Learning analytics and assessment: Emerging research trends, promises and future opportunities

Raković, Mladen; Gašević, Dragan; Hassan, Saeed‐Ul; Ruipérez‐Valiente, José A.; Aljohani, Naif Radi; Milligan, Sandra

doi:10.1111/bjet.13301

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…There has been a growing body of research dedicated to the use of multimodal and multichannel data to triangulate data from the learning process (eg, Azevedo et al., 2022; Molenaar et al., 2023; Olsen et al., 2020). Coupled with advancements in the field of learning analytics to better measure and support students' learning, even at a larger scale (Raković et al., 2023), as well as the implementation of and investigation into artificial intelligence approaches (Dijkstra et al., 2023; Järvelä et al., 2023), there is much potential for the further improvement of personalized scaffold support. However, in order to leverage technological solutions for adaptive support, the personalized scaffolding model used in the current study offers the basis on which other tech‐powered innovations may build upon.…”

Section: Discussionmentioning

confidence: 99%

How do students learn with real‐time personalized scaffolds?

Lim,

Bannert,

van der Graaf

et al. 2023

Brit J Educational Tech

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Scaffolds that support self‐regulated learning (SRL) have been found to improve learning outcomes. The effects of scaffolds can differ depending on how learners use them and how specific scaffolds might influence learning processes differently. Personalized scaffolds have been proposed to be more beneficial for learning due to their adaptivity to learning progress and individualized content to learning needs. The present study investigated finer‐grained effects of how personalized scaffolds driven by a rule‐based artificial intelligence system influenced SRL processes, especially how students learned with them. Using a pre‐post experimental design, we investigated personalized scaffolds based on university students' real‐time learning processes in a technologically enhanced learning environment. Students in the experimental group (n = 30) received personalized scaffolds, while the control group (n = 29) learned without scaffolds. All students completed a 45‐minute learning task with trace data recorded. Findings indicated scaffold effects on students' subsequent learning behaviour. Additionally, only scaffold interaction correlated to essay performance and suggests that the increase in frequencies of SRL activities alone does not contribute directly to learning outcomes. As guidelines for real‐time SRL support are lacking, this study provides valuable insights to enhance SRL support with adaptive learning technologies. Practitioner notesWhat is already known about this topic Self‐regulated learning scaffolds, especially adaptive scaffolds, improve learning. Personalized scaffolds have effects on self‐regulated learning activities. Past research focused on aggregated effects of scaffolds. What this paper adds Investigates how students learn with personalized scaffolds in terms of frequencies of learning activities and scaffold interaction. Takes a closer look at which learning activities and when the effects of personalized scaffolds occur. Examines how finer‐grained effects of personalized scaffolds correspond to learning outcomes. Implications for practice and/or policy Personalized scaffold effects vary across learning, and future research should consider finer‐grained investigations of SRL support in order to better understand their influence on learning. The number of personalized scaffolds provided should be reconsidered in the future as students only use some of the support provided, especially when task demands increase. Personalized scaffold interaction is linked to improvement in task performance, so future research should also focus on students' appropriate use of self‐regulated learning support.

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Section: Discussionmentioning

confidence: 99%

How do students learn with real‐time personalized scaffolds?

Lim,

Bannert,

van der Graaf

et al. 2023

Brit J Educational Tech

Self Cite

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Uncovering Students’ Temporal Quiz-Taking Behavior Patterns Using Sequence Analysis

Akçapınar,

Ilgaz,

Bayazıt

et al. 2024

Assessment Analytics in Education

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Do Predictive Analytics Dream of Risk-Free Education? The Politics of Risk Mitigation

Zakharova

Jarke

2023

Postdigit Sci Educ

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The future is always shaped and, to some extent, haunted by design decisions of the present and their future residues. Predictive learning analytics, as increasingly used in education, is an example of a technology that is future-oriented by design. Particularly risk prediction concerned with the students at risk, risk factors hindering educational success, and their management has a long history in education. Currently, identification of students ‘at risk’, risk prediction, and mitigation are being automated through learning analytics. Understanding risk as integral both to modern society and the educational processes, our goal in this paper is to trace the politics of risk prediction and educational futures. We argue that the future orientation of educational technologies materialise in form of design features. To analyse these future making features, we examine the risk prediction-related design features of five globally used learning management systems. We consider their politics for future making through the ways in which they define what is problematic, what is thinkable, and what is desirable in education. We discuss (1) the promises and aspirations these learning management systems promote to educators about the possibilities of a risk-free educational future; (2) how risk prediction features work, e.g., on which different data categories they operate; and (3) the resulting politics of who is perceived as a carrier of risk in education and who is called upon to act. We close with a discussion on the politics (and risk) of aspiring for risk-free learning and risk mitigation in datafied education.

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Learning analytics and assessment: Emerging research trends, promises and future opportunities

Cited by 5 publications

References 24 publications

How do students learn with real‐time personalized scaffolds?

How do students learn with real‐time personalized scaffolds?

Uncovering Students’ Temporal Quiz-Taking Behavior Patterns Using Sequence Analysis

Do Predictive Analytics Dream of Risk-Free Education? The Politics of Risk Mitigation

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