Educators in the United States (U.S.) are increasingly turning to intelligent tutoring systems (ITS) to provide differentiated math instruction to high school students. However, many struggling high school learners do not perform well on these platforms, which reinforces the need for more awareness about effective supports that influence the achievement of learners in these milieus. The purpose of this study was to determine what factors of the Assessment and Learning in Knowledge Spaces (ALEKS), an ITS, are predictive of struggling learners' performance in a blended-learning Algebra 1 course at an inner city technical high school located in the northeastern U.S. The theoretical framework consisted of knowledge base theory, the zone of proximal development, and cognitive learning theory. Three variables (student retention, engagement time, and the ratio of topics mastered to topics practiced) were used to predict the degree of association on the criterion variable (mathematics competencies), as measured by final course progress grades in algebra, and the Preliminary Scholastic Assessment Test (PSATm) math scores. A correlational predictive design was applied to assess the data of a purposive sample of 265 struggling students at the study site; multiple regression analysis was also used to investigate the predictability of these variables. Findings suggest that engagement time and the ratio of mastered to practiced topics were significant predictors of final course progress grades. Nevertheless, these factors were not significant contributors in predicting PSATm score. Retention was identified as the only statistically significant predictor of PSATm score. The results offer educators with additional insights that can facilitate improvements in mathematical content knowledge and promote higher graduation rates for struggling learners in high school mathematics.

An After Action Review (AAR) is the Army training system's performance feedback mechanism. The purpose of the AAR is to improve team (unit) and individual performance in order to increase organizational readiness. While a large body of knowledge exists that discusses instructional strategies, feedback and training systems, neither the AAR process nor the AAR systems have been examined in terms of learning effectiveness and efficiency for embedded trainers as part of a holistic training system. In this thesis, different feedback methods for embedded training are evaluated based on the timing and type of feedback used during and after training exercises. Those feedback methodologies include: providing Immediate Directive Feedback (IDF) only, the IDF Only feedback condition group; using Immediate Direct Feedback and delayed feedback with open ended prompts to elicit self-elaboration during the AAR, the IDF with AAR feedback condition group; and delaying feedback using opened ended prompts without any IDF, the AAR Only feedback condition group. The results of the experiment support the hypothesis that feedback timing and type do effect skill acquisition, retention and transfer in different ways. Immediate directive feedback has a significant effect in reducing the number of errors committed while acquiring new procedural skills during training. Delayed feedback, in the form of an AAR, has a significant effect on the acquisition, retention and transfer of higher order conceptual knowledge as well as procedural knowledge about a task. The combination of Immediate Directive Feedback with an After Action Review demonstrated the greatest degree of transfer on a transfer task.

Computer-based instructors, just like their human counterparts, should monitor the emotional and cognitive states of their students in order to adapt instructional technique. Doing so requires a model of student state to be available at run time, but this has historically been difficult. Because people are different, generalized models have not been able to be validated. As a person’s cognitive and affective state vary over time of day and seasonally, individualized models have had differing difficulties. The simultaneous creation and execution of an individualized model, in real time, represents the last option for modeling such cognitive and affective states. This dissertation presents and evaluates four differing techniques for the creation of cognitive and affective models that are created on-line and in real time for each individual user as alternatives to generalized models. Each of these techniques involves making predictions and modifications to the model in real time, addressing the real time datastream problems of infinite length, detection of new concepts, and responding to how concepts change over time. Additionally, with the knowledge that a user is physically present, this work investigates the contribution that the occasional direct user query can add to the overall quality of such models. The research described in this dissertation finds that the creation of a reasonable quality affective model is possible with an infinitesimal amount of time and without “ground truth” knowledge of the user, which is shown across three different emotional states. Creation of a cognitive model in the same fashion, however, was not possible via direct AI modeling, even with all of the “ground truth” information available, which is shown across four different cognitive states.

Emotional intelligence has a prominent role in education, health care, and day to day interaction. With the increasing use of computer technology, computers are interacting with more and more individuals. This interaction provides an opportunity to increase knowledge about human emotion for human consumption, well-being, and improved computer adaptation. This thesis explores the efficacy of using up to four different sensors in three domains for computational affect detection. We first consider computer-based education, where a collection of four sensors is used to detect student emotions relevant to learning, such as frustration, confidence, excitement and interest while students use a computer geometry tutor. The best classier of each emotion in terms of accuracy ranges from 78% to 87.5%. We then use voice data collected in a clinical setting to differentiate both gender and culture of the speaker. We produce classifiers with accuracies between 84% and 94% for gender, and between 58% and 70% for American vs. Asian culture, and we find that classifiers for distinguishing between four cultures do not perform better than chance. Finally, we use video and audio in a health care education scenario to detect students' emotions during a clinical simulation evaluation. The video data provides classifiers with accuracies between 63% and 88% for the emotions of confident, anxious, frustrated, excited, and interested. We find the audio data to be too complex to single out the voice source of the student by automatic means. In total, this work is a step forward in the automatic computational detection of affect in realistic settings.

This dissertation presents a novel effort to develop ITS technologies that adapt by observing student behavior. In particular, we define an evolving expert knowledge base (EEKB) that structures a domain's information as a set of nodes and the relationships that exist between those nodes. The structure of this model is not the particularly novel aspect of this work, but rather the model's evolving behavior. Past efforts have shown that this model, once created, is useful for providing students with expert feedback as they work within our ITS called Rashi. We present an algorithm that observes groups of students as they work within Rashi, and collects student contributions to form an accurate domain level EEKB. We then present experimentation that simulates more than 15,000 data points of real student interaction and analyzes the quality of the EEKB models that are produced. We discover that EEKB models can be constructed accurately, and with significant efficiency compared to human constructed models of the same form. We are able to make this judgment by comparing our automatically constructed models with similar models that were hand crafted by a small team of domain experts. We also explore several tertiary effects. We focus on the impact that gaming and game mechanics have on various aspects of this model acquisition process. We discuss explicit game mechanics that were implemented in the source ITS from which our data was collected. Students who are given our system with game mechanics contribute higher amounts of data, while also performing higher quality work. Additionally, we define a novel type of game called a knowledge-refinement game (KRG), which motivates subject matter experts (SMEs) to contribute to an already constructed EEKB, but for the purpose of refining the model in areas in which confidence is low. Experimental work with the KRG provides strong evidence that: 1) the quality of the original EEKB was indeed strong, as validated by KRG players, and 2) both the quality and breadth of knowledge within the EEKB are increased when players use the KRG.

No description available.

No description available.

In a technology driven world, efforts are being made to make education/learning available to individuals at any time with no compromise in the quality of teaching/training. To make learning flexible, different techniques such as distributing learning material, uploading audio lectures on the web, and creating intelligent tutoring systems (ITS) are being used. The technique considered here is an adaptive ITS, a system that replicates the learning that occurs in a student teacher relationship. This thesis develops an adaptive intelligent tutoring system architecture prototype where the addition, modification and removal of educational material are relatively easy. The resulting software will take into account: the goals of the specific educational experience, the concepts to be covered, the preferred learning style of the student, measures to detect misuse of the system functionality, behavior based on the student's performance and the generation of hint sequences and feedback messages to improve learning gain. The system will accomplish these objectives by assessing the student's prior knowledge level, observing the actions performed by the student and by adapting to his/her learning abilities. The ITS will attempt to be more intelligent by performing some actions traditionally done by a human teacher - such as diagnosing misconceptions, identifying the most suitable learning style, stressing concepts that the student is finding difficult to understand, switching back to the learning material if the student shows no improvement after a set of trials. The system makes sure that the student is getting feedback where appropriate. Using this prototype system, the student will be tutored to acquire declarative knowledge. A problem based learning (PBL) approach will be used to strengthen the acquired knowledge by providing a high degree of personal attention to the student. To show how the prototype system works, an example of analysis of a control system problem using bode plot technique will be used to assist the student in using the technique to perform the stability analysis of an analog, linear, time-invariant control system problems and to recommend a controller to attain stability (if the system is not stable). Ideas of porting the system from standalone to web-based architecture and features required for collaborative learning will be discussed and an architecture for a web-based tutoring system for supporting multiple students enabling communication between students and sharing data among them will be proposed. / Electrical and Computer Engineering

The purpose of this thesis was to develop a collection of fluid mechanics tutorials written in FORTRAN for the IBM PC. These programs are an improvement on existing software, covering more subjects that are typically covered in introductory and intermediate courses, and including improved graphics capabilities through the implementation of the Graphical Kernel System, or GKS. Fluid mechanics topics and GKS are brought together to form a package of 30 interactive programs in fluid mechanics. The thesis includes two chapters devoted to the Graphical Kernel System, and a sample program designed to serve as a tutorial to introduce the user to GKS. Each fluid mechanics program covers a specific topic and is given its own documentation in the thesis. Background information as well as program operations are discussed. "I Shall Return." - General Douglas MacArthur when asked of his desire to pursue a Ph.D. at the University of the Philippines / Master of Science

Tutoring is one of the most effective instruction methods. Computer as an Intelligent Tutor is an area of research since many decades. Technology advancement in Information and Communication Technology (ICT) can be used in developing Web – based Intelligent Tutoring System (WITS), which provides individualized tutoring at the same time to large number of students geographically distributed. Intelligent Tutoring System requires knowledge representation of expert, student and instructional strategy. While web technology promises many attractive features to build web based ITS, it would still be a challenge to represent knowledge objects that are scalable, reusable and platform independent. It is required to derive generalized knowledge representation framework which can be used in developing WITS for many courses. This research work proposes an instruction System Design (ISD) model based framework in development of WITS for Control Systems. ADDIE model is selected in development of WITS. Front end analysis is conducted to identify the learning goals of a course. Proposed research work presents a Bloom - Vincenti framework for preparing learning objectives for engineering courses. Problem Based Learning (PBL) is selected as instruction strategy. Then it presents an ontology based knowledge representation framework for expert module, tutoring module, and student module. Ontology for expert module is proposed on the course structure, instruction system, instruction material ontology, and Bloom – Vincenti Taxonomy. Ontology for student module is also proposed on course structure and Bloom – Vincenti Taxonomy. Tutoring module consists of ontology about the facts of the instruction material and rule base based on the categories of engineering knowledge (Vincenti) and cognitive skill (Bloom’s Taxonomy). Proposed way of knowledge representation supports scalability, and reusability. Prototype Web – based Intelligent Tutoring System for first level course on Control Systems is developed. JAVA technology used in development of Web – based Intelligent Tutoring System (WITS), makes WITS platform independent. Web – based Intelligent Tutoring System for Control Systems is deployed at laboratory level and its efficacy is tested for first two modules of a course.