Search results for author:"Ryan S. Baker"
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Stupid Tutoring Systems, Intelligent Humans
Ryan S. Baker
International Journal of Artificial Intelligence in Education Vol. 26, No. 2 (2016) pp. 600–614
The initial vision for intelligent tutoring systems involved powerful, multi-faceted systems that would leverage rich models of students and pedagogies to create complex learning interactions. But the intelligent tutoring systems used at scale today ...
Educational Data Mining and Learning Analytics: Applications to Constructionist Research
Matthew Berland; Ryan S. Baker; Paulo Blikstein
Technology, Knowledge and Learning Vol. 19, No. 1 (July 2014) pp. 205–220
Constructionism can be a powerful framework for teaching complex content to novices. At the core of constructionism is the suggestion that by enabling learners to build creative artifacts that require complex content to function, those learners will ...
Assessment of Robust Learning with Educational Data Mining
Ryan S. Baker; Albert T. Corbett
Research & Practice in Assessment Vol. 9 (2014) pp. 38–50
Many university leaders and faculty have the goal of promoting learning that connects across domains and prepares students with skills for their whole lives. However, as assessment emerges in higher education, many assessments focus on knowledge and ...
Unifying Computer-Based Assessment across Conceptual Instruction, Problem-Solving, and Digital Games
William L. Miller; Ryan S. Baker; Lisa M. Rossi
Technology, Knowledge and Learning Vol. 19, No. 1 (July 2014) pp. 165–181
As students work through online learning systems such as the Reasoning Mind blended learning system, they often are not confined to working within a single educational activity; instead, they work through various different activities such as...
Operationalizing and Detecting Disengagement within Online Science Microworlds
Janice D. Gobert; Ryan S. Baker; Michael B. Wixon
Educational Psychologist Vol. 50, No. 1 (2015) pp. 43–57
In recent years, there has been increased interest in engagement during learning. This is of particular interest in the science, technology, engineering, and mathematics domains, in which many students struggle and where the United States needs...
Replicating Studying Adaptive Learning Efficacy using Propensity Score Matching and Inverse Probability of Treatment Weighting
Shirin Mojarad; Ryan S. Baker; Alfred Essa; Steve Stalzer
Journal of Interactive Learning Research Vol. 32, No. 3 (August 2021) pp. 169–203
Despite the importance of replication, it remains rare in the interactive learning research community. In this paper, we attempt to replicate recent quasi-experimental results suggesting that the ALEKS intelligent tutoring system is effective at...
Incorporating Effective E-Learning Principles to Improve Student Engagement in Middle-School Mathematics
Kevin Mulqueeny; Victor Kostyuk; Ryan S. Baker; Jaclyn Ocumpaugh
International Journal of STEM Education Vol. 2, No. 1 (2015)
Background: The expanded use of online and blended learning programs in K-12 STEM education has led researchers to propose design principles for effective e-learning systems. Much of this research has focused on the impact on learning, but not how...
Predicting Robust Learning with the Visual Form of the Moment-by-Moment Learning Curve
Ryan S. Baker; Arnon Hershkovitz; Lisa M. Rossi; Adam B. Goldstein; Sujith M. Gowda
Journal of the Learning Sciences Vol. 22, No. 4 (2013) pp. 639–666
We present a new method for analyzing a student's learning over time for a specific skill: analysis of the graph of the student's moment-by-moment learning over time. Moment-by-moment learning is calculated using a data-mined model that...
Carelessness and Affect in an Intelligent Tutoring System for Mathematics
Maria Ofelia Z. San Pedro; Ryan S. J. de Baker; Ma Mercedes T. Rodrigo
International Journal of Artificial Intelligence in Education Vol. 24, No. 2 (June 2014) pp. 189–210
We investigate the relationship between students' affect and their frequency of careless errors while using an Intelligent Tutoring System for middle school mathematics. A student is said to have committed a careless error when the student'...
Comparing the Factors That Predict Completion and Grades among For-Credit and Open/MOOC Students in Online Learning
Ma. Victoria Almeda; Joshua Zuech; Chris Utz; Greg Higgins; Rob Reynolds; Ryan S. Baker
Online Learning Vol. 22, No. 1 (2018) pp. 1–18
Online education continues to become an increasingly prominent part of higher education, but many students struggle in distance courses. For this reason, there has been considerable interest in predicting which students will succeed in online...
Knowledge Elicitation Methods for Affect Modelling in Education
Kaska Porayska-Pomsta; Manolis Mavrikis; Sidney D'Mello; Cristina Conati; Ryan S. J. d. Baker
International Journal of Artificial Intelligence in Education Vol. 22, No. 3 (2013) pp. 107–140
Research on the relationship between affect and cognition in Artificial Intelligence in Education (AIEd) brings an important dimension to our understanding of how learning occurs and how it can be facilitated. Emotions are crucial to learning, but...
Towards Automatically Detecting Whether Student Learning Is Shallow
Sujith M. Gowda; Ryan S. Baker; Albert T. Corbett; Lisa M. Rossi
International Journal of Artificial Intelligence in Education Vol. 23, No. 1 (November 2013) pp. 50–70
Recent research has extended student modeling to infer not just whether a student knows a skill or set of skills, but also whether the student has achieved robust learning--learning that enables the student to transfer their knowledge and prepares...
Generalizing Automated Detection of the Robustness of Student Learning in an Intelligent Tutor for Genetics
Ryan S. J. d. Baker; Albert T. Corbett; Sujith M. Gowda
Journal of Educational Psychology Vol. 105, No. 4 (November 2013) pp. 946–956
Recently, there has been growing emphasis on supporting robust learning within intelligent tutoring systems, assessed by measures such as transfer to related skills, preparation for future learning, and longer term retention. It has been shown that...
From Log Files to Assessment Metrics: Measuring Students' Science Inquiry Skills Using Educational Data Mining
Janice D. Gobert; Michael Sao Pedro; Juelaila Raziuddin; Ryan S. Baker
Journal of the Learning Sciences Vol. 22, No. 4 (2013) pp. 521–563
We present a method for assessing science inquiry performance, specifically for the inquiry skill of designing and conducting experiments, using educational data mining on students' log data from online microworlds in the Inq-ITS system ...
Detecting Learning Moment-by-Moment
Ryan S. J. D. Baker; Adam B. Goldstein; Neil T. Heffernan
International Journal of Artificial Intelligence in Education Vol. 21, No. 1 (2011) pp. 5–25
Intelligent tutors have become increasingly accurate at detecting whether a student knows a skill, or knowledge component (KC), at a given time. However, current student models do not tell us exactly at which point a KC is learned. In this paper, we ...
On the Benefits of Seeking (and Avoiding) Help in Online Problem-Solving Environments
Ido Roll; Ryan S. J. d. Baker; Vincent Aleven; Kenneth R. Koedinger
Journal of the Learning Sciences Vol. 23, No. 4 (2014) pp. 537–560
Seeking the right level of help at the right time can support learning. However, in the context of online problem-solving environments, it is still not entirely clear which help-seeking strategies are desired. We use fine-grained data from 38 high...
Middle School Engagement with Mathematics Software and Later Interest and Self-Efficacy for STEM Careers
Jaclyn Ocumpaugh; Maria Ofelia San Pedro; Huei-yi Lai; Ryan S. Baker; Fred Borgen
Journal of Science Education and Technology Vol. 25, No. 6 (2016) pp. 877–887
Research suggests that trajectories toward careers in science, technology, engineering, and mathematics (STEM) emerge early and are influenced by multiple factors. This paper presents a longitudinal study, which uses data from 76 high school...
Development of a Workbench to Address the Educational Data Mining Bottleneck
Ma Mercedes T. Rodrigo; Ryan S. J. d. Baker; Bruce M. McLaren; Alejandra Jayme; Thomas T. Dy
International Conference on Educational Data Mining (EDM) 2012 (June 2012)
In recent years, machine-learning software packages have made it easier for educational data mining researchers to create real-time detectors of cognitive skill as well as of metacognitive and motivational behavior that can be used to improve...
Off-task behavior in elementary school children
Karrie E. Godwin; Ma. V. Almeda; Howard Seltman; Shimin Kai; Mandi D. Skerbetz; Ryan S. Baker; Anna V. Fisher
Learning and Instruction Vol. 44, No. 1 (August 2016) pp. 128–143
This paper reports results from a large-scale observational study investigating attention allocation during instructional activities in elementary school students (kindergarten through fourth-grade). In Study 1, 22 classrooms participated while a...
Detecting and Understanding the Impact of Cognitive and Interpersonal Conflict in Computer Supported Collaborative Learning Environments
David Nadler Prata; Ryan S. J. d. Baker; Evandro d. B. Costa; Carolyn P. Rose; Yue Cui; Adriana M. J. B. de Carvalho
International Conference on Educational Data Mining (EDM) 2009 (July 2009)
This paper presents a model which can automatically detect a variety of student speech acts as students collaborate within a computer supported collaborative learning environment. In addition, an analysis is presented which gives substantial insight ...
Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra
Ryan S. J. d. Baker; Sujith M. Gowda; Michael Wixon; Jessica Kalka; Angela Z. Wagner; Aatish Salvi; Vincent Aleven; Gail W. Kusbit; Jaclyn Ocumpaugh; Lisa Rossi
International Conference on Educational Data Mining (EDM) 2012 (June 2012)
In recent years, the usefulness of affect detection for educational software has become clear. Accurate detection of student affect can support a wide range of interventions with the potential to improve student affect, increase engagement, and...
More confusion and frustration, better learning: The impact of erroneous examples
J. Elizabeth Richey; Juan Miguel L. Andres-Bray; Michael Mogessie; Richard Scruggs; Juliana M.A.L. Andres; Jon R. Star; Ryan S. Baker; Bruce M. McLaren
Computers & Education Vol. 139, No. 1 (October 2019) pp. 173–190
Prior research suggests students can sometimes learn more effectively by explaining and correcting example problems that have been solved incorrectly, compared to problem-solving practice or studying correct solutions. It remains unclear, however,...
Detecting and Addressing Frustration in a Serious Game for Military Training
Jeanine A. DeFalco; Jonathan P. Rowe; Luc Paquette; Vasiliki Georgoulas-Sherry; Keith Brawner; Bradford W. Mott; Ryan S. Baker; James C. Lester
International Journal of Artificial Intelligence in Education Vol. 28, No. 2 (2018) pp. 152–193
Tutoring systems that are sensitive to affect show considerable promise for enhancing student learning experiences. Creating successful affective responses requires considerable effort both to detect student affect and to design appropriate...