Examining motivation in online distance learning environments: Complex, multifaceted and situation-dependent

Hartnett, Maggie; George, Alison St.; Dron, John

doi:10.19173/irrodl.v12i6.1030

Cited by 215 publications

(119 citation statements)

References 37 publications

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“…These findings are consistent with those of related research on online and distance learning settings (Centola, 2015;Hartnett, St. George, & Dron, 2011;Shea & Bidjerano, 2010). At present, there is a paucity of research on these types of self-directed learning environments in K-12 blended or face-to-face settings to further substantiate these findings.…”

Section: Discussionsupporting

confidence: 90%

Emerging Student Learning Networks: Self-Directed Learning in an Eighth-Grade Life Science Classroom

Ardito

2018

Issues and Trends in Educational Technology

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Although self-directed learning environments are common in higher education and online learning environments, they are uncommon in secondary educational settings. This mixed-method action research study, in which the author/researcher was also the teacher, reports on the design and implementation of a self-directed learning environment for eighth-grade life science students. This study investigated the impact of this type of learning environment on the behavior and achievement of students. The findings demonstrated an increase in self-regulatory behaviors, higher performance on a state-mandated final exam, and the development of complex and robust student learning networks. Implications for teaching and learning practices and suggestions for further research are discussed.

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Section: Discussionsupporting

confidence: 90%

Emerging Student Learning Networks: Self-Directed Learning in an Eighth-Grade Life Science Classroom

Ardito

2018

Issues and Trends in Educational Technology

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“…The existing literature on student motivation and engagement in online learning argue that the lack of student affinity to complete a course leads to higher dropout rates, and consequently failure to complete a course (Hartnett, George, & Dron, 2011). Thus, intention to complete a course and number of hours intended to devote to a course work, are commonly considered in predicting course persistence and achievement (i.e., included in 40% to 50% of studies that observed student motivation).…”

Section: Resultsmentioning

confidence: 99%

How Do We Model Learning at Scale? A Systematic Review of Research on MOOCs

Joksimovíc

Poquet

Kovanović

et al. 2017

Review of Educational Research

143

122

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Despite a surge of empirical work on student participation in online learning environments, the causal links between the learning-related factors and processes with the desired learning outcomes remain unexplored. This study presents a systematic literature review of approaches to model learning in Massive Open Online Courses offering an analysis of learningrelated constructs used in the prediction and measurement of student engagement and learning outcome. Based on our literature review, we identify current gaps in the research, including a lack of solid frameworks to explain learning in open online setting. Finally, we put forward a novel framework suitable for open online contexts based on a well-established 740335R ERXXX10.

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“…Thus the process of extrinsic motivation in terms of external regulation is the reason for doing online learning outside of the individual. External regulation is the type of extrinsic motivation most often contrasted with intrinsic motivation, where individuals are responsive to the threat of punishment or offer of reward and, as a result, tend to be obedient (Hartnett et al, 2011). The cluster analysis results show that three clusters are the fittest.…”

Section: Discussionmentioning

confidence: 99%

The Anomali on Technology Readiness Profile of Elementary School Teachers in Online Learning Amid Covid-19

Sulisworo¹,

Hidayati²,

Bala³

et al. 2021

ilkonline

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The Ministry of Education and Culture (MOEC) of Indonesia issued a policy for online learning in all schools, including provinces with limited internet facilities. This study aims to explain the readiness of elementary school teachers in the rural or remote areas in applying online learning. The method used in this study is was ex-post facto. Technology readiness is was measured based on four factors: Optimism (4 items), Innovativeness (4 items), Discomfort (4 items), and Insecurity (4 items). The Likert scale ranged from 1 (Strongly Disagree) to 5 (Strongly Agree). The analysis used Two-way ANOVA and Cluster Analysis. Data from field observations were used to provide a deeper explanation of the statistical analysis. The analysis shows that three segments appear namely Explorers, Pioneers, and Laggards. The immediate implementation of the online learning has caused the Skeptics and Paranoid segments not appear. This study implies that educational policy-makers need to pay attention to the Internet access and the availability of mobile phones as constraints in online learning in rural or remote areas.

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Examining motivation in online distance learning environments: Complex, multifaceted and situation-dependent

Cited by 215 publications

References 37 publications

Emerging Student Learning Networks: Self-Directed Learning in an Eighth-Grade Life Science Classroom

Emerging Student Learning Networks: Self-Directed Learning in an Eighth-Grade Life Science Classroom

How Do We Model Learning at Scale? A Systematic Review of Research on MOOCs

The Anomali on Technology Readiness Profile of Elementary School Teachers in Online Learning Amid Covid-19

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