scholarly journals Mining for gold: Learning analytics and design for learning - A review

2021 ◽  
pp. 74-82
Author(s):  
Claire Goode ◽  
Ana Terry ◽  
Hugh Harlow ◽  
Rachel Cash
Keyword(s):  
Learning Analytics ◽  
Design For Learning

scholarly journals Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ Behaviours

2018 ◽  
Vol 5 (3) ◽  
Author(s):  
Quan Nguyen ◽  
Michal Huptych ◽  
Bart Rienties
Keyword(s):  
Time Management ◽  
Learning Analytics ◽  
Virtual Learning Environment ◽  
Limited Attention ◽  
Learning Design ◽  
High Performing ◽  
Catching Up ◽  
Design For Learning ◽  
Learning Behaviours ◽  
Digital Footprints

Extensive research in learning science has established the importance of time management in online learning. Recently, learning analytics (LA) has shed further lights on the temporal characteristics of learning by allowing researchers to capture authentic digital footprints of student learning behaviours. Nonetheless, students’ timing of engagement and its relation to learning design (LD) and academic performance have received limited attention. This study investigates to what extent students’ timing of engagement aligned with instructor learning design, and how engagement varied across different levels of performance. Our findings revealed a mismatch between how instructors designed for learning and how students study. In most weeks, students spent less time studying the assigned materials on the virtual learning environment (VLE) compared to the number of hours recommended by instructors. The timing of engagement also varied, from in advance to catching up patterns. High-performing students spent more time studying in advance, while low-performing students spent a higher proportion of their time on catching-up activities. By incorporating the pedagogical context into learning analytics, not only we can understand what, why, and when students engage, but also how their behaviours are influenced by the way instructors design for learning.

scholarly journals Learning Analytics: Pathways to Impact

2020 ◽  
Vol 36 (6) ◽  
pp. 1-6
Author(s):  
Linda Corrin ◽  
Maren Scheffel ◽  
Dragan Gašević
Keyword(s):  
Higher Education ◽  
Learning Analytics ◽  
Negative Impact ◽  
Evidence Base ◽  
Personalised Learning ◽  
The Past ◽  
Design For Learning ◽  
Context Of Learning ◽  
The Impact ◽  
Effective Design

The field of learning analytics has evolved over the past decade to provide new ways to view, understand and enhance learning activities and environments in higher education. It brings together research and practice traditions from multiple disciplines to provide an evidence base to inform student support and effective design for learning. This has resulted in a plethora of ideas and research exploring how data can be analysed and utilised to not only inform educators, but also to drive online learning systems that offer personalised learning experiences and/or feedback for students. However, a core challenge that the learning analytics community continues to face is how the impact of these innovations can be demonstrated. Where impact is positive, there is a case for continuing or increasing the use of learning analytics, however, there is also the potential for negative impact which is something that needs to be identified quickly and managed. As more institutions implement strategies to take advantage of learning analytics as part of core business, it is important that impact can be evaluated and addressed to ensure effectiveness and sustainability. In this editorial of the AJET special issue dedicated to the impact of learning analytics in higher education, we consider what impact can mean in the context of learning analytics and what the field needs to do to ensure that there are clear pathways to impact that result in the development of systems, analyses, and interventions that improve the educational environment.

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