The Promises and Pitfalls of Self-regulated Learning Interventions in MOOCs

Vilkova, Kseniia

doi:10.1007/s10758-021-09580-9

Cited by 12 publications

(4 citation statements)

References 37 publications

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“…They were also able to show that a correlating strong but small negative effect occurred in men assigned to the female condition. Similar interesting results have been found by a number of other studies in MOOCs (Boaler et al, 2018;Hossain et al, 2015;Kizilcec et al, 2020;Vilkova, 2022), although they are far less common outside of the MOOC format. Interestingly, Motz et al (2018) demonstrate that these claims need not be made exclusively in a laboratory setting with controlled experiments.…”

Section: Education and Big Datasupporting

confidence: 87%

Using causal models to bridge the divide between big data and educational theory

Kitto

Hicks

Shum

2023

Brit J Educational Tech

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An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well‐known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory‐versus‐data debate in education, and extend an invitation to other investigators to join this exciting programme of research. Practitioner notesWhat is already known about this topic ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems. Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts. Causal inference is a well‐developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences. What this paper adds An overview of causal modelling to support educational data scientists interested in adopting this promising approach. A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories. An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent. Implications for practice and/or policy Causal models can help us to explicitly specify educational theories in a testable format. It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model. Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.

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Section: Education and Big Datasupporting

confidence: 87%

Using causal models to bridge the divide between big data and educational theory

Kitto

Hicks

Shum

2023

Brit J Educational Tech

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“…This study also highlights that, overall, the online SRL scores of participants graduating from the A-level and Blevel of accredited medical schools show similar results, except on the time management scale (p = 0.009), where the participants from the higher level accredited schools show higher scores than the participants from the lower level accredited schools. Prior online learning experience has been reported as an important determinant of online learning success (Vilkova, 2019). For newly graduated doctors completing an internship program, prior online learning experience may be largely attributed to the use of online learning formally in their medical school's curriculum.…”

Section: Discussionmentioning

confidence: 99%

“…Our study suggests that the higher accredited medical schools may provide more online learning experiences, leading to better time management skills among the participants from this group. Furthermore, MOOC completion and the fulfillment of learning outcomes were determined by the forethought phase in the students' SRL; thus, the goal-setting, self-efficacy, and task values should be emphasised by the participants and facilitated by the MOOC (Vilkova, 2019). Our study further indicates that time management in the use of the MOOC should be considered by learners in the forethought, performance, and self-reflection phases of their SRL; the planning stage of the MOOC development must determine the estimated completion time for the whole course and its sections about the course learning outcomes and the participant's characteristics (Stracke et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Measuring online self-regulated learning among early-career medical doctors in a Massive Open Online Course on COVID-19

Greviana

Kusumoningrum

Findyartini

et al. 2022

TAPS

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Introduction: As significant autonomy is given in a Massive Open Online Course (MOOC), online self-regulated learning (SRL) ability is crucial in such courses. We aim to measure the online SRL abilities of early-career medical doctors enrolled in a MOOC. Methods: We performed a cross-sectional study using the Self-Regulated Online Learning Questionnaire-revised version (SOL-Qr). We conducted a three-stage cross-cultural validation of the SOL-Qr, followed by Confirmatory Factor Analysis (CFA). The online SRL ability of 5,432 medical doctors enrolled in a MOOC was measured using the validated SOL-Qr. Results: The CFA of the cross-translated SOL-Qr confirmed its comparability to the original version, with excellent validity & reliability. Participants showed high levels of online SRL during their early careers. Despite high online SRL scores, MOOC completion rate was low. Male participants showed slightly better time management ability than female participants. Participants working in the primary epicentrum for COVID-19 in the country showed lower online SRL scores, while participants who graduated from higher accreditation levels showed better time management ability. Conclusion: The SOL-Qr and its subscales are suitable and valid for measuring the online SRL abilities of medical doctors in a MOOC during their early-career period. Time management ability was associated with previous experience during the medical education period, while other online SRL subscales were mostly associated with workload. However, as the scores did not correlate with the time spent for learning in MOOC, the corresponding learning effort or time spent may be beyond just the commitment to the described MOOC.

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“…Self-regulation is the process by which students take control of their learning process to enhance their academic performance and learning during a course (Vilkova 2022). Prior studies have shown that a lack of self-regulated learning (SRL) skills can be an important factor that leads to failure and students dropping out of courses (Kizilcec et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Informative Feedback and Explainable AI-Based Recommendations to Support Students’ Self-regulation

et al. 2023

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Self-regulated learning is an essential skill that can help students plan, monitor, and reflect on their learning in order to achieve their learning goals. However, in situations where there is a lack of effective feedback and recommendations, it becomes challenging for students to self-regulate their learning. In this paper, we propose an explainable AI-based approach to provide automatic and intelligent feedback and recommendations that can support the self-regulation of students’ learning in a data-driven manner, with the aim of improving their performance on their courses. Prior studies have predicted students’ performance and have used these predicted outcomes as feedback, without explaining the reasons behind the predictions. Our proposed approach is based on an algorithm that explains the root causes behind a decline in student performance, and generates data-driven recommendations for taking appropriate actions. The proposed approach was implemented in the form of a dashboard to support self-regulation by students on a university course, and was evaluated to determine its effects on the students’ academic performance. The results revealed that the dashboard significantly enhanced students’ learning achievements and improved their self-regulated learning skills. Furthermore, it was found that the recommendations generated by the proposed approach positively affected students’ performance and assisted them in self-regulation

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The Promises and Pitfalls of Self-regulated Learning Interventions in MOOCs

Cited by 12 publications

References 37 publications

Using causal models to bridge the divide between big data and educational theory

Using causal models to bridge the divide between big data and educational theory

Measuring online self-regulated learning among early-career medical doctors in a Massive Open Online Course on COVID-19

Informative Feedback and Explainable AI-Based Recommendations to Support Students’ Self-regulation

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