While games can be an innovative and a highly promising approach to education, creating effective educational games is a challenge. It requires effectively integrating educational content with game attributes and aligning cognitive and affective outcomes, which can be in conflict with each other. Intelligent Tutoring Systems (ITS), on the other hand, have proven to be effective learning environments that are conducive to strong learning outcomes. Direct comparisons between tutoring systems and educational games have found digital tutors to be more effective at producing learning gains. However, tutoring systems have had difficulties in maintaining students€™ interest and engagement for long periods of time, which limits their ability to generate learning in the long-term. Given the complementary benefits of games and digital tutors, there has been considerable effort to combine these two fields. This dissertation undertakes and analyzes three different ways of integrating Intelligent Tutoring Systems and digital games. We created three game-like systems with cognition, metacognition and affect as their primary target and mode of intervention. Monkey's Revenge is a game-like math tutor that offers cognitive tutoring in a game-like environment. The Learning Dashboard is a game-like metacognitive support tool for students using Mathspring, an ITS. Mosaic comprises a series of mini-math games that pop-up within Mathspring to enhance students' affect. The methodology consisted of multiple randomized controlled studies ran to evaluate each of these three interventions, attempting to understand their effect on students€™ performance, affect and perception of the intervention and the system that embeds it. Further, we used causal modeling to further explore mechanisms of action, the inter-relationships between student€™s incoming characteristics and predispositions, their mechanisms of interaction with the tutor, and the ultimate learning outcomes and perceptions of the learning experience.

An approach to education gaining popularity in the modern classroom, adaptive tutoring systems offer interactive learning environments in which students can access immediate feedback and rich tutoring while teachers can achieve organized assessment for targeted interventions. Yet despite the benefits that these systems provide, a number of questions remain regarding the optimal inner workings of adaptive platforms. What is the recipe for optimal student performance within these platforms? What elements should be taken into consideration when designing these learning environments? Can facets of these platforms be harnessed to increase students’ motivation to learn and to improve both immediate and robust learning gains? This thesis combines work conducted over the past two years through versatile approaches toward the goal of enhancing student motivation and learning within the ASSISTments platform. Approaches considered include a) enhancing motivation and performance through altered feedback using hypermedia elements, b) instilling motivational messages alongside media enhanced content and feedback, c) allowing students to choose their feedback medium, thereby exerting control over their assignment, d) altering content delivery by interleaving skills to enhance solution strategy development, and e) establishing partial credit assessments to drive motivation and proper system usage while enhancing student modeling. After a brief introduction regarding the main tenants of this research, each chapter highlights a randomized controlled trial focused around one of these approaches. All studies presented have been conducted or are still running within ASSISTments. Much of this work has already been published at peer reviewed conference venues, some with stringent acceptance rates as low as 25% for full papers. Two of the studies presented here are second iterations of previously published work that are still in progress, and only preliminary analyses are available. A chapter on conclusions and future work is included to discuss the contributions that have been made to the Learning Sciences community thus far, and to briefly discuss potential directions for my continued research.

An approach to education gaining popularity in the modern classroom, adaptive tutoring systems offer interactive learning environments in which students can access immediate feedback and rich tutoring while teachers can achieve organized assessment for targeted interventions. Yet despite the benefits that these systems provide, a number of questions remain regarding the optimal inner workings of adaptive platforms. What is the recipe for optimal student performance within these platforms? What elements should be taken into consideration when designing these learning environments? Can facets of these platforms be harnessed to increase students’ motivation to learn and to improve both immediate and robust learning gains? This thesis combines work conducted over the past two years through versatile approaches toward the goal of enhancing student motivation and learning within the ASSISTments platform. Approaches considered include a) enhancing motivation and performance through altered feedback using hypermedia elements, b) instilling motivational messages alongside media enhanced content and feedback, c) allowing students to choose their feedback medium, thereby exerting control over their assignment, d) altering content delivery by interleaving skills to enhance solution strategy development, and e) establishing partial credit assessments to drive motivation and proper system usage while enhancing student modeling. After a brief introduction regarding the main tenants of this research, each chapter highlights a randomized controlled trial focused around one of these approaches. All studies presented have been conducted or are still running within ASSISTments. Much of this work has already been published at peer reviewed conference venues, some with stringent acceptance rates as low as 25% for full papers. Two of the studies presented here are second iterations of previously published work that are still in progress, and only preliminary analyses are available. A chapter on conclusions and future work is included to discuss the contributions that have been made to the Learning Sciences community thus far, and to briefly discuss potential directions for my continued research.

In the field of Artificial Intelligence in Education, many contributions have been made toward estimating student proficiency in Intelligent Tutoring Systems (cf. Corbett & Anderson, 1995). Although the community is increasingly capable of estimating how much a student knows, this does not shed much light on when the knowledge was acquired. In recent research (Baker, Goldstein, & Heffernan, 2010), we created a model that attempts to answer that exact question. We call the model P(J), for the probability that a student just learned from the last problem they answered. We demonstrated an analysis of changes in P(J) that we call “spikiness", defined as the maximum value of P(J) for a student/knowledge component (KC) pair, divided by the average value of P(J) for that same student/KC pair. Spikiness is directly correlated with final student knowledge, meaning that spikes can be an early predictor of success. It has been shown that both over-practice and under-practice can be detrimental to student learning, so using this model can potentially help bias tutors toward ideal practice schedules. After demonstrating the validity of the P(J) model in both CMU's Cognitive Tutor and WPI's ASSISTments Tutoring System, we conducted a pilot study to test the utility of our model. The experiment included a balanced pre/post-test and three conditions for proficiency assessment tested across 6 knowledge components. In the first condition, students are considered to have mastered a KC after correctly answering 3 questions in a row. The second condition uses Bayesian Knowledge Tracing and accepts a student as proficient once they earn a current knowledge probability (Ln) of 0.95 or higher. Finally, we test P(J), which accepts mastery if a student's P(J) value spikes from one problem and the next first response is correct. In this work, we will discuss the details of deriving P(J), our experiment and its results, as well as potential ways this model could be utilized to improve the effectiveness of cognitive mastery learning.

Intelligent Tutoring Systems (ITS) have been proven to be efficient in providing students assistance and assessing their performance when they do their homework. Many research projects have been done to analyze how students' knowledge grows and to predict their performance from within intelligent tutoring system. Most of them focus on using correctness of the previous question or the number of hints and attempts students need to predict their future performance, but ignore how they ask for hints and make attempts. In this research work, we build a Sequence of Actions (SOA) model taking advantage of the sequence of hints and attempts a student needed for previous question to predict students' performance. A two step modeling methodology is put forward in the work, which is a combination of Tabling method and the Logistic Regression. We used an ASSISTments dataset of 66 students answering a total of 34,973 problems generated from 5010 questions over the course of two years. The experimental results showed that the Sequence of Action model has reliable predictive accuracy than Knowledge Tracing and Assistance Model and its performance of prediction is improved after combining with Knowledge Tracing.

Predicting student performance is an important part of the student modeling task in Intelligent Tutoring System (ITS). The state-of-art model for predicting student performance - Bayesian Knowledge Tracing (KT) has many critical limitations. One specific limitation is that KT has no underlying mechanism for memory decay represented in the model, which means that no forgetting is happening in the learning process. In addition we notice that numerous modification to the KT model have been proposed and evaluated, however many of these are often based on a combination of intuition and experience in the domain, leading to models without performance improvement. Moreover, KT is computationally expensive, model fitting procedures can take hours or days to run on large datasets. The goal of this research work is to improve the accuracy of student performance prediction by incorporating the memory decay factor which the standard Bayesian Knowledge Tracing had ignored. We also propose a completely data driven and inexpensive approach to model improvement. This alternative allows for researchers to evaluate which aspects of a model are most likely to result in model performance improvements based purely on the dataset features that are computed from ITS system logs.

Student gaming behavior in intelligent tutoring systems (ITS) has been correlated with lower learning rates. The goal of this work is to identify such behavior, produce interventions to discourage this behavior, and by doing so hopefully improve the learning rate of students who would normally display gaming behavior. Detectors have been built to identify gaming behavior. Interventions have been designed to discourage the behavior and their evaluation is discussed.

Over the past couple decades, intelligent tutoring systems (ITSs) have become popular in education. ITSs are effective at helping students learn (VanLehn, 2011; Razzaq, Mendicino & Heffernan, 2008; Koedinger et al, 1997) and help researchers understand how students learn. Such research has included modeling how students learn (Corbett & Anderson, 1995), the effectiveness of help given within an ITS (Beck et al, 2008), the difficulty of different problems (Pardos & Heffernan, 2011), and predicting long-term outcomes like college attendance (San Pedro et al, 2013a), among many other studies. While most studies have focused on ITSs from a cognitive perspective, a growing number of researchers are paying attention to the motivational and affective aspects of tutoring, which have been recognized as important components of human tutoring (Lepper et al, 1993). Recent work has shown that student affect within an ITS can be detected, even without physical sensors or cameras (D’Mello et al, 2008; Conati & Maclaren, 2009; Sabourin et al, 2011; San Pedro et al, 2013b). Initial studies with these sensor-less affect detectors have shown that certain problematic affective states, such as boredom, confusion and frustration, are prevalent within ITSs (Baker et al, 2010b). Boredom in particular has been linked to negative learning outcomes (Pekrun et al, 2010; Farmer & Sundberg, 1986) and long-term disengagement (Farrell, 1988). Therefore, reducing or responding effectively to these affective states within ITSs may improve both short- and long-term learning outcomes. This work is an initial attempt to determine what causes boredom in ITSs. First, we determine which is more responsible for boredom in ITSs: the content in the system, or the students themselves. Based on the findings of that analysis, we conduct a randomized controlled trial to determine the effects of monotony on student boredom. In addition to the work on boredom, we also perform analyses that concern student modeling, specifically how to improve Knowledge Tracing (Corbett & Anderson, 1995), a popular student model used extensively in real systems like the Cognitive Tutors (Koedinger et al, 1997) and in educational research.

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CoordenaÃÃo de AperfeiÃoamento de Pessoal de NÃvel Superior / A tutoria à uma das atividades docentes que acompanha, media e orienta o aluno na EducaÃÃo a DistÃncia (EaD) semipresencial. Este estudo busca, de modo geral, avaliar a atuaÃÃo do tutor a distÃncia. Especificamente, visa conhecer os saberes que sÃo necessÃrios na formaÃÃo docente para uma atuaÃÃo eficaz; analisar a aplicaÃÃo desses saberes e avaliÃ-los na atuaÃÃo do tutor a distÃncia nos cursos de graduaÃÃo em EaD semipresenciais da Universidade Federal do Cearà (UFC). Realizou-se, para esses propÃsitos, uma revisÃo de literatura contextualizando a EducaÃÃo em trÃs dimensÃes: EducaÃÃo Superior e EducaÃÃo a DistÃncia; formaÃÃo docente: tutoria e elementos formadores com a discussÃo dos saberes nas dimensÃes atitudinais, conceituais e procedimentais conforme as perspectivas pedagÃgica, didÃtica e tecnolÃgica; e a AvaliaÃÃo Educacional, no que diz respeito à avaliaÃÃo do ensino-aprendizagem e à avaliaÃÃo formativa. A natureza da investigaÃÃo caracterizou o estudo como exploratÃrio, transversal e quantitativo, realizado no Instituto Universidade Virtual. A amostra foi aplicada em 175 tutores e 1.957 alunos matriculados em 7 cursos de graduaÃÃo oferecidos no semestre letivo 2011.1, na modalidade EaD semipresencial. Os dados foram obtidos atravÃs de uma escala de avaliaÃÃo do tipo likert com 16 itens e analisados atravÃs do software SPSS versÃo 15.0. O instrumento aplicado apresentou precisÃo de sensibilidade alta e erro padrÃo da medida inferior a 10% da amplitude da escala. A validade de conteÃdo atravÃs da anÃlise fatorial indicou que a escala à unifatorial. A distribuiÃÃo do escore total na escala de avaliaÃÃo foi transformado para a escala de notas [0;10]; apresentou, na amostra, mÃdia 7,7, mediana 8,4 e coeficiente de variaÃÃo 28,2, com a mÃdia populacional variando no intervalo [7,6;7,8] para uma confianÃa de 95%. A distribuiÃÃo das notas à nÃo-normal, conforme teste de normalidade de Kolmorogov-Smirnov, com as notas se concentrando em valores superiores à mÃdia (mediana 8,4). Na anÃlise de regressÃo linear mÃltipla dos saberes, os coeficientes padronizados indicaram que a dimensÃo conceitual apresentou maior peso na composiÃÃo da nota de avaliaÃÃo. A dimensÃo atitudinal ficou em segundo lugar e a dimensÃo procedimental apresentou menor peso. Essa dimensÃo merece ser objeto de estudo e ser investigada com maior profundidade, pois trata do saber didÃtico, do saber-fazer na atuaÃÃo do tutor a distÃncia. A anÃlise dos resultados indicou que os alunos avaliaram a atuaÃÃo dos tutores classificando-a nos nÃveis de regular a bom. / Tutoring is one of the educational act ivities that accompanies, mediates and guides the student at the semipresential Distance Education (D.Ed.). This study intends, in general, to evaluate the performance of the tutor. Specifically, this study intends to know the necessary knowledges in teaching formation for an effective performance; to analyze the application of those knowledges and evaluate them in the performance of the tutor of semipresential courses of D.Ed. in Universidade Federal do Cearà (UFC). Thus, it was accomplished a literature revew about Education in three dimensions: Higher Education and D. Ed.; teaching formation: tutoring and formation with a discussion of the knowledges in its attitudinal, conceptual and procedural dimensions, under pedagogical, didact and technological perspectives; and Educational Assessment concerning to learning evaluation and formative evaluation. The nature of this investigation characterized the study as an exploratory, traverse and quantitative one, achieved in Instituto Universidade Virtual. The sample was applied in 175 tutors and 1.957 students enrolled in 7 undergraduate courses at the first semester of 2011, in the modality of D. Ed. The data were obtained through a likert scale with 16 items and analyzed by the software SPSS version 15.0. The applied scale presented a precision of high sensibility and standard error lower than 10% of the amplitude of the scale. The content validity was accomplished through factorial analysis that indicated that the scale is unifatorial. The distribution of total scale scores was converted into a scale of grades [0;10]; it presented, in the sample, average 7,7, median 8,4 and coefficient of variation 28,2, with the population average varying in the interval [7,6;7,8] for a confidence interval of 95%. The distribution of the grades is nonstandard, according to test of normality of Kolmorogov-Smirnov, with the concentration of the grades in values above the average (median 8,4). In the linear regression analysis of the knowledges, the standard coefficients indicated that the conceptual dimension was stronger in the composition of the grade. The attitudinal dimension came in second place and the procedural presented was weaker. Therefore, this dimension deserves to be more studied and investigated, because is related to didact knowledge, to doing knowledge in the performance of the tutor in D. Ed. The analysis of the results indicated that the students evaluated the performance of the tutors classifying it in levels from regular to good.