Learning Analytics for Monitoring Students Participation Online: Visualizing Navigational Patterns on Learning Management System

Poon, Leonard K. M.; Kong, Siu Cheung; Yau, Thomas S. H.; Wong, Michael Y. W.; Ling, Man Ho

doi:10.1007/978-3-319-59360-9_15

Cited by 28 publications

(20 citation statements)

References 20 publications

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“…However, this approach neglects the benefits and affordances that LMS offers. That is why Poon et al (2017) proposed using visualization techniques to mine for information based on log data from LMSs. Nonetheless, the suggested approach still offered only delayed analytics at best.…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Learning Analytics for Face-to-Face Lessons

Chui

Polytechnic

Foo

2020

pijtel

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Even though the use of digital technology and e-learning has grown over the years, most of the time spent in schools around the world is still in face-to-face lessons. Traditional classroom teaching encounters fundamental constraints like the difficulty faced by one educator to track the understanding of a group of learners. Numerous tools exist to help educators but they are mostly detached from the actual teaching and learning materials, and hence necessitate a breaking away from the flow of the lesson to collect, visualize and understand the data collected. In this paper, we present a real-time learning analytics system that can provide both educators and learners with a real-time view of the data collected from learners' interaction with a mobileoptimized lesson embedded in a learning management system and accessible via mobile phones or computers. Data collection and visualization is automated and achieved with no friction to the flow of the lesson. The educator could use the data to keep track of individual students' responses, as well as moderate the pace of the whole class. Action research was done on a total of four classes of students to test the benefits of using the real-time learning analytics system.

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Section: Literature Reviewmentioning

confidence: 99%

Real-Time Learning Analytics for Face-to-Face Lessons

Chui

Polytechnic

Foo

2020

pijtel

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“…They also discussed the various limitations of the learning analytics system. The researchers in (Poon et al, 2017) talked about a toolkit name "eLAT" for performing the task of learning analytics. The "eLAT" is capable of graphically indicating the learner behaviour, assessment and feedback in order to facilitate inclusive teaching and suggests improvements.…”

Section: Related Work In Briefmentioning

confidence: 99%

Learning analytics for IoE based educational model using deep learning techniques: architecture, challenges and applications

Ahad¹,

Tripathi²,

Agarwal³

2018

Smart Learn. Environ.

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The new generation teaching-learning pedagogy has created a complete paradigm shift wherein the teaching is no longer confined to giving the content knowledge, rather it fosters the "how, when and why" of applying this knowledge in real world scenarios. By exploiting the advantages of deep learning technology, this pedagogy can be further fine-tuned to develop a repertoire of teaching strategies. This paper presents a secured and agile architecture of an Internet of Everything (IoE) based Educational Model and a Learning Analytics System (LAS) model using the concept of deep learning which can be used to gauge the degree of learning, retention and achievements of the learners and suggests improvements and corrective measures. The paper also puts forward the advantages, applications and challenges of using deep learning techniques for gaining insights from the data generated from the IoE devices within the educational domain for creating such learning analytics systems. Finally a feature wise comparison is provided between the proposed Learning Analytics (LA) based approach and conventional teaching-learning approach in terms of performance parameters like cognition, attention, retention and attainment of learners.

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“…Poon et al suggest the use of data‐mining process for the analytical analysis of the learning process in the mixed learning environment, in order to discover significant navigation patterns by implementing sequential techniques for selecting samples. The use of methods for hierarchy clustering and visualization for the interpretation of patterns has been presented.…”

Section: Literature Overviewmentioning

confidence: 99%

Association analysis of moodle e‐tests in blended learning educational environment

Dimić¹,

Predić

Rančić

et al. 2017

Comp Applic In Engineering

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The paper suggests the implementation of association analysis for improving the process of e‐testing in blended learning environment. The research has been conducted using knowledge tests at the Computer Graphics Moodle Course. In the preprocessing phase, data matrices have been created and prepared for the process of discovering significant relationships and links between students' answers to the questions from preparatory tests and those for testing knowledge, the ways of doing, and achieved results. By implementing Apriori and Predictive Apriori algorithms, a great number of association rules has been discovered. Important and interesting rules have been singled out by implementing objective and subjective assessment measures. The examples of interesting rules, as well as discovered patterns in items' relationships, have also been presented. The contribution of the described case study is visible in providing important feedback which enables the teacher to get a better insight into the concepts of created tests and decide on how to make changes to improve testing.

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Learning Analytics for Monitoring Students Participation Online: Visualizing Navigational Patterns on Learning Management System

Cited by 28 publications

References 20 publications

Real-Time Learning Analytics for Face-to-Face Lessons

Real-Time Learning Analytics for Face-to-Face Lessons

Learning analytics for IoE based educational model using deep learning techniques: architecture, challenges and applications

Association analysis of moodle e‐tests in blended learning educational environment

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