Modeling player engagement is a key challenge in games. However, the gameplay signatures of engaged players can be highly context-sensitive, varying based on where the game is used or what population of players is using it. Traditionally, models of player engagement are investigated in a particular context, and it is unclear how effectively these models generalize to other settings and populations. In this work, we investigate a Bayesian hierarchical linear model for multi-task learning to devise a model of player engagement from a pair of datasets that were gathered in two complementary contexts: a Classroom Study with middle school students and a Laboratory Study with undergraduate students. Both groups of players used similar versions of Crystal Island, an educational interactive narrative game for science learning. Results indicate that the Bayesian hierarchical model outperforms both pooled and context-specific models in cross-validation measures of predicting player motivation from in-game behaviors, particularly for the smaller Classroom Study group. Further, we find that the posterior distributions of model parameters indicate that the coefficient for a measure of gameplay performance significantly differs between groups. Drawing upon their capacity to share information across groups, hierarchical Bayesian methods provide an effective approach for modeling player engagement with data from similar, but different, contexts.
The challenges of effective health risk communication are well known. This paper provides pointers to the health communication literature that discuss these problems. Tailoring printed information, visual displays, and interactive multimedia have been proposed in the health communication literature as promising approaches. On-line risk communication applications are increasing on the internet. However, potential effectiveness of applications using conventional computer technology is limited. We propose that use of artificial intelligence, building upon research in Intelligent Tutoring Systems, might be able to overcome these limitations.
This paper describes an effort to measure the effectiveness of tutor help in an intelligent tutoring system. Although conventional pre-and post-test experiments can determine whether tutor help is effective, they are expensive to conduct. Furthermore, pre-and post-test experiments often do not model student knowledge explicitly and thus are ignoring a source of information: students often request help about words they do not know. Therefore, we construct a dynamic Bayes net (which we call the Help model) that models tutor help and student knowledge in one coherent framework. The Help model distinguishes two different effects of help: scaffolding immediate performance vs. teaching persistent knowledge that improves long term performance. We train the Help model to fit student performance data gathered from usage of the Reading Tutor (Mostow & Aist, 2001). The parameters of the trained model suggest that students benefit from both the scaffolding and teaching effects of help. That is, students are more likely to perform correctly on the current attempt and learn persistent knowledge if tutor help is provided. Thus, our framework is able to distinguish two types of influence that tutor help has on the student, and can determine whether help helps learning without an explicit controlled study.
Fancsali, Stephen E. (Carnegie Learning, Inc.) | Ritter, Steven (Carnegie Learning, Inc.) | Yudelson, Michael (Carnegie Mellon University) | Sandbothe, Michael (Carnegie Learning, Inc.) | Berman, Susan R (Carnegie Learning, Inc.)
While many expect that the use of advanced learning technologies like intelligent tutoring systems (ITSs) will substitute for human teaching and thus reduce the influence of teachers on student outcomes, studies consistently show that outcomes vary substantially across teachers and schools (Pane et al. 2010; Pane et al. 2014; Ritter et al. 2007a; Koedinger et al. 1997; Koedinger and Sueker 2014). Despite these findings, there have been few efforts (e.g., Schofield 1995) to understand the mechanisms by which teacher practices influence student learning on such systems. We present analyses of Carnegie Learning’s Cognitive Tutor ITS data from a large school district in the southeastern United States, which present a variety of usage and implementation profiles that illuminate disparities in deployments in practical, day-to-day educational settings. We focus on differential effectiveness of teachers’ implementations and how implementations may drive learner efficiency in ITS usage, affecting long term learning outcomes. These results are consistent with previous studies of predictors and causes of learning outcomes for students using Cognitive Tutor. We provide recommendations for practitioners seeking to deploy intelligent learning technologies in real world settings.
This paper presents an out-of-sample prediction comparison between major machine learning models and the structural econometric model. Over the past decade, machine learning has established itself as a powerful tool in many prediction applications, but this approach is still not widely adopted in empirical economic studies. To evaluate the benefits of this approach, I use the most common machine learning algorithms, CART, C4.5, LASSO, random forest, and adaboost, to construct prediction models for a cash transfer experiment conducted by the Progresa program in Mexico, and I compare the prediction results with those of a previous structural econometric study. Two prediction tasks are performed in this paper: the out-of-sample forecast and the long-term within-sample simulation. For the out-of-sample forecast, both the mean absolute error and the root mean square error of the school attendance rates found by all machine learning models are smaller than those found by the structural model. Random forest and adaboost have the highest accuracy for the individual outcomes of all subgroups. For the long-term within-sample simulation, the structural model has better performance than do all of the machine learning models. The poor within-sample fitness of the machine learning model results from the inaccuracy of the income and pregnancy prediction models. The result shows that the machine learning model performs better than does the structural model when there are many data to learn; however, when the data are limited, the structural model offers a more sensible prediction. The findings of this paper show promise for adopting machine learning in economic policy analyses in the era of big data.