2017 journal article

Assessing implicit science learning in digital games

COMPUTERS IN HUMAN BEHAVIOR, 76, 617–630.

By: E. Rowe, J. Asbell-Clarke, R. Baker*, M. Eagle n, A. Hicks n, T. Barnes n, R. Brown n, T. Edwards

author keywords: Computer-based assessment; Implicit science learning; Game-based learning; Educational data mining; Learning analytics
TL;DR: Results suggest GBLA shows promise for future work such as adaptive games and in-class, data-driven formative assessments, but design of the assessment mechanics must be carefully crafted for each game. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science; OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2015 article

Exploring Missing Behaviors with Region-Level Interaction Network Coverage

ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015, Vol. 9112, pp. 831–835.

By: M. Eagle n & T. Barnes n

TL;DR: The possibility of using frequency estimation to uncover locations in the network with differing amounts of student-saturation can be used to locate specific problem approaches and strategies that would be most improved by additional student-data, as well as provide a measure of confidence when comparing across networks or between groups. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (Web of Science; OpenAlex)
Source: Web Of Science
Added: August 6, 2018

2014 conference paper

Modeling student dropout in tutoring systems

Intelligent tutoring systems, its 2014, 8474, 676–678.

By: M. Eagle n & T. Barnes n

TL;DR: This work explores how to use previously collected data to build models of student dropout over time, and uses survival analysis, a statistical method of measuring time to event data, to model how long students can expect to interact with a tutor. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: NC State University Libraries
Added: August 6, 2018

2014 conference paper

Survival analysis on duration data in intelligent tutors

Intelligent tutoring systems, its 2014, 8474, 178–187.

By: M. Eagle n & T. Barnes n

TL;DR: It is demonstrated that survival analysis is applicable to duration data collected from intelligent tutors and is particularly useful when a study experiences participant attrition. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: NC State University Libraries
Added: August 6, 2018

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