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160 results on '"Baker, Ryan S. J. d."'

1. Affective States and State Tests: Investigating How Affect and Engagement during the School Year Predict End-of-Year Learning Outcomes

Author

Pardos, Zachary A., Baker, Ryan S. J. D., San Pedro, Maria O. C. Z., Gowda, Sujith M., and Gowda, Supreeth M.

Abstract

In this paper, we investigate the correspondence between student affect and behavioural engagement in a web-based tutoring platform throughout the school year and learning outcomes at the end of the year on a high-stakes mathematics exam in a manner that is both longitudinal and fine-grained. Affect and behaviour detectors are used to estimate student affective states and behaviour based on post-hoc analysis of tutor log-data. For every student action in the tutor, the detectors give us an estimated probability that the student is in a state of boredom, engaged concentration, confusion, or frustration, and estimates of the probability that the student is exhibiting off-task or gaming behaviours. We used data from the ASSISTments math tutoring system and found that boredom during problem solving is negatively correlated with performance, as expected; however, boredom is positively correlated with performance when exhibited during scaffolded tutoring. A similar pattern is unexpectedly seen for confusion. Engaged concentration and, surprisingly, frustration are both associated with positive learning outcomes. In a second analysis, we build a unified model that predicts student standardized examination scores from a combination of student affect, disengaged behaviour, and performance within the learning system. This model achieves high overall correlation to standardized exam score, showing that these types of features can effectively infer longer-term learning outcomes.

Published
2014

2. What Different Kinds of Stratification Can Reveal about the Generalizability of Data-Mined Skill Assessment Models

Author

Sao Pedro, Michael A., Baker, Ryan S. J. d., and Gobert, Janice D.

Abstract

When validating assessment models built with data mining, generalization is typically tested at the student-level, where models are tested on new students. This approach, though, may fail to find cases where model performance suffers if other aspects of those cases relevant to prediction are not well represented. We explore this here by testing if scientific inquiry skill models built and validated for one science topic can predict skill demonstration for new students and a new science topic. Test cases were chosen using two methods: student-level stratification, and stratification based on the amount of trials ran during students' experimentation. We found that predictive performance of the models was different on each test set, revealing limitations that would have been missed from student-level validation alone.

Published
2013

3. The Potentials of Educational Data Mining for Researching Metacognition, Motivation and Self-Regulated Learning

Author

Winne, Philip H. and Baker, Ryan S. J. D.

Abstract

Our article introduces the "Journal of Educational Data Mining's" Special Issue on Educational Data Mining on Motivation, Metacognition, and Self-Regulated Learning. We outline general research challenges for data mining researchers who conduct investigations in these areas, the potential of EDM to advance research in this area, and issues in validating findings generated by EDM.

Published
2013

4. Leveraging Machine-Learned Detectors of Systematic Inquiry Behavior to Estimate and Predict Transfer of Inquiry Skill

Author

Sao Pedro, Michael A., Baker, Ryan S. J. d., Gobert, Janice D., Montalvo, Orlando, and Nakama, Adam

Abstract

We present work toward automatically assessing and estimating science inquiry skills as middle school students engage in inquiry within a physical science microworld. Towards accomplishing this goal, we generated machine-learned models that can detect when students test their articulated hypotheses, design controlled experiments, and engage in planning behaviors using two inquiry support tools. Models were trained using labels generated through a new method of manually hand-coding log files, "text replay tagging". This approach led to detectors that can automatically and accurately identify these inquiry skills under student-level cross-validation. The resulting detectors can be applied at run-time to drive scaffolding intervention. They can also be leveraged to automatically score all practice attempts, rather than hand-classifying them, and build models of latent skill proficiency. As part of this work, we also compared two approaches for doing so, Bayesian Knowledge-Tracing and an averaging approach that assumes static inquiry skill level. These approaches were compared on their efficacy at predicting skill before a student engages in an inquiry activity, predicting performance on a paper-style multiple choice test of inquiry, and predicting performance on a transfer task requiring data collection skills. Overall, we found that both approaches were effective at estimating student skills within the environment. Additionally, the models' skill estimates were significant predictors of the two types of inquiry transfer tests. [This paper was published in "User Modeling and User-Adapted Interaction" v23 p1-39 2013.]

Published
2013
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5. Discovery with Models: A Case Study on Carelessness in Computer-Based Science Inquiry

Author

Hershkovitz, Arnon, Baker, Ryan S. J. d., Gobert, Janice, Wixon, Michael, and Sao Pedro, Michael

Abstract

In recent years, an increasing number of analyses in Learning Analytics and Educational Data Mining (EDM) have adopted a "Discovery with Models" approach, where an existing model is used as a key component in a new EDM/analytics analysis. This article presents a theoretical discussion on the emergence of discovery with models, its potential to enhance research on learning and learners, and key lessons learned in how discovery with models can be conducted validly and effectively. We illustrate these issues through discussion of a case study where discovery with models was used to investigate a form of disengaged behavior, i.e., carelessness, in the context of middle school computer-based science inquiry. This behavior has been acknowledged as a problem in education as early as the 1920s. With the increasing use of high-stakes testing, the cost of student carelessness can be higher. For instance, within computer-based learning environments careless errors can result in reduced educational effectiveness, with students continuing to receive material they have already mastered. Despite the importance of this problem, it has received minimal research attention, in part due to difficulties in operationalizing carelessness as a construct. Building from theory on carelessness and a Bayesian framework for knowledge modeling, we use machine-learned detectors to predict carelessness within authentic use of a computer-based learning environment. We then use a discovery with models approach to link these validated carelessness measures to survey data, to study the correlations between the prevalence of carelessness and student goal orientation. [This paper was published in "American Behavioral Scientist" v57 n10 p1479-1498 2013.]

Published
2013
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6. Development of a Workbench to Address the Educational Data Mining Bottleneck

Author

International Educational Data Mining Society, Rodrigo, Ma. Mercedes T., Baker, Ryan S. J. d., and McLaren, Bruce M.

Abstract

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 student learning. However, there remain challenges to overcome for these methods to become available to the wider educational research and practice communities, including developing the labels that support supervised learning, distilling relevant and appropriate data features, and setting up appropriate cross-validation and configuration algorithms. We discuss the development of an Educational Data Mining (EDM) Workbench designed to address these challenges. (Contains 3 figures.) [This research project was made possible through a grant from the Philippines Department of Science and Technology's Engineering Research and Technology for Development program entitled "Development of an Educational Data Mining Workbench." For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]

Published
2012

7. Towards Sensor-Free Affect Detection in Cognitive Tutor Algebra

Author

International Educational Data Mining Society, Baker, Ryan S. J. d., Gowda, Sujith M., and Wixon, Michael

Abstract

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 improve learning. In addition, accurate detection of student affect could play an essential role in research attempting to understand the root causes and impacts of different forms of affect. However, current approaches to affect detection have largely relied upon sensor systems, which are expensive and typically not physically robust to classroom conditions, reducing their potential real-world impact. Work towards sensor-free affect detection has produced detectors that are better than chance, but not substantially better--especially when subject to stringent cross-validation processes. In this paper we present models which can detect student engaged concentration, confusion, frustration, and boredom solely from students' interactions within a Cognitive Tutor for Algebra. These detectors are designed to operate solely on the information available through students' semantic actions within the interface, making these detectors applicable both for driving interventions and for labeling existing log files in the PSLC DataShop, facilitating future discovery with models analyses at scale. (Contains 1 figure and 2 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]

Published
2012

8. Leveraging Educational Data Mining for Real-Time Performance Assessment of Scientific Inquiry Skills within Microworlds

Author

Gobert, Janice D., Sao Pedro, Michael A., Baker, Ryan S. J. D., Toto, Ermal, and Montalvo, Orlando

Abstract

We present "Science Assistments," an interactive environment, which assesses students' inquiry skills as they engage in inquiry using science microworlds. We frame our variables, tasks, assessments, and methods of analyzing data in terms of "evidence-centered design." Specifically, we focus on the "student model," the "task model," and the "evidence model" in the "conceptual assessment framework." In order to support both assessment and the provision of scaffolding, the environment makes inferences about student inquiry skills using models developed through a combination of text replay tagging [cf. Sao Pedro et al. 2011], a method for rapid manual coding of student log files, and "educational data mining." Models were developed for multiple inquiry skills, with particular focus on detecting if students are testing their articulated hypotheses, and if they are designing controlled experiments. Student-level cross-validation was applied to validate that this approach can automatically and accurately identify these inquiry skills for new students. The resulting detectors also can be applied at run-time to drive scaffolding intervention.

Published
2012

9. Improving Construct Validity Yields Better Models of Systematic Inquiry, Even with Less Information

Author

Sao Pedro, Michael A., Baker, Ryan S. J. d., and Gobert, Janice D.

Abstract

Data-mined models often achieve good predictive power, but sometimes at the cost of interpretability. We investigate here if selecting features to increase a model's construct validity and interpretability also can improve the model's ability to predict the desired constructs. We do this by taking existing models and reducing the feature set to increase construct validity. We then compare the existing and new models on their predictive capabilities within a held-out test set in two ways. First, we analyze the models' overall predictive performance. Second, we determine how much student interaction data is necessary to make accurate predictions. We find that these reduced models with higher construct validity not only achieve better agreement overall, but also achieve better prediction with less data. This work is conducted in the context of developing models to assess students' inquiry skill at designing controlled experiments and testing stated hypotheses within a science inquiry microworld.

Published
2012

10. [Proceedings of the] International Conference on Educational Data Mining (EDM) (3rd, Pittsburgh, PA, July 11-13, 2010)

Author

International Working Group on Educational Data Mining, Baker, Ryan S. J. d., Merceron, Agathe, and Pavlik, Philip I.

Abstract

The Third International Conference on Data Mining (EDM 2010) was held in Pittsburgh, PA, USA. It follows the second conference at the University of Cordoba, Spain, on July 1-3, 2009 and the first edition of the conference held in Montreal in 2008, and a series of workshops within the AAAI, AIED, EC-TEL, ICALT, ITS, and UM conferences. EDM 2011 will be held in Eindhoven, Netherlands. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented educational software and databases of student test scores, has created large repositories of data reflecting how students learn. The EDM conference focuses on computational approaches for analyzing the data to address important educational questions. The broad collection of research disciplines ensures cross fertilization of ideas, with the central questions of educational research serving as a unifying focus. This publication presents the following papers: (1) Effort-based Tutoring: An Empirical Approach to Intelligent Tutoring (Ivon Arroyo, Hasmik Mehranian and Beverly P. Woolf); (2) An Analysis of the Differences in the Frequency of Students' Disengagement in Urban, Rural, and Suburban High Schools (Ryan S.J.d. Baker and Sujith M. Gowda); (3) On the Faithfulness of Simulated Student Performance Data (Michel C. Desmarais and Ildiko Pelczer); (4) Mining Bodily Patterns of Affective Experience during Learning (Sidney D'Mello and Art Graesser); (5) Can We Get Better Assessment From A Tutoring System Compared to Traditional Paper Testing? Can We Have Our Cake (Better Assessment) and Eat It too (Student Learning During the Test)? (Mingyu Feng and Neil Heffernan); (6) Using Neural Imaging and Cognitive Modeling to Infer Mental States while Using an Intelligent Tutoring System (Jon M. Fincham, John R. Anderson, Shawn Betts and Jennifer Ferris); (7) Using multiple Dirichlet distributions to improve parameter plausibility (Yue Gong, Joseph E. Beck and Neil T. Heffernan); (8) Examining Learner Control in a Structured Inquiry Cycle Using Process Mining (Larry Howard, Julie Johnson and Carin Neitzel); (9) Analysis of Productive Learning Behaviors in a Structured Inquiry Cycle Using Hidden Markov Models (Hogyeong Jeong, Gautam Biswas, Julie Johnson and Larry Howard); (10) Data Mining for Generating Hints in a Python Tutor (Anna Katrina Dominguez, Kalina Yacef and James R. Curran); (11) Off Topic Conversation in Expert Tutoring: Waste of Time or Learning Opportunity (Blair Lehman, Whitney Cade and Andrew Olney); (12) Sentiment Analysis in Student Experiences of Learning (Sunghwan Mac Kim and Rafael A. Calvo); (13) Online Curriculum Planning Behavior of Teachers (Keith E. Maull, Manuel Gerardo Saldivar and Tamara Sumner); (14) A Data Model to Ease Analysis and Mining of Educational Data (Andre Kruger, Agathe Merceron and Benjamin Wolf); (15) Identifying Students' Inquiry Planning Using Machine Learning (Orlando Montalvo, Ryan S.J.d. Baker, Michael A. Sao Pedro, Adam Nakama and Janice D. Gobert); (16) Skill Set Profile Clustering: The Empty K-Means Algorithm with Automatic Specification of Starting Cluster Centers (Rebecca Nugent, Nema Dean and Elizabeth Ayers); (17) Navigating the parameter space of Bayesian Knowledge Tracing models: Visualizations of the convergence of the Expectation Maximization algorithm (Zachary Pardos and Neil Heffernan); (18) Mining Rare Association Rules from e-Learning Data (Cristobal Romero, Jose Raul Romero, Jose Maria Luna and Sebastian Ventura); (19) Using Text Replay Tagging to Produce Detectors of Systematic Experimentation Behavior Patterns (Michael Sao Pedro, Ryan S.J.d. Baker, Orlando Montalvo, Adam Nakama and Janice D. Gobert); (20) Identifying High-Level Student Behavior Using Sequence-based Motif Discovery (David H. Shanabrook, David G. Cooper, Beverly Park Woolf and Ivon Arroyo); (21) Unsupervised Discovery of Student Strategies (Benjamin Shih, Kenneth R. Koedinger and Richard Scheines); (22) Assessing Reviewer's Performance Based on Mining Problem Localization in Peer-Review Data (Wenting Xiong, Diane Litman and Christian Schunn); (23) Using Numeric Optimization To Refine Semantic User Model Integration Of Adaptive Educational Systems (Michael Yudelson, Peter Brusilovsky, Antonija Mitrovic and Moffat Mathews); (24) An Annotations Approach to Peer Tutoring (John Champaign and Robin Cohen); (25) Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning (Mohammad Hassan Falakmasir and Jafar Habibi); (26) Mining Students' Interaction Data from a System that Support Learning by Reflection (Rajibussalim); (27) Process Mining to Support Students' Collaborative Writing (Vilaythong Southavilay, Kalina Yacef and Rafael A. Callvo); (28) Automatic Rating of User-Generated Math Solutions (Turadg Aleahmad, Vincent Aleven and Robert Kraut); (29) Tracking Students' Inquiry Paths through Student Transition Analysis (Matt Bachmann, Janice Gobert and Joseph Beck); (30) DISCUSS: Enabling Detailed Characterization of Tutorial Interactions Through Dialogue Annotation (Lee Becker, Wayne H. Ward and Sarel vanVuuren); (31) Data Mining of both Right and Wrong Answers from a Mathematics and a Science M/C Test given Collectively to 11,228 Students from India [1] in years 4, 6 and 8 (James Bernauer and Jay Powell); (32) Mining information from tutor data to improve pedagogical content knowledge (Suchismita Srinivas, Muntaquim Bagadia and Anupriya Gupta); (33) Clustering Student Learning Activity Data (Haiyun Bian); (34) Analyzing Learning Styles using Behavioral Indicators in Web based Learning Environments (Nabila Bousbia, Jean-Marc Labat, Amar Balla and Issam Rebai); (35) Using Topic Models to Bridge Coding Schemes of Differing Granularity (Whitney L. Cade and Andrew Olney); (36) A Distillation Approach to Refining Learning Objects (John Champaign and Robin Cohen); (37) A Preliminary Investigation of Hierarchical Hidden Markov Models for Tutorial Planning (Kristy Elizabeth Boyer, Robert Phillips, Eun Young Ha, Michael D. Wallis, Mladen A. Vouk, and James C. Lester); (38) Higher Contributions Correlate with Higher Learning Gains (Carol Forsyth, Heather Butler, Arthur C. Graesser, Diane Halpern); (39) Pinpointing Learning Moments; A finer grain P(J) model (Adam Goldstein, Ryan S.J.d. Baker and Neil T. Heffernan); (40) Predicting Task Completion from Rich but Scarce Data (Jose P. Gonzalez-Brenes and Jack Mostow); (41) Hierarchical Structures of Content Items in LMS (Sharon Hardof-Jaffe, Arnon Hershkovitz, Ronit Azran and Rafi Nachmias); (42) Is Students' Activity in LMS Persistent? (Arnon Hershkovitz and Rafi Nachmias); (43) EDM Visualization Tool: Watching Students Learn (Matthew M. Johnson and Tiffany Barnes); (44) Inferring the Differential Student Model in a Probabilistic Domain Using Abduction inference in Bayesian networks (Nabila Khodeir, Nayer Wanas, Nevin Darwish and Nadia Hegazy); (45) Using LiMS (the Learner Interaction Monitoring System) to Track Online Learner Engagement and Evaluate Course Design (Leah P. Macfadyen and Peter Sorenson); (46) Observing Online Curriculum Planning Behavior of Teachers (Keith E. Maull, Manuel Gerardo Saldivar and Tamara Sumner); (47) When Data Exploration and Data Mining meet while Analysing Usage Data of a Course (Andre Kruger, Agathe Merceron and Benjamin Wolf); (48) AutoJoin: Generalizing an Example into an EDM query (Jack Mostow and Bao Hong (Lucas) Tan); (49) Conceptualizing Procedural Knowledge Targeted at Students with Different Skill Levels (Martin Mozina, Matej Guid, Aleksander Sadikov, Vida Groznik, Jana Krivec, and Ivan Bratko); (50) Data Reduction Methods Applied to Understanding Complex Learning Hypotheses (Philip I. Pavlik Jr.); (51) Analysis of a causal modeling approach: a case study with an educational intervention (Dovan Rai and Joseph E. Beck); (52) Peer Production of Online Learning Resources: A Social Network Analysis (Beijie Xu and Mimi M. Recker); (53) Class Association Rules Mining from Students' Test Data (Cristobal Romero, Sebastian Ventura, Ekaterina Vasilyeva and Mykola Pechenizkiy); (54) Modeling Learning Trajectories with Epistemic Network Analysis: A Simulation-based Investigation of a Novel Analytic Method for Epistemic Games (Andre A. Rupp, Shauna J. Sweet and Younyoung Choi); (55) Multiple Test Forms Construction based on Bees Algorithm (Pokpong Songmuang and Maomi Ueno); (56) Can Order of Access to Learning Resources Predict Success? (Hema Soundranayagam and Kalina Yacef); (57) A Data Driven Approach to the Discovery of Better Cognitive Models (Kenneth R. Koedinger and John C. Stamper); (58) Using a Bayesian Knowledge Base for Hint Selection on Domain Specific Problems (John C. Stamper, Tiffany Barnes and Marvin Croy); (59) A Review of Student Churn in the Light of Theories on Business Relationships (Jaan Ubi and Innar Liiv); (60) Towards EDM Framework for Personalization of Information Services in RPM Systems (Ekaterina Vasilyeva, Mykola Pechenizkiy, Aleksandra Tesanovic, Evgeny Knutov, Sicco Verwer and Paul De Bra); (61) A Case Study: Data Mining Applied to Student Enrollment (Cesar Vialardi, Jorge Chue, Alfredo Barrientos, Daniel Victoria, Jhonny Estrella, Juan Pablo Peche and Alvaro Ortigosa); (62) Representing Student Performance with Partial Credit (Yutao Wang, Neil T. Heffernan and Joseph E. Beck); (63) Where in the World? Demographic Patterns in Access Data (Mimi M. Recker, Beijie Xu, Sherry Hsi, and Christine Garrard); and (64) Pundit: Intelligent Recommender of Courses (Ankit Ranka, Faisal Anwar, Hui Soo Chae). Individual papers contain tables, figures, footnotes and references

Published
2010

11. Detecting and Understanding the Impact of Cognitive and Interpersonal Conflict in Computer Supported Collaborative Learning Environments

Author

International Working Group on Educational Data Mining, Prata, David Nadler, Baker, Ryan S. J. d., Costa, Evandro d. B., Rose, Carolyn P., Cui, Yue, and de Carvalho, Adriana M. J. B.

Abstract

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 as to how students' learning is associated with students' speech acts, knowledge that will significantly influence how this model is utilized by running learning software. Within Piagetian theory, the cognitive conflict of ideas between students is seen as beneficial for learning. Which sorts of interpersonal behaviors lead to most effective learning, however, is open to debate, with some researchers arguing that cooperation is most effective and others arguing that interpersonal conflict is a natural part of collaborative learning. We find that, in fact, interpersonal conflict is associated with positive learning, a finding that must be taken into account, in designing interventions that rely upon detectors of students' speech acts in CSCL environments. (Contains 1 figure and 3 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.]

Published
2009

12. Differences between Intelligent Tutor Lessons, and the Choice to Go Off-Task

Author

International Working Group on Educational Data Mining and Baker, Ryan S. J. d.

Abstract

Recent research has suggested that differences between intelligent tutor lessons predict a large amount of the variance in the prevalence of gaming the system. Within this paper, we investigate whether such differences also predict how much students choose to go off-task, and if so, which differences predict how much off-task behavior will occur. We utilize an enumeration of the differences between intelligent tutor lessons, the Cognitive Tutor Lesson Variation Space 1.1 (CTLVS1.1), to identify 79 differences between tutor lessons, within 20 lessons from an intelligent tutoring system for Algebra. We utilize a machine-learned detector of off-task behavior to predict 58 students' off-task behavior within that tutor, in each lesson. Surprisingly, the best model predicting off-task behavior from lesson features contains only one feature: lessons that involve equation-solving. We discuss possible explanations for this finding, and further studies that could shed light on this relationship. (Contains 1 figure and 2 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.]

Published
2009

13. The State of Educational Data Mining in 2009: A Review and Future Visions

Author

Baker, Ryan S. J. D. and Yacef, Kalina

Abstract

We review the history and current trends in the field of Educational Data Mining (EDM). We consider the methodological profile of research in the early years of EDM, compared to in 2008 and 2009, and discuss trends and shifts in the research conducted by this community. In particular, we discuss the increased emphasis on prediction, the emergence of work using existing models to make scientific discoveries ("discovery with models"), and the reduction in the frequency of relationship mining within the EDM community. We discuss two ways that researchers have attempted to categorize the diversity of research in educational data mining research, and review the types of research problems that these methods have been used to address. The most-cited papers in EDM between 1995 and 2005 are listed, and their influence on the EDM community (and beyond the EDM community) is discussed.

Published
2009

14. Learning Bayesian Knowledge Tracing Parameters with a Knowledge Heuristic and Empirical Probabilities

Author

Hawkins, William J., Heffernan, Neil T., Baker, Ryan S. J. D., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Trausan-Matu, Stefan, editor, Boyer, Kristy Elizabeth, editor, Crosby, Martha, editor, and Panourgia, Kitty, editor

Published
2014
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15. Sensor-Free Affect Detection for a Simulation-Based Science Inquiry Learning Environment

Author

Paquette, Luc, Baker, Ryan S. J. D., Sao Pedro, Michael A., Gobert, Janice D., Rossi, Lisa, Nakama, Adam, Kauffman-Rogoff, Zakkai, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Trausan-Matu, Stefan, editor, Boyer, Kristy Elizabeth, editor, Crosby, Martha, editor, and Panourgia, Kitty, editor

Published
2014
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16. On the Benefits of Seeking (and Avoiding) Help in Online Problem-Solving Environments

Author

Roll, Ido, Baker, Ryan S. J. d., Aleven, Vincent, and Koedinger, Kenneth R.

Abstract

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 school students who worked with the Geometry Cognitive Tutor for 2 months to better understand the associations between specific help-seeking patterns and learning. We evaluate how students' help-seeking behaviors on each step in a tutored problem are associated with their success on subsequent steps that require the same skills. Analyzing learning at the skill level allows us to compare different help-seeking patterns within a single student, controlling for between-student variations. Overall, asking for help on challenging steps is associated with productive learning, and overusing help is associated with poorer learning. However, contrary to many help-seeking theories, avoiding help (and failing repeatedly) is associated with better learning than seeking help on steps for which students have low prior knowledge. These results suggest that novice learners may benefit from engaging in solution attempts before they can make sense of given assistance. Methodological benefits for using local measures of learning are discussed, and comparisons are drawn to other forms of productive failure in problem solving.

Published
2014
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18. Differential Impact of Learning Activities Designed to Support Robust Learning in the Genetics Cognitive Tutor

Author

Corbett, Albert, MacLaren, Ben, Wagner, Angela, Kauffman, Linda, Mitchell, Aaron, Baker, Ryan S. J. d., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Lane, H. Chad, editor, Yacef, Kalina, editor, Mostow, Jack, editor, and Pavlik, Philip, editor

Published
2013
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19. The Interplay between Affect and Engagement in Classrooms Using AIED Software

Author

Hershkovitz, Arnon, Baker, Ryan S. J. d., Moore, Gregory R., Rossi, Lisa M., van Velsen, Martin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Lane, H. Chad, editor, Yacef, Kalina, editor, Mostow, Jack, editor, and Pavlik, Philip, editor

Published
2013
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20. Field Observations of Engagement in Reasoning Mind

Author

Ocumpaugh, Jaclyn, Baker, Ryan S. J. d., Gaudino, Steven, Labrum, Matthew J., Dezendorf, Travis, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Lane, H. Chad, editor, Yacef, Kalina, editor, Mostow, Jack, editor, and Pavlik, Philip, editor

Published
2013
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21. Towards an Understanding of Affect and Knowledge from Student Interaction with an Intelligent Tutoring System

Author

San Pedro, Maria Ofelia Z., Baker, Ryan S. J. d., Gowda, Sujith M., Heffernan, Neil T., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Lane, H. Chad, editor, Yacef, Kalina, editor, Mostow, Jack, editor, and Pavlik, Philip, editor

Published
2013
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22. Exploring the Relationships between Design, Students’ Affective States, and Disengaged Behaviors within an ITS

Author

Doddannara, Lakshmi S., Gowda, Sujith M., Baker, Ryan S. J. d, Gowda, Supreeth M., de Carvalho, Adriana M. J. B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Lane, H. Chad, editor, Yacef, Kalina, editor, Mostow, Jack, editor, and Pavlik, Philip, editor

Published
2013
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23. Predicting Successful Inquiry Learning in a Virtual Performance Assessment for Science

Author

Baker, Ryan S. J. D., Clarke-Midura, Jody, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carberry, Sandra, editor, Weibelzahl, Stephan, editor, Micarelli, Alessandro, editor, and Semeraro, Giovanni, editor

Published
2013
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24. WTF? Detecting Students Who Are Conducting Inquiry Without Thinking Fastidiously

Author

Wixon, Michael, Baker, Ryan S. J. d., Gobert, Janice D., Ocumpaugh, Jaclyn, Bachmann, Matthew, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Masthoff, Judith, editor, Mobasher, Bamshad, editor, Desmarais, Michel C., editor, and Nkambou, Roger, editor

Published
2012
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25. Content Learning Analysis Using the Moment-by-Moment Learning Detector

Author

Gowda, Sujith M., Pardos, Zachary A., Baker, Ryan S. J. D., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cerri, Stefano A., editor, Clancey, William J., editor, Papadourakis, Giorgos, editor, and Panourgia, Kitty, editor

Published
2012
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26. Towards Automatically Detecting Whether Student Learning Is Shallow

Author

Baker, Ryan S. J. D., Gowda, Sujith M., Corbett, Albert T., Ocumpaugh, Jaclyn, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Cerri, Stefano A., editor, Clancey, William J., editor, Papadourakis, Giorgos, editor, and Panourgia, Kitty, editor

Published
2012
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28. The Dynamics between Student Affect and Behavior Occurring Outside of Educational Software

Author

Baker, Ryan S. J. d., Moore, Gregory R., Wagner, Angela Z., Kalka, Jessica, Salvi, Aatish, Karabinos, Michael, Ashe, Colin A., Yaron, David, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, D’Mello, Sidney, editor, Graesser, Arthur, editor, Schuller, Björn, editor, and Martin, Jean-Claude, editor

Published
2011
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29. The Relationship between Carelessness and Affect in a Cognitive Tutor

Author

San Pedro, Maria Ofelia Clarissa Z., Rodrigo, Ma. Mercedes T., Baker, Ryan S. J. d., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, D’Mello, Sidney, editor, Graesser, Arthur, editor, Schuller, Björn, editor, and Martin, Jean-Claude, editor

Published
2011
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30. Exploring the Relationship between Novice Programmer Confusion and Achievement

Author

Lee, Diane Marie C., Rodrigo, Ma. Mercedes T., Baker, Ryan S. J. d., Sugay, Jessica O., Coronel, Andrei, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, D’Mello, Sidney, editor, Graesser, Arthur, editor, Schuller, Björn, editor, and Martin, Jean-Claude, editor

Published
2011
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31. Ensembling Predictions of Student Knowledge within Intelligent Tutoring Systems

Author

Baker, Ryan S. J. d., Pardos, Zachary A., Gowda, Sujith M., Nooraei, Bahador B., Heffernan, Neil T., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Konstan, Joseph A., editor, Conejo, Ricardo, editor, Marzo, José L., editor, and Oliver, Nuria, editor

Published
2011
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32. Carelessness and Goal Orientation in a Science Microworld

Author

Hershkovitz, Arnon, Wixon, Michael, Baker, Ryan S. J. d., Gobert, Janice, Sao Pedro, Michael, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Biswas, Gautam, editor, Bull, Susan, editor, Kay, Judy, editor, and Mitrovic, Antonija, editor

Published
2011
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33. Towards Predicting Future Transfer of Learning

Author

Baker, Ryan S. J. d., Gowda, Sujith M., Corbett, Albert T., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Biswas, Gautam, editor, Bull, Susan, editor, Kay, Judy, editor, and Mitrovic, Antonija, editor

Published
2011
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34. Detecting Carelessness through Contextual Estimation of Slip Probabilities among Students Using an Intelligent Tutor for Mathematics

Author

San Pedro, Maria Ofelia Clarissa Z., Baker, Ryan S. J. d., Rodrigo, Ma. Mercedes T., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Biswas, Gautam, editor, Bull, Susan, editor, Kay, Judy, editor, and Mitrovic, Antonija, editor

Published
2011
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36. Contextual Slip and Prediction of Student Performance after Use of an Intelligent Tutor

Author

Baker, Ryan S. J. d., Corbett, Albert T., Gowda, Sujith M., Wagner, Angela Z., MacLaren, Benjamin A., Kauffman, Linda R., Mitchell, Aaron P., Giguere, Stephen, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, De Bra, Paul, editor, Kobsa, Alfred, editor, and Chin, David, editor

Published
2010
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37. Detecting Gaming the System in Constraint-Based Tutors

Author

Baker, Ryan S. J. d., Mitrović, Antonija, Mathews, Moffat, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, De Bra, Paul, editor, Kobsa, Alfred, editor, and Chin, David, editor

Published
2010
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38. Detecting the Moment of Learning

Author

Baker, Ryan S. J. d., Goldstein, Adam B., Heffernan, Neil T., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Aleven, Vincent, editor, Kay, Judy, editor, and Mostow, Jack, editor

Published
2010
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39. More Accurate Student Modeling through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing

Author

Baker, Ryan S. J. d., Corbett, Albert T., Aleven, Vincent, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Woolf, Beverley P., editor, Aïmeur, Esma, editor, Nkambou, Roger, editor, and Lajoie, Susanne, editor

Published
2008
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40. Comparing Learners’ Affect While Using an Intelligent Tutoring System and a Simulation Problem Solving Game

Author

Rodrigo, Ma. Mercedes T., Baker, Ryan S. J. d., D’Mello, Sidney, Gonzalez, Ma. Celeste T., Lagud, Maria C. V., Lim, Sheryl A. L., Macapanpan, Alexis F., Pascua, Sheila A. M. S., Santillano, Jerry Q., Sugay, Jessica O., Tep, Sinath, Viehland, Norma J. B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Woolf, Beverley P., editor, Aïmeur, Esma, editor, Nkambou, Roger, editor, and Lajoie, Susanne, editor

Published
2008
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41. Generalizing Automated Detection of the Robustness of Student Learning in an Intelligent Tutor for Genetics

Author

Baker, Ryan S. J. d., Corbett, Albert T., and Gowda, Sujith M.

Abstract

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 different pedagogical strategies promote robust learning to different degrees. However, the student modeling methods embedded within intelligent tutoring systems remain focused on assessing basic skill learning rather than robust learning. Recent work has proposed models, developed using educational data mining, that infer whether students are acquiring learning that transfers to related skills, and prepares the student for future learning (PFL). In this earlier work, evidence was presented that these models achieve superior prediction of robust learning to what can be achieved by traditional methods for student modeling. However, using these models to drive intervention by educational software depends on evidence that these models remain effective within new populations. To this end, we analyze the degree to which these detectors remain accurate for an entirely new population of high school students. We find limited evidence of degradation for transfer. More degradation is seen for PFL. This degradation appears to occur in part because it is generally more difficult to infer this construct within the new population.

Published
2013
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42. Knowledge Elicitation Methods for Affect Modelling in Education

Author

Porayska-Pomsta, Kaska, Mavrikis, Manolis, D'Mello, Sidney, Conati, Cristina, and Baker, Ryan S. J. d.

Abstract

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 their nature, the conditions under which they occur, and their exact impact on learning for different learners in diverse contexts still needs to be mapped out. The study of affect during learning can be challenging, because emotions are subjective, fleeting phenomena that are often difficult for learners to report accurately and for observers to perceive reliably. Context forms an integral part of learners' affect and the study thereof. This review provides a synthesis of the current knowledge elicitation methods that are used to aid the study of learners' affect and to inform the design of intelligent technologies for learning. Advantages and disadvantages of the specific methods are discussed along with their respective potential for enhancing research in this area, and issues related to the interpretation of data that emerges as the result of their use. References to related research are also provided together with illustrative examples of where the individual methods have been used in the past. Therefore, this review is intended as a resource for methodological decision making for those who want to study emotions and their antecedents in AIEd contexts, i.e. where the aim is to inform the design and implementation of an intelligent learning environment or to evaluate its use and educational efficacy.

Published
2013
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43. Student Off-Task Behavior in Computer-Based Learning in the Philippines: Comparison to Prior Research in the USA

Author

Rodrigo, Ma. Mercedes T., Baker, Ryan S. J. D., and Rossi, Lisa

Abstract

Background: Off-task behavior can be defined as any behavior that does not involve the learning task or material, or where learning from the material is not the primary goal. One suggested path for understanding how to address off-task behavior is to study classrooms where off-task behavior is less common, particularly in Asia, in order to understand why off-task behavior is less common in those settings. Purpose: In this paper, we discuss three studies that examine the differences in the off-task behavior of students using intelligent tutoring software in the Philippines and the United States. We investigate whether Filipino students using intelligent tutors exhibit significantly less off-task behavior than their American counterparts, in line with the previously observed patterns of off-task behavior in traditional classrooms in East Asia and Southeast Asia. Using identical intelligent tutors in both countries allows us to control for confounds due to differences in curricula. Research Design: In the first study, approximately matched cohorts of students in classrooms in the United States and Philippines used the same educational software (the Scatterplot Tutor) for an equal amount of time and according to an identical research protocol. In the second and third studies, students in the Philippines used different intelligent tutoring systems for a fixed amount of time. Data Collection and Analysis: As students used the software, researchers used a quantitative field observation protocol to record their behaviors. From the quantitative field observations, we computed an approximate percentage of time that each student was off-task and then calculated the average time off-task for each group under study. An additional behavior, gaming the system (intentional misuse of educational software) was also coded. Findings: In the first study, students in the United States were off-task, on average, seven times more than students in the Philippines. Interestingly, however, the proportion of gaming the system was higher in the Philippines than in the United States. In the second and third studies, the incidence of off-task behavior was comparable to the frequency of off-task behavior in the Philippines in the first study, and substantially lower than the frequency of off-task behavior in the United States in the first study or in prior research on off-task behavior in the United States. Conclusions/Recommendations: The results of the first study suggest that the previously observed differences in off-task behavior between the United States and Asia cannot be attributed simply to differences in curricula. Understanding the relative roles of cultural factors and past experiences with adaptive educational technology in student off-task behavior is an important area of future work.

Published
2013

45. The Dynamics of Affective Transitions in Simulation Problem-Solving Environments

Author

Baker, Ryan S. J. d., Rodrigo, Ma. Mercedes T., Xolocotzin, Ulises E., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Paiva, Ana C. R., editor, Prada, Rui, editor, and Picard, Rosalind W., editor

Published
2007
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46. The Help Tutor: Does Metacognitive Feedback Improve Students’ Help-Seeking Actions, Skills and Learning?

Author

Roll, Ido, Aleven, Vincent, McLaren, Bruce M., Ryu, Eunjeong, Baker, Ryan S. J. d., Koedinger, Kenneth R., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ikeda, Mitsuru, editor, Ashley, Kevin D., editor, and Chan, Tak-Wai, editor

Published
2006
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47. Adapting to When Students Game an Intelligent Tutoring System

Author

Baker, Ryan S. J. d., Corbett, Albert T., Koedinger, Kenneth R., Evenson, Shelley, Roll, Ido, Wagner, Angela Z., Naim, Meghan, Raspat, Jay, Baker, Daniel J., Beck, Joseph E., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ikeda, Mitsuru, editor, Ashley, Kevin D., editor, and Chan, Tak-Wai, editor

Published
2006
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48. Generalizing Detection of Gaming the System Across a Tutoring Curriculum

Author

Baker, Ryan S. J. d., Corbett, Albert T., Koedinger, Kenneth R., Roll, Ido, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Ikeda, Mitsuru, editor, Ashley, Kevin D., editor, and Chan, Tak-Wai, editor

Published
2006
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49. Detecting Learning Moment-by-Moment

Author

Baker, Ryan S. J. D., Goldstein, Adam B., and Heffernan, Neil T.

Abstract

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 present a machine-learned model that assesses the probability that a student learned a KC at a specific problem step (instead of at the next or previous problem step). We use this model to analyze which KCs are learned gradually, and which are learned in "eureka" moments. We also discuss potential ways that this model could be used to improve the effectiveness of cognitive mastery learning. (Contains 5 figures and 2 tables.)

Published
2011
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50. Observations of Collaboration in Cognitive Tutor Use in Latin America

Author

Walker, Erin, Ogan, Amy, Baker, Ryan S. J. d., de Carvalho, Adriana, Laurentino, Tania, Rebolledo-Mendez, Genaro, Castro, Maynor Jimenez, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Biswas, Gautam, editor, Bull, Susan, editor, Kay, Judy, editor, and Mitrovic, Antonija, editor

Published
2011
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