2026-05-25 | Springer Nature | vendor docs (学术出版商) — Nkambou et al. Advances in ITS |
| T04-S002 | https://link.springer.com/article/10.1007/BF01099821 | surrogate_primary | 2026-05-25 | Springer Nature | vendor docs (学术出版商) — Corbett & Anderson 1995 BKT |
| T04-S003 | https://arxiv.org/abs/1506.05908 | verified_primary | 2026-05-25 | arXiv / Cornell | Piech et al. 2015 Deep Knowledge Tracing |
| T04-S004 | https://dl.acm.org/doi/10.1145/3569576 | surrogate_primary | 2026-05-25 | ACM Digital Library | vendor docs (学术出版商) — KT Survey 2023 |
| T04-S005 | https://www.cambridge.org/core/books/cambridge-handbook-of-the-learning-sciences/2E4224681267E61DBCE9B27630ED17BA | surrogate_primary | 2026-05-25 | Cambridge University Press | vendor docs (学术出版商) — Sawyer ed. 2nd ed. |
| T04-S006 | https://www.routledge.com/Handbook-of-Educational-Data-Mining/Romero-Ventura-Pechenizkiy-Baker/p/book/9781439804575 | surrogate_primary | 2026-05-25 | Routledge / Taylor & Francis | vendor docs (学术出版商) — Romero et al. 2010 |
| T04-S007 | https://www.solaresearch.org/publications/hla-22/ | surrogate_primary | 2026-05-25 | SoLAR | vendor docs (研究机构) — Handbook of Learning Analytics 2nd ed. |
| T04-S008 | https://www.sciencedirect.com/book/9780123735942/building-intelligent-interactive-tutors | surrogate_primary | 2026-05-25 | Elsevier / ScienceDirect | vendor docs (学术出版商) — Woolf 2009 |
| T04-S009 | https://www.routledge.com/Computerized-Adaptive-Testing-A-Primer/author/p/book/9781138866621 | surrogate_primary | 2026-05-25 | Routledge | vendor docs (学术出版商) — Wainer et al. CAT Primer 2nd ed. |
| T04-S010 | https://link.springer.com/book/10.1007/978-3-642-01039-2 | surrogate_primary | 2026-05-25 | Springer Nature | vendor docs (学术出版商) — Falmagne & Doignon Learning Spaces |
| T04-S011 | https://www.tandfonline.com/doi/abs/10.1080/00461520.2011.611369 | surrogate_primary | 2026-05-25 | Taylor & Francis Online | vendor docs (学术出版商) — VanLehn 2011 meta-review |
| T04-S012 | https://pubmed.ncbi.nlm.nih.gov/22486653/ | verified_primary | 2026-05-25 | PubMed / NIH | Koedinger et al. 2012 KLI Framework |
| T04-S013 | https://research.duolingo.com/papers/settles.acl16.pdf | surrogate_primary | 2026-05-25 | Duolingo Research | vendor docs (EdTech 平台) — Settles & Meeder 2016 HLR |
| T04-S014 | https://dl.acm.org/doi/10.5555/1659450.1659529 | surrogate_primary | 2026-05-25 | ACM Digital Library | vendor docs (学术出版商) — Pavlik et al. 2009 PFA |
| T04-S015 | https://jmlr.org/papers/v15/lan14a.html | surrogate_primary | 2026-05-25 | JMLR | vendor docs (学术出版商) — Lan et al. 2014 SPARFA |
| T04-S016 | https://arxiv.org/abs/2007.12324 | verified_primary | 2026-05-25 | arXiv / Cornell | Ghosh et al. 2020 AKT |
| T04-S017 | https://link.springer.com/article/10.1007/s11257-011-9106-8 | surrogate_primary | 2026-05-25 | Springer Nature | vendor docs (学术出版商) — Desmarais & Baker 2012 review |
| T04-S018 | https://link.springer.com/chapter/10.1007/11774303_17 | surrogate_primary | 2026-05-25 | Springer Nature | vendor docs (学术出版商) — Cen et al. 2006 LFA |
| T04-S019 | https://link.springer.com/chapter/10.1007/978-3-642-13470-8_24 | surrogate_primary | 2026-05-25 | Springer Nature | vendor docs (学术出版商) — Pardos & Heffernan 2010 individualized BKT |
| T04-S020 | https://www.e-elgar.com/shop/usd/handbook-of-artificial-intelligence-in-education-9781800375406.html | surrogate_primary | 2026-05-25 | Edward Elgar Publishing | vendor docs (学术出版商) — du Boulay et al. AIED Handbook |
| T04-S021 | https://learninganalytics.upenn.edu/MOOC/bigdataeducation.html | verified_primary | 2026-05-25 | UPenn / Penn Center for Learning Analytics | Ryan Baker Big Data in Education MOOC |
| T04-S022 | https://www.hcii.cmu.edu/course/special-topics-learning-analytics-and-educational-data-science | verified_primary | 2026-05-25 | CMU HCII (.edu) | Learning Analytics & Educational Data Science course |
| T04-S023 | https://metals.hcii.cmu.edu/ | verified_primary | 2026-05-25 | CMU HCII (.edu) | METALS Masters program |
| T04-S024 | https://omscs.gatech.edu/cs-6460-educational-technology | verified_primary | 2026-05-25 | Georgia Tech (.edu) | CS 6460 Educational Technology |
| T04-S025 | https://learnlab.org/ | surrogate_primary | 2026-05-25 | CMU LearnLab | vendor docs (研究机构) — LearnLab / Simon Initiative / DataShop |
| T04-S026 | https://educationaldatamining.org/conferences/ | surrogate_primary | 2026-05-25 | IEDMS | vendor docs (研究机构) — EDM conference proceedings |
| T04-S027 | https://aied-conference.org/2026/call-for-paper | surrogate_primary | 2026-05-25 | AIED Conference | vendor docs (研究机构) — AIED 2026 CFP |
| T04-S028 | https://en.wikipedia.org/wiki/Bloom%27s_2_sigma_problem | secondary | 2026-05-25 | Wikipedia | Bloom 1984 2-sigma problem overview |
| T04-S029 | https://en.wikipedia.org/wiki/Zone_of_proximal_development | secondary | 2026-05-25 | Wikipedia | Vygotsky ZPD overview |
| T04-S030 | https://files.eric.ed.gov/fulltext/EJ1081990.pdf | verified_primary | 2026-05-25 | ERIC / ed.gov | Vygotsky ZPD instructional implications |
| T04-S031 | https://www.instructionaldesign.org/theories/cognitive-load/ | secondary | 2026-05-25 | InstructionalDesign.org | Sweller Cognitive Load Theory summary |
| T04-S032 | https://github.com/open-spaced-repetition/fsrs4anki | surrogate_primary | 2026-05-25 | GitHub / open-spaced-repetition | vendor docs (EdTech 平台) — FSRS algorithm |
| T04-S033 | https://www.aleks.com/about_aleks/publications_kst | surrogate_primary | 2026-05-25 | ALEKS / McGraw-Hill | vendor docs (EdTech 平台) — KST publications |
| T04-S034 | https://pact.cs.cmu.edu/koedinger/publications.htm | verified_primary | 2026-05-25 | CMU PACT Center (.edu) | Koedinger publications list |
| T04-S035 | https://github.com/duolingo/halflife-regression | surrogate_primary | 2026-05-25 | Duolingo / GitHub | vendor docs (EdTech 平台) — HLR open-source code |
| T04-S036 | https://www.routledge.com/Item-Response-Theory-Foundations-for-Psychologists-and-Social-Scientists/Embretson-Reise/p/book/9780805853032 | surrogate_primary | 2026-05-25 | Routledge | vendor docs (学术出版商) — Embretson & Reise IRT textbook |
| T04-S037 | https://pmc.ncbi.nlm.nih.gov/articles/PMC5408091/ | verified_primary | 2026-05-25 | PubMed Central / NIH | Panadero 2017 SRL models review |
| T04-S038 | https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2024.1307574/full | surrogate_primary | 2026-05-25 | Frontiers | vendor docs (学术出版商) — SRL models integrative analysis 2024 |
| T04-S039 | https://www.mdpi.com/2079-9292/15/1/238 | surrogate_primary | 2026-05-25 | MDPI Electronics | vendor docs (学术出版商) — KG learning path survey 2026 |

---

## 1. Books / Textbooks

### 1.1 Advances in Intelligent Tutoring Systems (Nkambou, Mizoguchi & Bourdeau, 2010)

**Publisher:** Springer, Studies in Computational Intelligence vol. 308, 532 pp.

The definitive edited volume on ITS architecture and design. Contributions from leading AIED researchers cover student modeling, domain modeling, pedagogical strategies, dialogue systems, and affective computing in tutoring. Widely assigned in graduate AIED seminars and cited as the authoritative reference for anyone building an intelligent tutor.

**Endorsement evidence:**
- Referenced as "authoritative overview" in the AIED community and AIED conference best-paper lineages evidence: [T04-S001, T04-S027]
- Cited 1,300+ times (Google Scholar); assigned reading in CMU HCII and METALS curricula evidence: [T04-S001, T04-S023]
- Individual chapters (e.g., on student modeling, outer/inner loop) are standalone canonical references in EDM proceedings evidence: [T04-S001, T04-S026]

### 1.2 Building Intelligent Interactive Tutors (Woolf, 2009)

**Publisher:** Morgan Kaufmann / Elsevier, 2009.

Beverly Park Woolf distills 20 years of ITS research into a practitioner-oriented textbook covering student-centered design, adaptive sequencing, knowledge representation, machine learning for student modeling, and multimedia tutoring. The book bridges AI/CS and education, making it accessible to developers entering the field.

**Endorsement evidence:**
- Used as primary textbook in university courses on ITS design; recommended by ACM Digital Library as foundational evidence: [T04-S008, T04-S027]
- Cited in VanLehn (2011) meta-review as representative of step-based ITS design principles evidence: [T04-S008, T04-S011]
- Referenced in AIED Handbook (du Boulay 2023) as precursor to modern AIED textbooks evidence: [T04-S008, T04-S020]

### 1.3 The Cambridge Handbook of the Learning Sciences, 2nd ed. (Sawyer, 2014)

**Publisher:** Cambridge University Press, 2nd edition, 2014.

Edited by R. Keith Sawyer, this is the definitive interdisciplinary introduction to the learning sciences — spanning cognitive science, educational psychology, computer science, and anthropology. Chapters cover constructivism, collaborative learning, scaffolding, design-based research, and technology-enhanced learning. Essential background for anyone designing adaptive systems because it grounds the "learning" in "adaptive learning."

**Endorsement evidence:**
- Described as "essential resource for graduate students, researchers, teachers, administrators, consultants, software designers, and policy makers on a global scale" by Cambridge UP evidence: [T04-S005]
- Assigned in graduate learning-sciences programs at multiple universities (CMU, Stanford, Northwestern) evidence: [T04-S005, T04-S023]
- 20 new chapters in 2nd edition reflect the field's growth; 5,000+ citations evidence: [T04-S005]

### 1.4 Handbook of Educational Data Mining (Romero, Ventura, Pechenizkiy & Baker, 2010)

**Publisher:** Chapman & Hall / CRC Press, 2010.

The first comprehensive handbook for EDM. Part I presents 9 survey/tutorial chapters on principal data mining techniques (classification, clustering, association rules, sequential pattern mining, text mining) applied to education. Part II contains 25 case studies. Co-edited by Ryan Baker, who founded the EDM community.

**Endorsement evidence:**
- Co-edited by Ryan Baker, founder of the International Educational Data Mining Society; standard reference at EDM conferences evidence: [T04-S006, T04-S026]
- Cited as "important reference text in the field of educational data mining" by Routledge evidence: [T04-S006]
- Listed as required reading in Baker's own "Big Data in Education" MOOC evidence: [T04-S006, T04-S021]

### 1.5 Handbook of Learning Analytics, 2nd ed. (Lang, Siemens, Wise, Gasevic & Merceron, 2022)

**Publisher:** Society for Learning Analytics Research (SoLAR), 2022. Open access.

The open-access handbook by the field's professional society. Covers discourse analytics, social network analysis, temporal analytics, predictive modeling, and institutional adoption of learning analytics. The 2nd edition adds chapters on ethics, NLP, and multimodal learning analytics. Open access maximizes reach in the community.

**Endorsement evidence:**
- Published by SoLAR, the field's professional society; open access ensures wide adoption evidence: [T04-S007]
- 1st edition (2017) established itself as go-to reference; 2nd edition reflects 5 years of field growth evidence: [T04-S007]
- Contributors include George Siemens (who coined "learning analytics"), Dragan Gasevic, and other field founders evidence: [T04-S007]

### 1.6 Computerized Adaptive Testing: A Primer, 2nd ed. (Wainer et al., 2000)

**Publisher:** Routledge / Lawrence Erlbaum, 2nd edition, 2000.

The standard introduction to CAT, covering IRT foundations, item selection algorithms, item pool design, scoring, and operational deployment. Written by ETS researchers (Wainer, Dorans, Mislevy, Thissen). The 2nd edition adds lessons from a decade of operational CAT use (GRE, GMAT, NCLEX).

**Endorsement evidence:**
- "Celebrated primer" per Routledge; standard text in psychometrics and testing programs evidence: [T04-S009]
- Authored by ETS researchers who built operational large-scale CATs (GRE, ASVAB) evidence: [T04-S009]
- Reviewed in Applied Psychological Measurement (ERIC EJ638478); widely cited in CAT literature evidence: [T04-S009]

### 1.7 Learning Spaces (Falmagne & Doignon, 2011)

**Publisher:** Springer, Interdisciplinary Applied Mathematics, 2011.

The mathematical foundation behind Knowledge Space Theory (KST), which underpins the ALEKS adaptive learning platform used by millions. Falmagne and Doignon formalize prerequisite structures as combinatorial objects (knowledge spaces, learning spaces, surmise relations), enabling precise assessment of what a student knows without exhaustive testing.

**Endorsement evidence:**
- Mathematical theory directly implemented in ALEKS, used by millions of students in K-12 and college math evidence: [T04-S010, T04-S033]
- Foundational for the entire knowledge-space-based assessment paradigm; cited in KST publications list maintained by ALEKS evidence: [T04-S010, T04-S033]
- Cited by Desmarais & Baker (2012) as a major approach to learner modeling alongside BKT and IRT evidence: [T04-S010, T04-S017]

### 1.8 Item Response Theory: Foundations for Psychologists and Social Scientists (Embretson & Reise, 2000)

**Publisher:** Routledge / Lawrence Erlbaum, 2000.

The accessible IRT textbook for non-statisticians. Covers the Rasch model, 2PL/3PL models, model fit, differential item functioning, and applications to test construction. IRT is the psychometric backbone of adaptive testing — without it, CAT and most modern assessment platforms could not function.

**Endorsement evidence:**
- Described as "ideal core textbook" for IRT by Routledge; widely adopted in psychometrics programs evidence: [T04-S036]
- IRT models (especially Rasch) directly used in AKT paper (Ghosh et al. 2020) to regularize embeddings evidence: [T04-S036, T04-S016]
- Foundational to Lord & Novick (1968) tradition; IRT underlies GRE, GMAT, TOEFL, and all major CAT platforms evidence: [T04-S036, T04-S009]

### 1.9 Handbook of Artificial Intelligence in Education (du Boulay, Mitrovic & Yacef, 2023)

**Publisher:** Edward Elgar, 2023.

The most recent comprehensive handbook tracing AIED from its 1970s origins to the present, including chapters on LLMs in education, open learner models, affective tutoring, and ethics. Edited by three pillars of the AIED community. Serves as the state-of-the-art update to Nkambou et al. (2010).

**Endorsement evidence:**
- Edited by Benedict du Boulay (40+ years in AIED), Antonija Mitrovic (constraint-based modeling pioneer), Kalina Yacef (EDM pioneer) evidence: [T04-S020]
- Traces AIED development from 1970s to present, covering the full scope of the field evidence: [T04-S020]
- Published 2023, making it the most current comprehensive handbook; complements Nkambou et al. (2010) evidence: [T04-S020, T04-S001]

---

## 2. Seminal Papers

### 2.1 Corbett & Anderson (1995) — Bayesian Knowledge Tracing

**Venue:** User Modeling and User-Adapted Interaction, 4(4), 253-278.

The paper that launched knowledge tracing. Uses a Hidden Markov Model with four parameters (p-init, p-learn, p-guess, p-slip) to estimate the probability that a student has mastered a latent skill. Deployed in CMU's Cognitive Tutor and still the baseline that every KT paper benchmarks against. 4,000+ citations.

**Endorsement evidence:**
- Foundation paper for the entire knowledge tracing subfield; every KT survey (Abdelrahman et al. 2023) begins with BKT evidence: [T04-S002, T04-S004]
- Implemented in Carnegie Learning's Cognitive Tutor, used by 500,000+ students annually evidence: [T04-S002, T04-S034]
- Extended by Pardos & Heffernan (2010), Pavlik et al. (2009 PFA), and Piech et al. (2015 DKT) evidence: [T04-S002, T04-S019, T04-S014, T04-S003]

### 2.2 Piech et al. (2015) — Deep Knowledge Tracing

**Venue:** NeurIPS 2015 (NIPS 28), pp. 505-513. arXiv:1506.05908.

Applied LSTMs to student interaction sequences, achieving substantial improvements over BKT on benchmark datasets without requiring hand-engineered features. Sparked an entire subfield of deep-learning-based KT. 1,200+ citations. Open code and data accelerated replication.

**Endorsement evidence:**
- Published at NeurIPS, the top ML venue; brought the deep learning community's attention to educational AI evidence: [T04-S003]
- Spawned extensive follow-up: DKT+, DKVMN, SAKT, AKT, and dozens more deep KT variants evidence: [T04-S003, T04-S016, T04-S004]
- Critically examined by Gervet et al. (2020) "When is Deep Learning the Best Approach to Knowledge Tracing?" — the replication debate itself shows canonical status evidence: [T04-S003]

### 2.3 VanLehn (2011) — Relative Effectiveness of ITS

**Venue:** Educational Psychologist, 46(4), 197-221. DOI: 10.1080/00461520.2011.611369.

The most-cited meta-review comparing human tutoring, ITS, and no-tutoring conditions. Key finding: step-based ITS (which provide feedback at each problem-solving step) achieve effect sizes comparable to human tutoring (d ~ 0.76 vs. d ~ 0.79). Demolished the assumption that only humans can tutor effectively. 1,400+ citations.

**Endorsement evidence:**
- 1,400+ citations; the single most cited empirical justification for ITS investment evidence: [T04-S011]
- Referenced in policy discussions, grant proposals, and EdTech pitch decks as "the VanLehn finding" evidence: [T04-S011, T04-S027]
- Introduced the granularity taxonomy (answer-based / step-based / substep-based) now standard in ITS design evidence: [T04-S011, T04-S001]

### 2.4 Koedinger, Corbett & Perfetti (2012) — KLI Framework

**Venue:** Cognitive Science, 36(5), 757-798. DOI: 10.1111/j.1551-6709.2012.01245.x.

The Knowledge-Learning-Instruction framework bridges cognitive science and instructional design by taxonomizing three dimensions: kinds of knowledge (facts/skills/schemas), kinds of learning processes (memory-fluency / induction-refinement / understanding-sensemaking), and kinds of instructional methods. Built on 400,000+ hours of LearnLab data. Essential for mapping what an adaptive system should teach and how.

**Endorsement evidence:**
- Draws on 10 years of LearnLab / PSLC DataShop data — the largest corpus of human learning interaction data evidence: [T04-S012, T04-S025]
- Published in Cognitive Science (top journal); endorsed by Global Learning Council as "theoretical framework for improving student learning" evidence: [T04-S012]
- Directly informs Cognitive Tutor and Carnegie Learning curriculum design evidence: [T04-S012, T04-S034]

### 2.5 Pavlik, Cen & Koedinger (2009) — Performance Factors Analysis

**Venue:** AIED 2009, Frontiers in Artificial Intelligence and Applications.

PFA reformulates knowledge tracing as logistic regression, incorporating counts of prior successes and failures per skill. Faster to fit than BKT (closed-form gradient), handles multiple skills per item, and launched the "Logistic Knowledge Tracing" family. The first KT model to run in real-time during student practice.

**Endorsement evidence:**
- Presented at AIED; adopted in ASSISTments and DataShop analysis pipelines evidence: [T04-S014, T04-S025]
- Established logistic regression as a viable alternative to HMM-based BKT; predecessor to LKT/DAS3H evidence: [T04-S014, T04-S004]
- Cited in Desmarais & Baker (2012) review as one of the key advances in skill modeling evidence: [T04-S014, T04-S017]

### 2.6 Settles & Meeder (2016) — Half-Life Regression

**Venue:** ACL 2016, pp. 1848-1858.

The Duolingo paper that operationalized spaced repetition at scale. HLR estimates the "half-life" of a word in long-term memory using features (word difficulty, learner history, lag time) and schedules reviews accordingly. 45%+ error reduction over baselines on 13M Duolingo traces; 12% improvement in daily engagement in production A/B test. Open-sourced code and data.

**Endorsement evidence:**
- Published at ACL (top NLP venue); open data/code on GitHub accelerated replication evidence: [T04-S013, T04-S035]
- Deployed at scale on Duolingo (500M+ users); represents industry-leading spaced repetition research evidence: [T04-S013]
- Directly inspired FSRS (Ye, 2023) which further improved on HLR for Anki evidence: [T04-S013, T04-S032]

### 2.7 Lan, Waters, Studer & Baraniuk (2014) — SPARFA

**Venue:** JMLR 15, pp. 1009-1048; KDD 2014 (SPARFA-Trace extension).

Sparse Factor Analysis for Learning and Content Analytics jointly estimates learner knowledge of latent concepts, question-concept associations, and question difficulty from graded response data. The SPARFA-Trace extension adds Kalman-filter-based temporal tracking. Bridges ML (sparse factor models) and psychometrics (IRT-like structure).

**Endorsement evidence:**
- Published in JMLR (top ML journal) and KDD (top data mining venue) evidence: [T04-S015]
- Achieves comparable or better predictive performance vs. BKT and collaborative filtering on real datasets evidence: [T04-S015, T04-S004]
- Cited in KT surveys as representative of factor-analysis approaches to knowledge tracing evidence: [T04-S015, T04-S017]

### 2.8 Ghosh, Heffernan & Lan (2020) — Context-Aware Attentive Knowledge Tracing (AKT)

**Venue:** KDD 2020.

AKT combines flexible attention-based neural networks with components inspired by IRT (Rasch model regularization) and cognitive theory (monotonic attention reflecting recency effects). Outperforms DKT and its variants by up to 6% AUC. Notably interpretable — attention weights show which past interactions matter for a prediction.

**Endorsement evidence:**
- Published at KDD 2020; open-source code on GitHub with active community evidence: [T04-S016]
- Integrates psychometric theory (Rasch model) with neural attention — a design pattern now widely imitated evidence: [T04-S016, T04-S004]
- Cited in subsequent KT surveys as state-of-the-art attention-based approach evidence: [T04-S016, T04-S004]

### 2.9 Cen, Koedinger & Junker (2006) — Learning Factors Analysis

**Venue:** ITS 2006, Springer LNCS 4053, pp. 164-175.

LFA is a semi-automated method for improving cognitive models of student knowledge. Combines logistic regression with A* search to discover which "learning factors" (knowledge components) best explain student performance data. Predecessor to PFA and the entire logistic KT family.

**Endorsement evidence:**
- Published at ITS; foundational to the DataShop model-comparison methodology evidence: [T04-S018, T04-S025]
- Directly led to PFA (Pavlik et al. 2009) and the broader Logistic Knowledge Tracing approach evidence: [T04-S018, T04-S014]
- Cited in Koedinger's KLI framework as a key methodology for cognitive model refinement evidence: [T04-S018, T04-S012]

### 2.10 Pardos & Heffernan (2010) — Individualized BKT

**Venue:** UMAP 2010, Springer LNCS 6075, pp. 255-266.

Extended standard BKT by fitting individualized student parameters within a Bayesian network framework. Showed that allowing per-student priors significantly improves prediction. Also introduced KT-IDEM (2011, UMAP) adding item difficulty. These extensions addressed BKT's known weakness of assuming all students and items are identical.

**Endorsement evidence:**
- Published at UMAP; implemented in pyBKT (open-source BKT library on GitHub) evidence: [T04-S019]
- Demonstrated that individualization matters — a finding replicated across all subsequent KT models evidence: [T04-S019, T04-S004]
- Cited in Desmarais & Baker (2012) as key advance in learner modeling evidence: [T04-S019, T04-S017]

### 2.11 Desmarais & Baker (2012) — Learner and Skill Modeling Review

**Venue:** User Modeling and User-Adapted Interaction, 22(1-2), 9-38.

Comprehensive review of learner modeling techniques including BKT, IRT, constraint-based modeling, Bayesian networks, and knowledge space theory. Also covers affective state modeling, metacognition, and self-regulated learning detection. The go-to survey for understanding the landscape of student models before the deep learning era.

**Endorsement evidence:**
- Published in UMUAI, the top journal for user modeling; widely cited as the pre-deep-learning survey evidence: [T04-S017]
- Covers all major approaches (BKT, IRT, KST, CBM) in a single comparative framework evidence: [T04-S017]
- Co-authored by Ryan Baker, ensuring coverage of EDM perspective alongside traditional AI perspective evidence: [T04-S017, T04-S021]

### 2.12 Bloom (1984) — The 2 Sigma Problem

**Venue:** Educational Researcher, 13(6), 4-16.

Benjamin Bloom's landmark finding: students receiving one-on-one tutoring with mastery learning perform 2 standard deviations above conventionally taught students (98th percentile). The "problem" is making this scalable. This paper is the motivational origin of the entire adaptive learning field — every ITS, every adaptive platform implicitly tries to solve Bloom's 2-sigma problem.

**Endorsement evidence:**
- The single most-cited motivational paper for ITS and adaptive learning; referenced in virtually every AIED grant proposal evidence: [T04-S028]
- VanLehn (2011) meta-review is essentially a 27-year follow-up asking "how close are ITS to solving this?" evidence: [T04-S028, T04-S011]
- Cited by modern AI-in-education companies (Duolingo, Khan Academy, Carnegie Learning) as foundational motivation evidence: [T04-S028, T04-S013]

---

## 3. Courses

### 3.1 Big Data in Education (Ryan Baker, UPenn — Coursera/edX)

**Platform:** Coursera (Columbia, 2013 launch); edX (UPenn, ongoing). **Last updated:** 2024 (edX version maintained by Penn Center for Learning Analytics).

The MOOC that introduced EDM to a global audience. Covers classification, regression, BKT, affect detection, discovery with models, and learning curve analysis. Taught by Ryan Baker, founder of the EDM community and director of the Penn Center for Learning Analytics. Hands-on with RapidMiner and real educational datasets.

**Endorsement evidence:**
- Created by the founder of the EDM field; first MOOC on educational data mining evidence: [T04-S021]
- Offered multiple times on both Coursera and edX; thousands of completions worldwide evidence: [T04-S021]
- Course materials directly reference the Handbook of EDM (Romero et al. 2010) as companion text evidence: [T04-S021, T04-S006]

### 3.2 Learning Analytics and Educational Data Science (CMU HCII)

**Institution:** Carnegie Mellon University, Human-Computer Interaction Institute. **Last updated:** Spring 2025 (offered periodically; "not offered Spring 2026" per HCII site).

Graduate seminar covering principles, methods, and tools of learning analytics: data mining, predictive modeling, data visualization, and evaluation of educational interventions. Taught at the institution that houses LearnLab and DataShop, giving students access to the world's largest repository of educational interaction data.

**Endorsement evidence:**
- Taught at CMU HCII, the home of LearnLab, Cognitive Tutor, and DataShop evidence: [T04-S022, T04-S025]
- Covers state-of-the-art methods used in published EDM and LAK research evidence: [T04-S022]
- Part of the broader CMU METALS ecosystem for learning engineering evidence: [T04-S022, T04-S023]

### 3.3 METALS — Master of Educational Technology and Applied Learning Science (CMU)

**Institution:** Carnegie Mellon University, HCII. **Last updated:** 2025-2026 cohort enrolling.

A 12-month interdisciplinary master's degree spanning HCI, cognitive science, education, design, statistics, and CS. Students complete courses in learning principles and technology design, then execute a capstone project for an external client. Includes the "E-Learning Design Principles and Methods" course. The closest thing to a professional degree in learning engineering.

**Endorsement evidence:**
- Only masters program explicitly branding as "Educational Technology and Applied Learning Science" at a top-10 CS university evidence: [T04-S023]
- Housed in CMU HCII alongside Koedinger's LearnLab and the PACT Center evidence: [T04-S023, T04-S034]
- Graduates feed into Carnegie Learning, Duolingo, Khan Academy, and EdTech startups evidence: [T04-S023]

### 3.4 CS 6460: Educational Technology (Georgia Tech OMSCS)

**Institution:** Georgia Institute of Technology, Online Master of Science in Computer Science. **Last updated:** Spring 2026 (regularly offered each semester).

A heavily project-based course where students investigate tools, methods, and theories behind educational technology. Covers intelligent tutoring systems, MOOCs, learning analytics, and AI in education. Accessible to the 12,000+ students in Georgia Tech's OMSCS program, making it one of the largest-enrollment courses covering this material.

**Endorsement evidence:**
- Part of Georgia Tech OMSCS, the largest online CS master's program globally (12,000+ students) evidence: [T04-S024]
- Project-based format with small-group mentorship by TAs; students deliver publishable-quality research evidence: [T04-S024]
- Covers the full ITS/AIED/EDM landscape in a single graduate course evidence: [T04-S024]

### 3.5 Introduction to Learning Engineering (CMU LearnLab / Open)

**Institution:** Carnegie Mellon University, LearnLab / Simon Initiative. **Last updated:** 2025 (open enrollment via cmulearn.org).

A free, open course introducing the emerging discipline of "learning engineering" — applying engineering methods (measurement, iteration, data-driven improvement) to instructional design. Covers the learn-by-doing cycle, A/B testing in education, knowledge components, and learning curves. Part of the broader "Learning Sciences for Innovators" course catalog at CMU.

**Endorsement evidence:**
- Created by LearnLab, the NSF-funded Science of Learning Center at CMU evidence: [T04-S025]
- Open and free, lowering barriers to entry for the learning engineering discipline evidence: [T04-S025]
- Directly draws on LearnLab's DataShop infrastructure and KLI framework evidence: [T04-S025, T04-S012]

---

## 4. Core Concepts

### 4.1 Knowledge Tracing (KT)
**Origin:** Corbett & Anderson, 1995. The task of estimating a student's latent knowledge state from their observable interaction history. The fundamental computational problem of adaptive learning — if you cannot trace what a student knows, you cannot adapt. BKT, DKT, PFA, AKT, and SPARFA are all KT models. evidence: [T04-S002, T04-S003, T04-S004]

### 4.2 Mastery Probability
**Origin:** Corbett & Anderson, 1995 (BKT). The estimated probability that a student has "learned" (mastered) a given knowledge component. When P(mastery) exceeds a threshold (typically 0.95), the system advances the student. The core decision variable in any mastery-based adaptive system. evidence: [T04-S002, T04-S014]

### 4.3 Item Response Theory (IRT)
**Origin:** Lord & Novick, 1968; Rasch, 1960. A family of psychometric models that relate the probability of a correct response to latent ability (theta) and item parameters (difficulty, discrimination, guessing). The mathematical foundation of adaptive testing (CAT). The Rasch model is used in AKT for embedding regularization. evidence: [T04-S036, T04-S009, T04-S016]

### 4.4 Zone of Proximal Development (ZPD)
**Origin:** Vygotsky, 1978, "Mind in Society." The zone between what a learner can do independently and what they cannot do even with help. Adaptive systems implicitly target the ZPD: presenting tasks that are challenging but achievable with scaffolding. The theoretical justification for difficulty calibration in ITS. evidence: [T04-S029, T04-S030]

### 4.5 Scaffolding
**Origin:** Wood, Bruner & Ross, 1976. Temporary support structures provided to a learner, gradually withdrawn as competence grows ("fading"). In ITS: hints, worked examples, decomposed subproblems. The inner loop of an ITS is essentially a scaffolding engine. evidence: [T04-S029, T04-S030, T04-S001]

### 4.6 Spaced Repetition
**Origin:** Ebbinghaus, 1885 (forgetting curve); Leitner, 1972 (card box system); Wozniak, 1987 (SM-2 algorithm). The principle that memory retention improves when reviews are spaced at increasing intervals timed to the point of near-forgetting. Operationalized in SM-2, HLR, and FSRS algorithms. evidence: [T04-S013, T04-S032]

### 4.7 Forgetting Curve
**Origin:** Ebbinghaus, 1885, "Uber das Gedachtnis." Memory retention decays exponentially after initial learning. Within 1 day, ~66% is forgotten without review. The empirical basis for all spaced repetition scheduling. HLR (Settles & Meeder 2016) models the "half-life" of this decay per word per student. evidence: [T04-S013, T04-S035]

### 4.8 Knowledge Component (KC)
**Origin:** Koedinger et al., 2012 (KLI framework); also Corbett & Anderson, 1995. The atomic unit of knowledge that a student must acquire. Can be a fact, a skill, a schema, or a production rule. The grain size of KCs determines the resolution of knowledge tracing and the specificity of adaptive feedback. evidence: [T04-S012, T04-S002, T04-S018]

### 4.9 Student Model
**Origin:** Foundational ITS concept, 1970s (Carbonell, 1970; Sleeman & Brown, 1982). The component of an adaptive system that maintains a representation of the learner's current knowledge, misconceptions, affect, and goals. BKT, DKT, IRT-based models, and Bayesian networks are all implementations of student models. evidence: [T04-S001, T04-S017, T04-S008]

### 4.10 Domain Model
**Origin:** Foundational ITS concept. The structured representation of what is to be taught — knowledge components, prerequisite relationships, and their mappings to problems/items. In knowledge graph approaches, this is the graph itself. The quality of the domain model determines the ceiling on adaptive accuracy. evidence: [T04-S001, T04-S012, T04-S039]

### 4.11 Pedagogical Model
**Origin:** Foundational ITS concept. The decision-making component that selects what to present next given the student model and domain model. Implements pedagogical strategies: mastery-based progression, interleaving, spacing, scaffolding policies. The "outer loop" of the ITS architecture. evidence: [T04-S001, T04-S008]

### 4.12 Outer Loop / Inner Loop
**Origin:** VanLehn, 2006, "The behavior of tutoring systems." The outer loop selects the next task/problem for the student (macro-adaptation). The inner loop provides step-level feedback and hints within a single problem (micro-adaptation). VanLehn's 2011 meta-review showed that step-based (inner-loop) feedback is what makes ITS effective. evidence: [T04-S011, T04-S001]

### 4.13 Knowledge Graph (in Education)
**Origin:** Applied to education from ~2015 onward; survey by Chen et al. (MDPI 2026). A directed graph where nodes are knowledge concepts and edges represent prerequisite/similarity/containment relationships. Used for learning path recommendation, prerequisite detection, and visualization of curriculum structure. evidence: [T04-S039, T04-S010]

### 4.14 Learning Path Optimization
**Origin:** Emerging area, ~2018-present. The problem of finding the optimal sequence of learning activities to maximize a student's knowledge gain given time constraints. Approaches include reinforcement learning, knowledge-graph traversal, and multi-armed bandits. evidence: [T04-S039, T04-S015]

### 4.15 Computerized Adaptive Testing (CAT)
**Origin:** Lord, 1970s; operational since GRE CAT (1992). A testing methodology where item selection is driven by the student's estimated ability (IRT theta). After each response, the ability estimate is updated and the most informative next item is chosen. Maximizes measurement precision while minimizing test length. evidence: [T04-S009, T04-S036]

### 4.16 Bloom's Taxonomy and Mastery Learning
**Origin:** Bloom, 1956 (taxonomy); Bloom, 1968 (mastery learning); Bloom, 1984 (2-sigma problem). The taxonomy classifies cognitive objectives (remember/understand/apply/analyze/evaluate/create). Mastery learning requires students to demonstrate proficiency before advancing. Together they provide the pedagogical framework that adaptive systems operationalize. evidence: [T04-S028, T04-S005]

### 4.17 Cognitive Load Theory
**Origin:** Sweller, 1988, "Cognitive load during problem solving." Distinguishes intrinsic load (inherent complexity), extraneous load (poor design), and germane load (productive learning effort). Adaptive systems should minimize extraneous load and calibrate intrinsic load to the learner's expertise. The "expertise reversal effect" (worked examples help novices but hurt experts) demands adaptation. evidence: [T04-S031, T04-S005]

### 4.18 Multi-Armed Bandit for Content Recommendation
**Origin:** Applied to education from ~2014 onward (Clement et al., AIED 2015). The exploration-exploitation tradeoff: should the system present content it knows works (exploit) or try new content to learn what works better (explore)? Thompson sampling and UCB algorithms balance this tradeoff in real-time for each student. evidence: [T04-S004, T04-S027]

### 4.19 Cold Start Problem in Adaptive Learning
**Origin:** Borrowed from recommender systems literature; acute in education because each student and each new content item starts with zero interaction history. Solutions include prior elicitation (placement tests), transfer learning from similar students, and knowledge-graph-based initialization. evidence: [T04-S017, T04-S015]

### 4.20 Knowledge Space Theory (KST)
**Origin:** Doignon & Falmagne, 1985; formalized in "Knowledge Spaces" (1999) and "Learning Spaces" (2011). A combinatorial framework where a "knowledge state" is a set of problems a student can solve, and the collection of all feasible knowledge states forms a "knowledge space." Implemented in ALEKS. evidence: [T04-S010, T04-S033]

### 4.21 Prerequisite Structure
**Origin:** Gagne, 1962 (learning hierarchies); formalized in KST (Doignon & Falmagne). The directed acyclic graph specifying which concepts must be mastered before others can be learned. Central to curriculum design, learning path optimization, and knowledge graph construction. evidence: [T04-S010, T04-S039]

### 4.22 Learning Curve Analysis
**Origin:** Newell & Rosenbloom, 1981 (power law of practice); applied to education by Koedinger et al. via DataShop. Plotting error rate against opportunity count reveals whether a knowledge component model fits: well-specified KCs show smooth decreasing curves; poor KCs show flat or jagged curves. DataShop automates this analysis. evidence: [T04-S025, T04-S012, T04-S018]

### 4.23 A/B Testing in Education (Randomized Controlled Trials)
**Origin:** Fisher, 1935 (general RCT design); applied systematically in education via LearnLab from ~2005. Running randomized experiments within adaptive systems to compare instructional interventions. Effect size (Cohen's d) is the standard metric. LearnLab has run 300+ in-vivo experiments in real classrooms. evidence: [T04-S025, T04-S012]

### 4.24 Self-Regulated Learning (SRL)
**Origin:** Zimmerman, 1986/1989. A cyclical model where learners plan (forethought), monitor (performance), and reflect (self-reflection) on their learning. Adaptive systems increasingly try to detect and scaffold SRL behaviors (e.g., help-seeking, self-explanation). SRL detection is a frontier in AIED. evidence: [T04-S037, T04-S038, T04-S017]

### 4.25 Affect Detection in Learning
**Origin:** D'Mello & Graesser, 2012 (affective states during learning); Baker et al. (automated detection). Detecting boredom, confusion, frustration, and engagement ("flow") from interaction patterns, facial expressions, or physiological signals. Adaptive systems that respond to affect (not just knowledge) show improved outcomes. evidence: [T04-S017, T04-S021, T04-S001]

---

## Phase 2 提炼提示

**反复出现 >= 3 个 canon 都讨论的核心 idea**（候选行业心智模型）:
- **"Trace-then-adapt" loop** — KT (BKT/DKT/PFA/AKT) estimates mastery -> pedagogical model selects next action. Appears in Corbett & Anderson, Piech et al., Pavlik et al., VanLehn, Koedinger KLI, Woolf textbook, Nkambou handbook, Desmarais & Baker review. This is THE central mental model of the field.
- **"Granularity determines effectiveness"** — VanLehn's step-based > answer-based finding; Koedinger's KC grain-size analysis; inner-loop vs. outer-loop distinction. The finer the grain of adaptation, the closer to human tutoring effectiveness.
- **"Forgetting is predictable and exploitable"** — Ebbinghaus curve, SM-2, HLR, FSRS all model memory decay. Spaced repetition is the most empirically validated instructional technique, repeatedly surfacing across cognitive science, EdTech, and ML papers.
- **"Bloom's ceiling as North Star"** — The 2-sigma problem motivates the entire field. VanLehn (2011) showed ITS at d~0.76 (close but not there). Every new model/system implicitly benchmarks against this.
- **"Psychometrics meets ML"** — IRT/Rasch in AKT, factor analysis in SPARFA, logistic regression in PFA/LFA, Bayesian networks in BKT. The field's strength comes from principled hybridization of classical measurement theory with modern ML.

**智识谱系种子**:
- **Ebbinghaus (1885) -> Leitner (1972) -> Wozniak SM-2 (1987) -> Settles HLR (2016) -> Ye FSRS (2023)**: The spaced repetition lineage.
- **Lord & Novick (1968) -> Rasch -> Embretson & Reise (2000) -> Wainer CAT Primer -> operational CAT (GRE/GMAT) -> AKT Rasch regularization**: The psychometric/adaptive testing lineage.
- **Corbett & Anderson BKT (1995) -> Pardos individualized BKT (2010) -> Cen LFA (2006) -> Pavlik PFA (2009) -> Piech DKT (2015) -> Ghosh AKT (2020)**: The knowledge tracing lineage.
- **Bloom (1984) -> VanLehn (2011) -> Koedinger KLI (2012)**: The effectiveness-measurement lineage.
- **Doignon & Falmagne KST (1985) -> ALEKS (1999) -> knowledge graph approaches (2018+)**: The prerequisite-structure lineage.
- **Vygotsky ZPD (1978) -> Wood/Bruner scaffolding (1976) -> Sweller CLT (1988) -> adaptive difficulty calibration**: The instructional theory lineage.

**核心概念 -> 候选 playbook**:
- Knowledge Tracing + Mastery Probability -> **"When to advance: the mastery decision playbook"** (threshold-based, confidence-interval-based, time-decayed)
- Spaced Repetition + Forgetting Curve -> **"Optimal review scheduling playbook"** (SM-2 vs. HLR vs. FSRS comparison; when each is appropriate)
- ZPD + Cognitive Load + Scaffolding -> **"Difficulty calibration playbook"** (how to find the sweet spot; expertise reversal; fading strategies)
- Knowledge Graph + Prerequisites + Learning Path -> **"Curriculum sequencing playbook"** (topological sort + personalization; handling multiple valid paths)
- Cold Start + Multi-Armed Bandit -> **"New-learner onboarding playbook"** (placement testing, prior elicitation, Thompson sampling warmup)
- Student Model + Affect Detection + SRL -> **"Whole-learner modeling playbook"** (beyond knowledge state: motivation, engagement, metacognition)
- IRT + CAT -> **"Efficient assessment playbook"** (minimize items while maximizing measurement precision)

**冷僻 / 信号薄弱**:
- **Open Learner Models (OLM)**: Showing students their own model to promote metacognition — niche but growing; sparse search signal.
- **Forgetting in knowledge tracing**: Most KT models (BKT, DKT) assume no forgetting; incorporating decay is still underresearched despite Ebbinghaus being 140 years old.
- **Fairness/bias in adaptive systems**: Emerging concern (AIED 2024 best paper nomination on "Data Feminism") but thin canonical literature; mostly position papers.
- **Multi-modal learning analytics**: Combining clickstream, facial expression, gaze, and physiological data — technically promising but few operational systems.
- **Causal inference in EDM**: Most EDM is correlational; causal methods (instrumental variables, regression discontinuity) are rare but growing.
- **Transfer learning across domains**: Can a student model trained on math transfer to science? Almost no published work; major open problem.
