Do individual characteristics affect online learning behaviors? An analysis of learners sequential patterns

Çebi, Ayça; Araújo, Rafael Dias; Brusilovsky, Peter

doi:10.1080/15391523.2022.2027301

Cited by 9 publications

(5 citation statements)

References 55 publications

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“…In addition to domicile, other individual characteristics that influence attitudes towards smart village are gender, occupation, age, education level, and intensity in using social media. These results are consistent with previous studies that have resulted in the finding that individual characteristics will influence a person's attitude in various cases (Afridi et al, 2022;Aschemann-Witzel & Grunert, 2015;Çebi et al, 2022;Colledani et al, 2021;Fancourt & Mak, 2020;Flor & Finger, 2006;Fujii et al, 2021;Jafarkarimi et al, 1 C.E. ;Kim et al, 2019;Losada-Rojas & Gkritza, 2021;Villani et al, 2019).…”

Section: Figure 3 Smart Economy Readiness Smart Mobilitysupporting

confidence: 92%

Smart Village Readiness Using Decision Tree Analysis the Case of Banyumas Regency Indonesia

Rokhman,

Tobirin,

Faozanudin

2023

ICSS

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Digital transformation is now inevitable for anyone, individually and organizationally, including village government organizations. Therefore, the village government must be ready to transform into a smart village. The smart village component consists of smart government, smart environment, smart economy, smart mobility, smart people, and smart living. This study aims to analyze the readiness of local communities for smart villages. This research uses quantitative methods, where the population is local people. Kalikidang Village, located in Sokaraja, Banyumas regency, was used as a case study. The number of respondents was 20, taken through proportional random sampling techniques. Data analysis techniques use decision trees that are processed through Rapidminer software. The results showed that individual characteristics significantly influence the readiness of smart villages where the domicile attribute is the most influential

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Section: Figure 3 Smart Economy Readiness Smart Mobilitysupporting

confidence: 92%

Smart Village Readiness Using Decision Tree Analysis the Case of Banyumas Regency Indonesia

Rokhman,

Tobirin,

Faozanudin

2023

ICSS

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“…With the constant increase in the implementation of interactive content available to learners through modern personalised e‐learning environments, the problem of navigating learners to an appropriate learning path arises. Personalised navigation represents a group of techniques that aim to assist the individual learner in locating relevant information in the context of personalised content [62]. Due to the lack of appropriate guidance, students access too complex or too simple content concerning their current level of knowledge, which results in discouragement and loss of interest in the course [273].…”

Section: Discussionmentioning

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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“…Based on those previous studies, we included perception of the ability to succeed and academic performance (i.e., their grades at the final exam of the course) in our measured characteristics. We also measured prior knowledge, as it is considered one of the "core factors for adapting instruction to an individual learner" (Narciss et al, 2014) and has been consistently used in similar studies (C ¸ebi et al, 2023;Wittwer & Renkl, 2008;Wulfeck, 2009). For instance, Smits et al (2008) found that learners with high prior knowledge could learn more when provided with less detailed feedback, despite their preference for more specific feedback.…”

Section: Positioning and Research Questionsmentioning

confidence: 99%

Personal Characteristics and Explanations in Technology-Enhanced Environments: An Exploratory Study in Computer Science Education

Félix,

Amadieu,

Broisin

2024

Preprint

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The study of explainable artificial intelligence in education is a major preoccupation of the research community. In this paper, we introduce explanations in a competency-based system, in the context of computer education. Our overall aim is to increase learners' understanding of the levels of mastery of their competencies that are computed automatically, and thus to increase their trust in the system. In particular, we are investigating which of the students' personal characteristics affect their consultation and understanding of the explanations, and what impact these explanations have on their perception and behavior within the system. Our study focuses on 98 first-year computer science students in higher education and combines qualitative and quantitative analyses. The quantitative analyses show significant correlations between certain characteristics studied and the use, perception and understanding of explanations on the one hand, as well as learners' general perception of the system on the other hand. For example, students with a low perception of their ability to succeed are less likely to access and understand explanations, and students with low engagement and performance are less likely to understand explanations. Qualitative analyses, meanwhile, help us to identify certain learner needs in terms of the content, form, and timing of explanations: combining local and counterfactual explanations; combining textual and visual formats; providing explanations in real time.

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Do individual characteristics affect online learning behaviors? An analysis of learners sequential patterns

Cited by 9 publications

References 55 publications

Smart Village Readiness Using Decision Tree Analysis the Case of Banyumas Regency Indonesia

Smart Village Readiness Using Decision Tree Analysis the Case of Banyumas Regency Indonesia

Personalisation methods in e‐learning‐A literature review

Personal Characteristics and Explanations in Technology-Enhanced Environments: An Exploratory Study in Computer Science Education

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