As is rapidly becoming clear, data science increasingly permeates many aspects of life. Educational research recognizes the importance and complexity of learning data science. In line with this imperative, there is a growing need to investigate the factors that influence student performance in data science tasks. In this paper, we aimed to apply…

This study addressed the cognitive impacts of providing correct and incorrect machine learning (ML) outputs in support of an object detection task. The study consisted of five experiments that manipulated the accuracy and importance of mock ML outputs. In each of the experiments, participants were given the T and L task with T-shaped targets and…

The artificial intelligence methods might be applied to see through the education problems, and make effective prediction and decision. The transformation from data to decision are inseparable from the learning analytics. In order to solve the dynamic multi-objective decision problems, a decision learning algorithm is designed to analyze the…

To provide intelligent learning guidance for students in e-learning systems, it is necessary to accurately predict their performance in future exams by analyzing score data in past exams. However, existing research has not addressed the uncertain and dynamic features of students' cognitive status, whereas these features are essential for improving…

Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include a higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems (LMS) and learning analytics (LA), indicators of such behavior can be detected, enabling predictions of…

This study explored whether executive functioning skills and maths test anxiety were associated with children's untimed and timed algorithmic computational performance and their discrepancy. It also investigated whether such relations were moderated by children's basic maths fact fluency. One hundred and thirty third-graders were rated by teachers…

Purpose: The goal is to assess performance prediction systems (PPS) that are used to assist at-risk learners. Design/methodology/approach: The authors propose time-dependent metrics including earliness and stability. The authors investigate the relationships between the various temporal metrics and the precision metrics in order to identify the…

Develops and analyzes four algorithms for feature selection in the context of rough set methodology. Experimental results confirm the expected relationship between the time complexity of these algorithms and the classification accuracy of the resulting upper classifiers. When compared, results of upper classifiers perform better than lower…

Student performance on division exercises in the recent National Assessment of Educational Progress (NAEP) is reviewed. Pupil performance on selected exercises is reported and followed by some suggestions for improvement in the teaching of this skill. (MP) Aspect of National Assessment (NAEP) dealt with in this document: Results (Utilization).

Highlights semantic links and shows how the semantic content of hypertext links can be used for information retrieval. Discussion includes indexing and retrieval algorithms that exploit link content and node content; retrieval strategies exploiting semantic links, including conventional retrieval and constrained spreading activation techniques;…

Describes a detailed algorithm for fast text compression. Related to the PPM (prediction by partial matching) method, it simplifies the modeling phase by eliminating the escape mechanism and speeds up coding by using a combination of quasi-arithmetic coding and Rice coding. Details of the use of quasi-arithmetic code tables are given, and their…

Describes the design of optimal tree-structured vector quantizers that minimize the expected distortion subject to cost functions related to storage cost, encoding rate, or quantization time. Since the optimal design problem is intractable in most cases, the performance of a general design heuristic based on successive partitioning is analyzed.…

Presents a heuristic, attribute-based, noise-tolerant data mining program, HCV (Version 2.0) based on the newly-developed extension matrix approach. Outlines some techniques implemented in the HCV program for noise handling and discretization of continuous domains; an empirical comparison shows that rules generated by HCV are more compact than the…

Discussion of checklists and their variations--job aids, algorithms, heuristics, and decision tables--covers what they are, how they are generated, and some implications of checklisting for instruction, evaluation, and learning. Examples of various kinds of checklists and 11 references are provided. (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)