Package: LKT 1.7.0

Philip I. Pavlik Jr.

LKT: Logistic Knowledge Tracing

Computes Logistic Knowledge Tracing ('LKT') which is a general method for tracking human learning in an educational software system. Please see Pavlik, Eglington, and Harrel-Williams (2021) <https://ieeexplore.ieee.org/document/9616435>. 'LKT' is a method to compute features of student data that are used as predictors of subsequent performance. 'LKT' allows great flexibility in the choice of predictive components and features computed for these predictive components. The system is built on top of 'LiblineaR', which enables extremely fast solutions compared to base glm() in R.

Authors:Philip I. Pavlik Jr. [aut, ctb, cre], Luke G. Eglington [aut, ctb]

LKT_1.7.0.tar.gz
LKT_1.7.0.zip(r-4.7)LKT_1.7.0.zip(r-4.6)LKT_1.7.0.zip(r-4.5)
LKT_1.7.0.tgz(r-4.6-x86_64)LKT_1.7.0.tgz(r-4.6-arm64)LKT_1.7.0.tgz(r-4.5-x86_64)LKT_1.7.0.tgz(r-4.5-arm64)
LKT_1.7.0.tar.gz(r-4.7-arm64)LKT_1.7.0.tar.gz(r-4.7-x86_64)LKT_1.7.0.tar.gz(r-4.6-arm64)LKT_1.7.0.tar.gz(r-4.6-x86_64)
LKT_1.7.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
LKT/json (API)

# Install 'LKT' in R:
install.packages('LKT', repos = c('https://optimal-learning-lab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/optimal-learning-lab/lkt/issues

Datasets:

On CRAN:

Conda:

4.66 score 12 stars 38 scripts 334 downloads 1 mentions 25 exports 27 dependencies

Last updated from:7231b4f884. Checks:11 WARNING, 1 ERROR, 1 OK. Indexed: yes.

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Exports:buildLKTModelcomputefeaturescomputeSpacingPredictorscountOutcomeoldFastOnlineSimpleAdaptiveModelLASSOLKTDataLASSOLKTModelLibLinearModelLKTLKT_HDILKTCustomModelLKTFeatureInputLKTModelFitLKTOnlineSimpleAdaptiveDecayEvalLKTOnlineSimpleAdaptiveEvalLKTOnlineSimpleAdaptiveGradientLKTOnlineSimpleAdaptiveInputLKTOptimizeOnlineSimpleAdaptiveLKTOptimizeOnlineSimpleAdaptiveAlphaLKTOptimizeOnlineSimpleAdaptiveDecayAlphaOnlineAdaptiveModelOnlineCalibrationModelpredict_lktsmallSetViewExcel

Dependencies:bootclustercodetoolscrayondata.tableforeachglmnetHDIntervaliteratorslatticeLiblineaRlme4MASSMatrixminqanlmenloptrpROCrbibutilsRcppRcppEigenRdpackreformulasrlangshapeSparseMsurvival

Readme and manuals

Help Manual

Help pageTopics
Search for an LKT model specificationbuildLKTModel
Compute an LKT feature vectorcomputefeatures
Compute spacing predictors for LKT featurescomputeSpacingPredictors
Count prior outcomes within an indexcountOutcomeold
Raw Memphis DataShop samplelargerawsample
Build LASSO input data for LKT feature searchLASSOLKTData
Fit a LASSO-selected LKT modelLASSOLKTModel
Define the default LibLinear LKT modelLibLinearModel
Fit a logistic knowledge tracing modelLKT
Estimate coefficient HDIs by bootstrap resamplingLKT_HDI
Define a custom LKT modelLKTCustomModel
Create model-agnostic LKT feature inputLKTFeatureInput
Create an LKT model fit resultLKTModelFit
Evaluate online simple-adaptive decay learningLKTOnlineSimpleAdaptiveDecayEval
Evaluate the fast online simple-adaptive objectiveLKTOnlineSimpleAdaptiveEval
Gradient for the fast online simple-adaptive objectiveLKTOnlineSimpleAdaptiveGradient
Fast online simple-adaptive objective inputLKTOnlineSimpleAdaptiveInput
Optimize the fast online simple-adaptive modelLKTOptimizeOnlineSimpleAdaptive
Optimize only the online learning ratesLKTOptimizeOnlineSimpleAdaptiveAlpha
Optimize online beta and decay learning ratesLKTOptimizeOnlineSimpleAdaptiveDecayAlpha
Define the online adaptive LKT modelFastOnlineSimpleAdaptiveModel OnlineAdaptiveModel
Define the online calibration LKT modelOnlineCalibrationModel
Predict with an LKT model objectpredict_lkt
Sample practice trial sequencessamplelkt
Sample a subset of studentssmallSet
Open a data frame in the system spreadsheet viewerViewExcel