Search

Exploratory learning environments (ELEs) promote a view of learning that encourages students to construct and/or explore models and observe the effects of modifying their parameters. The freedom given to learners in this exploration context leads to a variety of learner approaches for constructing models and makes modelling of learner behavior a challenging task. To address this issue, we propose a learner modelling mechanism for monitoring learners’ actions when constructing/exploring models by modelling sequences of actions reflecting different strategies in solving a task. This is based on a modified version of case-based reasoning for problems with multiple solutions. In our formulation, approaches to explore the task are represented as sequences of simple cases linked by temporal and dependency relations, which are mapped to the learners’ behavior in the system by means of appropriate similarity metrics. This paper presents the development and validation of the modelling mechanism. The model was validated in the context of an ELE for mathematical generalisation using data from classroom sessions and pedagogically-driven learning scenarios. [ABSTRACT FROM PUBLISHER]

The increase in crime data recording coupled with data analytics resulted in the growth of research approaches aimed at extracting knowledge from crime records to better understand criminal behavior and ultimately prevent future crimes. While many of these approaches make use of clustering and association rule mining techniques, there are fewer approaches focusing on predictive models of crime. In this paper, we explore models for predicting the frequency of several types of crimes by LSOA code (Lower Layer Super Output Areas - an administrative system of areas used by the UK police) and the frequency of anti-social behavior crimes. Three algorithms are used from different categories of approaches: instance-based learning, regression and decision trees. The data are from the UK police and contain over 600,000 records before preprocessing. The results, looking at predictive performance as well as processing time, indicate that decision trees (M5P algorithm) can be used to reliably predict crime frequency in general as well as anti-social behavior frequency. [ABSTRACT FROM AUTHOR]

Dramatic advancements in Geographic Information Systems (GIS) and computer technologies resulted in a wide availability of rich-knowledge GIS digital vector maps that are easily accessible and downloadable through the world wide web. This led to the need of copyright protection systems to protect the rights of the producers of these maps. Unlike the research on copyright protection for raster/image data types, the research for vector map data is less documented. This article surveys and classifies the GIS vector map copyright protection research papers published between 2000 and 2014, towards a thorough understanding of the-state-of-the-art, addressing significant limitations of previous review articles, and outlining effective recommendations for future research directions. [ABSTRACT FROM AUTHOR]

Individual and/or hybrid AI techniques are often used in learning environments for well-structured domains to perform learner diagnosis, create and update a learner model and provide support at individual or group level. This paper presents a conceptual model that employs a synergistic approach based on Case-Based Reasoning (CBR) and Multicriteria Decision Making (MDM) components for learner modelling and feedback generation during exploration in an ill-defined domain of mathematical generalisation. The CBR component is used to diagnose what students are doing on the basis of simple and composite cases; simple cases represent parts of the models that the learners could possibly construct during an exploratory learning activity, while composite cases, which are assembled from simple cases, correspond to strategies that learners may adopt to construct their models. Similarity measures are used to identify how close/far are the learners from solutions pre-specified and stored in the knowledge base. This information is then fed into the MDM component that is responsible for prioritising types of feedback depending on the context. The operation of the two components and the effectiveness of the synergistic approach are validated through user scenarios in the context of an exploratory learning environment for mathematical generalisation. [ABSTRACT FROM AUTHOR]

Most e-Learning systems store data about the learner’s actions in log files, which give us detailed information about learner behaviour. Data mining and machine learning techniques can give meaning to these data and provide valuable information for learning improvement. One area that is of particular importance in the design of e-Learning systems is learner motivation as it is a key factor in the quality of learning and in the prevention of attrition. One aspect of motivation is engagement, a necessary condition for effective learning. Using data mining techniques for log file analysis, our research investigates the possibility of predicting users’ level of engagement, with a focus on disengaged learners. As demonstrated previously across two different e-Learning systems, HTML-Tutor and iHelp, disengagement can be predicted by monitoring the learners’ actions (e.g. reading pages and taking test/quizzes). In this paper we present the findings of three studies that refine this prediction approach. Results from the first study show that two additional reading speed attributes can increase the accuracy of prediction. The second study suggests that distinguishing between two different patterns of disengagement (spending a long time on a page/test and browsing quickly through pages/tests) may improve prediction in some cases. The third study demonstrates the influence of exploratory behaviour on prediction, as most users at the first login familiarize themselves with the system before starting to learn. [ABSTRACT FROM AUTHOR]

Learning environments aim to deliver efficacious instruction, but rarely take into consideration the motivational factors involved in the learning process. However, motivational aspects like engagement play an important role in effective learning—engaged learners gain more. E-Learning systems could be improved by tracking students' disengagement that, in turn, would allow personalized interventions at appropriate times in order to reengage students. This idea has been exploited several times for Intelligent Tutoring Systems, but not yet in other types of learning environments that are less structured. To address this gap, our research looks at online learning-content-delivery systems using educational data mining techniques. Previously, several attributes relevant for disengagement prediction were identified by means of log-file analysis on HTML-Tutor, a web-based learning environment. In this paper, we investigate the extendibility of our approach to other systems by studying the relevance of these attributes for predicting disengagement in a different e-learning system. To this end, two validation studies were conducted indicating that the previously identified attributes are pertinent for disengagement prediction, and two new meta-attributes derived from log-data observations improve prediction and may potentially be used for automatic log-file annotation. [ABSTRACT FROM PUBLISHER]