1,721,067 research outputs found
Recommended from our members
Developing an Affordable Authoring Tool For Intelligent Tutoring Systems
No full textIntelligent tutoring systems (ITSs) are computer based tutoring systems that provide individualized tutoring to the students. Building an ITS is recognized to be expensive task in terms of cost and resources. Authoring tools provide a framework and an environment for building the ITSs that help to reduce the resources like skills, time and cost required to build an intelligent tutoring system.
In this thesis we have implemented the Cognitive Tutor Authoring Tools (CTAT) and performed experiments to empirically determine the common programming errors that authors tend to make while building an ITS and study what is hard in authoring an ITS. The CTAT were used in a graduate class at Worcester Polytechnic Institute and also at the 4th Summer school organized at the Carnegie Mellon University. Based on the analysis of the experiments we suggest future work to reduce the debugging time and thereby reduce the time required to author an ITS. We also implemented the model tracing algorithm in JESS, evaluated its performance and compared to that of the model tracing algorithm in TDK.
This research is funded by the Office of Naval Research (Grant # N00014-0301-0221)
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Recommended from our members
Boredom and student modeling in intelligent tutoring systems
No full textOver 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.</P
Recommended from our members
Can a computer adaptive assessment system determine, better than traditional methods, whether students know mathematics skills?
No full textSchools use commercial systems specifically for mathematics benchmarking and longitudinal assessment. However these systems are expensive and their results often fail to indicate a clear path for teachers to differentiate instruction based on students’ individual strengths and weaknesses in specific skills. ASSISTments is a web-based Intelligent Tutoring System used by educators to drive real-time, formative assessment in their classrooms. The software is used primarily by mathematics teachers to deliver homework, classwork and exams to their students. We have developed a computer adaptive test called PLACEments as an extension of ASSISTments to allow teachers to perform individual student assessment and by extension school-wide benchmarking. PLACEments uses a form of graph-based knowledge representation by which the exam results identify the specific mathematics skills that each student lacks. The system additionally provides differentiated practice determined by the students’ performance on the adaptive test. In this project, we describe the design and implementation of PLACEments as a skill assessment method and evaluate it in comparison with a fixed-item benchmark
Recommended from our members
Visual Feedback for Gaming Prevention in Intelligent Tutoring Systems
No full textA major issue in Intelligent Tutoring Systems is off-task student behavior, especially performance-based gaming, where students systematically exploit tutor behavior in order to advance through a curriculum quickly and easily, with as little active thought directed at the educational content as possible. The goal of this research was to explore the phenomena of off-task gaming behavior within the Assistments system, as well as to develop a passive visual indicator to deter and prevent off-task gaming behavior without active intervention via graphical feedback to the student and teachers. Traditional active intervention approaches were also constructed for comparison purposes, and machine-learned gaming-detection models were developed as a potential invocation and evaluation mechanism. Passive graphical interventions have been well received by teachers, and results are suggestive that they are effective at reducing off-task gaming behavior
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Recommended from our members
Increasing parent engagement in student learning using an Intelligent Tutoring System with Automated Messages
No full textThis study explores the ability of an Intelligent Tutoring System (ITS) to increase parental engagement in student learning. A parental notification feature was developed for the web-based ASSISTments ITS that allows parents to log into their own accounts and access detailed data about their students’ performance. Parents from a local middle school were then invited to create accounts and answer a survey assessing how engaged they felt they were in their students’ education. A randomized controlled experiment was run during which weekly automated messages were sent home to parents regarding their students’ assignments and how they were performing. After having them take a post-survey, it was found that access to this data caused parents to become more involved in their students’ education. Additionally, this led to increased student performance in the form of higher homework completion rates. Qualitative feedback from parents was very positive
Recommended from our members
Refining Learning Maps with Data Fitting Techniques
No full textLearning maps have been used to represent student knowledge for many years. These maps are usually hand made by experts in a given domain. However, these hand-made maps have not been found to be predictive of student performance. Several methods have been proposed to find bet-ter fitting learning maps. These methods include the Learning Factors Analysis (LFA) model and the Rule-space method.
In this thesis we report on the application of one of the proposed operations in the LFA method to a small section of a skill graph and develop a greedy search algorithm for finding better fitting models for this graph. Additionally an investigation of the factors that influence the search for better data fitting models using the proposed algorithm is reported. We also present an empirical study in which PLACEments, an adaptive testing system that employs a skill graph, is modified to test the strength of prerequisite skill links in a given learning map and propose a method for refining learning maps based on those findings.
It was found that the proposed greedy search algorithm performs as well as an original skill graph but with a smaller set of skills in the graph. Additionally it was found that, among other factors, the number of unnecessary skills, the number of items in the graph, and the guess and slip rates of the items tagged with skills in the graph have an impact on the search. Further, the size of the evaluation data set impacts the search. The more data there is for the search, the more predictive the learned skill graph.
Additionally, PLACEments, an adaptive testing feature of ASSISTments, has been found to be useful for refining skill graphs by detecting the strengths of prerequisite links between skills in a graph
Recommended from our members
Incorporating Rich Features into Deep Knowledge Tracing
No full textThe desire to follow student learning within intelligent tutoring systems in near real time has led to the development of several models anticipating the correctness of the next item as students work through an assignment. Such models have in- cluded Bayesian Knowledge Tracing (BKT), Performance Factors Analysis (PFA), and more recently with developments in Deep Learning, Deep Knowledge Tracing (DKT). The DKT model, based on the use of a recurrent neural network, exhibited promising results in paper [PBH+15]. Thus far, however, the model has only considered the knowledge components of the problems and correctness as input, neglecting the breadth of other features col- lected by computer-based learning platforms. This work seeks to improve upon the DKT model by incorporating more features at the problem-level and student-level. With this higher dimensional input, an adaption to the original DKT model struc- ture is also proposed, incorporating an Autoencoder network layer to convert the input into a low dimensional feature vector to reduce both the resource requirement and time needed to train. Experimental results show that our adapted DKT model, which includes more combinations of features, can effectively improve accuracy
- …
