The thesis of this research is that database queries can be translated to corresponding English descriptions for the use in applications in intelligent tutoring (in particular, problem generation and feedback generation) for the subject domain of database query construction. To demonstrate this thesis, a rule-based graph-rewriting algorithm and a concrete set of rules for systematically transforming queries in a subset of SQL to English descriptions are presented. Further, through an implementation of this technique, this study demonstrates an evaluation of its performance on SQL queries from database textbooks.

Hypertext is the dominant method to navigate the Internet, providing user freedom and control over navigational behaviour. There has been an increase in converting existing educational material into Internet web pages but weaknesses have been identified in current WWW learning systems. There is a lack of conceptual support for learning from hypertext, navigational disorientation and cognitive overload. This implies the need for an established pedagogical approach to developing the web as a teaching and learning medium. Guided Discovery Learning is proposed as an educational pedagogy suitable for supporting WWW learning. The hypothesis is that a guided discovery environment will produce greater gains in learning and satisfaction, than a non-adaptive hypertext environment. A second hypothesis is that combining concept maps with this specific educational paradigm will provide cognitive support. The third hypothesis is that student learning styles will not influence learning outcome or user satisfaction. Thus, providing evidence that the guided discovery learning paradigm can be used for many types of learning styles. This was investigated by the building of a guided discovery system and a framework devised for assessing teaching styles. The system provided varying discovery steps, guided advice, individualistic system instruction and navigational control. An 84 subject experiment compared a Guided discovery condition, a Map-only condition and an Unguided condition. Subjects were subdivided according to learning styles, with measures for learning outcome and user satisfaction. The results indicate that providing guidance will result in a significant increase in level of learning. Guided discovery condition subjects, regardless of learning styles, experienced levels of satisfaction comparable to those in the other conditions. The concept mapping tool did not appear to affect learning outcome or user satisfaction. The conclusion was that using a particular approach to guidance would result in a more supportive environment for learning. This research contributes to the need for a better understanding of the pedagogic design that should be incorporated into WWW learning environments, with a recommendation for a guided discovery approach to alleviate major hypertext and WWW issues for distance learning.

published_or_final_version / abstract / toc / Educational Psychology / Master / Master of Social Sciences

Throughout history education has been restricted to a relatively small percentage of the world's population. The cause can be attributed to a number of factors; how- ever, it has been chiefly due to excessive cost. As we enter the information age it becomes conceivable to make education freely available to anyone, anywhere, any- time. The Intelligent Tutoring System is an automated teaching system designed to improve through experience, eventually learning to tailor its teaching to perfectly match each individual student's needs and preferences. In this dissertation we describe a template which we use for building problem-oriented skill teaching intelligent tutoring systems based on a Dynamic Bayes network framework. We present two case studies in which the template is adapted to very different teaching domains, documenting in each case the process of building, training, and testing the resulting ITS. In both case studies, the performance of the ITS is validated through human subject experiments. The results of these studies show that our template is a viable technique for designing ITSs that teach in skill based domains. We also show that, while conducting artificial intelligence research on the design of an ITS and collecting data for use in that regard, we can concurrently run educational research experiments. We find that the two are quite inextricably tied and that showing good general results regarding the performance of the ITS is not sufficient; a strong understanding of the experience of the students is also required. We report some interesting results covering the effect of choice in learning and a gender bias that shows up in our tutoring system.

No description available.

Personal tutoring is a term commonly used in the policy and practice of higher education. Extant literature utilizes the term, but there is no common understanding of its ethos within the higher education profession. Consequently the tacit nature, purpose and outcomes of one-to-one interactions between tutors and students, which have been at the heart of UK higher education since medieval times, risk invasion by policy imperatives such as employability and student retention, or risk marginalization as off-stage activities that occur in invisible space at the periphery of higher education practice. The thesis begins by exploring research and literature on the social and institutional contexts of activities which involve personal, supportive interaction between tutors and students, alongside literature on emotion work and emotional labour, counselling supervision and therapy culture, using a theoretical lens of critical social interactionism. This produced themes which were used to frame part of the data production and analysis. The purpose of the research is to explore the essence of the personal tutorial from the tutor’s practice perspective, and to locate this in its social and institutional contexts, enabling tutors to illuminate the essence of practice that takes place behind closed doors. The focus of data production is the reflective accounts of tutors participating in the study. Ten participants from a range of UK universities produced brief written reflections about one-to-one interactions with students, followed by an individual interaction between researcher and participant, based on exploring the written reflection. These methods are underpinned by critical theory which relates to the emancipatory, transformative outcomes of facilitated critical reflective practice. Participants revealed critical reflection is unlikely to occur in the absence of facilitation. The opportunity for tutors to take part in facilitated, critical reflective practice to explore personal interactions with students produced awareness of what shapes the nature and outcomes of personal tutoring, often resulting in transformation and articulation of practice. Contextualization by participants tended to be limited to institutional and personal factors, there was less engagement with wider social policy issues. There was little evidence that participants were aware of literature and practice models relating to personal tutoring, and little evidence of professional development opportunities in this area. Practice generally occurred in invisible space and time, and tended to be based on personal judgement rather than practice ethos. If personal tutoring is to become established as an essential practice at the heart of higher education, action will be needed to recognize and value its ethos, including social and pedagogical purpose.

"After decades of development, Intelligent Tutoring Systems (ITSs) have become a common learning environment for learners of various domains and academic levels. ITSs are computer systems designed to provide instruction and immediate feedback, which is customized to individual students, but without requiring the intervention of human instructors. All ITSs share the same goal: to provide tutorial services that support learning. Since learning is a very complex process, it is not surprising that a range of technologies and methodologies from different fields is employed. Student modeling is a pivotal technique used in ITSs. The model observes student behaviors in the tutor and creates a quantitative representation of student properties of interest necessary to customize instruction, to respond effectively, to engage students¡¯ interest and to promote learning. In this dissertation work, I focus on the following aspects of student modeling. Part I: Student Knowledge: Parameter Interpretation. Student modeling is widely used to obtain scientific insights about how people learn. Student models typically produce semantically meaningful parameter estimates, such as how quickly students learn a skill on average. Therefore, parameter estimates being interpretable and plausible is fundamental. My work includes automatically generating data-suggested Dirichlet priors for the Bayesian Knowledge Tracing model, in order to obtain more plausible parameter estimates. I also proposed, implemented, and evaluated an approach to generate multiple Dirichlet priors to improve parameter plausibility, accommodating the assumption that there are subsets of skills which students learn similarly. Part II: Student Performance: Student Performance Prediction. Accurately predicting student performance is one of the most desired features common evaluations for student modeling. for an ITS. The task, however, is very challenging, particularly in predicting a student¡¯s response on an individual problem in the tutor. I analyzed the components of two common student models to determine which aspects provide predictive power in classifying student performance. I found that modeling the student¡¯s overall knowledge led to improved predictive accuracy. I also presented an approach, which, rather than assuming students are drawn from a single distribution, modeled multiple distributions of student performances to improve the model¡¯s accuracy. Part III: Wheel-spinning: Student Future Failure in Mastery Learning. One drawback of the mastery learning framework is its possibility to leave a student stuck attempting to learn a skill he is unable to master. We refer to this phenomenon of students being given practice with no improvement as wheel-spinning. I analyzed student wheel-spinning across different tutoring systems and estimated the scope of the problem. To investigate the negative consequences of see what wheel-spinning could have done to students, I investigated the relationships between wheel-spinning and two other constructs of interest about students: efficiency of learning and ¡°gaming the system¡±. In addition, I designed a generic model of wheel-spinning, which uses features easily obtained by most ITSs. The model can be well generalized to unknown students with high accuracy classifying mastery and wheel-spinning problems. When used as a detector, the model can detect wheel-spinning in its early stage with satisfying satisfactory precision and recall. "

ASSISTments is a web-based intelligent tutoring system that can provide students with immediate feedback when they are doing math homework. Until now, ASSISTments required internet access in order to do nightly homework. Without ASSISTments, students do their work on paper and are not told if they are correct or given help for wrong answers until the next morning at best. We've developed a component that supports 'offline-mode', enabling students without internet access at home to still receive immediate feedback on their responses. Students with laptops download their assignments at school, and then run ASSISTments at home in offline mode, utilizing the browser's application cache and Web Storage API. To evaluate the benefit of having the offline feature, we ran a randomized controlled study that tests the effect of immediate feedback on student learning. Intuition would suggest that providing a student with tutoring and feedback immediately after they submit an answer would lead to better understanding of the material than having them wait until the next day. The results of the study confirmed our hypothesis, and validated the need for 'offline mode.'

This 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.

Adapting to learner characteristics is essential when selecting exercises for learners in an intelligent tutoring system. This thesis investigates how humans adapt next exercise selection (in particular difficulty level) to learner personality (self-esteem), invested mental effort, and performance to inspire an adaptive exercise selection algorithm. First, we describe the investigations to produce validated materials for the main studies, namely the creation and validation of self-esteem personality stories, mental effort statements, and mathematical exercises with varying levels of difficulty. Next, through empirical studies, we investigate the impact on exercise selection of learner's selfesteem (low versus high self-esteem) and effort (minimal, little, moderate, much, and all possible effort). Three studies investigate this for learners who had different performances on a previous exercise: just passing, just failing, and performed well. Participants considered a fictional learner with a certain performance, self-esteem and effort, and selected the difficulty level of the next mathematical exercise. We found that self-esteem, mental effort, and performance all impacted the difficulty level of the exercises selected for learners. Using the results from the studies, we generated an algorithm that selects exercises with varying difficulty levels adapted to learner characteristics. Finally, through a survey with professional teachers, we evaluated our algorithm and found that the algorithm's adaptations were appropriate in general.