Sequential Analysis of Online Learning Behaviors According to E-Learning Readiness

Şahi̇n, Muhittin; Keskin, Sinan; Yurdugül, Halil

doi:10.1007/978-3-030-48190-2_7

Cited by 15 publications

(5 citation statements)

References 12 publications

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“…For this reason, profiling and modeling studies in online learning will guide researchers and practitioners in data-driven decision-making processes at different levels such as teaching, adaptation, and recommendation. In the e-learning literature, various studies examine sequential behavior patterns (Cheng & Chu, 2019;Şahin et al, 2020), social interactions (Cela et al, 2015;Yıldırım, 2018), learner profiles (Eryılmaz, 2019;Liang, 2017), emotional states (Osmanoğlu et al, 2020), and discussion forum interactions (Huang et al, 2014;Huang et al, 2019;Kent et al, 2016;Wong et al, 2015). In addition to these, studies are carried out to predict students' success (Saa, 2016;Shahiri, & Husain, 2015) or drop out (Baker et al, 2015;Mubarak et al, 2020) using educational data mining techniques.…”

Section: Introductionmentioning

confidence: 99%

E-learning experience: Modeling students’ e-learning interactions using log data

Keskin

Yurdugül

2022

Journal of Educational Technology and Online Learning

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This study aims to examine e-learning experiences of the learners by using learner system interaction metrics. In this context, an e-learning environment has been structured within the scope of a course. Learners interacted with learning activities and leave various traces when they interact with others, contents, and assessment tasks. Log data were formed on these e-learning interactions. In the data analysis phase, firstly, a data pre-processing was performed, and then confirmatory factor analysis (CFA) was used to test how well the measured learning activity variables represent the latent system component variables. Then it was tested whether these components compose a latent e-learning experience variable (second-order CFA). The results showed that the learners interacted with five different system components: hypertext, the content package, video, discussion, and eassessment. In conclusion, there is a factorial relationship between the system components and learning activities. These components taken together constitute an elearning experience variable. When the factor loadings between the e-learning experience structure and subcomponents were examined, the discussion interactions in which the learner structured knowledge highlighted. In summary, the discussions, formative assessments, and content activities formed the learners' e-learning experience together. In order to form a well-structured e-learning environment, these activities together should be experienced by the learners. Research Article * This article is based on PhD thesis titled "Determining user profiles for adaptive feedback system design" completed by the first author under the consultancy of the second author.

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Section: Introductionmentioning

confidence: 99%

E-learning experience: Modeling students’ e-learning interactions using log data

Keskin

Yurdugül

2022

Journal of Educational Technology and Online Learning

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“…As LSA reveals user behaviors and sequential situations, statistically significant transitions emerge. Accordingly, when looking at the LSA visualizations, it was discovered that transition diagrams are used in relevant studies (Hwang et al, 2021;Pohl et al, 2016;Şahin et al, 2020). Based on this, transition diagrams were presented, and the arrows were thickened according to the transition probabilities.…”

Section: Resultsmentioning

confidence: 99%

Navigational Behavior Patterns of Learners on Dashboards Based on Assessment Analytics

Bayrak

Aydın

Yurdugül

2021

Advances in Analytics for Learning and Teaching

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“…The online discussion serves as a vital channel for online collaborative learning (Darabi et al, 2010; Mercimek & Çaka, 2022; Şahin et al, 2020; Sun & Gao, 2017). Through interactive discussions, students can reorganize their concepts, compare views with others, revise ideas, and develop previously unexplored thoughts (Liu & Yin, 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

Enhance adult students' online knowledge construction: Exploring effective instructional designs and addressing barriers

2024

Computer Assisted Learning

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Background StudyAsynchronous online discussions are vital venues for collaborative knowledge construction. However, the lack of appropriate instruction designs poses challenges in promoting deep and substantive engagement with the core subject matter. This paper explores how to enhance adult students' knowledge construction in the context of asynchronous online discussions at an open university in Taiwan.ObjectivesThis study explores instructional designs aimed at promoting online knowledge construction among adult students and overcoming barriers to this process.MethodsThis study used a convergent parallel mixed methods design to collect concurrently both quantitative data from the students' online discussion postings for content analysis and qualitative data from the focus group and individual interviews, the online open‐ended questionnaire, and the instructor's observation logs for thematic analysis. Then the results were merged in the interpretation stage.Results and ConclusionThe research highlights the significance of instructional designs in influencing students' level of knowledge construction during online discussions. The findings suggest that instructional designs that provide teacher‐led interim summaries, pose Socratic questions, and incorporate problem‐solving projects can promote higher‐level knowledge construction. However, time constraints, cultural influences, and unfamiliarity with peers negatively affected higher‐order knowledge construction. To address these challenges, culturally responsive and technologically inclusive instructional designs that take into account the specific challenges faced by adult learners are proposed to enhance learning outcomes through active participation.LimitationThe study's limited sample size warrants further research with a larger and more diverse cohort to validate the effectiveness of the proposed instructional designs. Another limitation is the lack of a robust theoretical base for the instructional strategies presented by the current action‐research study. Further examination and broader exploration in online education are needed to prop up proposed pedagogical approaches.

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Sequential Analysis of Online Learning Behaviors According to E-Learning Readiness

Cited by 15 publications

References 12 publications

E-learning experience: Modeling students’ e-learning interactions using log data

E-learning experience: Modeling students’ e-learning interactions using log data

Navigational Behavior Patterns of Learners on Dashboards Based on Assessment Analytics

Enhance adult students' online knowledge construction: Exploring effective instructional designs and addressing barriers

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