A Framework to Automatically Analyze Regulation

Zheng, Lanqin; El-Bishouty, Moushir M.; Pinnell, Colin; Bell, Jason; Kumar, Vive; Kinshuk, Kinshuk

doi:10.1007/978-3-662-44188-6_3

Cited by 2 publications

(4 citation statements)

References 19 publications

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“…For instance, SCRL (Zheng et al 2014(Zheng et al , 2015) is a tool developed for the purposes of explicitly engaging students in self-regulated and co-regulated learning activities in an online education environment. Figure 11.1 depicts an example of interaction with SCRL, where the student reviews proficiencies and initiates regulatory actions.…”

Section: Contribution Of Lambda To Mi-idemmentioning

confidence: 99%

Measurement of Quality of a Course

Seanosky

Boulanger

Pinnell

et al. 2015

The Future of Ubiquitous Learning

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Traditionally, the quality of a course offering is measured based on learner feedback at the end of the offering. This chapter offers a method to measure the quality of a course offering-continually, formatively, and summatively-using factors such as the quality of resources used, learner motivation, learner capacity, learner competency growth, and instructor competence. These factors are represented in a Bayesian belief network (BBN) in a system called MI-IDEM. MI-IDEM receives streams of data corresponding to these factors as and when they become available, which leads to estimates of quality of the course offering based on individual factors as well as an overall quality of the offering. Continuous, formative, and summative course quality measurements are imperative to identify weaknesses in the learning process of students and to assist them when they need help.

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Section: Contribution Of Lambda To Mi-idemmentioning

confidence: 99%

Measurement of Quality of a Course

Seanosky

Boulanger

Pinnell

et al. 2015

The Future of Ubiquitous Learning

Self Cite

View full text Add to dashboard Cite

show abstract

“…SCRL is a tool created to measure and improve the student ability during self-regulated and co-regulated learning (Pinnell et al, 2015). Self-regulated learning involves students being self-directed, taking initiatives to "set their goals, select and deploy tactics, monitor their own actions and evaluate their own effectiveness" (Zheng et al, 2015). Also, students can manage their own learning tasks and engage the coursework in a robust manner (Boulanger et al, 2015).…”

Section: Self-/co-regulated Learning (Scrl)mentioning

confidence: 99%

“…They can employ an immersive communication system to create a learner group for students with similar goals and create shared initiatives to address specific weakness of certain groups of learners. These features promote effective self-regulated and co-regulated learning, which are proven to be essential components of successful online learning environments (Zheng et al, 2015).…”

Section: Self-/co-regulated Learning (Scrl)mentioning

confidence: 99%

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Advancing learning through smart learning analytics: a review of case studies

Kinshuk

Kumar

2018

AAOUJ

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. Vivekanandan KumarAthabasca University, Athabasca, Canada Abstract Purpose -Smart learning analytics (Smart LA) -i.e. the process of collecting, analyzing and interpreting data on how students learn -has great potentials to support opportunistic learning and offer better -and more personalized -learning experiences. The purpose of this paper is to provide an overview of the latest developments and features of Smart LA by reviewing relevant cases. Design/methodology/approach -The paper studies several representative cases of Smart LA implementation, and highlights the key features of Smart LA. In addition, it discusses how instructors can use Smart LA to better understand the efforts their students make, and to improve learning experiences.Findings -Ongoing research in Smart LA involves testing across various learning domains, learning sensors and LA platforms. Through the collection, analysis and visualization of learner data and performance, instructors and learners gain more accurate understandings of individual learning behavior and ways to effectively address learner needs. As a result, students can make better decisions when refining their study plans (either by themselves or in collaboration with others), and instructors obtain a convenient monitor of student progress. In summary, Smart LA promotes self-regulated and/or co-regulated learning by discovering opportunities for remediation, and by prescribing materials and pedagogy for remedial instruction. Originality/value -Characteristically, Smart LA helps instructors give students effective and efficient learning experiences, by integrating the advanced learning analytics technology, fine-grained domain knowledge and locale-based information. This paper discusses notable cases illustrating the potential of Smart LA.

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A Framework to Automatically Analyze Regulation

Cited by 2 publications

References 19 publications

Measurement of Quality of a Course

Measurement of Quality of a Course

Advancing learning through smart learning analytics: a review of case studies

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