Data-informed nudges for student engagement and success

Blumenstein, Marion; Liu, Danny Y. T.; Richards, Deborah; Leichtweis, S.; Stephens, J. W. W.

doi:10.4324/9781351113038-12

Cited by 7 publications

(15 citation statements)

References 25 publications

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“…In the context of realizing strategic educational goals, LA promises to play an important role in practice and policy. There is already much evidence for this trend worldwide through nationally funded projects (e.g., Blumenstein et al, 2018;West, Huijser, & Heath, 2018). Typically, LA in practice is approached either top-down or bottom-up (Colvin et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…Maybe the emphasis needs to shift to a more holistic perspective of learning gains across behavioural and academic dimensions, including student well-being and emotional support. Despite recent advances in personalizing learning using embedded LA tools (e.g., Pardo et al, 2019;Blumenstein, Liu, Richards, Leichtweis, & Stephens, 2018), we need to gain a better understanding of what nudges mean to the learner. Students, especially first years, generally appreciated nudges (Pardo et al, 2017;Pardo et al, 2019), surprised that someone cared about their individual progress and well-being.…”

Section: Personalization -Tailored Learning and Predictive Analyticsmentioning

confidence: 99%

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Synergies of Learning Analytics and Learning Design: A Systematic Review of Student Outcomes

Blumenstein

2020

JLA

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The field of learning analytics (LA) has seen a gradual shift from purely data-driven approaches to more holistic views of improving student learning outcomes through data-informed learning design (LD). Despite the growing potential of LA in higher education (HE), the benefits are not yet convincing to the practitioner, in particular aspects of aligning LA data with LD toward desired learning outcomes. This review presents a systematic evaluation of effect sizes reported in 38 key studies in pursuit of effective LA approaches to measuring student learning gain for the enhancement of HE pedagogy and delivery. Large positive effects on student outcomes were found in LDs that fostered socio-collaborative and independent learning skills. Recent trends in personalization of learner feedback identified a need for the integration of student-idiosyncratic factors to improve the student experience and academic outcomes. Finally, key findings are developed into a new three-level framework, the LA Learning Gain Design (LALGD) model, to align meaningful data capture with pedagogical intentions and their learning outcomes. Suitable for various settings — face to face, blended, or fully online — the model contributes to data-informed learning and teaching pedagogies in HE.

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Section: Discussionmentioning

confidence: 99%

Section: Personalization -Tailored Learning and Predictive Analyticsmentioning

confidence: 99%

Synergies of Learning Analytics and Learning Design: A Systematic Review of Student Outcomes

Blumenstein

2020

JLA

Self Cite

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“…Although the attribute "timely feedback about my progress" (Q10) was sitting at the border of the quadrant "Concentrate here", this attribute still performed rather highly. These positive perceptions of students toward their personalised, data-informed feedback in the form of instructor emails demonstrate Blumenstein et al's (2019) principle of translating and applying learning analytics for the classroom, namely, to ensure the presence of the 'human element', as the student-instructor relationship is an important precursor to student engagement. Thus, personalised, data-informed feedback should emphasise the care of the instructor toward the student, and written in such a way that shows it was a personal communication from the instructor herself.…”

Section: Implications For Using Learning Analytics To Support Student...mentioning

confidence: 98%

“…Armed with greater control over their learning, they were subsequently more motivated to learn, signifying an increase in affective-emotional engagement (Fredricks, Blumenfeld, & Paris, 2004;Kahu & Nelson, 2018). This engagement is a psychological response which is elicited when students engage in academic activities (Blumenstein, Liu, Richards, Leichtweis, & Stephens, 2019).…”

Section: Students' Recipience and Perceptions Of Personalised Data-in...mentioning

confidence: 99%

Feeling supported: Enabling students in diverse cohorts through personalised, data-informed feedback

Lim¹,

Fudge²,

Dawson³

2022

APUBS

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Students entering enabling programs as an alternative pathway to University tend to have higher rates of attrition than their peers admitted via more traditional pathways. Students in enabling programs require high levels of personalised feedback to support their transition to study. However, the size and diversity of the enabling student cohort presents formidable challenges for instructors. The field of learning analytics offers a viable solution for scaling the communication of personalised, data-informed feedback to support student learning. This study describes the use of a novel learning analytics-based feedback system called OnTask, to provide personalised feedback and support to students in an enabling course at one Australian higher education institution. An end-of-course student survey (N=41; 17% response rate) was employed to gain insights into their perceptions of personalised, data-informed feedback messages. Using importance-performance analysis (IPA), the survey results indicated that this technology-mediated form of feedback exceeded students’ expectations of learning support, as well as the enhancement of their overall course experience. The implications for using learning analytics and data-informed feedback mechanisms in teaching and learning are discussed.

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“…Activity in the learning management system (LMS) has been used in studies as a proxy for student engagement (e.g. Beer et al, 2010; Blumenstein et al, 2019; Chaka and Nkhobo, 2019; Fritz, 2013; Henrie et al, 2018; Macfadyen and Dawson, 2010), as activity in the LMS has been found to contribute to better outcomes as measured by final grade (Fritz, 2013; Macfadyen and Dawson, 2010).…”

Section: Student Engagementmentioning

confidence: 99%

The creation of a nudging protocol to support online student engagement in higher education

Brown

Lawrence

Basson

et al. 2022

Active Learning in Higher Education

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Combining nudge theory with learning analytics, ‘nudge analytics’, is a relatively recent phenomenon in the educational context. Used, for example, to address such issues as concerns with student (dis)engagement, nudging students to take certain action or to change a behaviour towards active learning, can make a difference. However, knowing who to nudge, how to nudge or when to nudge can be a challenge. Providing students with strategic, sensitive nudges that help to move them forward is almost an art form. It requires not only technical skills to use appropriate software and interpret data, but careful consideration of what to say and how to say it. In this article a nudge protocol is presented that can be used in online courses to encourage student engagement with key course resources that are integral to supporting their learning.

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Data-informed nudges for student engagement and success

Cited by 7 publications

References 25 publications

Synergies of Learning Analytics and Learning Design: A Systematic Review of Student Outcomes

Synergies of Learning Analytics and Learning Design: A Systematic Review of Student Outcomes

Feeling supported: Enabling students in diverse cohorts through personalised, data-informed feedback

The creation of a nudging protocol to support online student engagement in higher education

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