Course signals at Purdue: using learning analytics to increase student success

By Kimberly E. Arnold, M. Pistilli. International Conference on Learning Analytics and Knowledge, 2012

1,095 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b50f3076848aff91139dd19229618c3d21b346c2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Education

In Media Technology

Citations in the corpus

Models for early prediction of at-risk students in a course using standards-based grading (2016, background): “Use of an early warning system in a course, along with intervention guidelines, can increase students ’ success in a course (Arnold & Pistilli, 2012; Essa & Ayad, 2012; Macfadyen & Dawson, 2010).”
Let’s not forget: Learning analytics are about learning (2014, background): “However, far less attention has been dedicated to the other elements of COPES, such as how and to what extent the conditions affect the operations performed, the products, and the standards used for metacognitive monitoring.”
An overview of learning analytics (2013, background): “Results so far are impressive (Arnold and Pistilli 2012).”
Learning dashboards: an overview and future research opportunities (2013, background): “In contrast to Course Signals, this dashboard presents at a more fine grained level which concepts may need additional attention of the student and how she is performing on different course activities.”
Predicting academic performance of students from VLE big data using deep learning models (2020, background): “These applications induce a positive drive in students learning, consequently impacting performance (Arnold & Pistilli, 2012).”
Stupid Tutoring Systems, Intelligent Humans (2016, background): “Course Signals has been found to lead to significantly higher course and university retention (Arnold and Pistilli 2012).”
Utilising learning analytics to support study success in higher education: a systematic review (2020, background): “Visualisation is realised through optical signals (Arnold and Pistilli 2012) and learning analytics features, which are distinguishing elements for supporting individual learning processes (Few 2013) or aiming to facilitate reflection (Dorodchi et al. 2018).”
Analyzing undergraduate students' performance using educational data mining (2017, background): “…as well as the appropriateness of classical classi ﬁ cation algorithms and clustering algorithms to predict students' success or failure in a course; Arnold and Pistilli (2012) provide an early intervention solution for dif ﬁ cult courses based on students' activity in a Learning Management System.”
Learning Analytics Dashboard Applications (2013, background): “Course Signals (Arnold & Pistilli, 2012) was evaluated on a very large scale for 3 academic years.”
Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores (2018, background): “Instructors can use ML algorithms to obtain studentrelated information in real time, which helps them intervene during early course stages [29, 30].”
MOOCs and the funnel of participation (2013, background): “Course Signals at Purdue: Using learning analytics to increase student success.”
Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning (2019, background): “Previous research [14] supports and promotes evidencebased practices of learning analytics’ potential in understanding and optimizing the complexities of the learning process.”