Local Goals Driven Hierarchical Reinforcement Learning

* This research was partially supported by the Latvian Science Foundation under grant No.02-86d.

Knowledge Learning Technology for Intelligent Tutoring Systems

In this work we suggest the technology of creation of intelligent tutoring systems which are oriented to teach knowledge. It is supposed the acquisition of expert’s knowledge by using of the Formal Concept Analysis method, then construction the test questions which are used for verification of the pupil's knowledge with the expert’s knowledge. Then the further tutoring strategy is generated by the results of this verification.

Facilitating intuitive-guided learning in a serious game through integration with a learning content management system

Increased global uptake of entertainment gaming has the potential to lead to high expectations of engagement and interactivity from users of technology-enhanced learning environments. Blended approaches to implementing game-based learning as part of distance or technology-enhanced education have led to demonstrations of the benefits they might bring, allowing learners to interact with immersive technologies as part of a broader, structured learning experience. In this article, we explore how the integration of a serious game can be extended to a learning content management system (LCMS) to support a blended and holistic approach, described as an 'intuitive-guided' method. Through a case study within the EU-Funded Adaptive Learning via Intuitive/Interactive, Collaborative and Emotional Systems (ALICE) project, a technical integration of a gaming engine with a proprietary LCMS is demonstrated, building upon earlier work and demonstrating how this approach might be realized. In particular, how this method can support an intuitive-guided approach to learning is considered, whereby the learner is given the potential to explore a non-linear environment whilst scaffolding and blending provide guidance ensuring targeted learning objectives are met. Through an evaluation of the developed prototype with 32 students aged 14-16 across two Italian schools, a varied response from learners is observed, coupled with a positive reception from tutors. The study demonstrates that challenges remain in providing high-fidelity content in a classroom environment, particularly as an increasing gap in technology availability between leisure and school times emerges.

Automorphisms of the Planar Tree Power Series Algebra and the Non-Associative Logarithm

2000 Mathematics Subject Classification: 17A50, 05C05.

Symmetric and Asymmetric Gaps in Some Fields of Formal Power Series

2000 Mathematics Subject Classification: 03E04, 12J15, 12J25.

A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining

Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data begging to be analyzed efficiently and effectively. In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness. The dimensionality p of the input space and the sample size n are usually the main ingredients in the characterization of data bigness. The specific statistical machine learning technique used to handle a particular big data set will depend on which category it falls in within the bigness taxonomy. Large p small n data sets for instance require a different set of tools from the large n small p variety. Among other tools, we discuss Preprocessing, Standardization, Imputation, Projection, Regularization, Penalization, Compression, Reduction, Selection, Kernelization, Hybridization, Parallelization, Aggregation, Randomization, Replication, Sequentialization. Indeed, it is important to emphasize right away that the so-called no free lunch theorem applies here, in the sense that there is no universally superior method that outperforms all other methods on all categories of bigness. It is also important to stress the fact that simplicity in the sense of Ockham’s razor non-plurality principle of parsimony tends to reign supreme when it comes to massive data. We conclude with a comparison of the predictive performance of some of the most commonly used methods on a few data sets.

Learner support and discovery in a virtual non-judgmental environment

This thesis is a qualitative case study that draws upon a grounded genre analysis approach situated within the social constructivist paradigm. The study describes the various obligatory, desired, and optional moves used by post-graduate students as they interacted within an online, non-judgmental environment in order to seek solutions to issues they were experiencing with their research projects or teaching. The postgraduate students or case participants met individually online with me at pre-arranged times to take part in Instant Messenger Cooperative Development (IMCD) (Boon, 2005) 30-minute to one hour sessions via the text-chat function of Skype. Participants took on the role of ‘Explorer’ in order to articulate their thoughts and ideas about their research. I took on the role of ‘Understander’ to provide support to each Explorer by reflecting my understanding of the ongoing articulations as the Explorers investigated their specific issues, determined possible ways to overcome them, made new discoveries, and formulated plans of action regarding the best way for them to move forward. The description of generic moves covers 32 IMCD sessions collected over a threeyear period (2009-2012) from 10 different participants (A-J). Data collected is drawn from live IMCD sessions, field notes, and post-session email feedback from participants. In particular, the thesis focuses on describing the specific generic moves of Explorers within IMCD sessions as they seek satisfactory resolutions to particular research or pedagogic puzzles. It also provides a detailed description of a longitudinal case (Participant A – four sessions), a one-session case (Participant B – one session), and an outlier case in which the Explorer underwent a negative IMCD experience. The thesis concludes by arguing that IMCD is a highly effective tool that helps facilitate the research process for both distance-learning and on-campus students and has the potential to be utilized across all disciplines at the tertiary level.