A Global South Perspective on Learning Analytics in an Open Distance E-learning (ODeL) Institution

Fynn, Angelo; Adamiak, J.P.; Young, Kelly Anne

doi:10.1007/978-981-19-0786-9_3

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Additionally, as with most of the literature regarding learning analytics (Fynn et al, 2022;Prinsloo, 2018), this study demonstrates the voices of those from the Global North and neglects to amplify voices from the Global South. Therefore, we encourage future studies to build on this research with perspectives across socio-cultural, political, and economic contexts to guide the implementation and practice of equitable learning analytics.…”

Section: Study Limitationssupporting

confidence: 69%

Amplifying Student and Administrator Perspectives on Equity and Bias in Learning Analytics

et al. 2023

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When higher education institutions (HEIs) have the potential to collect large amounts of learner data, it is important to consider the spectrum of stakeholders involved with and impacted by the use of learning analytics. This qualitative research study aims to understand the degree of concern with issues of bias and equity in the uses of learner data as perceived by students, diversity and inclusion leaders, and senior administrative leaders in HEIs. An interview study was designed to investigate stakeholder voices that generate, collect, and utilize learning analytics from eight HEIs in the United States. A phased inductive coding analysis revealed similarities and differences in the three stakeholder groups regarding concerns about bias and equity in the uses of learner data. The study findings suggest that stakeholders have varying degrees of data literacy, thus creating conditions of inequality and bias in learning data. By centring the values of these critical stakeholder groups and acknowledging that intersections and hierarchies of power are critical to authentic inclusion, this study provides additional insight into proactive measures that institutions could take to improve equity, transparency, and accountability in their responsible learning analytics efforts.

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Section: Study Limitationssupporting

confidence: 69%

Amplifying Student and Administrator Perspectives on Equity and Bias in Learning Analytics

et al. 2023

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“…Regarding the functions of EDM and LA, artificial intelligence (AI) technology is the forerunner to the birth of these two research groups (Fynn et al, 2022). AI studies have created predictive technology processed through machine learning (ML) for several purposes, including education (Leitner et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Predicting Physics Students’ Achievement Using In-Class Assessment Data: A Comparison of Two Machine Learning Models

Santoso,

Santosa,

Istiyono

et al. 2023

PERJ

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Data is the primary source to scaffold physics teaching and learning for teachers and students, mainly reported through in-class assessment. Machine learning (ML) is an axis of artificial intelligence (AI) study that immensely attracts the development of physics education research (PER). ML is built to predict students’ learning that can support students’ success in an effective physics achievement. In this paper, two ML algorithms, logistic regression and random forest, were trained and compared to predict students’ achievement in high school physics (N = 197). Data on students’ achievement was harvested from in-class assessments administered by a physics teacher regarding knowledge (cognitive) and psychomotor during the 2020/2021 academic year. Three assessment points of knowledge and psychomotor were employed to predict students’ achievement on a dichotomous scale on the final term examination. Combining in-class assessment of knowledge and psychomotor, we could discover the plausible performance of students’ achievement prediction using the two algorithms. Knowledge assessment was a determinant in predicting high school physics students’ achievement. Findings reported by this paper recommended open room for the implementation of ML for educational practice and its potential contribution to supporting physics teaching and learning.

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A Global South Perspective on Learning Analytics in an Open Distance E-learning (ODeL) Institution

Cited by 2 publications

References 16 publications

Amplifying Student and Administrator Perspectives on Equity and Bias in Learning Analytics

Amplifying Student and Administrator Perspectives on Equity and Bias in Learning Analytics

Predicting Physics Students’ Achievement Using In-Class Assessment Data: A Comparison of Two Machine Learning Models

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