Single-Case Learning Analytics to Support Social-Emotional Learning: The Case of Doctoral Education

Prieto, Luis P.; Rodríguez‐Triana, María Jesús; Odriozola-González, Paula; Dimitriadis, Yannis

doi:10.1007/978-3-031-06333-6_12

Cited by 2 publications

(2 citation statements)

References 40 publications

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“…While such networks can reveal relevant causal relations among variables, they require longitudinal data and are typically used in idiographic studies as within-subjects networks (e.g., Fisher et al, 2017). Therefore, such networks are out of the scope of the current paper and interested readers are referred to, for example, Prieto et al (2022) and Saqr and L opez-Pernas (2021) to learn about the potentials of temporal within-subject networks in the educational domain.…”

Section: Psychological Network In Education Researchmentioning

confidence: 99%

Explaining trace‐based learner profiles with self‐reports: The added value of psychological networks

Jovanovic,

Gašević,

Yan

et al. 2024

Computer Assisted Learning

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BackgroundLearner profiles detected from digital trace data are typically triangulated with survey data to explain those profiles based on learners' internal conditions (e.g., motivation). However, survey data are often analysed with limited consideration of the interconnected nature of learners' internal conditions.ObjectivesAiming to enable a thorough understanding of trace‐based learner profiles, this paper presents and evaluates a comprehensive approach to analysis of learners' self‐reports, which extends conventional statistical methods with psychological networks analysis.MethodsThe study context is a massive open online course (MOOC) aimed at promoting physical activity (PA) for health. Learners' (N = 497) perceptions related to PA, as well as their self‐efficacy and intentions to increase the level of PA were collected before and after the MOOC, while their interactions with the course were logged as digital traces. Learner profiles derived from trace data were further examined and interpreted through a combined use of conventional statistical methods and psychological networks analysis.Results and ConclusionsThe inclusion of psychological networks in the analysis of learners' self‐reports collected before the start of the MOOC offers better understanding of trace‐based learner profiles, compared to the conventional statistical analysis only. Likewise, the combined use of conventional statistical methods and psychological networks in the analysis of learners' self‐reports before and after the MOOC provided more comprehensive insights about changes in the constructs measured in each learner profile.Major TakeawaysThe combined use of conventional statistical methods and psychological networks presented in this paper sets a path for a comprehensive analysis of survey data. The insights it offers complement the information about learner profiles derived from trace data, thus allowing for a more thorough understanding of learners' course engagement than any individual method or data source would allow.

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Section: Psychological Network In Education Researchmentioning

confidence: 99%

Explaining trace‐based learner profiles with self‐reports: The added value of psychological networks

Jovanovic,

Gašević,

Yan

et al. 2024

Computer Assisted Learning

View full text Add to dashboard Cite

show abstract

“…Even though international HDR students contribute to national economies and revitalise cultural diversity in society, their social-emotional learning is less talked about publicly and usually sidelined by how well they can perform academically (Chen et al, 2023;Prieto et al, 2022). The elephant in the room is that international students often struggle to cope with not only intellectually challenging demands in academic writing and research but also barriers raised by the new culture and language different from their own.…”

Section: Background Storymentioning

confidence: 99%

Mentoring international postgraduate students and early career researchers through transnational telecollaboration: a supervisor’s autoethnography

2023

JALT

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Single-Case Learning Analytics to Support Social-Emotional Learning: The Case of Doctoral Education

Cited by 2 publications

References 40 publications

Explaining trace‐based learner profiles with self‐reports: The added value of psychological networks

Explaining trace‐based learner profiles with self‐reports: The added value of psychological networks

Mentoring international postgraduate students and early career researchers through transnational telecollaboration: a supervisor’s autoethnography

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