Student Modeling From Different Aspects

With the wide usage of online tutoring systems, researchers become interested in mining data from logged files of these systems, so as to get better understanding of students. Varieties of aspects of students’ learning have become focus of studies, such as modeling students’ mastery status and affects. On the other hand, Randomized Controlled Trial (RCT), which is an unbiased method for getting insights of education, finds its way in Intelligent Tutoring System. Firstly, people are curious about what kind of settings would work better. Secondly, such a tutoring system, with lots of students and teachers using it, provides an opportunity for building a RCT infrastructure underlying the system. With the increasing interest in Data mining and RCTs, the thesis focuses on these two aspects. In the first part, we focus on analyzing and mining data from ASSISTments, an online tutoring system run by a team in Worcester Polytechnic Institute. Through the data, we try to answer several questions from different aspects of students learning. The first question we try to answer is what matters more to student modeling, skill information or student information. The second question is whether it is necessary to model students’ learning at different opportunity count. The third question is about the benefits of using partial credit, rather than binary credit as measurement of students’ learning in RCTs. The fourth question focuses on the amount that students spent Wheel Spinning in the tutoring system. The fifth questions studies the tradeoff between the mastery threshold and the time spent in the tutoring system. By answering the five questions, we both propose machine learning methodology that can be applied in educational data mining, and present findings from analyzing and mining the data. In the second part, we focused on RCTs within ASSISTments. Firstly, we looked at a pilot study of reassessment and relearning, which suggested a better system setting to improve students’ robust learning. Secondly, we proposed the idea to build an infrastructure of learning within ASSISTments, which provides the opportunities to improve the whole educational environment

End-to-end deep reinforcement learning in computer systems

Abstract The growing complexity of data processing systems has long led systems designers to imagine systems (e.g. databases, schedulers) which can self-configure and adapt based on environmental cues. In this context, reinforcement learning (RL) methods have since their inception appealed to systems developers. They promise to acquire complex decision policies from raw feedback signals. Despite their conceptual popularity, RL methods are scarcely found in real-world data processing systems. Recently, RL has seen explosive growth in interest due to high profile successes when utilising large neural networks (deep reinforcement learning). Newly emerging machine learning frameworks and powerful hardware accelerators have given rise to a plethora of new potential applications. In this dissertation, I first argue that in order to design and execute deep RL algorithms efficiently, novel software abstractions are required which can accommodate the distinct computational patterns of communication-intensive and fast-evolving algorithms. I propose an architecture which decouples logical algorithm construction from local and distributed execution semantics. I further present RLgraph, my proof-of-concept implementation of this architecture. In RLgraph, algorithm developers can explore novel designs by constructing a high-level data flow graph through combination of logical components. This dataflow graph is independent of specific backend frameworks or notions of execution, and is only later mapped to execution semantics via a staged build process. RLgraph enables high-performing algorithm implementations while maintaining flexibility for rapid prototyping. Second, I investigate reasons for the scarcity of RL applications in systems themselves. I argue that progress in applied RL is hindered by a lack of tools for task model design which bridge the gap between systems and algorithms, and also by missing shared standards for evaluation of model capabilities. I introduce Wield, a first-of-its-kind tool for incremental model design in applied RL. Wield provides a small set of primitives which decouple systems interfaces and deployment-specific configuration from representation. Core to Wield is a novel instructive experiment protocol called progressive randomisation which helps practitioners to incrementally evaluate different dimensions of non-determinism. I demonstrate how Wield and progressive randomisation can be used to reproduce and assess prior work, and to guide implementation of novel RL applications

Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold: Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction

Refining Prerequisite Skill Structure Graphs Using Randomized Controlled Trials

Prerequisite skill structure graphs represent the relationships between knowledge components. Prerequisite structure graphs also propose the order in which students in a given curriculum need to be taught specific knowledge components in order to assist them build on previous knowledge and improve achievement in those subject domains. The importance of accurate prerequisite skill structure graphs can therefore not be overemphasized. In view of this, many approaches have been employed by domain experts to design and implement these prerequisite structures. A number of data mining techniques have also been proposed to infer these knowledge structures from learner performance data. These methods have achieved varied degrees of success. Moreover, to the best of our knowledge, none of the methods have employed extensive randomized controlled trials to learn about prerequisite skill relationships among skills. In this dissertation, we motivate the need for using randomized controlled trials to refine prerequisite skill structure graphs. Additionally, we present PLACEments, an adaptive testing system that uses a prerequisite skill structure graph to identify gaps in students’ knowledge. Students with identified gaps are assisted with more practice assignments to ensure that the gaps are closed. PLACEments additionally allows for randomized controlled experiments to be performed on the underlying prerequisite skill structure graph for the purpose of refining the structure. We present some of the different experiment categories which are possible in PLACEments and report the results of one of these experiment categories. The ultimate goal is to inform domain experts and curriculum designers as they create policies that govern the sequencing and pacing of contents in learning domains whose content lend themselves to sequencing. By extension students and teachers who apply these policies benefit from the findings of these experiments

Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

Bubble World - A Novel Visual Information Retrieval Technique

With the tremendous growth of published electronic information sources in the last decade and the unprecedented reliance on this information to succeed in day-to-day operations, comes the expectation of finding the right information at the right time. Sentential interfaces are currently the only viable solution for searching through large infospheres of unstructured information, however, the simplistic nature of their interaction model and lack of cognitive amplification they can provide severely limit the performance of the interface. Visual information retrieval systems are emerging as possible candidate replacements for the more traditional interfaces, but many lack the cognitive framework to support the knowledge crystallization process found to be essential in information retrieval. This work introduces a novel visual information retrieval technique crafted from two distinct design genres: (1) the cognitive strategies of the human mind to solve problems and (2) observed interaction patterns with existing information retrieval systems. Based on the cognitive and interaction framework developed in this research, a functional prototype information retrieval system, called Bubble World, has been created to demonstrate that significant performance gains can be achieved using this technique when compared to more traditional text-based interfaces. Bubble World does this by successfully transforming the internal mental representation of the information retrieval problem to an efficient external view, and then through visual cues, provides cognitive amplification at key stages of the information retrieval process. Additionally, Bubble World provides the interaction model and the mechanisms to incorporate complex search schemas into the retrieval process either manually or automatically through the use of predefined ontological models