The research in this dissertation consists of two parts: An active learning algorithm for hierarchical labels and an embedding-based retrieval algorithm. In the first part, we present a new approach for learning hierarchically decomposable concepts. The approach learns a high-level classifier (e.g., location vs. non-location) by separately…

To meet the national and international call for creative and innovative engineers, many engineering departments and classrooms are striving to create more authentic learning spaces where students are actively engaging with design and innovation activities. For example, one model for teaching innovation is Innovation-Based Learning (IBL) where…

The early detection of students' academic performance or final grades helps instructors prepare their online courses. In the Open University Learning Analytics Dataset, I found many online students clicked the course materials before the first day of class. This study aims to investigate how data mining models can use this student interaction data…

A vast amount of published scholarly literature is generated every day. Today, it is one of the biggest challenges for organisations to extract knowledge embedded in published scholarly literature for business and research applications. Application of text mining is gaining popularity among researchers and applications are growing exponentially in…

The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…

Discussion of semantic networks and ranked retrieval focuses on two models, the semantic network model with spreading activation and the vector space model with dot product. Suggests a formal method to analyze the two models in terms of their relative performance in the same universe of objects. (Author/LRW)

Presents a relevance-feedback strategy that improves the effectiveness of information-retrieval systems based on back-propagation of the relevance of retrieved documents using an algorithm developed in a neural approach. Describes a neural information-retrieval model and reports results obtained with the algorithm in three different environments.…

Discussion of hypertext browsing proposes a filtering algorithm which restricts the amount of information made available to the user by calculating the set of most relevant hypertext nodes for the user, utilizing the user profile and data clustering technique. An example is provided of an optimal cluster of relevant data items. (Author/LRW)

Presents a preliminary empirical test of a maximum likelihood approach to using relevance data for training information retrieval parameters. Discusses similarities to language models; the unification of document-oriented and query-oriented views; tests on data sets; algorithms and scalability; and the effectiveness of maximum likelihood…

The Hepatitis Knowledge Base (text of prototype information system) was used for modifying and testing "A Navigator of Natural Language Organized (Textual) Data" (ANNOD), a retrieval system which combines probabilistic, linguistic, and empirical means to rank individual paragraphs of full text for similarity to natural language queries…

Discussion of user queries in information retrieval highlights the experimental evaluation of an adaptive linear model that constructs improved query vectors from user preference judgments on a sample set of documents. The performance of this method is compared with that of standard relevance feedback techniques. (28 references) (LRW)

Presents a coordination level matching algorithm to be used in document retrieval systems that incorporate relevance feedback strategies. It is argued that this algorithm may eliminate the need for the frequent reevaluation of documents that is currently found in such systems, and conditions under which reranking is unnecessary are given.…

Describes the three subsystems of an information retrieval system (document descriptions, queries, and matching algorithms) and argues that the interdependency of these subsystems requires adaptation for the system to perform when any component changes. An algorithm for redescribing documents, in response to changes in queries and retrieval rules,…

Discussion of the use of Latent Semantic Indexing to determine relevancy in information retrieval focuses on statistical regression and Bayesian methods. Topics include keyword searching; a multiple regression model; how the regression model can aid search methods; and limitations of this approach, including complexity, linearity, and…

This study develops an integrated conceptual representation scheme for multimedia documents that are viewed to comprise an object-oriented database; the necessary abstractions for the conceptual model and extensions to the relational model used as the search structure; a retrieval model that includes associative, semantic and media-specific…