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1. Using a Single Model Trained across Multiple Experiments to Improve the Detection of Treatment Effects

Author

Patikorn, Thanaporn, Selent, Douglas, Heffernan, Neil T., Beck, Joseph E., and Zou, Jian

Abstract

In this work, we describe a new statistical method to improve the detection of treatment effects in interventions. We call our method TAME (Trained Across Multiple Experiments). TAME takes advantage of multiple experiments with similar designs to create a single model. We use this model to predict the outcome of the dependent variable in unseen experiments. We use the predictive accuracy of the model on the conditions of the experiment to determine if the treatment had a statistically significant effect. We validated the effectiveness of our model using a large-scale simulation study, where we showed that our model can detect treatment effects with 10% more statistical power than an ANOVA in certain settings. We also applied our model to real data collected from the ASSISTments online learning platform and showed that the treatment effects detected by our model were comparable to the effects detected by the ANOVA. [For the full proceedings, see ED596512.]

Published
2017

2. Going Deeper with Deep Knowledge Tracing

Author

Xiong, Xiaolu, Zhao, Siyuan, Van Inwegen, Eric G., and Beck, Joseph E.

Abstract

Over the last couple of decades, there have been a large variety of approaches towards modeling student knowledge within intelligent tutoring systems. With the booming development of deep learning and large-scale artificial neural networks, there have been empirical successes in a number of machine learning and data mining applications, including student knowledge modeling. Deep Knowledge Tracing (DKT), a pioneer algorithm that utilizes recurrent neural networks to model student learning, reports substantial improvements in prediction performance. To help the EDM community better understand the promising techniques of deep learning, we examine DKT alongside two well-studied models for knowledge modeling, PFA and BKT. In addition to sharing a primer on the internal computational structures of DKT, we also report on potential issues that arise from data formatting. We take steps to reproduce the experiments of Deep Knowledge Tracing by implementing a DKT algorithm using Google's TensorFlow framework; we also reproduce similar results on new datasets. We determine that the DKT findings do not hold an overall edge when compared to the PFA model, when applied to properly prepared datasets that are limited to main (i.e. non-scaffolding) questions. More importantly, during the investigation of DKT, we not only discovered a data quality issue in a public available data set, but we also detected a vulnerability of DKT at how it handles multiple skill sequences. [Additional funding for this paper was provided by the U.S. Department of Education's Graduate Assistance in Areas of National Need (GAANN). For the full proceedings, see ED592609.]

Published
2016

3. Developing Early Detectors of Student Attrition and Wheel Spinning Using Deep Learning

Author

Botelho, Anthony F., Varatharaj, Ashvini, Patikorn, Thanaporn, Doherty, Diana, Adjei, Seth A., and Beck, Joseph E.

Abstract

The increased usage of computer-based learning platforms and online tools in classrooms presents new opportunities to not only study the underlying constructs involved in the learning process, but also use this information to identify and aid struggling students. Many learning platforms, particularly those driving or supplementing instruction, are only able to provide aid to students who interact with the system. With this in mind, student persistence emerges as a prominent learning construct contributing to students success when learning new material. Conversely, high persistence is not always productive for students, where additional practice does not help the student move toward a state of mastery of the material. In this paper, we apply a transfer learning methodology using deep learning and traditional modeling techniques to study high and low representations of unproductive persistence. We focus on two prominent problems in the fields of educational data mining and learner analytics representing low persistence, characterized as student "stopout," and unproductive high persistence, operationalized through student "wheel spinning," in an effort to better understand the relationship between these measures of unproductive persistence (i.e., stopout and wheel spinning) and develop early detectors of these behaviors. We find that models developed to detect each within and across-assignment stopout and wheel spinning are able to learn sets of features that generalize to predict the other. We further observe how these models perform at each learning opportunity within student assignments to identify when interventions may be deployed to best aid students who are likely to exhibit unproductive persistence.

Published
2019
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4. Using Student Modeling to Estimate Student Knowledge Retention

Author

International Educational Data Mining Society, Wang, Yutao, and Beck, Joseph E.

Abstract

The goal of predicting student behavior on the immediate next action has been investigated by researchers for many years. However, a fair question is whether this research question is worth all of the attention it has received. This paper investigates predicting student performance after a delay of 5 to 10 days, to determine whether, and when, the student will "retain" the material seen. Although this change in focus sounds minor, two aspects make it interesting. First, the factors influencing retention are different than those influencing short-term performance. Specifically, we found that the number of student correct and incorrect responses were not reliable predictors of long-term performance. This result is in contrast to most student-modeling efforts on predicting performance on the next response. Second, we argue that answering the question of whether a student will retain a skill is more useful for guiding decision making of intelligent tutoring systems (ITS) than predicting correctness of next response. We introduce an architecture that identifies two research topics that are meaningful for ITS decision making. Our experiments found one feature in particular that was relevant for student retention: the number of distinct days in which a student practiced a skill. This result provides additional evidence for the spaced practice effect, and suggests our models need to be aware of features known to impact retention. (Contains 1 figure and 1 table.) [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

5. Why, What, and How to Log? Lessons from LISTEN

Author

International Working Group on Educational Data Mining, Mostow, Jack, and Beck, Joseph E.

Abstract

The ability to log tutorial interactions in comprehensive, longitudinal, fine-grained detail offers great potential for educational data mining--but what data is logged, and how, can facilitate or impede the realization of that potential. We propose guidelines gleaned over 15 years of logging, exploring, and analyzing millions of events from Project LISTEN's Reading Tutor and its predecessors. (Contains 1 figure.) [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

6. Using Dirichlet Priors to Improve Model Parameter Plausibility

Author

International Working Group on Educational Data Mining, Rai, Dovan, Gong, Yue, and Beck, Joseph E.

Abstract

Student modeling is a widely used approach to make inference about a student's attributes like knowledge, learning, etc. If we wish to use these models to analyze and better understand student learning there are two problems. First, a model's ability to predict student performance is at best weakly related to the accuracy of any one of its parameters. Second, a commonly used student modeling technique, knowledge tracing, suffers from having multiple sets of parameters providing equally good model fits. Furthermore, common methods for estimating parameters, including conjugate gradient descent and expectation maximization, suffer from finding local maxima that are heavily dependent on their starting values. We propose a technique that estimates Dirichlet priors directly from the data, and show that using those priors produces model parameters that provide a more plausible picture of student knowledge. Although plausibility is difficult to quantify, we employed external measures to show the parameter estimates were indeed improved, even if our model did not predict student behavior any more accurately. (Contains 3 figures and 5 tables.) [Additional funding was provided by the Fulbright Program. 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

7. Does Self-Discipline Impact Students' Knowledge and Learning?

Author

International Working Group on Educational Data Mining, Gong, Yue, Rai, Dovan, Beck, Joseph E., and Heffernan, Neil T.

Abstract

In this study, we are interested to see the impact of self-discipline on students' knowledge and learning. Self-discipline can influence both learning rate as well as knowledge accumulation over time. We used a Knowledge Tracing (KT) model to make inferences about students' knowledge and learning. Based on a widely used questionnaire, we measured students' level of self-discipline. When we analyzed the relation of students' self-discipline with their knowledge attributes, we found that high self-discipline students had significantly higher initial knowledge, but there is no consistent relationship of learning while using the tutor. Moreover, higher self-discipline students seemed more careful with respect to making careless mistakes. (Contains 5 figures and 5 tables.) [Additional funding was provided by the Fulbright Program. 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

8. Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions on Learning

Author

International Working Group on Educational Data Mining, Feng, Mingyu, Beck, Joseph E., and Heffernan, Neil T.

Abstract

A basic question of instructional interventions is how effective it is in promoting student learning. This paper presents a study to determine the relative efficacy of different instructional strategies by applying an educational data mining technique, learning decomposition. We use logistic regression to determine how much learning is caused by different methods of teaching the same skill, relative to each other. We compare our results with a previous study, which used classical analysis techniques and reported no main effect. Our results show that there is a marginal difference, suggesting giving students scaffolding questions is less effective at promoting student learning than providing them delayed feedback. Our study utilizes learning decomposition, an easier and quicker approach of evaluating the quality of ITS interventions than experimental studies. We also demonstrate the usage of computer-intensive approach, bootstrapping, for hypothesis testing in educational data mining area. (Contains 4 tables and 1 footnote.) [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

9. Modeling the Incubation Effect Among Students Playing an Educational Game for Physics

Author

Talandron, May Marie P., Rodrigo, Ma. Mercedes T., Beck, Joseph E., 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, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, André, Elisabeth, editor, Baker, Ryan, editor, Hu, Xiangen, editor, Rodrigo, Ma. Mercedes T., editor, and du Boulay, Benedict, editor

Published
2017
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10. Wheel-Spinning in a Game-Based Learning Environment for Physics

Author

Palaoag, Thelma D., Rodrigo, Ma. Mercedes T., Andres, Juan Miguel L., Andres, Juliana Ma. Alexandra L., Beck, Joseph E., 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, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Micarelli, Alessandro, editor, Stamper, John, editor, and Panourgia, Kitty, editor

Published
2016
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11. A Study of Exploring Different Schedules of Spacing and Retrieval Interval on Mathematics Skills in ITS Environment

Author

Xiong, Xiaolu, Beck, Joseph E., 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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12. Investigate Performance of Expected Maximization on the Knowledge Tracing Model

Author

Gu, Junjie, Cai, Hang, Beck, Joseph E., 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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13. Class Distinctions: Leveraging Class-Level Features to Predict Student Retention Performance

Author

Xiong, Xiaolu, Beck, Joseph E., Li, Shoujing, 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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14. Repairing Deactivating Negative Emotions with Student Progress Pages

Author

Rai, Dovan, Arroyo, Ivon, Stephens, Lynn, Lozano, Cecil, Burleson, Winslow, Woolf, Beverly Park, 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, 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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15. Wheel-Spinning: Students Who Fail to Master a Skill

Author

Beck, Joseph E., Gong, Yue, 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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16. Modeling Multiple Distributions of Student Performances to Improve Predictive Accuracy

Author

Gong, Yue, Beck, Joseph E., Ruiz, Carolina, 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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17. WEBsistments: Enabling an Intelligent Tutoring System to Excel at Explaining Rather Than Coaching

Author

Gong, Yue, Beck, Joseph E., 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, Cerri, Stefano A., editor, Clancey, William J., editor, Papadourakis, Giorgos, editor, and Panourgia, Kitty, editor

Published
2012
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18. Math Learning Environment with Game-Like Elements: An Incremental Approach for Enhancing Student Engagement and Learning Effectiveness

Author

Rai, Dovan, 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, 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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19. Looking Beyond Transfer Models: Finding Other Sources of Power for Student Models

Author

Gong, Yue, Beck, Joseph E., 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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20. The Fine-Grained Impact of Gaming (?) on Learning

Author

Gong, Yue, Beck, Joseph E., Heffernan, Neil T., Forbes-Summers, Elijah, 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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21. Comparing Knowledge Tracing and Performance Factor Analysis by Using Multiple Model Fitting Procedures

Author

Gong, Yue, Beck, Joseph E., 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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22. How Who Should Practice: Using Learning Decomposition to Evaluate the Efficacy of Different Types of Practice for Different Types of Students

Author

Beck, Joseph E., Mostow, Jack, 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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23. Does Help Help? Introducing the Bayesian Evaluation and Assessment Methodology

Author

Beck, Joseph E., Chang, Kai-min, Mostow, Jack, Corbett, Albert, 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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24. A Case Study Empirical Comparison of Three Methods to Evaluate Tutorial Behaviors

Author

Zhang, Xiaonan, Mostow, Jack, 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, 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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25. Trying to Reduce Bottom-Out Hinting: Will Telling Student How Many Hints They Have Left Help?

Author

Guo, Yu, Beck, Joseph E., 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, Woolf, Beverley P., editor, Aïmeur, Esma, editor, Nkambou, Roger, editor, and Lajoie, Susanne, editor

Published
2008
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26. Lessons Learned from Scaling Up a Web-Based Intelligent Tutoring System

Author

Patvarczki, Jozsef, Almeida, Shane F., Beck, Joseph E., 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, Woolf, Beverley P., editor, Aïmeur, Esma, editor, Nkambou, Roger, editor, and Lajoie, Susanne, editor

Published
2008
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27. Computer-Guided Oral Reading versus Independent Practice: Comparison of Sustained Silent Reading to an Automated Reading Tutor That Listens

Author

Mostow, Jack, Nelson-Taylor, Jessica, and Beck, Joseph E.

Abstract

A 7-month study of 178 students in grades 1-4 at two Blue Ribbon schools compared two daily 20-minute treatments. Eighty-eight students used the 2000-2001 version of Project LISTEN's Reading Tutor (www.cs.cmu.edu/~listen) in 10-computer labs, averaging 19 hours over the course of the year. The Reading Tutor served as a computerized implementation of the National Reading Panel's recommended guided oral reading instruction (NRP, 2000). The Reading Tutor listened to students read aloud, giving spoken and graphical help when it noticed them click for help, make a mistake, or get stuck. Students using the Reading Tutor averaged significantly higher gains across measures of reading ability, especially those involving word level skills (word identification, blending words, and spelling) than their matched classmates who spent that time doing Sustained Silent Reading (SSR) in their classrooms. Additionally, these students trended toward higher gains in fluency and reading comprehension. Overall, use of the Reading Tutor resulted in the types of improvement that would be expected from guided oral reading, but with the benefit of scalability, a problem for human-guided oral reading practice.

Published
2013
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29. 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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30. Math Learning Environment with Game-Like Elements: An Experimental Framework

Author

Rai, Dovan and Beck, Joseph E.

Abstract

Educational games intend to make learning more enjoyable, but carry the potential cost of compromising learning efficiency by consuming both instructional time and student cognitive resources. Therefore, instead of creating an educational game, the authors create a learning environment with game-like elements, the aspects of games that are engaging but that do not negatively impact the learning effectiveness of the system. This paper presents an experimental framework for determining the effect of game-like elements in terms of their benefits such as enhancing engagement and learning, as well as their costs such as distraction and working memory overload. As a first experimental step, the authors develop four versions of a math tutor with different degrees of game-likeness, such as adding narrative and visual feedback. Based on a study with 297 students, it is found that students reported more satisfaction with the tutor with more game-like elements, but there was no conclusive difference in learning among the different versions.

Published
2012
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31. How to Construct More Accurate Student Models: Comparing and Optimizing Knowledge Tracing and Performance Factor Analysis

Author

Gong, Yue, Beck, Joseph E., and Heffernan, Neil T.

Abstract

Student modeling is a fundamental concept applicable to a variety of intelligent tutoring systems (ITS). However, there is not a lot of practical guidance on how to construct and train such models. This paper compares two approaches for student modeling, Knowledge Tracing (KT) and Performance Factors Analysis (PFA), by evaluating their predictive accuracy on individual student practice opportunities. We explore the space of design decisions for each approach and find a set of "best practices" for each. In a head to head comparison, we find that PFA has considerably higher predictive accuracy than KT. In addition to being more accurate, we found that PFA's parameter estimates were more plausible. Our best-performing model was a variant of PFA that ignored the tutor's transfer model; that is, it assumed all skills influenced performance on all problems. One possible implication is that this result is a general one suggesting there is benefit from considering models that incorporate information from more than the typical handful of skills associated with a problem in the transfer model. Alternately, an explanation for this result is the transfer model that our tutor uses is particularly weak. We also found that both KT and PFA have relatively low predictive accuracy for cases where students generate incorrect responses, and 2/3 of the model's errors are false positives, indicating a better means of determining when students will make mistakes is needed. (Contains 9 tables and 1 figure.)

Published
2011
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32. Can Automated Questions Scaffold Children’s Reading Comprehension?

Author

Beck, Joseph E., Mostow, Jack, Bey, Juliet, 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, Lester, James C., editor, Vicari, Rosa Maria, editor, and Paraguaçu, Fábio, editor

Published
2004
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33. Using Knowledge Tracing to Measure Student Reading Proficiencies

Author

Beck, Joseph E., Sison, June, 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, Lester, James C., editor, Vicari, Rosa Maria, editor, and Paraguaçu, Fábio, editor

Published
2004
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37. Causal Modeling of User Data from a Math Learning Environment with Game-Like Elements

Author

Rai, Dovan, Beck, Joseph E., 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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38. Mily’s World: A Coordinate Geometry Learning Environment with Game-Like Properties

Author

Rai, Dovan, Beck, Joseph E., 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. Coordinate Geometry Learning Environment with Game-Like Properties

Author

Rai, Dovan, Beck, Joseph E., 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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43. Using a Learning Agent with a Student Model

Author

Beck, Joseph E., Woolf, Beverly Park, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Goettl, Barry P., editor, Halff, Henry M., editor, Redfield, Carol L., editor, and Shute, Valerie J., editor

Published
1998
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44. Using Rollouts to Induce a Policy from a User Model

Author

Beck, Joseph E., Park Woolf, Beverly, Goos, G., editor, Hartmanis, J., editor, van Leeuwen, J., editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Bauer, Mathias, editor, Gmytrasiewicz, Piotr J., editor, and Vassileva, Julita, editor

Published
2001
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