Data mining has been successfully and widely utilized in educational information systems, and an important research field has been formed, which is educational data mining. Process mining inherits the characteristics of data mining which can not only use historical data in the system to analyze learning behavior and predict academic performance,…

Recently, deep learning has become a pervasive tool in prediction problems for structured and/or unstructured big data in various areas including science and engineering. In particular, deep neural network models (i.e. a basic core model of deep learning) can be viewed as an extension of statistical models by going through the incorporation of…

Recommender systems have been actively used in many areas like e-commerce, movie and video suggestions, and have proven to be highly useful for its users. But the use of recommender systems in online learning platforms is often underrated and less likely used. But many of the times it lacks personalisation especially in collaborative approach…

Selecting undergraduate majors or specializations is a crucial decision for students since it considerably impacts their educational and career paths. Moreover, their decisions should match their academic background, interests, and goals to pursue their passions and discover various career paths with motivation. However, such a decision remains…

Machine learning (ML) methods are among the most promising technologies with wide-ranging research opportunities, particularly in the field of education, where they can be used to enhance student learning outcomes. This study explores the potential of machine learning algorithms to build and train models using log data from the "3D…

Presents three text compression methods of increasing power and evaluates each based on the trade-off between compression gain and processing time. The advantages of using hash coding for speed and optimal arithmetic coding to successor information for compression gain are discussed. (26 references) (Author/CLB)

Describes the theoretical framework of a retrieval system that uses data structures called bitmaps to improve search algorithms for medium-sized systems. Information retrieval systems that contain the full text of the documents are discussed, and the use of inverted files such as concordances for small texts is explained. (24 references) (LRW)

Discusses properties of multiple attribute tree (MAT) and inverted file structures; establishes suitability of MAT data structure for bibliographic files using worst-case performance measures; provides arguments to establish MAT average case superiority; and proposes an efficient adaptation of MAT data structure to exploit special features of MAT…

Discusses the problem of duplicate citations resulting from file overlaps in multidatabase searching and shows that such duplicates could be identified automatically and eliminated by a host computer as a complementary service to online retrieval. Steps involved in the realization of this service are described, and 11 references are listed. (RBF)

Investigates applicability of inverted file processing in magnetic bubble memories by presenting four magnetic bubble memory models as storage mediums: one based on a major-minor loop configuration; one utilizing a decoder design on a magnetic bubble memory chip; and two based on different structural configurations of the first two. (MBR)

Presents a new image compression algorithm that employs some of the most successful approaches to adaptive lossless compression to perform adaptive online (single pass) vector quantization with variable size codebook entries. Results of tests of the algorithm's effectiveness on standard test images are given. (12 references) (KRN)

Presents an inference algorithm that produces a weighted finite automata (WFA), in particular, the grayness functions of graytone images. Image-data compression results based on the new inference algorithm produces a WFA with a relatively small number of edges. Image-data compression results alone and in combination with wavelets are discussed.…

Describes WebACE, the architecture of a client-side agent that explores and classifies Web documents in clusters automatically and discusses the details of the algorithms within its key components. Highlights principal direction divisive partitioning (PDDP), a scalable hierarchical clustering algorithm; compares it to other clustering methods; and…

This paper addresses the problem of data management techniques for efficiently retrieving requested subsets of large data sets from mass storage devices. Describes the development of algorithms and software that facilitate the partitioning of a large data set into multiple "clusters" that reflect their expected access. (Author/JKP)

The computer-based system for routing school buses in rural areas which is discussed minimizes both transportation costs and the average student ride time. Route generation methodology is described and a flowchart of the algorithm is included. (Author/JJD)