Mining Sequential Patterns of Students’ Access on Learning Management System

Poon, Leonard K. M.; Kong, Siu Cheung; Wong, Michael Y. W.; Yau, Thomas S. H.

doi:10.1007/978-3-319-61845-6_20

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…It was defined as the ability to "(…) use digital technology, communication tools and networks to acquire and evaluate information, communicate with others and perform practical tasks". PSTRE items focused on the ability to solve problems for personal, work and civic purposes, by setting up appropriate goals and plans, and accessing and making use of information through computers and computer networks (OECD, 2009 [11]).…”

Section: Assessing Problem-solving In Technology-rich Environments Inmentioning

confidence: 99%

Using process data to understand adults’ problem-solving behaviour in the Programme for the International Assessment of Adult Competencies (PIAAC)

2019

OECD Education Working Papers

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JT03451134 OFDE This document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. 2  EDU/WKP(2019)13 Unclassified OECD EDUCATION WORKING PAPERS SERIES OECD Working Papers should not be reported as representing the official views of the OECD or of its member countries. The opinions expressed and arguments employed herein are those of the author(s). Working Papers describe preliminary results or research in progress by the author(s) and are published to stimulate discussion on a broad range of issues on which the OECD works. Comments on Working Papers are welcome, and may be sent to the Directorate for Education and Skills, OECD,

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Section: Assessing Problem-solving In Technology-rich Environments Inmentioning

confidence: 99%

Using process data to understand adults’ problem-solving behaviour in the Programme for the International Assessment of Adult Competencies (PIAAC)

2019

OECD Education Working Papers

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“…Methodological implications. Previous research has identified the challenges of using sequential pattern mining algorithms in educational studies [68]: 1) the generation of excessive patterns with limited relevancy and value, and 2) the involvement of domain experts for filtering and labeling purposes. For the first challenge, we proposed a benchmarking process to select the most suitable algorithm, dataset and algorithm parametrisation so that we maximise how informative and representative the results are.…”

Section: Discussionmentioning

confidence: 99%

Modeling micro-interactions in self-regulated learning: A data-driven methodology

Harper

Vigo

2021

International Journal of Human-Computer Studies

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“…It can also examine information produced by any e-learning system and concentrate on various factors, both individual and collective, and administrative and motivational information, which contain numerous layers of context and historical data about the learner. Throughout literature, several commonly used data mining techniques were identified: prediction [4,87,233], clustering [50,93,94], relationship mining [222,234,256], distillation for human judgement [29,282] and Discovery with models [126,127].…”

Section: Other Intelligent Techniques Used In E-learningmentioning

confidence: 99%

Personalisation methods in e‐learning‐A literature review

Mikić¹,

Ilić²,

Kopanja³

et al. 2022

Comp Applic In Engineering

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Over the years, personalisation in e‐learning has evolved as a promising paradigm for matching learners' specific requirements. Although many literature reviews on personalised e‐learning have been conducted, there are still voids and limitations in the comprehensive literature survey regarding how personalisation might occur in e‐learning environments and benefit the teaching and learning process. In addition, no design guidelines, frameworks, or tools to guide its implementation exist, so practice can vary widely. The principal purpose of this literature review is to examine the subject, provide insights into the use of personalised techniques and systems in e‐learning, and investigate its impact on the engagement and success of the students. This study applies methods of precise selection criteria to determine which of the selected publications have the strongest interconnection and relevance with the topic of e‐learning personalisation. It also categorises the personalisation techniques employed by each identified system and highlights new perspectives and advances in the area.

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Mining Sequential Patterns of Students’ Access on Learning Management System

Cited by 16 publications

References 8 publications

Using process data to understand adults’ problem-solving behaviour in the Programme for the International Assessment of Adult Competencies (PIAAC)

Using process data to understand adults’ problem-solving behaviour in the Programme for the International Assessment of Adult Competencies (PIAAC)

Modeling micro-interactions in self-regulated learning: A data-driven methodology

Personalisation methods in e‐learning‐A literature review

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