Lukasz Kidzinski
École Polytechnique Fédérale de Lausanne
Bayesian statisticsDisseminationNonparametric statisticsMotion (physics)StatisticsHuman–computer interactionMachine learningSeries (mathematics)Support vector machineMathematical optimizationEngineeringMathematical analysisWorld Wide WebFunction spaceMathematics educationNonverbal communicationSmart environmentRegression analysisArtificial intelligenceHumanitiesMarkov processMarkov modelEducational technologyEmpirical evidenceTime managementUsabilityConstruct (philosophy)Expectation–maximization algorithmEstimatorPedagogyMonte Carlo methodBody languageClass (computer programming)Data sciencePrincipal component analysisTracking (education)Order (business)SimplicityEveningOrchestration (computing)Learning analyticsTechnology integrationThursdayQuality (business)GazePopularityScale (chemistry)Learning stylesApplied mathematicsR packageAnalysis toolsLearning experienceVideo based learningEducational scienceContinuous educationIndependent samplesComputer visionMathematicsFocus (computing)Computer scienceProcess (engineering)SimulationCurriculumMultimediaMedical educationAdaptive systemEye trackingCognitive loadHilbert spaceReflection (computer programming)Unobtrusive researchDimensionality reductionCluster analysisAnalyticsRegressionTeaching methodCross-sectional regressionCategorization
17Publications
7H-index
152Citations
Publications 17
Newest
#1Luis P. Prieto (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 24
#2Kshitij Sharma (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 18
Last. Pierre Dillenbourg (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 64
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Orchestration load is the effort a teacher spends in coordinating multiple activities and learning processes. It has been proposed as a construct to evaluate the usability of learning technologies at the classroom level, in the same way that cognitive load is used as a measure of usability at the individual level. However, so far this notion has remained abstract. In order to ground orchestration load in empirical evidence and study it in a more systematic and detailed manner, we propose a metho...
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#1Lukasz KidzinskiH-Index: 7
Last. Piotr KokoszkaH-Index: 40
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Every teacher understands that different students benefit from different activities. Recent advances in data processing allow us to detect and use behavioral variability for adapting to a student. This approach allows us to optimize learning process but does not focus on understanding it. Conversely, classical findings in educational sciences allow us to understand the learner but are hard to embed in a large scale adaptive system. In this study we design and build a framework to investigate whe...
Last. Abelardo PardoH-Index: 35
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Jan 1, 2016 in EDM (Educational Data Mining)
#1Louis Faucon (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 4
#2Lukasz Kidzinski (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 7
Last. Pierre Dillenbourg (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 64
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Large-scale experiments are often expensive and time consuming. Although Massive Online Open Courses (MOOCs) provide a solid and consistent framework for learning analytics, MOOC practitioners are still reluctant to risk resources in experiments. In this study, we suggest a methodology for simulating MOOC students, which allow estimation of distributions, before implementing a large-scale experiment. To this end, we employ generative models to draw independent samples of artificial students in M...
#1Kshitij SharmaH-Index: 18
#2Lukasz KidzinskiH-Index: 7
Last. Pierre DillenbourgH-Index: 64
view all 4 authors...
Students of programming languages in massive on-line open courses (MOOCs) solve programming assignments in order to internalize the concepts. Programming assignments also constitute the assessment procedure for such courses. Depending on their motivation and learning styles, students pursue different strategies. We identify which approach to attempt these assignments results in better performance. We predict students’ success from their online behaviour; and identify different paths students cho...
Jan 1, 2016 in EDM (Educational Data Mining)
#1Mina Shirvani Boroujeni (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 7
#2Lukasz Kidzinski (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 7
Last. Pierre Dillenbourg (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 64
view all 3 authors...
Massive Open Online Courses (MOOCs) changed the way continuous education is perceived. Employees willing to progress their careers can take high quality courses. Students can develop skills outside curriculum. Studies show that most of the MOOC users are pursuing or have received a university degree. Therefore it is beneficial to consider motives and constraints of this class of participants while designing a course. In this study we focus on time constraints experienced by full-time and part-ti...
#1Pierre DillenbourgH-Index: 64
#2Nan LiH-Index: 8
Last. Lukasz KidzinskiH-Index: 7
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Reference EPFL-CHAPTER-218182 URL: http://www.portlandpresspublishing.com/sites/default/files/Editorial/Wenner/PPL_Wenner_Ch02.pdf Record created on 2016-04-24, modified on 2017-05-12
Sep 15, 2015 in EC-TEL (European Conference on Technology Enhanced Learning)
#1Nan Li (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 8
#2Lukasz Kidzinski (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 7
Last. Pierre Dillenbourg (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 64
view all 4 authors...
For MOOC learners, lecture video viewing is the central learning activity. This paper reports a large-scale analysis of in-video interactions. We categorize the video behaviors into patterns by employing a clustering methodology, based on the available types of interactions, namely, pausing, forward and backward seeking and speed changing. We focus on how learners view MOOC videos with these interaction patterns, especially on exploring the relationship between video interaction and perceived vi...
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#1Siegfried Hörmann (ULB: Université libre de Bruxelles)H-Index: 19
#2Lukasz Kidzinski (EPFL: École Polytechnique Fédérale de Lausanne)H-Index: 7
Last. Piotr Kokoszka (CSU: Colorado State University)H-Index: 40
view all 3 authors...
type="main" xml:id="jtsa12114-abs-0001"> The paper introduces a functional time series (lagged) regression model. The impulse-response coefficients in such a model are operators acting on a separable Hilbert space, which is the function space L-super-2 in applications. A spectral approach to the estimation of these coefficients is proposed and asymptotically justified under a general nonparametric condition on the temporal dependence of the input series. Since the data are infinite-dimensional, ...
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