The Positive Impact of Deliberate Writing Course Design on Student Learning Experience and Performance

Lancaster, Alia; Moses, P. Scott; Clark, Martyn; Masters, Megan C.

doi:10.18608/jla.2020.73.5

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Although it is possible to collect multiple types of data about the interaction between students and an LMS, identifying which types of data is more effective to predict student performance is essential to deliver LA indicators that provide actionable insights to educators that will eventually guide them to improve their LD. For example, in Lancaster et al (2020) researchers concluded that students' page views of the course was not a good predictor of performance (which is also supported in a literature review by Mangaroska and Giannakos (2019)), while discussion in an internal forum did predict overall performance. As a form of augmentation to traditional LA, Mangaroska et al (2020) emphasize the need for 'learning analytics that are consequential for learning, rather than easy and convenient to collect'.…”

Section: Learning Analytics Support For Learning Designmentioning

confidence: 77%

“…Teachers may also involve making decisions about useful data sources that could support their subsequent reflections. Different types of LA indicators have been used in research related to LA and LD, namely students' pageviews in LMS (Kaliisa et al, 2020;Lancaster et al, 2020), entries posted in discussion forums (Kaliisa et al, 2020;Lancaster et al, 2020), final grades (Lancaster et al, 2020) and physiological data from involuntary reactions such as electrodermal activity or body temperature (Mangaroska et al, 2020). Although it is possible to collect multiple types of data about the interaction between students and an LMS, identifying which types of data is more effective to predict student performance is essential to deliver LA indicators that provide actionable insights to educators that will eventually guide them to improve their LD.…”

Section: Learning Analytics Support For Learning Designmentioning

confidence: 99%

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Learning analytics support to teachers' design and orchestrating tasks

Amarasinghe

Michos

Crespi

et al. 2022

Computer Assisted Learning

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Background: Data-driven educational technology solutions have the potential to support teachers in different tasks, such as the designing and orchestration of collaborative learning activities. When designing, such solutions can improve teacher understanding of how learning designs impact student learning and behaviour; and guide them to refine and redesign future learning designs. When orchestrating educational scenarios, data-driven solutions can support teacher awareness of learner participation and progress and enhance real time classroom management. Objectives: The use of learning analytics (LA) can be considered a suitable approach to tackle both problems. However, it is unclear if the same LA indicators are able to satisfactorily support both the designing and orchestration of activities. This study aims to investigate the use of the same LA indicators for supporting multiple teacher tasks, that is, design, redesign and orchestration, as a gap in the existing literature that requires further exploration. Methods: In this study, first we refer to the previous work to study the use of different LA to support both tasks. Then we analyse the nature of the two tasks focusing on a case study that uses the same collaborative learning tool with LA to support both tasks. Implications: The study findings led to derive design considerations on LA support for teachers' design and orchestrating tasks.

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Section: Learning Analytics Support For Learning Designmentioning

confidence: 77%

Section: Learning Analytics Support For Learning Designmentioning

confidence: 99%

Learning analytics support to teachers' design and orchestrating tasks

Amarasinghe

Michos

Crespi

et al. 2022

Computer Assisted Learning

View full text Add to dashboard Cite

show abstract

“…However, the majority of studies appealed to relevant concepts and approaches rather than established theoretical frameworks, approaching the issue of LA‐LD integration. These included the following examples: LA process model and stage‐based model of personal informatics systems (Kokoç & Altun, 2021), technology‐enhanced learning design patterns (Kaliisa et al, 2020), learning analytics implementation principles of coordination, comparison, and customization (Lancaster et al, 2020), ADDIE instructional design model (Molina‐Carmona et al, 2017).…”

Section: Resultsmentioning

confidence: 99%

Learning analytics driven improvements in learning design in higher education: A systematic literature review

Drugova,

Zhuravleva,

Zakharova

et al. 2023

Computer Assisted Learning

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BackgroundDriven by the ongoing need to provide high‐quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear.ObjectivesThis study is aimed to evaluate the maturity of research discussing LA‐driven LD improvements in higher education.MethodsThe systematic review analyses 49 empirical papers, assesses their quality and suggests further research directions. The review elaborates on methodological (research questions, strategy and methods, LA‐LD integration theoretical backgrounds) and substantial (LA‐driven LD improvements, types of data used, LA software development) features of the papers.Results and ConclusionsThe findings demonstrated the lack of theoretical alignment between LA and LD, with research tending towards user experience studies. The most frequently used research strategy was a case study; experiments were very rare. Researchers predominantly used parsing for collecting data and AI methods for analysing it; mostly used data types related to registering learners' engagement with learning activities as well as resources and tools provided in digital learning environments.TakeawaysThe research area discussing LA‐driven LD improvements still has a way to go before attaining the level of full maturity. Only a third of the papers reported actual LA‐driven LD improvements; moreover, only three papers measured their effectiveness. The presented LA software was mostly at the beta or implementation stages and did not assess the impact of using this software.

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“…This seems to be a promising approach since previous research has already used analytics to inform the design of policies in the educational context. Computational methods have, e.g., been used to evaluate how the design of courses on learning management systems affects learning goals (Lancaster et al, 2020) or to predict students' progress on computer-assisted tasks (Faucon et al, 2020). This paper will explore whether predictive analytics can be used to design affirmative action policies.…”

Section: Affirmative Actionmentioning

confidence: 99%

Designing Affirmative Action Policies under Uncertainty

Hertweck¹

2022

Learning Analytics

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We study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs. In the context of this system, we explore affirmative action policies that seek to narrow the gap between the admission rates of different socio-demographic groups while still accepting students with high scores. Since there is uncertainty about the score distribution of the students who will apply to each program, it is unclear what policy would have the desired effect on the admission rates of different groups. We address this challenge by using a predictive model trained on historical data to help optimize the parameters of such policies. We find that a learned predictive model does significantly better than relying on the ideal parameters for the last year. At the same time, we also find that a large pool of historical data yields similar results as our predictive approach for our data. Due to the more complex nature of the predictive approach, we conclude that a simpler approach should be preferred if enough data is available (e.g., long-standing, traditional university programs), but not for newer programs and other cases in which our predictive strategy can prove helpful.

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The Positive Impact of Deliberate Writing Course Design on Student Learning Experience and Performance

Cited by 5 publications

References 8 publications

Learning analytics support to teachers' design and orchestrating tasks

Learning analytics support to teachers' design and orchestrating tasks

Learning analytics driven improvements in learning design in higher education: A systematic literature review

Designing Affirmative Action Policies under Uncertainty

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