library(Saqrlab)
#>
#> Attaching package: 'Saqrlab'
#> The following object is masked from 'package:stats':
#>
#> simulateOverview
Saqrlab includes over 180 learning action verbs organized into 8 categories, designed for educational research simulations. This reference documents all available learning states and how to use them.
Available Categories
list_learning_categories()
#> Category Count
#> 1 metacognitive 20
#> 2 cognitive 30
#> 3 behavioral 30
#> 4 social 30
#> 5 motivational 30
#> 6 affective 30
#> 7 group_regulation 9
#> 8 lms 30
#> Examples
#> 1 Plan, Monitor, Evaluate, Reflect, Regulate, ...
#> 2 Read, Study, Analyze, Summarize, Memorize, ...
#> 3 Practice, Annotate, Research, Review, Revise, ...
#> 4 Collaborate, Discuss, Seek_help, Question, Explain, ...
#> 5 Focus, Persist, Explore, Create, Strive, ...
#> 6 Enjoy, Appreciate, Value, Interest, Curious, ...
#> 7 Adapt, Cohesion, Consensus, Coregulate, Discuss, ...
#> 8 View, Access, Download, Upload, Submit, ...Complete Learning States Reference
Metacognitive (20 verbs)
Self-regulation and awareness actions for planning, monitoring, and evaluating one’s own learning.
get_learning_states("metacognitive")
#> [1] "Plan" "Monitor" "Evaluate" "Reflect" "Regulate"
#> [6] "Adjust" "Adapt" "Check" "Assess" "Judge"
#> [11] "Strategize" "Prioritize" "Set_goals" "Track" "Self_assess"
#> [16] "Calibrate" "Diagnose" "Forecast" "Anticipate" "Reconsider"Cognitive (30 verbs)
Mental processing actions for understanding, analyzing, and synthesizing information.
get_learning_states("cognitive")
#> [1] "Read" "Study" "Analyze" "Summarize"
#> [5] "Memorize" "Connect" "Apply" "Comprehend"
#> [9] "Synthesize" "Compare" "Contrast" "Infer"
#> [13] "Interpret" "Elaborate" "Encode" "Retrieve"
#> [17] "Process" "Understand" "Learn" "Recognize"
#> [21] "Recall" "Integrate" "Differentiate" "Abstract"
#> [25] "Generalize" "Classify" "Categorize" "Deduce"
#> [29] "Reason" "Conclude"Behavioral (30 verbs)
Observable study actions including reading, writing, practicing, and note-taking.
get_learning_states("behavioral")
#> [1] "Practice" "Annotate" "Research" "Review" "Revise" "Test"
#> [7] "Write" "Note" "Highlight" "Underline" "Reread" "Skim"
#> [13] "Scan" "Draft" "Edit" "Copy" "Record" "Complete"
#> [19] "Submit" "Attempt" "Repeat" "Drill" "Exercise" "Rehearse"
#> [25] "Outline" "Diagram" "Map" "List" "Organize" "Structure"Social (30 verbs)
Interaction and collaboration actions for discussing, helping, teaching, and asking.
get_learning_states("social")
#> [1] "Collaborate" "Discuss" "Seek_help" "Question" "Explain"
#> [6] "Share" "Teach" "Tutor" "Debate" "Argue"
#> [11] "Negotiate" "Consult" "Ask" "Answer" "Present"
#> [16] "Participate" "Engage" "Contribute" "Support" "Help"
#> [21] "Clarify" "Communicate" "Listen" "Respond" "Feedback"
#> [26] "Critique" "Peer_review" "Co_create" "Brainstorm" "Network"Motivational (30 verbs)
Effort and persistence actions for focusing, persisting, engaging, and striving.
get_learning_states("motivational")
#> [1] "Focus" "Persist" "Explore" "Create" "Strive"
#> [6] "Commit" "Motivate" "Endure" "Overcome" "Challenge"
#> [11] "Aspire" "Dedicate" "Invest" "Concentrate" "Attend"
#> [16] "Sustain" "Maintain" "Initiate" "Continue" "Pursue"
#> [21] "Drive" "Hustle" "Push" "Achieve" "Accomplish"
#> [26] "Excel" "Improve" "Grow" "Develop" "Progress"Affective (30 verbs)
Emotional and attitudinal states including enjoying, coping, managing stress, and curiosity.
get_learning_states("affective")
#> [1] "Enjoy" "Appreciate" "Value" "Interest" "Curious"
#> [6] "Worry" "Stress" "Relax" "Cope" "Manage"
#> [11] "Calm" "Frustrate" "Satisfy" "Excite" "Bore"
#> [16] "Confuse" "Resolve" "Embrace" "Accept" "Tolerate"
#> [21] "Celebrate" "Doubt" "Confident" "Anxious" "Hopeful"
#> [26] "Discourage" "Encourage" "Inspire" "Overwhelm" "Relief"Group Regulation (9 verbs)
Socially shared regulation of learning (SSRL) actions for collaborative planning, monitoring, and adapting.
get_learning_states("group_regulation")
#> [1] "Adapt" "Cohesion" "Consensus" "Coregulate" "Discuss"
#> [6] "Emotion" "Monitor" "Plan" "Synthesis"LMS Actions (30 verbs)
Learning Management System interactions including viewing content, accessing resources, and submitting assignments.
get_learning_states("lms")
#> [1] "View" "Access" "Download" "Upload" "Submit"
#> [6] "Click" "Navigate" "Browse" "Login" "Logout"
#> [11] "Post" "Reply" "Forum" "Quiz" "Assignment"
#> [16] "Video" "Resource" "Grade" "Attempt" "Complete"
#> [21] "Module" "Page" "File" "Link" "Course"
#> [26] "Content" "Discussion" "Message" "Announcement" "Calendar"Using Learning States
Basic Retrieval
# Get all verbs from a category
metacognitive_verbs <- get_learning_states("metacognitive")
length(metacognitive_verbs)
#> [1] 20
# Get verbs from multiple categories
srl_verbs <- get_learning_states(c("metacognitive", "cognitive", "motivational"))
length(srl_verbs)
#> [1] 80
# Get all verbs
all_verbs <- get_learning_states("all")
length(all_verbs)
#> [1] 202Random Sampling
# Random 8 verbs from any category
random_8 <- get_learning_states(n = 8, seed = 42)
random_8
#> [1] "Reason" "Edit" "Satisfy" "Rehearse" "Worry" "Dedicate" "Calendar"
#> [8] "Initiate"
# Random 6 verbs from specific categories
random_cog <- get_learning_states(c("cognitive", "behavioral"), n = 6, seed = 42)
random_cog
#> [1] "Submit" "Write" "Read" "Generalize" "Compare"
#> [6] "Test"Smart Selection
The select_states() function intelligently selects
states based on network size:
# Small network: auto-selects from one category
small_net <- select_states(5, seed = 42)
small_net
#> [1] "Regulate" "Discourage" "Judge" "Plan" "Appreciate"
# Medium network: balanced across categories
medium_net <- select_states(10, seed = 42)
medium_net
#> [1] "Sustain" "Strive" "Negotiate" "Judge" "Plan"
#> [6] "Create" "Manage" "Forecast" "Tolerate" "Brainstorm"
# Large network: wide variety
large_net <- select_states(20, seed = 42)
large_net
#> [1] "Link" "Maintain" "Forecast" "Strive" "Commit"
#> [6] "Memorize" "Categorize" "Evaluate" "Create" "Calm"
#> [11] "Test" "Develop" "Reconsider" "Drive" "Respond"
#> [16] "Improve" "Brainstorm" "Curious" "Module" "Accept"Biased Selection
# Prioritize metacognitive verbs
meta_focus <- select_states(
n_states = 10,
primary_categories = "metacognitive",
secondary_categories = "cognitive",
primary_ratio = 0.7, # 70% metacognitive
seed = 42
)
meta_focus
#> [1] "Reconsider" "Judge" "Diagnose" "Reflect" "Monitor"
#> [6] "Encode" "Regulate" "Abstract" "Process" "Plan"Integration with Simulation Functions
With simulate_sequences()
# Auto-generates with learning states by default
sequences <- simulate_sequences(
n_sequences = 50,
seq_length = 15,
n_states = 5,
seed = 42
)
# Check states used
unique(unlist(sequences))
#> [1] "Appreciate" "Judge" "Discourage" "Plan" "Regulate"Specifying Categories
# Use specific categories
sequences <- simulate_sequences(
n_sequences = 50,
seq_length = 15,
n_states = 6,
categories = c("metacognitive", "cognitive"),
seed = 42
)
unique(unlist(sequences))
#> [1] "Plan" "Process" "Retrieve" "Memorize" "Reason" "Judge"With simulate_matrix()
# Matrix uses random category by default
mat <- simulate_matrix(n_nodes = 5, seed = 42)
rownames(mat)
#> [1] "Regulate" "Plan" "Judge" "Reflect" "Monitor"
# Run again - different category selected
mat2 <- simulate_matrix(n_nodes = 5, seed = 123)
rownames(mat2)
#> [1] "Consensus" "Emotion" "Synthesis" "Cohesion" "Plan"With simulate_htna()
# HTNA uses different category per type
net <- simulate_htna(seed = 42)
lapply(net$node_types, head, 3)
#> $Metacognitive
#> [1] "Diagnose" "Regulate" "Plan"
#>
#> $Cognitive
#> [1] "Understand" "Classify" "Process"
#>
#> $Behavioral
#> [1] "Write" "Review" "Outline"
#>
#> $Social
#> [1] "Help" "Critique" "Contribute"
#>
#> $Motivational
#> [1] "Overcome" "Accomplish" "Improve"Direct Access to LEARNING_STATES
# Access the raw data
names(LEARNING_STATES)
#> [1] "metacognitive" "cognitive" "behavioral" "social"
#> [5] "motivational" "affective" "group_regulation" "lms"
# Get specific category
LEARNING_STATES$metacognitive
#> [1] "Plan" "Monitor" "Evaluate" "Reflect" "Regulate"
#> [6] "Adjust" "Adapt" "Check" "Assess" "Judge"
#> [11] "Strategize" "Prioritize" "Set_goals" "Track" "Self_assess"
#> [16] "Calibrate" "Diagnose" "Forecast" "Anticipate" "Reconsider"
# Count verbs per category
sapply(LEARNING_STATES, length)
#> metacognitive cognitive behavioral social
#> 20 30 30 30
#> motivational affective group_regulation lms
#> 30 30 9 30Global Names Dataset
Saqrlab also includes 300 diverse names for simulation:
# Access names
head(GLOBAL_NAMES, 20)
#> $western_europe
#> [1] "Emma" "Liam" "Chloe" "Jack" "Lily" "Oliver"
#> [7] "Grace" "Harry" "Aoife" "Sean" "Niamh" "Finn"
#> [13] "Saoirse" "Ciaran" "Roisin" "Declan" "Lea" "Hugo"
#> [19] "Manon" "Lucas" "Chloe" "Jules" "Camille" "Louis"
#> [25] "Eloise" "Antoine" "Amelie" "Theo" "Juliette" "Raphael"
#> [31] "Margot" "Adrien" "Max" "Mia" "Felix" "Lena"
#> [37] "Leon" "Anna" "Paul" "Clara" "Lukas" "Sophie"
#> [43] "Jonas" "Lea" "Elias" "Marie" "Noah" "Emilia"
#> [49] "Daan" "Sanne" "Sem" "Julia" "Lotte" "Ruben"
#> [55] "Fien" "Bram" "Thijs" "Eva" "Lars" "Lisa"
#> [61] "Milan" "Noor" "Tim" "Fleur"
#>
#> $eastern_europe
#> [1] "Ivan" "Olga" "Dmitri" "Natasha" "Alexei" "Katya" "Yuri"
#> [8] "Anya" "Boris" "Mila" "Sasha" "Vera" "Vlad" "Daria"
#> [15] "Oleg" "Irina" "Nikita" "Lena" "Andrei" "Tanya" "Maxim"
#> [22] "Yulia" "Artem" "Sveta" "Jakub" "Zuzanna" "Kacper" "Maja"
#> [29] "Antoni" "Hanna" "Piotr" "Ewa" "Michal" "Anna" "Tomek"
#> [36] "Kasia" "Bartek" "Ola" "Pawel" "Magda" "Jiri" "Petra"
#> [43] "Tomas" "Hana" "Marek" "Eva" "Milan" "Jana" "Ondrej"
#> [50] "Lucie" "Adam" "Tereza" "Filip" "Klara" "Jakub" "Monika"
#> [57] "Andrei" "Elena" "Mircea" "Raluca" "Bela" "Eszter" "Matyas"
#> [64] "Zsofia" "Ion" "Maria" "Vlad" "Ana" "Attila" "Kata"
#> [71] "Levente" "Reka"
#>
#> $southern_europe
#> [1] "Pablo" "Lucia" "Diego" "Sofia" "Alvaro"
#> [6] "Carmen" "Sergio" "Marta" "Javier" "Ana"
#> [11] "Carlos" "Laura" "Alejandro" "Elena" "Daniel"
#> [16] "Paula" "Raul" "Ines" "Adrian" "Alba"
#> [21] "Jorge" "Nuria" "Ivan" "Cristina" "Marco"
#> [26] "Giulia" "Luca" "Francesca" "Matteo" "Chiara"
#> [31] "Andrea" "Sara" "Alessandro" "Valentina" "Lorenzo"
#> [36] "Martina" "Davide" "Federica" "Giuseppe" "Elisa"
#> [41] "Joao" "Maria" "Tiago" "Ines" "Diogo"
#> [46] "Beatriz" "Pedro" "Ana" "Miguel" "Mariana"
#> [51] "Rui" "Catarina" "Andre" "Sofia" "Bruno"
#> [56] "Rita" "Nikos" "Elena" "Dimitris" "Maria"
#> [61] "Kostas" "Sofia" "Yannis" "Anna" "Giorgos"
#> [66] "Eleni" "Christos" "Katerina" "Alexandros" "Ioanna"
#> [71] "Stavros" "Dimitra"
#>
#> $nordic
#> [1] "Erik" "Astrid" "Oscar" "Maja" "Axel" "Ella"
#> [7] "Viktor" "Saga" "Gustav" "Wilma" "William" "Ebba"
#> [13] "Linus" "Agnes" "Filip" "Freja" "Lars" "Ingrid"
#> [19] "Sven" "Liv" "Olaf" "Nora" "Leif" "Thea"
#> [25] "Magnus" "Emma" "Henrik" "Sofie" "Andreas" "Ida"
#> [31] "Kristian" "Julie" "Mikkel" "Freya" "Emil" "Clara"
#> [37] "Christian" "Emilie" "Noah" "Alma" "Oliver" "Freja"
#> [43] "Lucas" "Laura" "Frederik" "Anna" "Mathias" "Maja"
#> [49] "Onni" "Aino" "Eetu" "Venla" "Lauri" "Helmi"
#> [55] "Eero" "Siiri" "Matias" "Emilia" "Aleksi" "Sofia"
#> [61] "Veeti" "Aada" "Elias" "Ella" "Ragnar" "Sigrid"
#> [67] "Bjorn" "Gudrun" "Eirik" "Hildur" "Odin" "Frida"
#> [73] "Thor" "Helga" "Gunnar" "Unnur" "Bjarni" "Kristin"
#> [79] "Arnar" "Margret"
#>
#> $arab
#> [1] "Ali" "Fatima" "Omar" "Layla" "Yusuf" "Mariam" "Khalid"
#> [8] "Noor" "Sultan" "Sheikha" "Rashid" "Amal" "Hamad" "Moza"
#> [15] "Faisal" "Hessa" "Hassan" "Amira" "Amir" "Zara" "Tariq"
#> [22] "Salma" "Faris" "Rania" "Karim" "Leila" "Walid" "Dina"
#> [29] "Nabil" "Huda" "Samir" "Lina" "Ahmed" "Mona" "Mahmoud"
#> [36] "Heba" "Mostafa" "Yasmine" "Tarek" "Dalia" "Mohamed" "Noha"
#> [43] "Amr" "Aya" "Khaled" "Mai" "Hazem" "Nada" "Ziad"
#> [50] "Rana" "Sami" "Reem" "Adel" "Noura" "Mazen" "Asma"
#> [57] "Jamal" "Dalal" "Bassam" "Aisha" "Rami" "Hana" "Tamer"
#> [64] "Sara"
#>
#> $persian
#> [1] "Cyrus" "Shirin" "Dara" "Parisa" "Reza" "Maryam"
#> [7] "Arman" "Soraya" "Babak" "Mina" "Kaveh" "Nazanin"
#> [13] "Omid" "Sara" "Farhad" "Azar" "Behzad" "Niloufar"
#> [19] "Kian" "Mahsa" "Arash" "Pari" "Dariush" "Roxana"
#> [25] "Amir" "Leyla" "Kamran" "Shadi" "Nima" "Azadeh"
#> [31] "Siavash" "Setareh" "Ahmad" "Fatima" "Farid" "Zainab"
#> [37] "Hamid" "Maryam" "Jawad" "Hosna" "Rustam" "Gulnora"
#> [43] "Firdavs" "Munis" "Jamshed" "Dilbar" "Firuz" "Sitora"
#>
#> $turkish
#> [1] "Emre" "Elif" "Kaan" "Defne" "Cem" "Zeynep" "Deniz"
#> [8] "Aylin" "Burak" "Selin" "Mert" "Ceren" "Baris" "Ebru"
#> [15] "Arda" "Melis" "Kemal" "Leyla" "Ozan" "Nehir" "Alp"
#> [22] "Yasemin" "Umut" "Ezgi" "Onur" "Duygu" "Tolga" "Pinar"
#> [29] "Serkan" "Burcu" "Hakan" "Esra" "Eldar" "Gunel" "Tural"
#> [36] "Nigar" "Orhan" "Sevil" "Farid" "Aynur" "Rauf" "Gulnar"
#> [43] "Ilkin" "Samira" "Nurlan" "Leyla" "Vugar" "Arzu"
#>
#> $central_asia
#> [1] "Aibek" "Aida" "Nurlan" "Gulnara" "Bekzat" "Aigerim"
#> [7] "Ruslan" "Dana" "Nursultan" "Ainur" "Almas" "Dilnaz"
#> [13] "Miras" "Zhanna" "Saken" "Aliya" "Bobur" "Nilufar"
#> [19] "Sardor" "Malika" "Jasur" "Dilnoza" "Nodir" "Zarina"
#> [25] "Bakyt" "Madina" "Azamat" "Asel" "Damir" "Cholpon"
#> [31] "Timur" "Sabina" "Merdan" "Mahri" "Oraz" "Ogulgerek"
#> [37] "Serdar" "Aylar" "Dovlet" "Jennet" "Bahrom" "Zebo"
#> [43] "Farkhod" "Nigina" "Suhrab" "Madina" "Daler" "Firuza"
#>
#> $south_asia
#> [1] "Arun" "Priya" "Raj" "Devi" "Amit" "Sita" "Ravi"
#> [8] "Maya" "Ajay" "Lata" "Vijay" "Anita" "Rohit" "Pooja"
#> [15] "Arjun" "Kavita" "Rahul" "Sunita" "Kiran" "Aditi" "Nikhil"
#> [22] "Shreya" "Vikram" "Neha" "Sanjay" "Meera" "Aditya" "Divya"
#> [29] "Varun" "Isha" "Dev" "Tara" "Surya" "Lakshmi" "Karthik"
#> [36] "Deepa" "Ganesh" "Radha" "Suresh" "Vasuki" "Rajan" "Chitra"
#> [43] "Ashok" "Padma" "Senthil" "Bhavani" "Mohan" "Uma" "Imran"
#> [50] "Ayesha" "Bilal" "Sana" "Hamza" "Hira" "Zain" "Maryam"
#> [57] "Usman" "Fatima" "Ali" "Zoya" "Hasan" "Amina" "Faisal"
#> [64] "Rabia" "Rafiq" "Fatema" "Rahim" "Nasreen" "Shahid" "Rupa"
#> [71] "Kamal" "Shanta" "Tanvir" "Nusrat" "Mahbub" "Tahera" "Iqbal"
#> [78] "Hasina" "Jamal" "Rima" "Nimal" "Kumari" "Sandun" "Dilani"
#> [85] "Chamara" "Nimali" "Lasith" "Thilini" "Binod" "Sushma" "Rajan"
#> [92] "Kamala" "Sunil" "Gita" "Ramesh" "Sabita"
#>
#> $east_asia
#> [1] "Wei" "Lin" "Chen" "Mei" "Jing"
#> [6] "Xiao" "Ming" "Hua" "Feng" "Yan"
#> [11] "Jun" "Lei" "Bo" "Lan" "Tao"
#> [16] "Ying" "Hao" "Yue" "Zhen" "Xin"
#> [21] "Peng" "Qian" "Rui" "Shu" "Long"
#> [26] "Fang" "Hong" "Li" "Chao" "Na"
#> [31] "Gang" "Ling" "Yuki" "Hiro" "Akira"
#> [36] "Sakura" "Kenji" "Yuna" "Taro" "Haruki"
#> [41] "Koji" "Miki" "Ryu" "Emi" "Rin"
#> [46] "Sora" "Kaito" "Hana" "Yuto" "Aoi"
#> [51] "Sota" "Mei" "Ren" "Yui" "Haruto"
#> [56] "Koharu" "Takumi" "Himari" "Kenta" "Akari"
#> [61] "Daiki" "Riko" "Naoki" "Ayumi" "Minho"
#> [66] "Jisoo" "Joon" "Seo" "Tae" "Yuna"
#> [71] "Woo" "Minji" "Dae" "Eunji" "Sung"
#> [76] "Hana" "Hyun" "Jiwon" "Seok" "Nari"
#> [81] "Jiho" "Somin" "Junho" "Yuri" "Minsoo"
#> [86] "Soyeon" "Donghyun" "Chaeyoung" "Bataar" "Oyun"
#> [91] "Bold" "Altai" "Temuulen" "Sarnai" "Ganzorig"
#> [96] "Enkhtuya" "Erdene" "Narantsetseg" "Munkh" "Tsetseg"
#> [101] "Baatar" "Oyunbileg" "Chuluun" "Bolormaa"
#>
#> $southeast_asia
#> [1] "Linh" "Minh" "Anh" "Bao" "Mai" "Duc"
#> [7] "Thao" "Tuan" "Hoa" "Nam" "Lan" "Hung"
#> [13] "Hanh" "Cuong" "Ngoc" "Phong" "Trang" "Huy"
#> [19] "Nhi" "Khanh" "Vy" "Long" "Huong" "Dung"
#> [25] "Somchai" "Ploy" "Chai" "Nong" "Kiet" "Mali"
#> [31] "Somsak" "Araya" "Prem" "Dao" "Niran" "Malai"
#> [37] "Sakchai" "Achara" "Prasert" "Supaporn" "Rizal" "Putri"
#> [43] "Bagus" "Sari" "Wayan" "Ayu" "Gede" "Nisa"
#> [49] "Dewi" "Budi" "Rani" "Eko" "Sinta" "Agus"
#> [55] "Ratna" "Dimas" "Adi" "Fitri" "Rudi" "Wulan"
#> [61] "Hendra" "Lestari" "Yusuf" "Mega" "Jose" "Maria"
#> [67] "Juan" "Rosa" "Carlo" "Ana" "Miguel" "Luz"
#> [73] "Antonio" "Carmen" "Ramon" "Liza" "Francis" "Grace"
#> [79] "Paolo" "Joy" "Fajar" "Indah" "Hafiz" "Nurul"
#> [85] "Amir" "Siti" "Ismail" "Aishah" "Zul" "Farah"
#> [91] "Azlan" "Aisyah" "Firdaus" "Nur" "Hakim" "Amira"
#>
#> $west_africa
#> [1] "Chidi" "Adaora" "Emeka" "Ngozi" "Obi" "Amaka"
#> [7] "Chijioke" "Nneka" "Ikenna" "Chinwe" "Obinna" "Uchenna"
#> [13] "Nnamdi" "Chinyere" "Kelechi" "Adaobi" "Ade" "Yemi"
#> [19] "Tunde" "Funke" "Segun" "Bola" "Kunle" "Nike"
#> [25] "Dayo" "Sade" "Kayode" "Toyin" "Femi" "Lola"
#> [31] "Bayo" "Ronke" "Kofi" "Ama" "Kwame" "Akua"
#> [37] "Yaw" "Abena" "Kwesi" "Efua" "Kojo" "Akosua"
#> [43] "Kwadwo" "Adwoa" "Kweku" "Afua" "Kobi" "Afia"
#> [49] "Amadou" "Fatou" "Moussa" "Aissatou" "Oumar" "Mariama"
#> [55] "Sekou" "Binta" "Ibrahima" "Aminata" "Mamadou" "Kadiatou"
#> [61] "Boubacar" "Fatoumata" "Cheikh" "Oumou"
#>
#> $east_africa
#> [1] "Juma" "Amina" "Bakari" "Zuri" "Mwangi" "Wanjiku"
#> [7] "Kamau" "Njeri" "Ochieng" "Auma" "Otieno" "Adhiambo"
#> [13] "Kipchoge" "Chebet" "Korir" "Jepkosgei" "Baraka" "Neema"
#> [19] "Hamisi" "Saida" "Rajabu" "Rehema" "Salum" "Mwajuma"
#> [25] "Abebe" "Tigist" "Haile" "Makeda" "Tadesse" "Meron"
#> [31] "Dawit" "Sara" "Yohannes" "Bethlehem" "Girma" "Selam"
#> [37] "Tesfaye" "Hana" "Solomon" "Rahel" "Mohamed" "Halima"
#> [43] "Abdi" "Amina" "Hassan" "Fartun" "Omar" "Sahra"
#> [49] "Mugisha" "Uwimana" "Kato" "Akello" "Okello" "Nkunda"
#> [55] "Kagame" "Umutesi"
#>
#> $southern_africa
#> [1] "Themba" "Lindiwe" "Sipho" "Nomzamo" "Thabo"
#> [6] "Zanele" "Mandla" "Lerato" "Sibusiso" "Thandiwe"
#> [11] "Bongani" "Nolwazi" "Siyabonga" "Ayanda" "Luyanda"
#> [16] "Nomvula" "Mpho" "Kelebogile" "Neo" "Goitseone"
#> [21] "Tshepo" "Dineo" "Thato" "Palesa" "Tendai"
#> [26] "Rudo" "Tapiwa" "Chipo" "Tinashe" "Tariro"
#> [31] "Farai" "Nyasha" "Tawanda" "Rutendo" "Tatenda"
#> [36] "Rumbidzai" "Kudakwashe" "Ropafadzo" "Tanaka" "Rufaro"
#> [41] "Mulenga" "Mutinta" "Chisomo" "Thandie" "Kgosi"
#> [46] "Naledi" "Amantle" "Masego"
#>
#> $north_africa
#> [1] "Youssef" "Fatima" "Hassan" "Amina" "Said" "Khadija" "Rachid"
#> [8] "Zahra" "Mehdi" "Salma" "Amine" "Houda" "Yassine" "Imane"
#> [15] "Hamza" "Hajar" "Malik" "Samira" "Kamel" "Nadia" "Hamid"
#> [22] "Leila" "Sofiane" "Salima" "Riad" "Yasmina" "Farid" "Lamia"
#> [29] "Nassim" "Amira" "Djamel" "Sihem" "Hedi" "Sonia" "Nizar"
#> [36] "Amel" "Fares" "Meriem" "Slim" "Ines" "Mukhtar" "Hana"
#> [43] "Idris" "Asma" "Fathi" "Marwa" "Nuri" "Salwa"
#>
#> $central_africa
#> [1] "Patrice" "Solange" "Jean" "Marie" "Emmanuel"
#> [6] "Grace" "David" "Ruth" "Fiston" "Carine"
#> [11] "Christian" "Nadine" "Patrick" "Sylvie" "Serge"
#> [16] "Claudine" "Samuel" "Esther" "Paul" "Christiane"
#> [21] "Pierre" "Nadege" "Andre" "Simone" "Yves"
#> [26] "Blanche" "Roger" "Jeanne" "Fabrice" "Mireille"
#> [31] "Herve" "Colette" "Arsene" "Prudence" "Guy"
#> [36] "Diane" "Didier" "Lydie" "Landry" "Ornella"
#>
#> $north_america
#> [1] "James" "Emily" "Michael" "Sarah" "David" "Jessica"
#> [7] "John" "Ashley" "Robert" "Amanda" "William" "Jennifer"
#> [13] "Chris" "Nicole" "Matt" "Lauren" "Ryan" "Rachel"
#> [19] "Tyler" "Megan" "Brian" "Stephanie" "Kevin" "Brittany"
#> [25] "Andrew" "Samantha" "Justin" "Elizabeth" "Brandon" "Heather"
#> [31] "Josh" "Michelle" "Liam" "Emma" "Noah" "Olivia"
#> [37] "Ethan" "Ava" "Mason" "Sophia" "Logan" "Charlotte"
#> [43] "Jacob" "Amelia" "Lucas" "Harper" "Jack" "Evelyn"
#>
#> $latin_america
#> [1] "Juan" "Rosa" "Diego" "Luz" "Pablo" "Sol"
#> [7] "Luis" "Ana" "Carlos" "Lucia" "Miguel" "Elena"
#> [13] "Jose" "Sofia" "Pedro" "Camila" "Alejandro" "Valentina"
#> [19] "Fernando" "Daniela" "Ricardo" "Mariana" "Eduardo" "Fernanda"
#> [25] "Mateo" "Isabella" "Rafael" "Isabel" "Sergio" "Carmen"
#> [31] "Alvaro" "Paulina" "Andres" "Maria" "Santiago" "Martina"
#> [37] "Gabriel" "Florencia" "Nicolas" "Julieta" "Sebastian" "Catalina"
#> [43] "Benjamin" "Antonia" "Martin" "Emilia" "Tomas" "Isidora"
#> [49] "Joao" "Julia" "Pedro" "Fernanda" "Lucas" "Amanda"
#> [55] "Gustavo" "Beatriz" "Rafael" "Larissa" "Thiago" "Bruna"
#> [61] "Matheus" "Carolina" "Vitor" "Leticia" "Bruno" "Mariana"
#> [67] "Felipe" "Gabriela" "Leonardo" "Camila" "Gabriel" "Isabela"
#>
#> $caribbean
#> [1] "Marlon" "Keisha" "Dwayne" "Shanique" "Andre" "Natalie"
#> [7] "Wayne" "Tanya" "Usain" "Shelly" "Damian" "Khadija"
#> [13] "Tristan" "Yolanda" "Jermaine" "Sasha" "Jean" "Marie"
#> [19] "Pierre" "Rose" "Jacques" "Nadine" "Claude" "Carole"
#> [25] "Wyclef" "Michaelle" "Stanley" "Fabienne" "Herby" "Guerda"
#> [31] "Frantz" "Ketty" "Alejandro" "Yolanda" "Orlando" "Marisol"
#> [37] "Ramon" "Yesenia" "Jorge" "Luz" "Lazaro" "Yanelis"
#> [43] "Yunel" "Dania" "Pedro" "Yuliesky" "Miguel" "Yamilet"
#>
#> $indigenous_americas
#> [1] "Takoda" "Winona" "Koda" "Aiyana" "Chayton"
#> [6] "Aponi" "Ahanu" "Chenoa" "Hinto" "Halona"
#> [11] "Shilah" "Mika" "Nayeli" "Kimi" "Huritt"
#> [16] "Ayita" "Sequoia" "Cochise" "Sacagawea" "Tecumseh"
#> [21] "Hiawatha" "Pocahontas" "Dakota" "Cheyenne" "Taima"
#> [26] "Kaya" "Nanuq" "Sedna" "Amaruq" "Siku"
#> [31] "Tulok" "Atka" "Itzamna" "Xochitl" "Cuauhtemoc"
#> [36] "Citlali" "Quetzal" "Itzel" "Tlaloc" "Ixchel"
#> [41] "Tupac" "Qori" "Inti" "Sumaq" "Amaru"
#> [46] "Killa" "Rumi" "Wayra"
# Get random names
get_global_names(n = 10, seed = 42)
#> [1] "Shu" "Soraya" "Rangi" "Cuauhtemoc" "Gagik"
#> [6] "Anahera" "Oyunbileg" "Sem" "Tevita" "Eloise"Educational Research Applications
Self-Regulated Learning (SRL)
# Zimmerman's SRL phases
forethought <- get_learning_states("metacognitive")
performance <- get_learning_states(c("cognitive", "behavioral"))
reflection <- c("Evaluate", "Reflect", "Assess", "Judge")
cat("Forethought:", head(forethought, 5), "...\n")
#> Forethought: Plan Monitor Evaluate Reflect Regulate ...
cat("Performance:", head(performance, 5), "...\n")
#> Performance: Read Study Analyze Summarize Memorize ...
cat("Reflection:", reflection, "\n")
#> Reflection: Evaluate Reflect Assess JudgeSocially Shared Regulation of Learning (SSRL)
# SSRL components
ssrl_verbs <- get_learning_states("group_regulation")
ssrl_verbs
#> [1] "Adapt" "Cohesion" "Consensus" "Coregulate" "Discuss"
#> [6] "Emotion" "Monitor" "Plan" "Synthesis"Learning Analytics (LMS Data)
# Common LMS actions
lms_verbs <- get_learning_states("lms")
head(lms_verbs, 15)
#> [1] "View" "Access" "Download" "Upload" "Submit"
#> [6] "Click" "Navigate" "Browse" "Login" "Logout"
#> [11] "Post" "Reply" "Forum" "Quiz" "Assignment"Collaborative Learning
# Social and group regulation
collab_verbs <- get_learning_states(c("social", "group_regulation"))
length(collab_verbs)
#> [1] 38
head(collab_verbs, 10)
#> [1] "Collaborate" "Discuss" "Seek_help" "Question" "Explain"
#> [6] "Share" "Teach" "Tutor" "Debate" "Argue"Custom State Names
You can always use your own state names:
# With simulate_sequences
sequences <- simulate_sequences(
n_sequences = 50,
seq_length = 15,
n_states = 4,
states = c("Explore", "Learn", "Practice", "Master"),
seed = 42
)
unique(unlist(sequences))
#> [1] "Plan" "Regulate" "Discourage" "Judge"
# With simulate_matrix
mat <- simulate_matrix(
n_nodes = 4,
names = c("Phase1", "Phase2", "Phase3", "Phase4"),
seed = 42
)
rownames(mat)
#> [1] "Phase1" "Phase2" "Phase3" "Phase4"Complete Reference Table
# Create comprehensive reference
ref_table <- data.frame(
Category = names(LEARNING_STATES),
Count = sapply(LEARNING_STATES, length),
First_5 = sapply(LEARNING_STATES, function(x) {
paste(head(x, 5), collapse = ", ")
}),
row.names = NULL
)
ref_table
#> Category Count First_5
#> 1 metacognitive 20 Plan, Monitor, Evaluate, Reflect, Regulate
#> 2 cognitive 30 Read, Study, Analyze, Summarize, Memorize
#> 3 behavioral 30 Practice, Annotate, Research, Review, Revise
#> 4 social 30 Collaborate, Discuss, Seek_help, Question, Explain
#> 5 motivational 30 Focus, Persist, Explore, Create, Strive
#> 6 affective 30 Enjoy, Appreciate, Value, Interest, Curious
#> 7 group_regulation 9 Adapt, Cohesion, Consensus, Coregulate, Discuss
#> 8 lms 30 View, Access, Download, Upload, SubmitSummary
Key Functions
| Function | Purpose |
|---|---|
LEARNING_STATES |
Raw dataset of all verbs |
get_learning_states() |
Retrieve verbs by category |
list_learning_categories() |
Show category summary |
select_states() |
Intelligent selection |
GLOBAL_NAMES |
300 diverse names |
get_global_names() |
Retrieve names |
Category Summary
| Category | Count | Focus |
|---|---|---|
| metacognitive | 20 | Self-regulation |
| cognitive | 30 | Mental processing |
| behavioral | 30 | Observable actions |
| social | 30 | Interaction |
| motivational | 30 | Effort/persistence |
| affective | 30 | Emotions |
| group_regulation | 9 | SSRL |
| lms | 30 | System interactions |
| Total | 209 |
Usage Tips
- Default behavior: Most functions use learning states automatically
- Categories parameter: Control which categories to sample from
-
Custom names: Always supported via
statesornamesparameter -
Reproducibility: Use
seedparameter for consistent results - HTNA/MLNA: Each type automatically gets different category