Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Predicting students' behavioural engagement in microlearning using learning analytics model

Predicting students' behavioural engagement in microlearning using learning analytics model

For access to this article, please select a purchase option:

Buy chapter PDF
$16.00
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
E-learning Methodologies: Fundamentals, technologies and applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The student-centred learning trend is one of the e-learning service factors in universities and schools that have been improved with added values. Now, students can access the e-learning platform on a cloud server with their mobile devices. Several ways and practices in e-learning today include learning management systems (LMS), blended learning, microlearning, mobile learning, open learning, self-learning, and virtual learning. Microlearning refers to the micro perspective in learning contact, education, and exercise. Student engagement is one of the key indicators of a successful implementation of e-learning. Those studies were carried out based on the educational data mining technique, which is widely used in analysing the various patterns of online learning behaviour and predicting learning outcomes. Another popular technique that uses a similar approach but with different focus is learning analytics.

Chapter Contents:

  • 3.1 Introduction
  • 3.2 LA studies
  • 3.3 Methods
  • 3.4 Results
  • 3.4.1 Analysis of using NN
  • 3.4.2 Analysis using LR
  • 3.5 Comparison analysis using NN and LR
  • 3.6 Conclusion
  • 3.7 Future scope
  • References

Inspec keywords: cloud computing; mobile computing; computer aided instruction

Other keywords: microlearning; learning analytics model; open learning; educational data mining technique; self-learning; mobile learning; student behavioural engagement; LMS; online learning behaviour; cloud server; e-learning service factors; mobile devices; blended learning; learning management systems; student-centred learning; virtual learning

Subjects: Ubiquitous and pervasive computing; Internet software; Computer-aided instruction

Preview this chapter:
Zoom in
Zoomout

Predicting students' behavioural engagement in microlearning using learning analytics model, Page 1 of 2

| /docserver/preview/fulltext/books/pc/pbpc040e/PBPC040E_ch3-1.gif /docserver/preview/fulltext/books/pc/pbpc040e/PBPC040E_ch3-2.gif

Related content

content/books/10.1049/pbpc040e_ch3
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address