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.

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.

abstract: Embedded assessment constantly updates a model of the student as the student works on instructional tasks. Accurate embedded assessment allows students, instructors and instructional systems to make informed decisions without requiring the student to stop instruction and take a test. This thesis describes the development and comparison of several student models for Dragoon, an intelligent tutoring system. All the models were instances of Bayesian Knowledge Tracing, a standard method. Several methods of parameterization and calibration were explored using two recently developed toolkits, FAST and BNT-SM that replaces constant-valued parameters with logistic regressions. The evaluation was done by calculating the fit of the models to data from human subjects and by assessing the accuracy of their assessment of simulated students. The student models created using node properties as subskills were superior to coarse-grained, skill-only models. Adding this extra level of representation to emission parameters was superior to adding it to transmission parameters. Adding difficulty parameters did not improve fit, contrary to standard practice in psychometrics. / Dissertation/Thesis / Masters Thesis Computer Science 2015

abstract: EMBRACE (Enhanced Moved By Reading to Accelerate Comprehension in English) is an IPad application that uses the Moved By Reading strategy to help improve the reading comprehension skills of bilingual (Spanish speaking) English Language Learners (ELLs). In EMBRACE, students read the text of a story and then move images corresponding to the text that they read. According to the embodied cognition theory, this grounds reading comprehension in physical experiences and thus is more engaging. In this thesis, I used the log data from 20 students in grades 2-5 to design a skill model for a student using EMBRACE. A skill model is the set of knowledge components that a student needs to master in order to comprehend the text in EMBRACE. A good skill model will improve understanding of the mistakes students make and thus aid in the design of useful feedback for the student.. In this context, the skill model consists of vocabulary and syntax associated with the steps that students performed. I mapped each step in EMBRACE to one or more skills (vocabulary and syntax) from the model. After every step, the skill level is updated in the model. Thus, if a student answered the previous step incorrectly, the corresponding skills are decremented and if the student answered the previous question correctly, the corresponding skills are incremented, through the Bayesian Knowledge Tracing algorithm. I then correlated the students’ predicted scores (computed from their skill levels) to their posttest scores. I evaluated the students’ predicted scores (computed from their skill levels) by comparing them to their posttest scores. The two sets of scores were not highly correlated, but the results gave insights into potential improvements that could be made to the system with respect to user interaction, posttest scores and modeling algorithm. / Dissertation/Thesis / Masters Thesis Computer Science 2016

The educational industry has evolved with the development of computer technology. The online education system (OES) provides a more effective and efficient educational strategy for students benefiting from computer science technologies. There is a need for a mapping of knowledge definition from the traditional education system to the data gathered from the non-traditional OES. This would make the measurement of knowledge possible for OES. The study aims to (a) find an appropriate knowledge definition through a literature review process, and (b) based on the definition measure students’ knowledge in OES, such as Hypocampus, by using machine learning techniques. Experiments were conducted using a well-known Bayesian KnowledgeTracing (BKT) model. The evaluation was performed on 3300 students studying medicine in France using Hypocampus OES. As a result, the student’s knowledge was measured in skills, performance, and achievement per subject with 81% average accuracy. The obtained results suggest the potential of the presented approach for measuring students’ knowledge in OES.

Despite widespread recognition by science educators, researchers and K-12 frameworks that scientific inquiry should be an essential part of science education, typical classrooms and assessments still emphasize rote vocabulary, facts, and formulas. One of several reasons for this is that the rigorous assessment of complex inquiry skills is still in its infancy. Though progress has been made, there are still many challenges that hinder inquiry from being assessed in a meaningful, scalable, reliable and timely manner. To address some of these challenges and to realize the possibility of formative assessment of inquiry, we describe a novel approach for evaluating, tracking, and scaffolding inquiry process skills. These skills are demonstrated as students experiment with computer-based simulations. In this work, we focus on two skills related to data collection, designing controlled experiments and testing stated hypotheses. Central to this approach is the use and extension of techniques developed in the Intelligent Tutoring Systems and Educational Data Mining communities to handle the variety of ways in which students can demonstrate skills. To evaluate students' skills, we iteratively developed data-mined models (detectors) that can discern when students test their articulated hypotheses and design controlled experiments. To aggregate and track students' developing latent skill across activities, we use and extend the Bayesian Knowledge-Tracing framework (Corbett & Anderson, 1995). As part of this work, we directly address the scalability and reliability of these models' predictions because we tested how well they predict for student data not used to build them. When doing so, we found that these models demonstrate the potential to scale because they can correctly evaluate and track students' inquiry skills. The ability to evaluate students' inquiry also enables the system to provide automated, individualized feedback to students as they experiment. As part of this work, we also describe an approach to provide such scaffolding to students. We also tested the efficacy of these scaffolds by conducting a study to determine how scaffolding impacts acquisition and transfer of skill across science topics. When doing so, we found that students who received scaffolding versus students who did not were better able to acquire skills in the topic in which they practiced, and also transfer skills to a second topic when was scaffolding removed. Our overall findings suggest that computer-based simulations augmented with real-time feedback can be used to reliably measure the inquiry skills of interest and can help students learn how to demonstrate these skills. As such, our assessment approach and system as a whole shows promise as a way to formatively assess students' inquiry.

Despite widespread recognition by science educators, researchers and K-12 frameworks that scientific inquiry should be an essential part of science education, typical classrooms and assessments still emphasize rote vocabulary, facts, and formulas. One of several reasons for this is that the rigorous assessment of complex inquiry skills is still in its infancy. Though progress has been made, there are still many challenges that hinder inquiry from being assessed in a meaningful, scalable, reliable and timely manner. To address some of these challenges and to realize the possibility of formative assessment of inquiry, we describe a novel approach for evaluating, tracking, and scaffolding inquiry process skills. These skills are demonstrated as students experiment with computer-based simulations. In this work, we focus on two skills related to data collection, designing controlled experiments and testing stated hypotheses. Central to this approach is the use and extension of techniques developed in the Intelligent Tutoring Systems and Educational Data Mining communities to handle the variety of ways in which students can demonstrate skills. To evaluate students' skills, we iteratively developed data-mined models (detectors) that can discern when students test their articulated hypotheses and design controlled experiments. To aggregate and track students' developing latent skill across activities, we use and extend the Bayesian Knowledge-Tracing framework (Corbett & Anderson, 1995). As part of this work, we directly address the scalability and reliability of these models' predictions because we tested how well they predict for student data not used to build them. When doing so, we found that these models demonstrate the potential to scale because they can correctly evaluate and track students' inquiry skills. The ability to evaluate students' inquiry also enables the system to provide automated, individualized feedback to students as they experiment. As part of this work, we also describe an approach to provide such scaffolding to students. We also tested the efficacy of these scaffolds by conducting a study to determine how scaffolding impacts acquisition and transfer of skill across science topics. When doing so, we found that students who received scaffolding versus students who did not were better able to acquire skills in the topic in which they practiced, and also transfer skills to a second topic when was scaffolding removed. Our overall findings suggest that computer-based simulations augmented with real-time feedback can be used to reliably measure the inquiry skills of interest and can help students learn how to demonstrate these skills. As such, our assessment approach and system as a whole shows promise as a way to formatively assess students' inquiry.