Analytics of student behaviour in a learning management system as a predictor of learning success

Marques, Bruno Pereira; Villate, Jaime E.; Carvalho, Carlos Vaz de

doi:10.23919/cisti.2017.7975863

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…This work was developed in the context of a PhD project whose objective was to study, discuss, propose and validate a support model for the adoption of Information and Communication Technologies (ICT) for pedagogical purposes in the HE scenario, and to propose a coherent and consistent model of institutional and pedagogical activity, centered on teachers (Marques, 2015). This article is an improved, revised and extended version of the publication by Marques (2017).…”

Section: Introductionmentioning

confidence: 99%

Student Activity Analytics in an e-Learning Platfom: Anticipating Potential Failing Students

Marques

Villate²,

Carvalho

2018

Journal of Information Systems Engineering & Management

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The evolution of learning technology and tools changed the way students access information and build their knowledge. Registering the interaction of students with these tools generates a large amount of data that, once critically analysed, can provide important clues about the students' learning progress. Nevertheless research has still to be conducted to fully understand how (and if) the students' interaction with the learning technologies relates to their learning success. In parallel, new analytical tools must be developed to allow teachers to fully exploit the information embedded in this data, in a friendly but flexible way. This article is a contribution to this effort and presents a study where the use of a learning management system (LMS) in a specific semester-long course in an Engineering school produced data that was analysed and correlated to the students' success. Results indicate that some correlation exists between the effective use of some of the tools integrated in the LMS and student success which points the way to buld specific applications to provide teachers with indicators of students in danger of failing.

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Section: Introductionmentioning

confidence: 99%

Student Activity Analytics in an e-Learning Platfom: Anticipating Potential Failing Students

Marques

Villate²,

Carvalho

2018

Journal of Information Systems Engineering & Management

Self Cite

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“…In the seventh study, the relation was studied between learning analytics of learners' behavior following the learner's success [18]. The researchers have analyzed several online activities such as learner access and their interactions.…”

Section: Learning Analyticsmentioning

confidence: 99%

Clickstream Data Schema for Learning Analytics to Understand Learner Behaviour

2020

IJATCSE

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Learning analytics has caught numerous researchers' attention in the last decade due to its ability to analyze, report, and process information produced by learners. Several research studies have examined learning analytics in terms of learner behavior to provide quality and efficient reporting for educators and higher learning institutions. In particular, the click pattern of the learner has been examined in terms of its significant impact on determining learner performance. Yet, the clickstream data schema that has been addressed in the literature was limited to click patterns focusing on event-based click patterns and time in the logging history. However, learner interaction logging history available in clickstream data schema for major online learning management systems holds great potential to reveal diverse and complex learner behavior patterns based on various significant learner behavior elements that are waiting to be discovered. The literature has shown that great interest has been given in understanding specific behavior produced by the learner through an online learning management system. Thus, this study aims to review previous related studies that could reveal possible learner click patterns, which could be considered as an indicator to predict the learner's performance and behavior. Potential learner interaction data that could be applied to produce a clickstream data stream has been outlined. As an implication, the contribution of the study could open up new frontiers that trigger the development of a learning analytics dashboard for various online learning platforms.

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“…ICT must assist teachers in developing better pedagogical and didactical methods taking into account differences between students. Some studies such as those introduced by Marques, Villate, and Carvalho (2017); Meira Ferrão Luis, Llamas-Nistal, and Fernández Iglesias (2017); Morais, Alves, and Miranda (2017) highlight the importance of analysing data generated by Learning Management Systems (LMS). These systems gather a lot of information about student's action and activities, but teachers have difficulties to analyse and use it in order to improve their strategies.…”

Section: Introductionmentioning

confidence: 99%

Supporting self-regulated learning and personalization using ePortfolios: a semantic approach based on learning paths

Romero

Saucedo

Caliusco

et al. 2019

Int J Educ Technol High Educ

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In the area of e-learning, there are new challenges to be overcome. One of them is to allow students self-regulation of their learning. Within this self-regulation process, students and teachers use technological tools to carry out their tasks. In general, these tools do not consider pedagogical aspects. Emerging approaches propose the use of electronic portfolios and semantic technologies to provide pedagogical characteristics that make tools more user-friendly for both teachers and students. This paper presents a conceptual model of an intelligent system to support self-regulated learning. This model is based on semantic technologies and uses the concepts of learning paths and student e-portfolios. For learning path generation, the system considers the different learning levels that a student should achieve and metrics that measure student progress. In this way, it proposes a learning path with the intention that the student achieves as highest learning level as possible.

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Analytics of student behaviour in a learning management system as a predictor of learning success

Cited by 5 publications

References 7 publications

Student Activity Analytics in an e-Learning Platfom: Anticipating Potential Failing Students

Student Activity Analytics in an e-Learning Platfom: Anticipating Potential Failing Students

Clickstream Data Schema for Learning Analytics to Understand Learner Behaviour

Supporting self-regulated learning and personalization using ePortfolios: a semantic approach based on learning paths

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