Putting learning back into learning analytics: actions for policy makers, researchers, and practitioners

Abstract

This paper is based on (a) a literature review focussing on the impact of learning analytics on supporting learning and teaching, (b) a Delphi study involving international expert discussion on current opportunities and challenges of learning analytics as well as (c) outlining a research agenda for closing identified research gaps. Issues and challenges facing educators linked to learning analytics and current research gaps were organised into four themes, the further development of which by the expert panel, led to six strategy and action areas. The four themes are 1. development of data literacy in all stakeholders, 2. updating of guiding principles and policies of educational data, 3. standards needed for ethical practices with data quality assurance, and 4. flexible user-centred design for a variety of users of analytics, starting with learners and ensuring that learners and learning is not harmed. The strategies and actions are outcomes of the expert panel discussion and are offered as provocations to organise and focus the researcher, policymaker and practitioner dialogs needed to make progress in the field.

Desperately Seeking the Impact of Learning Analytics in Education at Scale

“…Taking the teacher perspective, in today's society such goals should focus not only on the improvement of students' subject knowledge, but also on development of their critical 21st century skills, including collaborative and self-regulated learning skills that are directly associated with academic performance, especially in online learning settings (e.g., Viberg, Khalil, & Baars, 2020). Moreover, such goals may be directed towards the development and improvements of students' data-, feedback-and digital literacy skills that are crucial for their successful navigation and study success in online learning settings (see e.g., Ifenthaler et al, 2020;Jivet, 2021).…”

“…There are many reasons behind the slow adoption of LA and data-driven decision making processes in educational settings, especially in K-12 education, including challenges related to data interoperability (Dodero et al, 2017;Samuelsen, Chen, & Wasson, 2019), ethics and privacy concerns (Beerwinkle, 2020;Livingstone, 2020;Viberg, Andersson et al, 2021), development of stakeholders' data literacy (Ifenthaler et al, 2020) as well as feedback literacy skills (Jivet, 2021), and a general lack of participatory approaches that take into account the needs and preferences of the students and teachers -even less actually engage them directlyin the LA design process (Buckingham Shum, Ferguson, & Martinez-Maldonado, 2019;Jivet, 2021). We should not forget that LA is about supporting learning, not just reporting it (Gasevic, Dawson, & Simiens, 2015).…”

Emergence of Learning Analytics in Education: Challenges and Issues of Learning Analysis

“…L'écriture collaborative a mené à différentes publications, dont une dans le livre électronique Learners and Learning Contexts. New Alignments for the Digital Age (Fisser & Phillips, 2020) et une autre dans une revue (Ifenthaler et al, 2020). La suite de notre article permettra de porter un regard sur le domaine de l'analyse de l'apprentissage et de rapporter les résultats des discussions du groupe de travail agrémentés de quelques réflexions complémentaires.…”

“…Les travaux du TWG6 ont mené le groupe à proposer des recommandations 10 , sous la forme de six stratégies précisées chacune par deux actions principales, adressées aux décideurs politiques, aux chercheurs et aux praticiens, la moitié des recommandations concernant toutes les parties prenantes (Ifenthaler et al, 2020). Ces stratégies et actions sont présentées et discutées dans cette section en abordant à la fois les possibilités, mais aussi les défis qu'il reste à relever en matière d'analyse de l'apprentissage.…”

Applying Learning Analytics in Mathematics and Science Education Lessons: Experiences of Teachers in Basic Education

“…Mokymosi analitikos naudojimo švietime modelyje (Chatti et al, 2012) apjungiami mokymosi analitikos naudojimo edukacijoje veiksniai bei išskiriamos būtinos sąlygos mokymosi analitikos veikmei, išskiriami ir apibūdinami mokymosi naudojimo edukacijoje tikslai atsakant į klausimą kodėl ir kam jos reikia. Dirbtiniu intelektu grįstos ir mokymosi analitiką integruojančios skaitmeninės mokymo ir mokymosi platformos suteikia mokytojams įžvalgas, reikalingas priimant sprendimus: dėl mokymo(si) klasėje tobulinimo (Long & Siemens, 2011); dėl mokymo(si) personalizavimo (siekiant suteikti galimybę patiems besimokantiesiems projektuoti savo mokymąsi pagal tai, kaip jie mokosi, kokius mokymosi poreikius turi) (Mangaroska et al, 2019, Ifenthaler et al, 2021; dėl efektyvaus grįžtamojo ryšio organizavimo (realiu laiku, pagrįstą ir efektyvesnį grįžtamąjį ryšį) (Weber, 2015); dėl mokymosi kokybės, siekiant mažinti atskirtį, nustatyti mokinių rizikos grupes (Mangaroska & Giannakos, 2018;Kurvinen et al, 2020). Šių bei kitų sprendimų klasėje priėmimui mokytojui būtini tinkamai surinkti ir apdoroti duomenys, pakankamai laiko šiems veiksmams atlikti.…”