Numbers Are Not Enough. Why e-Learning Analytics Failed to Inform an Institutional Strategic Plan

By Leah P. Macfadyen, Shane Dawson. J. Educ. Technol. Soc., 2012

341 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e8fdbe891218f83adf39fea91f4b373c38999785

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Citations in the corpus

Using learning analytics to predict (and improve) student success: a faculty perspective (2013, methodology): “Macfadyen and Dawson (2012) used LMS tool use frequency (e.g., number of discussion messages read, number of discussion replies posted) to predict student achievement (course grade).”
Let’s not forget: Learning analytics are about learning (2014, result): “The difference in instructional conditions is likely to shed light on the inconsistent results of the trace data-based predictors of academic success that are often reported in the literature (Jayaprakash et al., 2014; Macfadyen & Dawson, 2012).”
Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning (2014, methodology): “Furthermore, empirical data from almost 53,000 students showed a great difference between the four most used functionalities of a VLE – content pages, discussions, course organization and assessment – and the rest of activities (Macfadyen & Dawson, 2012).”
Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success (2016, background): “For example, where Macfadyen & Dawson (2012) identified a strong correlation between student discussion forum activity and overall academic grades at a large research intensive Canadian university (N=52,917), Lauría et al. (2012) found only weak correlations (ranging from 0.098 to 0.233) between…”
An overview of learning analytics (2013, background): “The nightmare vision of Big Data for individuals is that the system does not rely on self-surveillance to enforce a disciplinary regime: all actions are captured, logged, and analysed, so transgressions will be noted regardless of whether the jailer happens to be looking in the right direction.”
Big Data and analytics in higher education: Opportunities and challenges (2015, background): “The importance of communicating these ideas is also acknowledged by Macfadyen and Dawson (2012), who pointed out that analytics have a negative or neutral impact on educational planning.”
Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy (2020, background): “In line with Macfadyen and Dawson (2012), we can see a learning analytics approach has helped to give some constructive meaning to the data gathered on the eLearning platforms to understand better our students’ learning patterns and experiences.”
Utilising learning analytics to support study success in higher education: a systematic review (2020, unknown): “…as their geo-and socio-demographic backgrounds (Lacave et al. 2018), previous qualifications (Daud et al. 2017), their engagement in the recruitment journey (Berg et al. 2018), activities on websites (Seidel and Kutieleh 2017), and tracking information on their searches (Macfadyen and Dawson 2012).”