Abstract
The paper aims to analyse Educational Data Mining/Learning Analytics application trends to personalise learning. First of all, systematic literature review was performed. Based on the systematic review analysis, the main trends on applying educational data mining methods to personalise learning were identified. Second, three main tendencies on educational data mining/learning analytics application in education were formulated. They are: (a) Educational Data Mining/Learning Analytics support self-directed autonomous learning; (b) Educational Data Mining/Learning Analytics systems become essential tools of educational management; and (c) most teaching is delegated to computers, and Educational Data Mining/Learning Analytics based recommendations become better and more reliable than those that can be produced by even the best-trained teachers.
Highlights
ĮvadasStraipsnio tikslas yra dvejopas: pirma, atlikti sisteminę literatūros apžvalgą edukacinių duomenų gavybos
most teaching is delegated to computers
that can be produced by even the best-trained teachers
Summary
Straipsnio tikslas yra dvejopas: pirma, atlikti sisteminę literatūros apžvalgą edukacinių duomenų gavybos Educational Data Mining, EDM) / mokymosi analitikos Learning Analytics, LA) tema ir, antra, išanalizuoti ir pasiūlyti pagrindines EDM taikymo tendencijas mokymuisi personalizuoti (individualizuoti). Labiausiai paplitusieji yra asociacija, klasifikacija bei grupavimo ir išskirties pastebėjimai. Priemonių ir jos realizavimo algoritmų pasirinkimas priklauso nuo turimų duomenų, nustatytų mokslinių tyrimų tikslų ir planuotų rezultatų pasiekimo. Straipsnį sudaro įvadas (1 skyrius), sisteminė edukacinių duomenų gavybos / mokymosi analitikos literatūros apžvalga ir jos rezultatai (2 skyrius), pagrindinės tendencijos taikant edukacinių duomenų gavybą mokymuisi personalizuoti (3 skyrius). Paskutiniame, 4-ame skyriuje yra pateiktos straipsnio išvados
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