Academic Analytics: A New Tool for a New Era.

By John P. Campbell, Peter Deblois, Diana G. Oblinger. 2007

578 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e43590442141de49233ad0905f9d4557a58faa79

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Citations in the corpus

Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning (2019, background): “Thus, the mapping between Campbell and Oblinger’s five-step learning analytics model and the results from the review study are the following: 1) capture will incorporate the different data collection methods commonly used in the selected studies to gather user data; 2) report will refer to the techn”
Using learning analytics to predict (and improve) student success: a faculty perspective (2013, methodology): “Once the data have been warehoused, can anyone have access to it (Campbell et al., 2007; Greller & Drachsler, 2012)? Finally, there is the issue of whether or not we are really measuring student learning, or are we just attempting to boost student retention and course completion (Watters, 2012).”
Learning analytics: drivers, developments and challenges (2012, background): “The Educause Review (Campbell et al., 2007) presented a bleak view of the US education system trailing other developed countries, with some graduates lacking even basic competencies.”
A Systematic Review on Educational Data Mining (2017, background): “Educational institutions are increasingly being held accountable for the academic success of their students [7].”
Learning Analytics (2013, background): “Given the wide range of information that may be included in such models, there is a recognized danger of potential bias and oversimplification (Bienkowski et al., 2012; Campbell et al., 2007; May, 2011).”
Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review. (2016, methodology): “Academic analytics combines “large data sets, statistical techniques, and predictive modeling” (Campbell et al., 2007, p. 42).”
Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success (2016, methodology): “Regardless of the data source, the prediction of student grades is generally determined by applying logistic regression (Barber & Sharkey, 2012; Campbell, DeBlois, & Oblinger, 2007; Lauría, Baron, Devireddy, Sundararaju, & Jayaprakash, 2012; Palmer, 2013).”
Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory (2015, background): “For instance, Campbell, DeBlois, and Oblinger (2007) employed logistic regression, neural networks and other models to search for students that are at-risk of failing and alert instructors to potential issues.”
A reference model for learning analytics (2012, background): “3 student academic success and were restricted to statistical software (Campbell et al., 2007; Campbell and Oblinger, 2007; Goldstein and Katz, 2005; Norris et al., 2008a).”
The learning analytics cycle: closing the loop effectively (2012, background): “These include speeding up or shortening the cycle so feedback happens more quickly, and widening the audience for feedback (in particular, considering learners and teachers as audiences for analytics) so that it can have a larger impact.”
Social learning analytics: five approaches (2012, background): “The field of learning analytics has its roots in the appropriation of business intelligence concepts by educational institutions: the earlir terms ‘academic analytics’ [1] and ‘action analytics’ [2] refer respectively to the capture and report of data by educational administrators, and to the need f”
Educational data mining and learning analytics: An updated survey (2020, background): “…concerned with the collection, analysis, and visualization of academic program activities such as courses, degree programs; research, revenue of students’ fees, course evaluation, resource allocation and management to generate institutional insight (Campbell et al. 2007) (Long and Siemens, 2014).”