Gennady Agre

From Gamification to Gameful Design and Gameful Experience in Learning

Abstract Learning is a goal driven social activity determined by motivational factors. To be able to efficiently gamify learning for improved student motivation and engagement, the educators have to understand the related aspects studied in games, motivational psychology and pedagogy. This will help them to identify the factors that drive and explain desired learning behaviors. This paper presents a survey of the main approaches employed in gamification and the emerging new directions in the context of the relevant motivational psychology and pedagogy. The focus is on the motivational factors that impact learning and understanding of behavior change. The purpose of the paper is two-fold: on one side, to provide analysis and guide to relevant works related to gamification along with outlining the emerging trends, and on the other, to provide foundation for evaluation and identification of the areas of possible improvements.

KBS Maintenance as Learning Two-Tiered Domain Representation

The paper deals with the problem of improving problem-solving behavior of traditional KBS in the course of its real operation which is a part of the maintenance task. The solution of the problem is searched in integration of the KBS with a specially designed case-based reasoning module used for correcting solutions produced by the KBS. Special attention is paid to the methods of case matching and reconciling conflicts between CBR and RBR. The proposed solution for both problems is based on treating the maintenance task as a problem for learning two-tiered domain representation. From this view point rules form the first domain tier reflecting existing strong patterns in the representation of domain concepts, while the second tier is formed by the newly solved cases along with a special domain-dependent procedure for case matching. The main ideas of the approach are illustrated by the results of some experiments with the experimental system CoRCase.

Current Practices, Trends and Challenges in K-12 Online Learning

Abstract Online learning is one of the fastest growing trends in Technology- Enhanced Learning (TEL). Technology in combination with an instruction that addresses the cognitive and social processes of knowledge construction could offer more diverse and effective online learning opportunities than their face-to-face counterparts. In this review we attempt to summarize different forms and practices in K-12 online and blended learning as they appear in various regional, national and cultural contexts. The article starts with introducing some basic concepts and terminology, sums up the state of K-12 online learning around the world and ends with summarizing some trends and challenges observed in current K-12 online learning practices.

How to create a WSMO-based semantic service without knowing WSML

In order to make accessible new Semantic Web Services technologies to the end users, the level of tools supporting these technologies should be significantly raised. The paper presents such a tool - an INFRAWEBS Designer - a graphical ontology-driven development environment for creating semantic descriptions of Web services and goals according to WSMO Framework. The tool is oriented to the end users - providers of Web services and semantic Web services applications, who would like to convert their services into WSMO based semantic Web services. The most character features of the tool - intensive use of ontologies, automatic generation of logical description of a semantic service from graphical models and the use of similarity-based reasoning for finding similar service descriptions to be reused as initial templates for designing new services are discussed.

INFRAWEBS designer – a graphical tool for designing semantic web services

In order to make accessible new Semantic Web Services technologies to the end users, the level of tools supporting these technologies should be significantly raised. The paper presents the architecture of such a tool – an INFRAWBS Designer – a graphical ontology-driven tool for creating a semantic Web service description according to WSMO Framework. The tool is oriented to the end users – providers of Web services, who would like to convert their services into WSMO based semantic Web services. The most character features of the tool – intensive use of ontologies, automatic generation of logical description of a semantic service from graphical models and the use of similarity-based reasoning for finding similar service descriptions to be reused as initial templates for designing new services are discussed.

A Weighted Feature Selection Method for Instance-Based Classification

The paper presents a new method for selecting features that is suited for the instance-based classification. The selection is based on the ReliefF estimation of the quality of features in the orthogonal feature space obtained after PCA transformation, as well as on the interpretation of these weights as values proportional to the amount of explained concept changes. The user sets a threshold defining what percent of the whole concept variability the selected features should explain and only the first “stronger” features, which combine weights together exceed this threshold, are selected. During the classification phase the selected features are used along with their weights. The experiment results on 12 benchmark databases have shown the advantages of the proposed method in comparison with traditional ReliefF.

Bridging the gap between digital libraries and e-learning

Abstract Digital Libraries (DL) are offering access to a vast amount of digital content, relevant to practically all domains of human knowledge, which makes it suitable to enhance teaching and learning. Based on a systematic literature review, this article provides an overview and a gap analysis of educational use of DLs.

A customised metric for foods categorization

The paper presents a metric specifically tailored toward categorization of food products. Formally it is not a metric in mathematical sense but heuristic that consists of two functions -- one that estimates the distance between the target food product and the category instances and a second one that estimates the level of trust that we can have to the category implied by the category of the nearest category instance. All foods are described by a set of attributes that corresponds to their nutrients. This is the most appropriate representation of foods data as the content of the food is the base for developing healthy menus and plays a critical role in the support of the physical fitness and personal appearance. Several experiments were performed that demonstrate the metric is good enough when more general categories (categories from the upper levels of the food ontology) are in use or for finding the substitutes of a given product.

An Expert System for Healthful and Dietary Nutrition

In this paper we present an expert system for healthful and dietary nutrition. It was built as part of the research project financed by Bulgarian SME fund [1]. The main purpose of the system is to facilitate the classification of the unknown food products to the set of predefined classes related to different client groups. In our case these classes are food products appropriate/not appropriate for children, adults below 60 years with high physical activity, low physical activity, adults above 60, etc but the system is not limited to these classes and can be used with any other classification hierarchy. The base metric that the system uses to compare food products was thoroughly presented in our previous publication [5]. In this paper we introduce the remaining part of the system including overall architecture, classification process, believability propagation, different user roles, implementation, and general considerations.

On speeding up the implementation of nearest neighbour search and classification

The paper presents practical approaches and techniques to speeding up implementations of nearest neighbour search/classification algorithm for high dimensional data and/or many training examples. Such settings often appear in the fields of big data and data mining. We apply a fast iterative form of polar decomposition and use the computed matrix to pre-select smaller number of candidate classes for the query element. We show that additional speed up can be achieved when the training classes consists of many instances by subdividing them in subclasses by fast approximation of some clustering algorithm and the resulting classification is used for building the decomposition matrix. Our pre-processing (depends linearly or near linearly on the number of examples and dimensions) and pre-selection steps (depends on number of classes) can be used with any well-known indexing method as annulus method, kd-trees, metric trees, r-trees, cover trees, etc to limit the training instances used in the search/classification process. Finally we introduce what we name cluster index and show that in practice it extends the applicability of the indexing structures with higher order complexity to bigger datasets.