Eric Cosyn

dblp:92/4058 · DBLP profile ↗
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11ranked-venue papers
0as first author
5since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 10 · 5 since 2021Human-computer interaction and ubiquitous computing · 4 · 2 since 2021Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Theory of computation · 1
YearPublicationVenuePosition
2024 An Evaluation of a Placement Assessment for an Adaptive Learning System
Jeffrey Matayoshi, Eric Cosyn, Christopher Lechuga, Hasan Uzun
EDM2
2022 Does Practice Make Perfect? Analyzing the Relationship Between Higher Mastery and Forgetting in an Adaptive Learning System
Jeffrey Matayoshi, Eric Cosyn, Hasan Uzun
EDM2
2022 Using a Randomized Experiment to Compare the Performance of Two Adaptive Assessment Engines
Jeffrey Matayoshi, Hasan Uzun, Eric Cosyn
EDM3
2021 Pre-course Prediction of At-Risk Calculus Students
Raktim Mukhopadhyay, Rishabh Ranjit Kumar Jain, Jeffrey Matayoshi, Eric Cosyn, Hasan Uzun
AIED (2)5
2021 Evaluating the Impact of Research-Based Updates to an Adaptive Learning System
Jeffrey Matayoshi, Eric Cosyn, Hasan Uzun
AIED (2)2
2020 Studying Retrieval Practice in an Intelligent Tutoring System
abstract
Retrieval practice (also known as testing effect or test-enhanced learning) is a well-studied and established technique for improving the retention of knowledge. Many previous works have confirmed the benefits of retrieval practice in laboratory experiments involving the memorization of words or facts. In this study, we build on these works and analyze retrieval practice in an intelligent tutoring system. Using a large data set composed of the actions of almost 4 million students studying math and chemistry, we look at the possible benefits of retrieval practice in the ALEKS adaptive learning and assessment system. We compare two different types of retrieval practice---one involving the assessment of learned material, and another involving the learning of closely related content that builds on the learned material---leveraging the scale of the available data to control for several confounding variables. Finally, we look at the timing of retrieval practice within the system and the possible effect it has on forgetting. The results indicate that a delay in retrieval practice is associated with better retention and that, while being assessed on learned material is beneficial, the learning of closely related content is associated with an even higher rate of retention.
Jeffrey Matayoshi, Hasan Uzun, Eric Cosyn
L@S3
2019 Using Recurrent Neural Networks to Build a Stopping Algorithm for an Adaptive Assessment
Jeffrey Matayoshi, Eric Cosyn, Hasan Uzun
AIED (2)2
2019 Deep (Un)Learning: Using Neural Networks to Model Retention and Forgetting in an Adaptive Learning System
Jeffrey Matayoshi, Hasan Uzun, Eric Cosyn
AIED (1)3
2018 Forgetting curves and testing effect in an adaptive learning and assessment system
Jeffrey Matayoshi, Umberto Granziol, Christopher Doble, Hasan Uzun, Eric Cosyn
EDM5
2018 Identifying Student Learning Patterns with Semi-Supervised Machine Learning Models
Jeffrey Matayoshi, Eric Cosyn
ICCE2
2006 The Assessment of Knowledge, in Theory and in Practice
Jean-Claude Falmagne, Eric Cosyn, Jean-Paul Doignon, Nicolas Thiéry 0001
ICFCA2