By opening the learner model to both the learner and other peers within an e-learning system, the learner gains control over his or her learner model and is able to reflect on the contents presented in the model. Many current modeling systems translate an existing model to fit the context when information is needed. This thesis explores the observation that information in the model depends on the context in which it is generated and describes a method of generating the model for the specific user and purpose. The main advantage of this approach is that exactly the right information is generated to suit the context and needs of the learner. To explore the benefits and possible downsides of this approach, a learner model Query Tool was implemented to give instructors and learners the opportunity to ask specific questions (queries) of the content delivery system hosting several online courses. Information is computed in real time when the query is run by the instructor, so the data is always up-to-date. Instructors may then choose to allow students to run the query as well, enabling learner reflection on their progress in the course as the instructor has defined it. I have called this process active open learner modelling, referring to the open learner modelling community where learner models are accessible by learners for reflective purposes, and referring to the active learner modelling community which describes learner modelling as a context-driven process. Specific research questions explored in this thesis include "how does context affect the modelling process when learner models are opened to users", "how can privacy be maintained while useful information is provided", and "can an accurate and useful learner model be computed actively".

Augmented Reality can visually convey abstract concepts and 3D spatial information in context with real-world objects, which makes it an ideal tool for training and educational purposes. This masters thesis investigates the use of Augmented Reality to assist with training for manual assembly and maintenance tasks. Improving on prior research, this approach combines Augmented Reality with a robust Intelligent Tutoring System to provide a more effective learning experience. After developing a modular software framework, a prototype was created that teaches the user to assemble hardware components on a computer motherboard. A thorough evaluation of the prototype found that the new intelligent approach significantly improves the learning outcome over traditional Augmented Reality training methods that do not employ Intelligent Tutoring Systems.

Emotions play a significant role in many human mental activities, including decision-making, motivation, and cognition. Various intelligent and expert systems can be empowered with emotionally intelligent capabilities, especially systems that interact with humans and mimic human behaviour. However, most current methods in affect recognition studies use intrusive, lab-based, and expensive tools which are unsuitable for real-world situations. Inspired by studies on keystrokes dynamics, this thesis investigates the effectiveness of diagnosing users’ affect through their typing behaviour in an educational context. To collect users’ typing patterns, a field study was conducted in which subjects used a dialogue-based tutoring system built by the researcher. Eighteen dialogue features associated with subjective and objective ratings for users’ emotions were collected. Several classification techniques were assessed in diagnosing users’ affect, including discrimination analysis, Bayesian analysis, decision trees, and neural networks. An artificial neural network approach was ultimately chosen as it yielded the highest accuracy compared with the other methods. To lower the error rate, a hierarchical classification was implemented to first classify user emotions based on their valence (positive or negative) and then perform a finer classification step to determining which emotions the user experienced (delighted, neutral, confused, bored, and frustrated). The hierarchical classifier was successfully able to diagnose users' emotional valence, while it was moderately able to classify users’ emotional states. The overall accuracy obtained from the hierarchical classifier significantly outperformed previous dialogue-based approaches and in line with some affective computing methods.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: Intelligent Tutoring Systems; Model Tracing; Machine Learning; Artificial Intelligence; Programming by Demonstration. Includes bibliographical references.

abstract: Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply instructors with biology questions, a semantic network approach was developed for generating open response biology questions. The generated questions were compared to professional authorized questions. To boost students’ learning experience, adaptive selection was built on the generated questions. Bayesian Knowledge Tracing was used as embedded assessment of the student’s current competence so that a suitable question could be selected based on the student’s previous performance. A between-subjects experiment with 42 participants was performed, where half of the participants studied with adaptive selected questions and the rest studied with mal-adaptive order of questions. Both groups significantly improved their test scores, and the participants in adaptive group registered larger learning gains than participants in the control group. To explore the possibility of generating rich instructional feedback for machine-generated questions, a question-paragraph mapping task was identified. Given a set of questions and a list of paragraphs for a textbook, the goal of the task was to map the related paragraphs to each question. An algorithm was developed whose performance was comparable to human annotators. A multiple-choice question with high quality distractors (incorrect answers) can be pedagogically valuable as well as being much easier to grade than open-response questions. Thus, an algorithm was developed to generate good distractors for multiple-choice questions. The machine-generated multiple-choice questions were compared to human-generated questions in terms of three measures: question difficulty, question discrimination and distractor usefulness. By recruiting 200 participants from Amazon Mechanical Turk, it turned out that the two types of questions performed very closely on all the three measures. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2015

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: Concept maps are commonly used knowledge visualization tools and have been shown to have a positive impact on learning. The main drawbacks of concept mapping are the requirement of training, and lack of feedback support. Thus, prior research has attempted to provide support and feedback in concept mapping, such as by developing computer-based concept mapping tools, offering starting templates and navigational supports, as well as providing automated feedback. Although these approaches have achieved promising results, there are still challenges that remain to be solved. For example, there is a need to create a concept mapping system that reduces the extraneous effort of editing a concept map while encouraging more cognitively beneficial behaviors. Also, there is little understanding of the cognitive process during concept mapping. What’s more, current feedback mechanisms in concept mapping only focus on the outcome of the map, instead of the learning process. This thesis work strives to solve the fundamental research question: How to leverage computer technologies to intelligently support concept mapping to promote meaningful learning? To approach this research question, I first present an intelligent concept mapping system, MindDot, that supports concept mapping via innovative integration of two features, hyperlink navigation, and expert template. The system reduces the effort of creating and modifying concept maps while encouraging beneficial activities such as comparing related concepts and establishing relationships among them. I then present the comparative strategy metric that modes student learning by evaluating behavioral patterns and learning strategies. Lastly, I develop an adaptive feedback system that provides immediate diagnostic feedback in response to both the key learning behaviors during concept mapping and the correctness and completeness of the created maps. Empirical evaluations indicated that the integrated navigational and template support in MindDot fostered effective learning behaviors and facilitating learning achievements. The comparative strategy model was shown to be highly representative of learning characteristics such as motivation, engagement, misconceptions, and predicted learning results. The feedback tutor also demonstrated positive impacts on supporting learning and assisting the development of effective learning strategies that prepare learners for future learning. This dissertation contributes to the field of supporting concept mapping with designs of technological affordances, a process-based student model, an adaptive feedback tutor, empirical evaluations of these proposed innovations, and implications for future support in concept mapping. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019

The COVID-19 pandemic wiped off decades of educational gains in the developing world and added 24 million more children to 775 million illiterates in the world. To counteract such a huge predicament, human learning agility comes into action. This human characteristic of knowing what to do when one does not know what to do, invokes the Soft System Methodology (SSM) approach to analyze illiteracy as the worst of all pandemics since it infiltrates into generations. After evaluating different effective teaching methods and utilizing the SSM approach, this paper proposes suitable pedagogies to educate deprived students. It examines Massive Online Open Courseware (MOOC) as a viable solution for K-12 students and compares it with a more robust educational technology model of Intelligent Tutoring System (ITS). Using artificial intelligence, the ITS tailors the instructional content framework and teaching strategies after evaluating students' pre-existing knowledge, learning habits, & styles. The ITS engages the student with the lesson with a two-way dialog while providing customized instruction and immediate feedback. An ITS requires no human intervention and could be a suitable replacement for an inadequately qualified teacher or no teacher. Hence it could be a practical tool in tackling the global literacy catastrophe. A comprehensive literature review followed by a meta-analysis reveals the effectiveness of ITS as a feasible intervention. The major purpose of this study is to define the application of educational pedagogy behind AI-based tutoring and cognitive science in this learner-centered approach.

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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.