scholarly journals Informing learning design in online education using learning analytics of student engagement

2022 ◽  
pp. 189-207
Author(s):  
Quan Nguyen ◽  
Bart Rienties ◽  
Denise Whitelock
Keyword(s):  
Student Engagement ◽  
Online Education ◽  
Learning Analytics ◽  
Learning Design

scholarly journals Including Learning Analytics in the Loop of Self-Paced Online Course Learning Design

2020 ◽  
Author(s):  
Hongxin Yan ◽  
Fuhua Lin ◽  
Kinshuk
Keyword(s):  
Online Education ◽  
Learning Analytics ◽  
Course Design ◽  
Online Course ◽  
Full Potential ◽  
Learning Design ◽  
Design Strategies ◽  
Student Modelling ◽  
Tutoring Systems ◽  
Online Educators

AbstractOnline education is growing because of its benefits and advantages that students enjoy. Educational technologies (e.g., learning analytics, student modelling, and intelligent tutoring systems) bring great potential to online education. Many online courses, particularly in self-paced online learning (SPOL), face some inherent barriers such as learning awareness and academic intervention. These barriers can affect the academic performance of online learners. Recently, learning analytics has been shown to have great potential in removing these barriers. However, it is challenging to achieve the full potential of learning analytics with the traditional online course learning design model. Thus, focusing on SPOL, this study proposes that learning analytics should be included in the course learning design loop to ensure data collection and pedagogical connection. We propose a novel learning design-analytics model in which course learning design and learning analytics can support each other to increase learning success. Based on the proposed model, a set of online course design strategies are recommended for online educators who wish to use learning analytics to mitigate the learning barriers in SPOL. These strategies and technologies are inspired by Jim Greer’s work on student modelling. By following these recommended design strategies, a computer science course is used as an example to show our initial practices of including learning analytics in the course learning design loop. Finally, future work on how to develop and evaluate learning analytics enabled learning systems is outlined.

Designing Online Learning Strategies through Analytics

2014 ◽  
pp. 1-15 ◽  
Author(s):  
Prerna Lal
Keyword(s):  
Student Engagement ◽  
Online Education ◽  
Learning Strategies ◽  
Learning Process ◽  
Learning Analytics ◽  
Online Data ◽  
Institutional Planning ◽  
Education Environment ◽  
Day By Day ◽  
Video Format

The online education environment is becoming complex day-by-day. Nowadays, educational institutes are offering various types of courses online to a large number of students having a diverse background, with the flexibility of time and geography. This results in creating a large repository of online data regarding courses, students and instructors. These data may be in text, audio or video format. This chapter is an attempt to understand the use of Learning Analytics that advocates for analysis of these data and to understand the learning process better in terms of student engagement, pedagogy, content and assessment. Educational institutes can utilize the intelligence revealed by learning analytics processes, and communicate them to those involved in strategic institutional planning.

scholarly journals Assessing Student Engagement in Online Programmes: Using Learning Design and Learning Analytics

2019 ◽  
Vol 8 (6) ◽  
pp. 171 ◽  
Author(s):  
Maria Toro-Troconis ◽  
Jesse Alexander ◽  
Manuel Frutos-Perez
Keyword(s):  
Online Learning ◽  
Student Engagement ◽  
Student Performance ◽  
Cognitive Skills ◽  
Learning Analytics ◽  
Linear Regression Analysis ◽  
Learning Design ◽  
Online Forums ◽  
Postgraduate Students ◽  
Weak Evidence

This paper presents the learning design framework used in the design of the Online MA in Photography at Falmouth University. It discusses the importance of evaluating the success of online learning programmes by analysing learning analytics and student feedback within the overall pedagogic context and design of the programme. Linear regression analysis was used to analyse the engagement of three cohorts of students that completed four modules of the Online MA Photography (n=33) with over 80,000 entries in the dataset. The research explored student engagement with online content that promoted low-order cognitive skills (i.e. watching videos, reading materials and listening to podcasts) as well as high-order cognitive skills (i.e. participating in online forums and webinars). The results suggest there is weak evidence of an association between average overall mark in all modules and the level of engagement with self-directed content (P = 0.0187). There is also weak evidence of an association between average overall mark in all modules and the level of engagement in collaborative activities (P < 0.0528). Three major themes emerged from the focus group 1) weekly forums and webinars, 2) self-directed learning materials and 3) learning design and support. Online learning was acceptable and convenient to postgraduate students. These findings are discussed further in the paper as potential predictors of student performance in online programmes.

scholarly journals Theory-based learning analytics to explore student engagement patterns in a peer review activity

2021 ◽  
Author(s):  
Erkan Er ◽  
Cristina Villa-Torrano ◽  
Yannis Dimitriadis ◽  
Dragan Gasevic ◽  
Miguel L. Bote-Lorenzo ◽  
...  
Keyword(s):  
Student Engagement ◽  
Peer Review ◽  
Learning Analytics

scholarly journals Learning Analytics to Reveal Links Between Learning Design and Self-Regulated Learning

2021 ◽  
Author(s):  
Yizhou Fan ◽  
Wannisa Matcha ◽  
Nora’ayu Ahmad Uzir ◽  
Qiong Wang ◽  
Dragan Gašević
Keyword(s):  
Learning Analytics ◽  
High Performance ◽  
Process Mining ◽  
Learning Experience ◽  
Learning Design ◽  
Metacognitive Control ◽  
Self Regulated Learning ◽  
Regulated Learning ◽  
High Level ◽  
Evaluation Of Learning

AbstractThe importance of learning design in education is widely acknowledged in the literature. Should learners make effective use of opportunities provided in a learning design, especially in online environments, previous studies have shown that they need to have strong skills for self-regulated learning (SRL). The literature, which reports the use of learning analytics (LA), shows that SRL skills are best exhibited in choices of learning tactics that are reflective of metacognitive control and monitoring. However, in spite of high significance for evaluation of learning experience, the link between learning design and learning tactics has been under-explored. In order to fill this gap, this paper proposes a novel learning analytic method that combines three data analytic techniques, including a cluster analysis, a process mining technique, and an epistemic network analysis. The proposed method was applied to a dataset collected in a massive open online course (MOOC) on teaching in flipped classrooms which was offered on a Chinese MOOC platform to pre- and in-service teachers. The results showed that the application of the approach detected four learning tactics (Search oriented, Content and assessment oriented, Content oriented and Assessment oriented) which were used by MOOC learners. The analysis of tactics’ usage across learning sessions revealed that learners from different performance groups had different priorities. The study also showed that learning tactics shaped by instructional cues were embedded in different units of study in MOOC. The learners from a high-performance group showed a high level of regulation through strong alignment of the choices of learning tactics with tasks provided in the learning design. The paper also provides a discussion about implications of research and practice.

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