There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Individuals with strong self-regulated learning (SRL) skills, characterized by the ability to plan, manage and control their learning process, can learn faster and outperform those with weaker SRL skills. SRL is critical in learning environments that provide low levels of support and guidance, as is commonly the case in Massive Open Online Courses (MOOCs). Learners can be trained to engage in SRL and actively supported with prompts and activities. However, effective implementation of learner support systems in MOOCs requires an understanding of which SRL strategies are most effective and how these strategies manifest in online behavior. Moreover, identifying learner characteristics that are predictive of weaker SRL skills can advance efforts to provide targeted support without obtrusive survey instruments. We investigated SRL in a sample of 4,831 learners across six MOOCs based on individual records of overall course achievement, interactions with course content, and survey responses. We found that goal setting and strategic planning predicted attainment of personal course goals, while help seeking was associated with lower goal attainment. Learners with stronger SRL skills were more likely to revisit previously studied course materials, especially course assessments. Several learner characteristics, including demographics and motivation, predicted learners' SRL skills. We discuss implications for theory and the development of learning environments that provide adaptive support. Goal setting and strategic planning positively predict goal attainment in MOOCs.Help seeking negatively predicts goal attainment, e.g., earning a certificate.Self-reported SRL strategies manifest behaviorally in revisiting course content.Learner characteristics (demographics, motivation, etc.) predict self-reported SRL.

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Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses

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The existing literature suggests that self-regulated learning (SRL) strategies are relevant to student grade performance in both online and blended contexts, although few, if any, studies have compared them. However, due to challenges unique to each group, the variety of SRL strategies that are implicated, and their effect size for predicting performance may differ across contexts. One hundred and forty online students and 466 blended learning students completed the Motivated Strategies for Learning Questionnaire. The results show that online students utilised SRL strategies more often than blended learning students, with the exception of peer learning and help seeking. Despite some differences in individual predictive value across enrolment status, the key SRL predictors of academic performance were largely equivalent between online and blended learning students. Findings highlight the relative importance of using time management and elaboration strategies, while avoiding rehearsal strategies, in relation to academic subject grade for both study modes.

Comparing online and blended learner's self-regulated learning strategies and academic performance

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Many committed learners struggle to achieve their goal of completing a Massive Open Online Course (MOOC). This work investigates self-regulated learning (SRL) in MOOCs and tests if encouraging the use of SRL strategies can improve course performance. We asked a group of 17 highly successful learners about their own strategies for how to succeed in a MOOC. Their responses were coded based on a SRL framework and synthesized into seven recommendations. In a randomized experiment, we evaluated the effect of providing those recommendations to learners in the same course (N = 653). Although most learners rated the study tips as very helpful, the intervention did not improve course persistence or achievement. Results suggest that a single SRL prompt at the beginning of the course provides insufficient support. Instead, embedding technological aids that adaptively support SRL throughout the course could better support learners in MOOCs.

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Recommending Self-Regulated Learning Strategies Does Not Improve Performance in a MOOC

Study in a Massive Open and Online Courses (MOOCs) is challenging, since participants take the course without the support of a teacher. Taking a MOOC require the students to have the ability to self-regulate their learning. However, every person has its own learning style and the way each one interacts and self-regulate in a MOOC varies. In this work we present an exploratory study from a process-oriented perspective to study whether students with different learning styles and SRL profiles show differences in navigating through a MOOC. Specifically, we investigate using Process Mining Techniques to analyze log files recording the course behavior of 99 learners across an Open edX MOOC combined with data from self-reported surveys. Our findings show that learners with different SRL profiles follow similar navigation paths, but there are differences when differentiating students by their learning styles.

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Exploring differences in how learners navigate in MOOCs based on self-regulated learning and learning styles: A process mining approach

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This article described the process used for developing and validating an analysis framework that allows comparing design methodologies for the design creation of Learning Objects (LO) and selecting the one that best meets the needs of teachers based on the needs of a specific educational context. This framework is called MASMDOA (Analysis Framework for the Selection of a Learning Object Design and Deployment Methodology), and it has been applied to a case study where, from a compilation of LO design methodologies used in Ibero-America, one of these methodologies is selected to work in the context of a LO design and production workshop. MASMDOA proved useful for selecting a LO design and deployment methodology that is appropriate for the requirements of an educator in a specific context. To achieve this, MASMDOA proposes a set of criteria that are useful for recommending and characterizing the methodologies to be analyzed and, following a two-phase process, it recommends the methodology that is better suited for the educator's needs. In this paper, we present MASMDOA, the case study to which it was applied, and the results obtained.

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Jorge J. Maldonado

Papers

Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses

Recommending Self-Regulated Learning Strategies Does Not Improve Performance in a MOOC

Exploring differences in how learners navigate in MOOCs based on self-regulated learning and learning styles: A process mining approach

Preeclampsia associates with RECK-dependent decrease in human trophoblasts migration and invasion

Analysis framework for tailored selection of learning objects methodologies