scholarly journals Towards a Holistic Evaluation Concept for Personalised Learning in Flipped Classrooms

2018 ◽  
pp. 101-116 ◽  
Author(s):  
Philipp Melzer
Keyword(s):  
Personalised Learning ◽  
Flipped Classrooms

ANALYSIS OF THE CONCEPT OF PERSONALISED LEARNING PATHWAYS

2017 ◽  
Vol 1 (43) ◽  
pp. 47-56
Author(s):  
Karol Korczak ◽  
Konrad Szymański
Keyword(s):  
Personalised Learning ◽  
Learning Pathways

scholarly journals X5Learn: A Personalised Learning Companion at the Intersection of AI and HCI

2021 ◽  
Author(s):  
Maria Perez-Ortiz ◽  
Claire Dormann ◽  
Yvonne Rogers ◽  
Sahan Bulathwela ◽  
Stefan Kreitmayer ◽  
...  
Keyword(s):  
Personalised Learning

scholarly journals Designing Tasks for a Dynamic Online Environment: Applying Research into Students’ Difficulties with Linear Equations

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 557
Author(s):  
Morten Elkjær ◽  
Uffe Thomas Jankvist
Keyword(s):  
Design Theory ◽  
Teaching And Learning ◽  
Linear Equations ◽  
Task Design ◽  
Tree Structure ◽  
Learning Trajectory ◽  
Variation Theory ◽  
Online Environment ◽  
School Students ◽  
Personalised Learning

Despite almost half a century of research into students’ difficulties with solving linear equations, these difficulties persist in everyday mathematics classes around the world. Furthermore, the difficulties reported decades ago are the same ones that persist today. With the immense number of dynamic online environments for mathematics teaching and learning that are emerging today, we are presented with a perhaps unique opportunity to do something about this. This study sets out to apply the research on lower secondary school students’ difficulties with equation solving, in order to eventually inform students’ personalised learning through a specific task design in a particular dynamic online environment (matematikfessor.dk). In doing so, task design theory is applied, particularly variation theory. The final design we present consists of eleven general equation types—ten types of arithmetical equations and one type of algebraic equation—and a broad range of variations of these, embedded in a potential learning-trajectory-tree structure. Besides establishing this tree structure, the main theoretical contribution of the study and the task design we present is the detailed treatment of the category of arithmetical equations, which also involves a new distinction between simplified and non-simplified arithmetical equations.

Technology-mediated personalised learning for younger learners

2020 ◽  
Author(s):  
Stefanie Vanbecelaere ◽  
Frederik Cornillie ◽  
Fien Depaepe ◽  
Roger Gilabert Guerrero ◽  
Manolis Mavrikis ◽  
...  
Keyword(s):  
Personalised Learning

scholarly journals Demystifying Learning Analytics in Personalised Learning

2018 ◽  
Vol 7 (3) ◽  
pp. 1124 ◽  
Author(s):  
Andino Maseleno ◽  
Noraisikin Sabani ◽  
Miftachul Huda ◽  
Roslee Ahmad ◽  
Kamarul Azmi Jasmi ◽  
...  
Keyword(s):  
Learning Environment ◽  
Learning Process ◽  
Learning Analytics ◽  
Educational Institutions ◽  
Learning Resources ◽  
Personalised Learning ◽  
Report Data ◽  
Overall Performance ◽  
The Relationship ◽  
Specific Learning

This paper presents learning analytics as a mean to improve students’ learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper’s contribution to knowledge is in considering personalised learning within the context framework of learning analytics. 

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