A Framework for Using Web Usage Mining to Personalise E-learning

Ba-Omar, H.; Petrounias, Ilias; Anwar, Fahad

doi:10.1109/icalt.2007.13

Cited by 37 publications

(26 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the e-learning context, unlike other web based domains, user identification is a straightforward problem as in most cases learners must login using their unique ID [25].…”

Section: Data Pre-processingmentioning

confidence: 99%

Analysis of Stakeholders’ Behaviour Depending on Time in Virtual Learning Environment

Munk¹,

Drlk²

2014

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

Abstract:The aim of the paper is the probability modelling of accesses to the different parts of e-learning course in interactive learning environment depending on time. This problem belongs to the open questions of the contemporary emerging discipline Educational Data Mining. We are concerned with the access probabilities to the individual parts of e-learning course content. For the purpose of modelling of the stakeholders' behaviour dependence on time, we use multinominal logit model which is a special case of Generalized Linear Model. We pay attention to data preparation issues. Data about using e-learning courses are stored in databases. We assume that these data represent time data. Surprisingly, modelling of stakeholders' behaviour dependence on time is missing in Educational Data Mining discipline. The time variable, mostly stored in database in "unixtime" form, integrating date and time, is partially used by sequence rules extraction, where it is only used for the tracking of visited e-learning course parts during each session. Therefore, we describe the applied multinominal logit model and methodology of the modelling in detail. We deal with parameter estimations. Finally, we figure that the multinomial logit model finds its application mainly in the process of restructuring the existing e-learning courses. We discuss about its possible contribution to the improvement of the interactive learning environment as well as in the personalization of the course content and structure.

show abstract

“…In the e-learning context, unlike other web based domains, user identification is a straightforward problem as in most cases learners must login using their unique ID [25].…”

Section: Data Pre-processingmentioning

confidence: 99%

Analysis of Stakeholders’ Behaviour Depending on Time in Virtual Learning Environment

Munk¹,

Drlk²

2014

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…With the transition to Web-based learning technologies and with the broader adoption of LMS systems, several researchers (e.g., Ba-Omar et al, 2007;Marquardt et al, 2004) have adopted traditional WUM techniques to analyze learning data. It is important to note that certain characteristics of LMS systems make the process somewhat simpler.…”

Section: Web Usage Mining In Distance Educationmentioning

confidence: 99%

“…Typically, researchers adopt a heuristic approach (e.g., limit all activities to 10, 30, or 60 minutes) (Ba-Omar, Petrounias, & Anwar, 2007;Munk & Drlík, 2011) and do not address the consequences of such adopted heuristics on the produced statistical model. In this paper, we try to evaluate what are the consequences of the different estimation heuristics on the results of the final predictive model.…”

Section: Introductionmentioning

confidence: 99%

Does Time-on-task Estimation Matter? Implications on Validity of Learning Analytics Findings

et al. 2016

View full text Add to dashboard Cite

ABSTRACT:With widespread adoption of Learning Management Systems (LMS) and other learning technology, large amounts of data -commonly known as trace data -are readily accessible to researchers. Trace data has been extensively used to calculate time that students spend on different learning activities -typically referred to as time-on-task. These measures are used to build predictive models of student learning in order to understand and improve learning processes. While time-on-task measures have been used in Learning Analytics research, the consequences of their use are not fully described or examined. This paper presents findings from two experiments regarding different time-on-task estimation methods and their influence on research findings. Based on modelling different student performance measures with popular statistical methods in two datasets (one online, one blended), our findings indicate that time-on-task estimation methods play an important role in shaping the final study results, particularly in online settings where the amount of interaction with LMS is typically higher. The primary goal of this paper is to raise awareness and initiate debate on the important issue of time-on-task estimation within the broader learning analytics community. Finally, the paper provides an overview of commonly adopted time-on-task estimation methods in educational and related research fields.

show abstract

“…The E-learning system is an evolution of information technology, network technology and multimedia technology to achieve maximum efficiency in education. E-learning systems are now widely accepted because it has the flexibility to adjust content, which will be up to date, provides an opportunity for everyone to learn and gives freedom of research without restrictions of time and location [1]. In addition, e-learning systems are also used as a communication tool between teacher and learner by using modern communication tools such as e-mail, web-board, chat, etc [2].…”

Section: Introductionmentioning

confidence: 99%

“…In the research [1] the learner provided an answer for the online VARK questionnaire and studied e-learning. Then, obtained studying behaviors or used the main web of Web logs to analyze via data mining techniques to find the learning path's content for learners with different learning styles.…”

Section: Introductionmentioning

confidence: 99%

Conceptual Framework of a Synthesized Adaptive e-Learning and e-Mentoring System Using VARK Learning Styles with Data Mining Methodology

Pantho¹,

Tiantong²

2015

IJCTE

View full text Add to dashboard Cite

Abstract-Currently, e-learning systems are becoming more popular. This is because e-learning systems provide learners freedom to study with unlimited time and at any location. But, most of the e-learning systems present the same learning content without regard to different learning styles of learners. Many learners have to adapt to different learning styles such as learning content from images which is not specifically targeted at their needs. Meanwhile, other learners may have aptitude in reading or from listening, etc. Therefore, learning and teaching processes are important issues that teachers need to adjust their teaching according to individual learners. If each learner obtains content that aligns with their own learning style, it will lead to more achievement.The purpose of this research is to synthesize the learning model of adaptive e-learning and e-mentoring system in order to recommend learners and analyze the VARK learning style (VARK is an acronym for visual, aural, read/write, and kinesthetic) by using data mining methodology. The synthesized model consists of four modules which are 1) esaB eluR KRAV eludoM2) VARK Learner Module 3) Content Module and 4) Learning Module.Index Terms-E-learning, adaptive learning, learning style, VARK learning style. I. INTRODUCTIONLearning and teaching by using the e-learning system is a way to enhance learning and teaching capacity. The E-learning system is an evolution of information technology, network technology and multimedia technology to achieve maximum efficiency in education. E-learning systems are now widely accepted because it has the flexibility to adjust content, which will be up to date, provides an opportunity for everyone to learn and gives freedom of research without restrictions of time and location [1]. In addition, e-learning systems are also used as a communication tool between teacher and learner by using modern communication tools such as e-mail, web-board, chat, etc [2]. However, although e-learning is very useful, some e-learning systems are a self-learning system with no influence of achievement for the learner. Some learners may take more time than others to study contents as each learner has different ability to gain knowledge. The recognition of learner depends on individual factors, including experience, interest, aptitude, knowledge, wisdom, and learning plan. These factors influence the learning speed. In traditional e-learning systems, the focus was on the development of presentation rather than the consideration of individuality [3]. And traditional e-learning systems tend to lead to boredom, slow down of learning and underachieving. Therefore, the solution for an effective e-learning system is the development of an adaptive e-learning system [2] by focusing on the learner's learning style. Learning style means the physical characteristic, thought and feeling that individual uses to perceive, responsiveness and interaction with a stable environment. The learning style is one element that will help to enhance the learning process, pro...

show abstract

A Framework for Using Web Usage Mining to Personalise E-learning

Cited by 37 publications

References 1 publication

Analysis of Stakeholders’ Behaviour Depending on Time in Virtual Learning Environment

Analysis of Stakeholders’ Behaviour Depending on Time in Virtual Learning Environment

Does Time-on-task Estimation Matter? Implications on Validity of Learning Analytics Findings

Conceptual Framework of a Synthesized Adaptive e-Learning and e-Mentoring System Using VARK Learning Styles with Data Mining Methodology

Contact Info

Product

Resources

About